Twist Bioscience Corp. Stock

Welcome to Twist Bioscience's 2026 Third Quarter Financial Results Conference Call. [Operator Instructions] Please note, this call is being recorded.

I would now like to turn the call over to Angela Bitting, SVP of Corporate Affairs. Please go ahead.

Thank you, operator. Good morning, everyone. I'd like to thank you for joining us for Twist Bioscience's conference call to review our fiscal 2026 third quarter financial results and business progress. We issued our financial results press release before the market, and it is available at our website at www.twistbioscience.com. .

With me on the call today are Dr. Emily Leproust, CEO and Co-Founder of Twist; Adam Laponis, CFO of Twist; and Dr. Patrick Finn, President and COO of Twist. Today, we will discuss our business progress, financial and operational performance as well as growth opportunities. We'll then open the call for questions. We ask that you limit your questions to only one, and then requeue as a courtesy to others on the call. This call is being recorded, and the audio portion will be archived in the Investors section of our website and will be available for 2 weeks.

During today's presentation, we will make forward-looking statements within the meaning of the U.S. securities -- federal securities laws. Forward-looking statements generally relate to future events or future financial or operating performance. Our expectations and beliefs regarding these matters may not materialize, and actual results in financial periods are subject to risks and uncertainties that could cause actual results to differ materially from those projected. These risks include those set forth in the press release we issued earlier today as well as those more fully described in our filings with the Securities and Exchange Commission. The forward-looking statements in this presentation are based on information available to us as of the date hereof, and we disclaim any obligation to update any forward-looking statements, except as required by law.

We'll also discuss adjusted EBITDA, a financial measure that does not conform with generally accepted accounting principles. Information may be calculated differently than similar non-GAAP data presented by other companies. When reported, a reconciliation between GAAP and non-GAAP financial measures will be included in our earnings documents, which can be found on the Investors section of our website.

With that, I will now turn the call over to our CEO and Co-Founder, Emily Leproust.

Thank you, Angela, and good morning, everyone. On Slide 3, you will see that this morning, we reported our 14th quarter of consecutive growth with record revenue of $118.4 million, growth of 23% year-over-year. This growth reflects the diligence and execution we have employed over the last several years, building for opportunities we see ahead. Sometimes those opportunities are clearly visible many years in advance and others require us to be nimble and engage with our customers in real time to ensure we build and deliver what they need to be successful.

On Slide 4, you'll see that at Twist we sit at the unique junction where we have spent the last decade building the infrastructure to enable our customers to advance the therapeutics, diagnostics and industrial breakthroughs resulting from the innovation. As biology becomes increasingly computational, our ability to deliver precision and speed at scale becomes even more valuable.

On Slide 5, I'm sure you agree that AI is shifting workflows across multiple industries. And we see an inflection point in drug discovery with more and more groups leveraging AI their first antibody discovery screen. Where one year ago, just a handful of organizations we are pursuing this path, now we have seen large pharma [indiscernible] traditional biotech and even major technology companies, all employ AI [indiscernible] approaches following the pattern of design build [indiscernible]. Our customers cannot design more sequences, test more hypothesis and iterate faster than ever before. In parallel, we also see the benefit of data and genetic information resulting in diagnostic assets that monitor, select and determine next steps for a wide variety of diseases.

Turning to our product groups. Over the past year, we've talked about AI-enabled discovery as an important emerging opportunity. Today, we are seeing that opportunity mature into a durable growth engine. We'll continue to support customers in building new models while others are using Twist to conduct discovery for specific therapeutic targets and to iterate models. This combination of repeat business, new customer acquisition and new organizations forming are expanding into the life sciences area, gives us increasing confidence in the durability and longevity of AI-enabled discovery as a growth driver for the short, medium and long term.

On Slide 6, the promise of AI-enabled drug discovery is straight forward, identify better drug candidate faster, reduce the cost of bringing new [indiscernible] to market and improve the probability that candidates entering development ultimately succeed.

On Slide 7, you'll see the workflow where our customers use AI to [indiscernible] generate thousands to millions of potential DNA sequences and iteratively we find those designs to identify the most promising therapeutic candidates for a specific disease. Twist quickly translate those digital [indiscernible] into physical biology through high [indiscernible] DNA synthesis of individual or pool DNA followed by protein expression. Many customers then leverage our downstream capabilities to evaluate those proteins across assays measuring binding, durability, characterization and other properties associated with successful drug candidates.

[indiscernible] rapid design build test and learn cycles at the earlier stage of discovery supporting our customers' ultimate objectives. When taking a partner for this work, every customer needs a trusted provider capable of manufacturing increasingly complex sequences, expressing protein, generating high-quality characteristics of data and delivering consistent results at the speed modern drug discovery requires. This combination of capabilities has become one of Twist's most important competitive advantages.

At our Investor Day in May, we added to triple-digit percentage order growth for AI-enabled discovery in fiscal 2026 versus fiscal 2025. Now that we are through the third quarter of the year, we are increasingly confident that we will meet or exceed that target. Our confidence reflects durable demand [indiscernible] as existing customers return for iterative design build, test, learn cycles reflecting growing recognition at our platform is well suited to support AI-enabled drug discovery programs as well as traditional drug discovery. Importantly, our final opportunities continues to expand meaningfully, and we believe that we have the potential to again post triple-digit percentage order growth for AI-enabled drug discovery in fiscal 2027 when compared to fiscal 2026.

Turning to Slide 8. During the quarter, our platform again demonstrated its importance in supporting global public health. On May 15, 2026, public health officials in the [indiscernible] Congo and also developing outbreak of [indiscernible]. The outbreak went undetected for at least a month, escaping standard PCR assays, a gap that allowed the virus to spread further before it could be identified and contained. As of July 1, the virus has been confirmed in both [indiscernible] and Uganda with an estimated spread to several thousand people, at least 399 confirmed death.

Approximately one month into the outbreak, researchers use Twist comprehensive viral panel to identify the pathogen as the [indiscernible] Ebola virus, underscoring the value of all viral genome enrichment to cash emerging infectious disease that standard testing missed. Separately, we also synthesized [indiscernible] virus material in less than 24 hours to support rapid response efforts during the cruise ship outbreak in May. These are powerful examples of the breadth, speed and reliability of our platform and of our role in a global research and public health communities.

On Slide 9, we continue to see strong accelerating momentum for our NGS Applications Group, driven primarily by diagnostic customers, many of whom are focused on growing their commercial volumes with other than [indiscernible] commercialization. We have our eyes on several new opportunities as the pipeline remains rich for next-generation assays to [indiscernible] disease test, optimize workflows using proprietary enzymes and more.

During the quarter, we increased [indiscernible], strengthened our commercial pipeline and continue to execute on new product introductions to reinforce our position as a trusted partner for next-generation sequencing workflow. As sequencing volumes continues to grow globally, we believe our differentiated technology and expanding portfolio position us well for continued growth. We continue to see customers turn to Twist [indiscernible] precision and rapid execution matter most. This holds true when we are supporting academic research, cancer diagnostic, agricultural biotech, responding to emerging infectious diseases or myriad other work streams.

On Slide 10, one of the highlights of the quarter was hosting our Investor Day at our fab in Wilsonville, Oregon. Many of our customers shared our Twist products and services play a critical role in their internal workflow, providing tangible examples across multiple markets and applications. In addition, several Twist leaders showcased their expertise and leadership throughout different areas of the business.

As Twist has grown, we have built an organization designed to repeatedly convert scientific innovation into commercial success. Great science creates opportunity, operational excellence turn that opportunity to durable revenue growth, expanding profitability and long-term value creation. That requires manufacturing discipline, commercial execution, digital capabilities and above all an exceptional team. Investor Day gave us the opportunity to demonstrate the strength of that foundation through our leadership bench, differentiated technology, highly estimated manufacturing, expanding capacity and the proprietary software that powers our business.

The consistent feedback we received was that investors left with a broader appreciation of the scale of our opportunities, the strength of our execution and the depth of our organization we have built as well as the reality of the growth opportunities that lie ahead to drive confidence in our long-term trajectory. For me, Investor Day, we involved something I believed since founding Twist. We do things the Twist way. And this is not the easy path. When someone says it's hard, I say, yes, that's the point. If it were easy, everyone will be doing it. Building what has never existed before requires relentless innovation, disciplined execution and clear communication.

What began as a funding principle has become how Twist operates. It's embedded in our systems, our processes, and more importantly, our people. That's how we scale innovation consistently, build trust with customers and investors, strengthen our competitive advantage and create long-term shareholder value.

With that, I'd like to turn the call over to Paddy, who will discuss our competitive mode, how we're executing against our priorities and why we believe the operational foundation we've built positions Twist for its next phase of growth.

Thanks, Emily. Good morning, everyone. One of the things I enjoyed most about Investor Day is that people got to see Twist from the inside. They didn't just see the products, they saw how we operate. Twist's core technology differentiation is a semiconductor-based DNA synthesis platform that provides a structural advantage in cost, scale and speed that feeds into every product and service we offer. The Syn platform also enables a highly efficient new product introduction engine, allowing us to rapidly translate customer demand into scalable offerings and continuously expand our portfolio.

As we increase volume on the silicon chip, we expand our wallet share, accelerate product innovation and further strengthen our competitive advantage. However, as Twist has grown, one thing has become increasingly clear to me, you'll see on Slide 11, our competitive moat expands well beyond our robust IP position around the chip into a complex infrastructure for manufacturing, automation, software, commercial execution product development, customer engagement, scientific innovation and critically the customer experience all reinforce one another.

Every improvement we make strengthens the platform, every new capability benefits multiple parts of the business. Every automation project improves quality, productivity and throughput, and every customer we add helps make the platform stronger. It's something we've been building deliberately for years. It's also why I believe our advantage continues to expand. Customers are looking for a partner that can help them move faster, and we're becoming more deeply embedded in our customers' research and development activities. That creates more durable relationships and over time, a stronger business, and that doesn't happen without exceptional and consistent execution.

Every day, our teams are focused on delivering exceptional quality, increasing productivity, expanding capacity and reducing turnaround time. These priorities don't change, there's no finish line, and we measure ourselves by how consistently we improve.

On Slide 12, I talked earlier about our silicon chip shown on the left. It's been the foundation of our commercial manufacturing platform since 2015. By miniaturizing known [indiscernible] chemistry and dramatically reducing reagent consumption, our economics have always been strong as we've created structural advantages in cost, scale and throughput that extend across our entire portfolio.

Just as importantly, we continue to improve that platform, as shown on the right side of the slide. Over the last 3 years alone, we have reduced manufacturing costs by 60% and reduced waste by 70%, reduced turnaround time by roughly 73% and increased oligonucleotide capacity fourfold. Those gains improve our economics, strengthen our competitive position and create additional capacity to support future growth. Over the past several years, VC firms invested heavily in alternative DNA synthesis technologies. The marketing is compelling but commercial success depends upon much more than chemistry. It requires molecular quality, reproducibility, and manufacturing scale and continuous operational improvement. We believe our platform and our track record continues to set the standard.

Our leadership in the field is one of the many things that gives us confidence in the long-term opportunity. Our investments aren't tied to one product cycle. We're strengthening the foundation for everything we build [indiscernible]. That's how we think about creating long-term value. We do not optimize for a single quarter, we focus on building a stronger company every quarter.

On Slide 13, another tangible example of continued improvement leading to a specific product launch. Last quarter, I talked about an early access launch of our complex genes offering extending the range of DNA sequences researchers can order from Twist. Complex genes are highly challenging constructs that have historically been out of reach for most platforms to synthesize consistently because of high GC content repetitive elements or other complex characteristics. Today, we manufacture these sequences with the same automation, the same manufacturing line as part of our standard and express gene production workflow with the consistent speed, scale and quality customers expect from Twist.

Early customer adoption has been very encouraging. During early access, we've worked with a select handful of customers who have ordered more than 1,800 complex genes across well over 100 orders and execution has been super. We've completed the overwhelming majority of these highly complex constructs in 12 days within our expected delivery window. That matters because it's one thing to produce a complex sequence once in an R&D environment, it's something entirely different to manufacture thousands of highly complex constructs repeatedly at commercial scale with consistent quality.

While complex genes represent a relatively specialized market today, our execution demonstrates that we continue to expand the boundaries of what customers can manufacture on the Twist platform using the same highly automated production infrastructure. Each new capability increases the value of our platform expand wallet share with existing customers and further differentiates Twist from competitors.

At Twist, we focus on automating and operationalizing any new product offering within our manufacturing workflow so that what we can do for one customer, we can deliver for thousands of customers with consistent quality, speed and precision at scale. Two weeks ago, we launched a complex offering formally expanding availability broadly across the market.

In summary, we believe our silicon platform, automation, software and manufacturing infrastructure uniquely position us to serve a wide range of customers across industries, creating a competitive moat that would be exceptionally difficult to replicate.

With that, I'll turn the call over to Adam to review our financial results.

Thank you, Paddy. Turning to Slide 14. Q3 was another quarter of consistent execution against the financial model we've laid out. Revenue grew 23% year-over-year to $118.4 million, our 14th consecutive quarter of sequential growth. We focused on growing the top line while maintaining our commitment to adjusted EBITDA breakeven for the fourth quarter of fiscal 2026, and we understand the growth of 20% or more significantly differentiates Twist among our peers. Gross margin was 52.8%, growth of 120 basis points sequentially, with 70% of incremental revenue dropping to the gross margin line.

Let me walk you through the details. On Slide 15, you'll see DNA Synthesis and Protein Solutions revenue increased to $56.6 million compared to $40.8 million in the third quarter of fiscal '25, growth of 39% year-over-year and 6% sequentially. We shipped 369,000 genes in the third quarter, plus consecutive quarter-over-quarter growth in genes manufactured for data characterization.

On Slide 16, we show NGS applications revenue for the third quarter grew to approximately $61.8 million compared to $55.3 million in the third quarter of fiscal 2025, an increase of 12% year-over-year and up 8% sequentially, driven by growth in top accounts. For the quarter, revenue from our top 10 NGS applications customers accounted for approximately 48% of NGS applications revenue. We served 657 NGS applications customers in the quarter with 182 having adopted our products.

Looking geographically, on Slide 17. Americas revenue increased to approximately $77.3 million in the third quarter compared to $59.4 million in the same period of fiscal 2025, growth of 30% year-over-year. EMEA revenue rose to $33.6 million in the third quarter versus $30.7 million in the same period of fiscal 2025, growth 9% year-over-year. APAC revenue increased to $7.5 million in the third quarter compared to $5.9 million in the same period of fiscal 2025, an increase of 26% year-over-year.

On Slide 18, looking at revenue by industry. Therapeutic revenue was $40.4 million for the third quarter of 2026 compared to $27 million in the same period of fiscal '25, growth of 49%, reflecting the increased uptake of our products by pharma, dry lab biotech and large tech companies in their efforts on therapeutic discovery and including AI-enabled drug discovery.

Diagnostics revenue was $43.8 million in the third quarter of 2026 compared to $38.1 million in the same period of fiscal 2025, an increase of 15%. Diagnostics revenue grew 10% sequentially based on strong growth from top accounts. Industry and applied revenue were $5.7 million in the third quarter of 2026 compared to $6.1 million in the same period of fiscal '25. Academic and government revenue were $15.5 million in the third quarter of fiscal '26, an increase of 32% year-over-year and 21% sequentially, driven by strength in U.S. accounts, where we saw several large customers returned during the quarter.

The environment remains dynamic and order patterns may fluctuate but importantly, our outperformance this quarter for academic and government demonstrates the resilience of our business, the depth of our customer engagement and the growth of this customer group.

Global Supply Partner revenue was $12.9 million in the third quarter of 2026 compared to $13 million in the same period of fiscal 2025. This revenue stream continues to provide a stable recurring revenue base while our faster-growing therapeutics and diagnostics product groups drive overall company growth.

Moving down the P&L to Slide 19. You'll see our key financial metrics. Our gross margin for the third quarter was 52.8%, up sequentially and driven by strong revenue growth, even as we continue to make deliberate investments in new product offerings and manufacturing capacity that we expect to result in future margin gains as we accelerate growth and implement continuous process improvements.

Operating expenses, excluding cost of revenues, were $98.7 million for the quarter compared to $81.4 million in the prior year which includes approximately $2 million in employee transition costs as well as other onetime expenses that will result in more than $5 million reduction in OpEx in the fourth quarter.

Looking at our progress on our path to profitability and progress towards breakeven on Slide 20. For the third quarter of fiscal 2026, adjusted EBITDA was a loss of approximately $11.3 million reflecting planned onetime investments. We ended Q3 with $166.8 million in cash, cash equivalents and short-term investments versus $171.7 million as of March 31, 2026.

On Slide 21, turning to guidance. For fiscal '26, we are increasing our revenue guidance and now expect total revenue of $456 million to $457 million, up $12 million at the midpoint, representing growth of approximately 21% year-over-year. In the third quarter, total revenue growth reflected above 20% [indiscernible] customer demand, our current funnel and order growth we expect momentum to continue and look forward to sharing full year guidance for fiscal '27 in November. For Q4 of fiscal 2026, we expect total revenue of $123 million to $124 million, growth of approximately 25% year-over-year at the midpoint.

We expect sequential growth from both DSPS and NGS. We expect DSPS sequential growth to be driven by therapeutics and NGS to return to growth above 20% year-over-year. While in our final quarter of the fiscal year, we remain confident in our trajectory and continue to expect to achieve adjusted EBITDA breakeven this quarter. Our focus is now on sustaining that performance and continuing to execute against long-term financial objectives throughout fiscal 2027.

With that, I'll turn the call back to Emily.

Thank you, Adam. I'd like to leave you with one final thought. When Twist was founded more than 13 years ago, our goal wasn't simply to be the better way to synthesize DNA. It was remove [indiscernible] that slows scientific discovery. That vision is working. Today, our platform is enabling advances across cancer diagnostics, AI-enabled drug discovery, synthetic biogenes and academic research. We have translated [indiscernible] into durable business performance, delivering 14 consecutive quarters of revenue growth, expanding gross margin and remaining on track to achieve adjusted EBITDA breakeven this quarter.

And yes, I believe we are still in the early chapters. Biology is becoming increasingly digital, data-driven and AI enabled. Scientists are asking bigger questions, designing more complex molecules and moving faster than ever before. Those trends play directly to Twist strength and reinforce our confidence that the opportunity ahead is substantially larger than the one we set out to address 13 years ago.

Importantly, we continue to execute against our plan to deliver sustainable profitable growth. As we look ahead, we are confident not only because of the opportunities in front of us, but because of the foundation we have built to capture them. And we continue to see multiple durable drivers of growth, as you'll see on Slide 22.

AI-enabled drug discovery continues to expand. Molecular [indiscernible] disease continues to grow in application with the reimbursement environment expanding. We see opportunity to introduce proprietary enzyme in our workflows and our internal processes to optimize performance and cost as well as reduce our dependence on suppliers. And in the longer term, we believe [indiscernible] therapeutics provides a personalized diagnostic and therapeutic approach that we improve patient outcomes and positively impact the health care system.

With the growth across the market without expanding, our severable addressable market is projected to be a $13 billion by 2030. In addition, our innovation engine will continue introducing new capabilities that we haven't mentioned here today, expanding that upside opportunity.

On Slide 24, you'll see that we feel confident in our ability to drive continued growth moving forward. Specifically, we've just delivered our 14th consecutive quarter of revenue growth at 23% growth year-over-year. We believe we have a strong growth trajectory to more than double revenue from organic growth in 2031.

We guided to $123 million to $124 million for the fourth quarter of fiscal 2026, growth of approximately 25% year-over-year. Based on customer demand, our current [indiscernible] order growth, we expect momentum to continue. We expect to meet or exceed our guidance of triple-digit percent order growth for AI-enabled drug discovery in fiscal 2026. Based on our robust funnel, we expect to again deliver triple-digit percent growth for orders from AI-enabled drug discovery in fiscal 2027.

We expect gross margin above 52% for fiscal 2026 with a goal of margins of more than 60% as the business matures. We expect to achieve adjusted EBITDA breakeven for the fourth quarter of fiscal 2026 and we expect to maintain this commitment for fiscal '27. And we will continue to meet our customers where they are enabling them to truly change the world for the better.

At this time, let's open the call for questions.

[Operator Instructions] Our first question comes from Brendan Smith with TD Cowen.

Congrats on the quarter. I wanted to first ask actually just about the relative breakdown of FQ3 revenue, you talked a little bit about this already, but it looks like therapeutics revenues were incrementally down sequentially, the volumes do look good. So I guess just any additional color there on some of the ordering dynamics we should maybe be aware of?

And then on the new FY '27 outlook, repeating triple-digit AI growth next year. I appreciate all that as well. I guess, is that based on -- I know you said the funnel, but is this kind of based on some visibility into orders already coming in? Is this kind of reflective of ongoing conversations you're having any particular inflections in the type of quality of orders over the next year or so? Just kind of any color you can give us to support durability on that scale would be great.

Thank you, Brendan. We're very please this quarter. We're seeing a lot of strength from existing customers as well as new customers coming in. Obviously, we had a growth of more than 20%, and we are guiding a growth of more than 20% -- almost 25% for [indiscernible]. So things are going really well. In AI-enabled discovery in particular, made up a few things happening. First, customers that have [indiscernible] and then we're seeing new customers coming on top of that. And then even the existing customers [indiscernible] have been [indiscernible] look at new modalities. So [indiscernible] coming in with just a [indiscernible] and then expanding to [indiscernible]. And people are not quite broadly talking about AI [indiscernible], but we know it [indiscernible].

So talking to customers, looking at the costs we are giving them, the number of sequences that they have in mind that they want to build either a full DNA or [indiscernible] very, very strong confidence that triple-digit percentage growth that we see in AI-enabled drug discovery, as we know we're going to be able to deliver [indiscernible] is continuing again from [indiscernible].

Our next question comes from Puneet Souda with Leerink.

Thanks for the question here. I'll wrap my questions in one. So look, on the gene shipped, you're seeing quite a bit of acceleration here, 56% growth. But wondering why is the protein segment growing only 39%? Just wondering if there is any consider -- any pricing or ASP compression that you're seeing? And I think the bigger question here is that you're pointing out triple-digit growth this year next year or triple-digit order growth again for AI. But how is the AI order to revenue conversion I think that's a key question we're getting.

And for Adam, on adjusted EBITDA, if you could -- could you double-click on that and provide us how do we get to adjusted EBITDA breakeven in the fourth quarter and keep that sustained or higher in fiscal '27?

Yes. Thanks, Puneet, for the great questions. Yes, the number of genes is growing massively, which is great, right? I think for us, we can process them easily on our automated systems, and we [indiscernible] to uphold that kind of revenue growth. So the therapeutics business grew 48% year-over-year, so very much in line with the growth of genes. It's correct that DSPS [indiscernible] only grew 36%. I think that as the volume growth comes primarily from antibody type sequences. As you know, those sequences are shorter than maybe [indiscernible]. And so as we see the growth coming primarily from [indiscernible] therapeutics, maybe a shift to smaller gene which is [indiscernible].

But the key is AI is doing exactly what we told it to do. It just creates more sequences. And then -- and I mentioned is that biology is becoming more digital, [indiscernible] and more AI enabled and that just brings more demand for more and more sequences, and we're absolutely seeing it in our data.

I don't know if you take the second question?

No, absolutely. So thanks for the question, Puneet. In terms of order to revenue recognition, we're noticing, particularly with the AI drug discovery that the time lines for projects are usually [indiscernible]. So at the end of last year, when we had a pretty significant order step up towards the end of the fiscal year, we saw there was some separation there. But as we go into '26 and look towards '27, the 2 metrics converge pretty nicely.

The other question in terms of adjusted EBITDA and the path and the bridge to Q4 and beyond, no, great question. And we've been very disciplined over the last number of years with how we've managed the business and our investments, and we continue to do so. And so as we look into Q4, we had the onetimers associated with some of the employee transitions in Q3 as well as some continued investment in our new digital capabilities that we've here launched in Q4.

So we expect to see sequential improvement in the OpEx moving forward starting in Q4 [indiscernible] we see the path to adjusted EBITDA positive in Q4 and then being able to continue to make progress on that in '27 and beyond.

Our next question comes from David Westenberg with Piper Sandler.

And maybe I'll just add on to Puneet's question on the EBITDA bridge. I appreciate the color there. Do you expect 2027 to have -- continued momentum in 2027 with the EBITDA bridge, i.e., do you think that you could stay EBITDA positive throughout? Or do you think it's going to be quarter-on-quarter, but the year you're really focusing on that EBITDA?

And then kind of for a second question, I was hoping you can give maybe some color on the new product mix in NGS, particularly around some of your new whole genome offering and then maybe some of the more expansive or faster-growing new products from customers in NGS. I mean I'm thinking about yield, but any other ones like that, that could be just incredibly high growth potential and how they did?

Thanks, David, great to have you. Thanks for initiating [indiscernible] double question. I'll send it to Adam for your first one on the adjusted EBITDA momentum and then added for the new [indiscernible]. .

David, great to hear you on the call. In terms of our commitment, we said we're going to give full year guidance in November, and we plan to give a robust outlook. We're not in the business typically of going back [indiscernible]. And so we look at our full year adjusted EBITDA this year, improving year-on-year to being positive for the year, and we look to make sequential improvements wherever possible. We also recognize we love our employees and give them a raise at the beginning of every fiscal year. So we're balanced, we're going to balance that. We'll give full guidance in -- as we approach November.

And then also in terms of how we look at the business long term, very positive progress on both the growth trajectory as well as the commitment on continued progress [indiscernible]

David, just building off [indiscernible] coming over to your questions on products and NGS space. And from a workflow standpoint, it's a good time just as a reminder, and how we've built out some really well differentiated enzymes using AI-driven discovery and development methods. The critical in whole genome and other workflows is high-performing [indiscernible] high-performing preliminary, both of which are featuring in our kits, which are benchmarking [indiscernible], both internally and in our customers' hands. And so that's a long-term commitment to enhance our customer success [indiscernible], and it's going to be an ongoing drive into the market.

And as I move across into the emerging applications that we're very excited about, I'll just call out MRD again from [indiscernible] disease. Obviously, we have workflows for any approach. But in particular, maybe just spend 30 seconds on the tumor informed platform. And what you're seeing there, obviously, is a correlation between increasing [indiscernible] derived from knowledge gained from sequencing the patient's tumor leading to higher sensitivity tests and the better patient outcome.

And we see demand continue to increase [indiscernible] to talking about something like our current capacity for something like 32 million [indiscernible] per day. If you can imagine the situation of hundreds of thousands to millions of tests in the future, all demanding thousands of [indiscernible] tumor informed delivered to a patient in -- or sorry, delivered to a service lab or a hospital or wherever a customer is in a very, very short period of time. And by that, I mean a couple of days max, and we're pretty excited about where that segment is going. It's another beautiful application of our synthesis platform.

Our next question comes from Subbu Nambi with Guggenheim.

As we get closer to 2027 fiscal year, do you have any preliminary thoughts on '27 growth outlook? I know you said you'll provide us full guidance. But then you touched on this a little bit about 20% growth for both NGS and DSPS. Is that reasonable? And if yes, this would be above your LRP of mid-teens growth. And then I had a clarifying question. You had 25 million AI-related orders in '25, even assuming 100% as bare minimum triple digit, that would be 50 million. And now you're confident that 50 million growing to 100 million, did I get that right?

Yes. Thank you, Subbu. Yes, you got that right that for the [indiscernible] comments. In terms of long-term growth, we think that although the trends are in our favor. Paddy just mentioned the trends in MRD, where I will fully repeated [indiscernible] basically what we're hearing is for [indiscernible] people want more [indiscernible]. And in order to [indiscernible] what we've built is the [indiscernible]. So we think that trend there can be a very strong sales [indiscernible] wins. In terms of AI growth story, it's just [indiscernible]. But last year, a year ago, we had a big order in DSPS and now the [indiscernible]. Really, we've seen a change in the order pattern. We are [indiscernible] and then sticking to [indiscernible] what they need going forward. We've heard about the model for the AI [indiscernible] needing more and more data [indiscernible]. There's a growing number of data [indiscernible] needed, and we can feel that [indiscernible] We deliver high [indiscernible] and capitalization. And so we're going to detail the bucket.

So overall, we're not guiding to 2027. But Q4, we're going to [indiscernible] above 20% growth, we're guiding to adjusted EBITDA breakeven in Q4, and we couldn't be more excited about what's go-again, [indiscernible] Twist platform is really meeting the [indiscernible] on the NGS and the [indiscernible].

Our next question comes from Kyle Mikson with Canaccord Genuity.

Congrats on the 49% growth in therapeutics, very impressive. However, as was mentioned earlier, the revenues were down or kind of flat quarter-to-quarter. There was some competitive actions taken by other vendors in the field. There was some discount, I believe. Has that having any impact on the therapeutics and the AI drug discovery business at all? And then secondly, Adam, you raised the guidance by $10 million or so. The beat was $4 million. Where are you expecting this performance in the fiscal fourth quarter to come through on the different industries and the products?

Thanks, Kyle, for initiating on the question. Yes, 49% growth quarter-over-quarter is outstanding. We are raising the guide by $12 million, which is 3x the beat. So obviously, there's a lot of confidence. Maybe I'll say back one sentence that [indiscernible], which is that we do not optimize for single quarter. And so what we're doing is we are focusing on building a stronger company every quarter. So we have our [indiscernible] looking ahead. happy, not worried about competition at all. The kind of number in terms of thousands of sequences that people need in an [indiscernible]. I don't know if there's any other platform that can deliver that kind of number.

So we're not really worried with our position. It's about being there for customers, [indiscernible] what they need and meeting their [indiscernible]. And we think we are doing pretty really well and as people start to expand to new modalities. Again, a lot of the AI so far has been done for [indiscernible] maybe a year ago, we're on the sideline now well engaged. We -- again, we would not guide to triple-digit percent growth on [indiscernible]

Our next question comes from Matt Larew with William Blair.

You provided some details on customers who have sort of progressed from [indiscernible] building into [indiscernible] and then expansion of their overall work. I'm curious if you can maybe speak more to the new customer activity that you've seen over the last 12 months? And if there's any way you can help us put a number or direction on the number of customers still sort of just starting or getting into AI drug discovery as part of their workflow today. That's the first part.

The second is related, which is when we were at the Investor Day, it was clear that one area of physical investment you were making was broadly in the data capabilities -- capabilities to serve data characterization. And I would just be curious if you could, again, sort of put in numbers on how that business is growing, demand, what kind of services you've been asked for there that would be helpful again to contextualize the order growth for next year?

Thank you, Matt. The growth for next year is anticipated to be very broad-based. We are actually adding capacity now ahead of the demand in the data characterization. As you know, the capacity that we have on the DNA Synthesis and Protein Expression is really outstanding and ahead of what people need. And so we know that the data piece is very exciting to our customers. They don't have to be the lab. They'll have to worry about maintenance, calibration, about machine-to-machine reproducibility, we take care of all of that.

So being able to have a one-stop shop where you give sequences -- thousands of sequences and the target that you interested in and being able to get data is very appealing. At the same time, we are a custom business. Our customers want different things, different blends of text, different conditions, different buffer, different dilution. And so our platform is built for that as well.

So to your question in terms of where will the growth come from? Frankly, it doesn't really matter. We don't really care. We will be there for wherever the science takes our customer. At the same time, we anticipate that a lot of it will come from the data side.

Our next question comes from Mac Etoch with Stephens.

Maybe just to clarify, sorry, my connection is may be a little bit spotty. So if you answered this, I apologize already. But the change in expectations from 3Q to 4Q, 4Q implying a sequential step-up in DSPS now. Can you just double tap on the drivers that you're looking at there, what's changed? What's changed and how you're looking at that from the past quarter and now in 3Q?

Mac, thanks for the question. So in terms of the guide for Q4, we are expecting sequential growth in both DSPS and NGS applications. We continue to see both strength continuing in the diagnostics space sequentially as well in the therapeutics often driven by the AI drug discovery. As we look at that strength continuing, it's really [indiscernible] to the pipeline of our opportunities we see. We see growth both in terms of the new customers coming in as well as in the repeat business across the DSPS and the AI drug discovery space.

Our next question comes from Vijay Kumar with Evercore ISI.

Congrats on a nice print here. My first question was getting back to the AI orders. I think in the past, Twist [indiscernible] had its short-cycle order cycle, right, shorter duration order cycle. I'm curious on commenting on fiscal '27 triple-digit AI orders. That implies a lot of visibility. Maybe could you elaborate what is different about AI that gives you this longer-term visibility versus the typical order book, which seems to be more shorter cycle?

Yes, I think that's a great comment. It's true that -- your are 100% correct that in the past, we [indiscernible] companies and they will send us some sequences. And we would not -- we will ship them very quickly, we'd get paid. And we didn't always have visibility as to when the next batch will be. I think for AI, it's a little bit different because now we have to give -- [indiscernible] big number. Now we have all the -- from the DNA Synthesis and Protein Solutions side that's similar from what we are getting in the NGS side. And when you're thinking about a big number of millions of dollars and now you need an MSA and there is some discussion upfront of whether they need and it's more our customers making sure that convincing themselves that we have the capacity to do.

And so they are a lot more transparent in [indiscernible]. And then once the order comes in, again, those are our big numbers, there's a lot more engagement and transparency on their side with what they're going to do with it, what the next path is coming in. And so in some ways, we have been much more engaged in the science of what our customers are doing than we were before, where we were just a DNA provider or just a sequence provider. And now we will move to the next level of providing a solution. And they're looking to our CSO, [indiscernible] drug discover, right? And so it's a much higher level of engagement and that's why we have more visibility than we had before.

Our next question comes from Luke Sergott with Barclays.

Just a couple of cleanups. Did you already call out the amount of AI revs you guys had in the quarter? Or kind of what's embedded for the full year guide? So that's my first one. And the second one is more -- we're getting a more, I guess, more not pushed back from the market, but just more interest in the market from [ GenScript Turbo ] show. We're hearing about that side as we're doing more work on the AI labs like what this could mean for you guys, but we're also hearing them come up more in conversations, given the cost. And they have a different scale than you guys. So kind of talk about where you see the competitive dynamic shaking out, what you would need to do from a scale or turnaround time, anything that you guys can do there to continue to win share and on the market.

So I'll start and then I'll pass the question to Adam on the financial question. When we -- we don't worry too much with the competition, frankly. We try to hear what customers want and then do that. At the same time, having a healthy [indiscernible] is important. I think in this case, it's a little bit different. We started as a DNA company. And we didn't have [indiscernible] months ago, we did have a big presence in protein. And through engagement with customers, we understood that they indeed protein and data. And so over the last 18 months, we built something that we think is getting pretty unique.

In some ways, we are leaping ahead of GenScript because we don't believe they have the same data capacity and capabilities as we are. So in some ways, we are the distributor, not the other way around. [indiscernible] been around for a long time. And frankly, our customers don't care what kind of [indiscernible] you use. They want to know what direct capability we have, what capacity do we have and what speed we can deliver that data. And so it's not the differentiation, it's not around the true that we're using the [indiscernible] is around the breadth of the menu, the flexibility in the conditions that those [indiscernible] being produced.

And then very importantly the end, how many sequence can I test. If you're talking about millions of data forms in the [indiscernible] assets, I think we are unbeatable there. If you are talking about tens of thousands of sequences in an array arid format, I think we're also pretty unbeatable. Then [indiscernible] sequences you have a choice of supplier. But for a large number, I think we are in a very, very good position.

Adam?

Yes. Thanks for the question. In terms of AI orders and revenue, I hit on it earlier, but if you looked back a year ago at this time, in Q4 of 2025, we had a record order from an AI drug discovery project that really made the cutover at the end of the fiscal year, some of that order drifted into revenue recognition into Q1 fiscal '26. Looking at '26, the time lines are much more lined up. Just that the progress has been smoother, and we've had a broader base of customers upon which to grow on. And we're seeing that the orders and the revenue are much more lined up with each other. .

Although we haven't given specific breakout AI drug discovery quarter-on-quarter, I point everybody to the progress in therapeutics and really the outside growth in therapeutics, primarily being driven by AI drug discovery. So looking forward into '27 and beyond, we'd expect the revenue and the orders to be more lined up like they are in '26.

Our next question comes from Robert Bamberger with Baird.

Academic and government went from 3% growth last quarter to 32% this quarter and up pretty nicely sequentially. So any change in what you're seeing in academic and I guess, what caused those outsized gains? And then any commentary on the academic promotions as well.

Yes, a good question. Thanks for that. Yes, the academic segment, good quarter, team executed well. It remains a dynamic environment, and we'll continue to execute well into that segment. Our value proposition continues to resonate. You imagine a budget-constrained environment, cost, speed, quality, [indiscernible] something that as a segment we've underserved in the history of Twist and [indiscernible] into the segment continued, and we're just focused on like all of our customers, delivering a really fantastic customer experience [indiscernible] the platform and you're not going to go back to pay more or slower product. We'll continue to execute day by day, and we'll wait for our -- to continue our strength in that segment.

I'm showing no further questions at this time. I'd like to turn the call over to Emily Leproust for closing remarks.

Thank you for your question. [indiscernible] is simple. Customer demand continues to strengthen, our execution continues to deliver and the platform we've over -- for more than 10 years that we've been doing this, is creating an advantage that continues to be widen. We remain confident in our trajectory. I'm excited about the opportunities ahead. Thank you.

Thank you for your participation. This does conclude the program. You may now disconnect. Everyone, have a great day.

[Audio Gap]

We'll focus more on NGS and finance and our culture. So to tell us about our [ DNA synthesizer production ], I'm very glad to welcome our CTO, Siyuan Chen. He is going to tell us some of our strategy. Thank you, Siyuan.

Hey, good morning, everyone. My name is Siyuan Chen, I am the CTO on Twist Bio. I'm an oligo chemist by training. Actually, I was the first employee on the R&D side in Twist. So like I've been working in this company for 13 years, like in a long time. I'm really excited to be here because I don't think I've ever talked to like large group investors in this setting. So I'm a little bit nervous, but I'm very excited to show you what we're able to do.

So I think you guys all just went through the tour, right, the production tour. Like I'm sure you have seen a lot of really interesting, a lot of amazing stuff there. I want to say a lot of magic we do like really started with the silicon chip. So I think you have all seen a silicon chip, which is like [ 96 world play ] size. Instead of having 96 wells, we actually have 10,000 wells on it. We call them clusters. And like within each cluster, we can make 121 audios. So that's enough oligo to go to make 1 gene, and on a chip, we're able to make 10,000 genes.

And like on the silicon chip, we're also able to make about 1 million oligos right? And we have multiple riders. The riders can run actually really, really fast. I'll talk a little bit more about it later. That gives us a capacity of making 32 million oligos per day capacity. It enables a lot of different applications. And like we also like do the DNA is its base by base like using [indiscernible]. We have done so much optimization like to really improve the efficiency and every single layer.

So like right now, we're actually able to do as soon as it's all the way to [ 500 base bar ], so we call them [ 500 mer ], which is actually quite amazing if you really think about it. Because as I said, I was an oligo chemist. I remember when I was been work in graduate school trying to like making [ 30 mer ] on a chip, that's enough lens for hybridization like for micro array. That's quite amazing already, like you're able to do [ 30 mer ] and quality good enough to do habilitation, that's good. If you're able to do like [ 80 mer, 100 mer ], everybody look up to you saying, we can do [ 80 mer ] like, in a consistent basis. So that's quite amazing. But right now, we actually do [ 500 mer ] in a consistent basis on a production setting.

So like -- and also in everyway that's like industry-leading, I think we're able to get to 1 in 2,500 base bar, 1 in 3,000 base bar, every rate, that's like really unheard of, like compared to the traditional synthesizer you get 500 base, right, that's pretty much ask you can go.

So that's our foundational platform. And on the platform, we really built up a lot of DNA product, right, oligos, oligo pool, gene fragments, clonal genes and also like in the last couple of years instead of printing ATCG for different basis, we're actually able to add in modified basis to like make some siRNA antisense oligonucleotide. You're actually going to hear a talk from Dillon from GSK later today talking about how they use our platform to enable high-throughput siRNA screening.

And on top of it, actually, on top of the DNA layer, we also built up a protein layer where we can do different type of antibody discovery work and like antibody production and antibody characterization. So like -- so we have this amazing platform where we can make DNA and a scale and a very fast to speed. We really built up a very impressive NPI machine on top of it.

So I want to show you where we were back in 2021, like this is essentially the product we had back in 5 years ago. So we have SynBio product. We have NGS product. On the SynBio side, we have oligo pool, we have variance library, we can make gene fragments, we can make clonal genes. And on the NGL side, we have exome panels. We have some custom panels. We had a couple of library patients. They're all good product, like they're actually -- like we are still selling those products, very strong these days.

But at the same time, I want to say the footprint is -- the product line is somewhat limited. And I want to show you in the last 5 years, what we have done in terms of expanding our product road map here, our product -- like a product line. So this is where we are right now in 2026. So I'm not going to go over all the product here, but I want to highlight a few things we can do, right?

So on the gene side, we can do -- like we had clonal genes. 2.5 years ago, we launched Express Gene, which offers industry-leading like turnaround time for clonal gene product. And a few weeks ago, we actually announced Ultra Complex Gene and SynBio beta. That's really leveraging the 500 base-pay synthesis capability I mentioned like a couple of slides ago, like so that we don't have to really worry too much about the [ Iseco ] structure. We're just really going to make the DNA chemically and stitch only a few pieces together to make ultra-complex gene.

That serves as a backbone for like a very complex mRNA and help us get in the world of nucleic acid therapeutics. You're going to hear more from Paddy later today talking about that part. At the same time, on the SynBio side, actually, we built up like a protein layer on top of it, like where we can do antibody discovery in vitro, in vivo. And we also use AI tools to do in silico discovery work. And at the same time, we built up a very impressive antibody expression system and the antibody characterization generate lots of data to help our customers to do AI model training like refinement and help their AI enabled drug discovery work. And [ Toby ] is going to talk more about it, share more insights on the AI side.

And the NGS side, like we expanded our panel product like the loss of panel product. We put a lot of emphasis on MRD, molecular residue disease. So we should we really think that area kind of ramp up very, very quickly. So like Jimmy is going to cover that part later today. And on the library preparation side, like we had a couple of library per patient. We added quite a few more new products found some generic library patients, some standard fragmentation and also some more specific ones focusing on like in our cfDNA, folks on like FlexPrep like to really enable microwave conversion from -- like to NGS.

That's actually really like enabled by the enzyme engineering capability we developed in the last few years, leverage our synthetic like DNA sensitive capability. So it's a really nice product. What makes it even more exciting is I think that's actually just really the beginning of what we can do. Because if you really look into all the applications like can be enabled by oligos, by DNA, like in terms of oligo number, mass lens, like in different areas, like we can see there's a lot of things we can do like which is -- we just need to choose which one we want to go as we continue to move forward.

So yes, this is the table just to reference, like the way how we perceive like what DNA can do for different applications. So that's enough about NPI machine. We're going to talk a lot more about it throughout the day. What actually I want to spend like the rest of my talk here to share a little bit more about how we do operational excellence.

Because that's actually a really important part. We spend a lot of time on it. We're really excited about it. And that's also a part might be a little bit underappreciated by people like from the outside world. So like I want to share a little bit more of what we do. So we always come back to the silicon chip, right? So that's really our foundational platform. We do pretty much everything on top of it. And I want to say the chip itself is actually not static. It's actually like it's -- we continue to iterate and improve this platform.

So like a few things I want to share, like number one, we're able to maintain really, really good every rate in the last couple of years, like 1 in 3,000 base fare. Sometimes the best run we've ever seen was like is 1 in 4,000 base fare, which is like super, super amazing. And we also like improve the consistency of the DNA synthesis quite a bit like in the last couple of years.

So believe it or not, actually the DNA synthesis is quite -- can be pretty like variable, can fluctuate quite a lot. There's even a lot of seasonality in DNA synthesis. So I really think about it like just even like if you are in a rainy day, the moisture can be high, like them to kind of factor you're DNA recoupling. Like even the rush hour, the ozone from the rush hour can help -- can generate like cash effect quality of the oligos. And sometimes like for the vendor, we buy all the ball chemical from the vendor. They might sort of chemical outside in the hot summer. Like the quality is still going to be good, but it's actually going to have an impact on the oligo quality.

So we have done a lot of work actually to just really like tease out all the details, like trying to improve the performance. So like I want to say, right now, if you're looking at longitudinal data, like for the oligos has been very flat like in month by month and across all the machines that we have here. So which is something probably cannot be said by other competitors when they have like hundreds of machines running place over places. Like 1 common comment we hear from customers like they struggle with the current provider because like they get lots of batch-to-batch reproducibility issues, things like that. That's not something we have to worry about because of all the work we have done on the synthesis improvements.

And at the same time, like we continue to reduce the cost of the synthesis. As you see like in the last 3 years from 2023 to 2026, we're able to reduce the synthesis cost by 60%. And a lot of cost reduction come from the use of less solvents, right? So as you see, like in the middle, when back in 2023, took us about 51 liters of chemical to make [ 1,000,100 mer ]. That's quite amazing already because that's 1 million different sequences, 51 liter chemical. And we did a calculation back in the days, it's actually -- it's like 99.8% reduction compared to column-based synthesizer. So which is like we're talking about 3 orders magnitude lower than what people normally use.

Yet like in 2026, we're able to reduce that number to 14 liters for 1 million oligos. Like what does that mean? 14 liters, if you really think about it. So like looking at this bottle, this is a 500 ml bottle. So we're able -- the bottle of solvent like this can be easier to make 35,000 oligos, right? That's -- I think that's the scale we're talking about here. I think for the people who are on DNA synthesizers, a bottle of chemical probably normally used to make a few oligos. But here, we're able to pack in 35,000 oligos into a small bottle of solvents here.

And more impressively, I think we have done a lot of work to reduce the turnaround time. So like in the first half of 2023 took us 26 hours to make [ 1,000,100 ] nucleotide oligos, which was like really fast in the time. And when we launched Express Gene, we try to really look into everywhere, every place we can like shrink the time. So on the right are side, we're able to reduce it from 26 hours to 13 hours. And at that time, like I were joking, that's super fast already. I don't think there's much we can do to make it even faster.

Yet in 2026, we're able to shrink it down to 7 hours to make [ 100 mer ]. And that's actually a 73% reduction in turnaround time. And not only we get like kind of 37% reduction turnaround time, we actually means like we have 4x increase in capacity. So we have the same number of riders compared to 3 years ago. But like our oligo synthesizer capacity actually increased by 4x like with the same number of riders.

That actually enabled a lot of new applications like new product. For example, like I think I talked about the Ultra-Complex Gene, which need 500 nucleotide oligos. If we took the 2023 chemistry, it would take us 5.5 days to make [ 500 mer ] on the rider. And that's going to take up a lot of capacity and also like the product is not going to be competitive because like from get-go, you spend almost a week just to making the oligos. But nowadays, we can actually do it in less than 30 hours for the 500-mil, which like really enabled ultra-complex gene product.

And at the same time, like we built up enough, like enormous amount of capacity, 32 million oligos per day capacity, really support the needs for MRD customers because they all come in with personalized panels. Another thing we do, it's automation. We actually like take automation very seriously. We do -- try to do automation like in our front start to finish.

So when you go through the production tour, like I'm sure you see a lot of automation, like we actually use a lot of [ Hamilton ], right? That's the liquid handler to move liquid or pipe liquid, move place around -- so in a lot of applications like seems like Hamilton works well enough, right? Just to put the -- lost a deck, load the taps, load a sample. And like in an hour or so, they come back with what do you need to do.

So that works well for like a lot of applications, but not good enough for the gene production because the gene production is actually very -- like has many steps has 20-plus steps in the process there. If we were to use like stand-alone workstations like Hamilton, the issue we're running to is like take an operator to time to set up the deck, load the tips, low the plates,and run it. And once the run is done, have to move the plate from 1 machine to the other machine, do it again. So it's actually quite labor-intensive. That's created a lot of bottlenecks in our manufacturing process.

So what we did here is we actually -- we identified all the key bottlenecks in our production process, we build up integrated automation, integrated system to like to enable all the bottleneck steps here. So those are the 4 major systems we built from oligo fragmentation system to take the rider oligos to gene fragments. And then the second 1 to -- like then we clone the fragments like then we play them, we pick all the colon -- we have to pick millions of colonies to support our demand. And then once we pick your colonies, we all the colonies goes through next-generation sequencing sample prep system. We actually process about 10 million samples per year on gene facility, which I believe like we're probably more than like the highest volume like in terms of the sample volume for the NGS. Like I don't think anyone else in the world can match the scale, the number of samples we process sequencing every year.

And once we identified the like perfect clone, we can go through like plasma preparation to get purified plasma and ready to send to customers. And that's essentially how we enable the scaling with more and more integrated system, right? As you see, right now, we have a total of 20 integrated system on the production floor. And some of them you already see on the production like in IT-enabled genes and proteins, and we have quite a few systems in South San Francisco as well to support our NGS product. And that number was actually much, much lower than just a couple of years ago, like we only have 7 systems. So we continue to develop the tool, implement the tool like continue to improve our production by having more and more integrated solutions.

One thing I really want to highlight, it's like when we build tools like this, we always think through like scalability and trying to make it a future proof as much as we can. So like when we always look into when we brought a new product, new process, we really want to make sure multiple processes, multiple product lines can run the same machines.

So like back to the ultra-complex gene, which is -- like the workflow is somewhat different from what we normally do for like standard genes. But we're able to leverage all the systems you see on the left side, and there was a minimal addition for equipment. That's how we want to make it like future-proof and all like in our future compatible.

At the same time, like with auto automation, we're able to improve our capacity, improve our throughput and also with lower footprint, right? So like when you walk into the gene lab, the gene lab 1, that's actually the footprint of the gene lab 1. So we used to have 2 assembly lines, like as you see on the right side, those 2 green blocks. So that's all the like individual automation back in the days. So that's operators basically do work on automation one, finish the work, move to the station 2. As they work down the aisle, they go from oligos to gene fragments. That's what we used to do. So it's automated, but we still need like 3 to 4 operators running through the process. It takes about 9 to 10 hours to go through their process because it's automated, but like do you need like pretty much all hands on time to make it happen. And over 10 hours, you can make 12 plates.

Then in the last year, we actually put in 2 integrated systems, the 2 solid like in the green box on the right side. The footprint is 1/5 of what we have for the gene lab for the original workstations. But we're able to, like in these cases, operators only have to come in like a couple of people loading the plate, loading the tips. They come back in 6 to 8 hours, like with 16 plates ready to go. They don't have to worry too much about it. They can actually do other work like the end of the day.

So now like with the integrated system, we're able to manufacture 2x the fragment with 1/5 of the space. Now like actually, we opened up the gene lab 1, we're doing more specialty work. We can use it for cell-free work. We can use it for mRNA work in the future. That's how we ensure like scalability and sustainability as we continue to grow.

So Twist, like we always like go crazy about speed. I feel like we're always in pursuit of speed here. Like I think I already talked about the story about how we increase -- reduce the time for oligo synthesis like right from 26 hours to like 7 hours in the last 3 years. And we did something pretty similar for gene product. Like I remember like many, many years ago, when we launched the gene product, took us like 30-plus days to make a gene. It's a very slow process. And like we refined the process, we're able to make standard genes up to 5 kb in 10 to 15 business days. And when we launch like Express Gene, we're able to really shrink it from 10 to 15 days to 4 to 7 business days, which is faster than anyone else in the world.

And if 1 seems to be even faster, we can go with gene fragments like the 2 to 4 business days, super high quality. I think James just gave you an example, like the 1-day fragrance we made for [ Mike Wiley ], actually someone we worked with like a few years ago, during the Ebola outbreak like on the NGS side. So we're able to deliver genes in less than 1 day. Actually, that gene spend more time on the FedEx truck than like in our facility. That's just how fast we are, how crazy we are when it comes to keep continuing to improve the turnaround time.

And we're doing something pretty similar for the -- on the IgG side as well, right? When we launched the product, 20 to 25 business days. Good, but not fast enough. And are able to reduce it like 10 to 15 business as of today. And if 1 seems to be even faster, we can do them in cell-free manner and then you can get your antibodies in 5 business days.

So -- so like I think on the operational excellence side, like the -- we look at a speed like because we always want to go faster. We're also trying to reduce the turnaround time so that with the same number of people, we can do more work. We always look for opportunity where we can like save money, like reduce the cost and also ways for us to improve the capacity, like improve the throughput.

So like now this basically shows you like what we have done in the last like 3 years. Actually, even in the last 1.5 years, we have 46 tractor projects to improve like attractive CPI, continuous process improvements to improve our operational excellence, resulting in tens of million dollars of savings like in the last like 2 to 3 years.

I'm not going to be able to go over all the details about this like 46 projects. That's just like too many. But I do want to highlight 1 project we did on the panel side. So this is something like -- so we always make -- like on the NGS, we make panels. We're always the best when it comes to making panels, the highest quality, faster turnaround time. Like we can make panels in 2 to 3 weeks, which is like much faster than other people, which could be easily double our time.

And starting a couple of years ago, actually, we saw like MRD. We definitely see that personalized MRD is going to be a big trend. People are going to do more panels like it's going to be all personalized, there's going to be lots of them, right? So just to front our understanding, like we're like, hey, customers really need the panels to be incredibly fast because MRD patients cannot wait. The window for the test is very short, like we want to make the panel as fast as possible, ideally less than a week.

And we also need to build a very highly efficient process because we need to be able to make hundreds thousands of millions of panels because that's how many cancer patients we're talking about. And we cannot compromise the quality. We still have to maintain the highest possible quality because other patients deserve like the best panel like we can make for the cancer detection.

So we know that's what we need to do. Then like we planned a couple of years ahead of time, saying, "Hey, we see the surge coming. It's not there yet, but we need to be ready." So like we did a lot of planning, like a lot of preparation. We know we need to put in like fully walkaway automation system to make the panels. We need to build in like the software to really make this happen, like the integration of the software MES and hardware. And like then it's all up to execution.

I think like 1 thing we do really, really well and this company is execution. At the end of the day, like with the fixed headcount for the panel production, we're able to increase our capacity by 10x. And like actually, reduce the turnaround time very significantly from like 2 to 3 weeks to make a panel to like less than 5 days to make a panel. At the same time, like we're actually able to like take the learning we have to make MRD panels like can expand it for all the panels, actually resulting in more than $2 million saving. Like in the consumables when it comes to panels. That's actually the year 1 saving. The number is only getting bigger and bigger as we continue to scale our operation.

So I think this is my last slide here. I think just want to give you a taste of like how we see the world like when it comes to NPI, it comes to like operational excellence. And we're incredibly excited about what we're able to do, like I'm happy to talk to all of you like of you like off-line. I'm going to pass the mic back to you, Emily, here.

Thank you very much, Siyuan. Much appreciated. So next, we are going to have a fireside chat with Dr. Frances Arnold. It's my great pleasure to bring Frances here. I have my note counts, I feel like [ Alex Trevica ] on a game show. And we have a runner. So the with a microphone, thank you in the room. So I have my own questions, but if you have a burning question, please do not hesitate to get in.

So to start, thank you, Frances. I remember when we're talking about a Twist, we're a baby startup company, we are thinking about who can you bring in the SAB, we need the best. And I told the Board, you all know that, but I'll tell you now. We need Frances before she wins a Nobel prize because we'll never get her after. And so you graciously accepted it. And maybe my first question will start there is we had a number of SMB meetings. We're just at the beginning. A lot of start-up sales, you see. Did you think we were going to make it? Or do you think we are crazy was never going to work.

I thought it was crazy but that you were going to make it. Because the need was so great and the ideas were clearly the right ones, right, to miniaturize and go with the silicon. I'm an engineer by training. So this is deep engineering and not appreciated by many biologists. So I thought you were exactly the right person to make that happen. And you did it.

We did it. Well, if it's good, it's the team, if it's bad to me. So is I want into the bad stuff. We just did the two. What do you think? What surprised you, good or bad?

Well, I have to say I'm blown away because I moved away from the SAB in the early days. I had to deal with a whole bunch of other things at that time. So I didn't follow in detail, and this is the first time I've actually visited the manufacturing. So I love it. I have to say I love it. The attention to execution, although you were always attentive from the very beginning. You put in quality control is #1, careful engineering is #1. So I'm not surprised. But it's fantastic because I use these products all the time in my research. And we love it. We absolutely love it, but I'm not surprised to see how well you've been able to scale this.

That's great. So you are an expert in enzyme. I think a lot of your career has been focused on enzyme. I know you have to catch a plane after this, so you'll miss our part on enzyme engineering. But just tell us why enzymes are so important from your perspective?

I've been working on enzymes forever. And these are those remarkable molecular machines that convert really cheap materials like carbon dioxide and sunlight into complex chemicals like trees or you and me. All the chemistry of the biological world is done using these machines.

And now we're coming into a period of synthetic biology. It's not just all about health care folks, it's about everything. And biology can make virtually anything. It's just bringing the cost down just. It's bringing the cost down and opening up, expanding the possibilities for biology. Because biology makes you and does a pretty limited set of chemistries. But evolution is an algorithm that can go out well beyond.

So I see now enabled by what you're doing in AI, a combination is enabling biology to do any chemistry you want, to make any pharmaceutical, to make any fuel, to make textiles to make all sorts of things we need in our daily lives that are done using dirty chemistry today or not even done at all. So I think we're at an inflection point really that's more exciting even than it was 12 years ago.

I totally agree. Yes. And so enzymes are proteins, right? So there are nontherapeutic floating in the 2 big categories. There's also the therapeutic protein side of it. You're an academic, you're in the SAB or fund of a number of companies on the Board of Alphabet. So you have, I think, a unique view into protein engineering and also as it relates to therapeutics.

AI is challenging that field. And 1 of the questions, and I'm sure a lot of investors in the room have the question is around AI-driven drug discovery, is it a flash in the pan? Is it here to stay?

Well, what do you see? What are you seeing? Because you're seeing the same thing I'm seeing, and I want to hear it from your math.

In is we're going to need a bigger boat. In terms of capacity, I think that yes, what we see is there is a first pass of model building, where people need a lot of sequences, expressing protein, tested against a number of targets to build in the model than we find the model. And then you have to turn the crank and the turn the crank needs a lot more DNA.

And I think what we're seeing is AI is going to be the first path for drug discovery. In vivo and in vitro are second needed. But AI has a huge advantage is that it's fast. It's much faster than having to either inoculate a mouse. You have to wait for the mouse to be your drug company. And all you have to do page display or use display and that takes days. So that's what we were seeing is AI as the first pass. Does that jive with what you're seeing?

Well, of course, because enzymes are more complicated than the binding proteins used in therapeutics. It's the next generation, right? So it's still -- AI is still not good enough unless you combine it with these optimization methods for which I won the Nobel Prize. So imagine that you can combine AI to get to a good starting point for chemistry, but then you combine that with iterative optimization, all of which uses synthetic DNA. In an active learning cycle, you just press the button. And I think in the next 5 years, we'll be able to genetically encode any chemistry.

So I'm super excited about this. And I know it's not a flash in the pan. I write the checks for the alphabet. And they're really big checks. It's not a flash in the pan because whatever you can't do today that won't be true tomorrow. And we're just going to get better at it.

So then do you think it's going to make DNA obsolete? Or these things are going to create more demand for DNA because they're going to have to try more things?

So that's a really good question. If we get perfect at AI, right? You just make 1 gene and you're done. That is completely unrealistic, right? Because there are so many specifications that we don't even know to make on the DNA, how it cures the disease, how it catalyzes a reaction as in a particular laundry detergent formulation. I mean there's all sorts of things that we can't write down in the specifications that only become real when you translate the DNA into the sequence that you compose using AI into the real world.

And what -- I think it was said -- you said it this morning. DNA is the point at which you translate your computation into the real world. That is the physical manifestation of your computation. And I think it always will be because it's not efficient just to go and make proteins synthetically. Nature does it by DNA and she does it for sugar.

I do it for sugar too. Lots of sugar. So if there are any questions, raise your hand and we have a run that will bring your questions. So the last 9 years that you were to write down, we had a number of projects together. We -- early on, we did a membrane protein cameras and then with the machine learning design charger optogenetics tools. And then very recently, we did a carbon transferases project.

So as a user of DNA, how do you choose a provider? What -- you're using Twist a lot, but there are other providers. What's the criteria for you to make a decision of where to go? Or do you leave that to your team?

Well, I come from academics. We never have enough money. So price is very, very important. That's why I yank on your chain and say, "Hey, I want a better price, but I'll give something in return." So yes, price matters a lot for the academics, but I also sit on the industrial side. And their time and quality are the key things. So there are different metrics. And I have to say I'm pretty impressed that you can meet all of those metrics.

Yes. A lot of companies, between the speed, quality or price, usually, you have to choose to. Now any 2, but we wish we pride ourselves in you get all 3.

And that will be important because graduate students are relatively cheap. They used to be almost free. And so time was not manipulations, that wasn't important. Now they've become a little bit more expensive. And so we actually do a little bit of calculation over the trade-off. Do we want the whole gene or do we do all that ourselves?

But in industry, time is -- especially in this new AI or a time is -- turnaround time is really critical. And as we move into active learning test build design cycles, the ability to turn that around and generate the key data will mark who wins in this race. And believe me, it is a big race. There's a huge amount of interest in biology as a manifestation of AI. And not just therapeutics, as I say, it's all of chemistry. It's going to be a race who can do this.

That's why we love speed. So there's a question. Great. Puneet?

Thanks for the question, and great to see you here. So the question is really about there are a lot of assumptions being put forth in AI in terms of speed, speed to therapeutics, the speed to drug discovery. Can you talk a little bit about in terms of scaling? Where do you see as you talked about briefly about chemists' role is going to change a bit, too. But can you talk about the scaling we're going to see? Or there is an assumption that we are hearing in the market that in 10 years, we can see a number of therapeutics coming to market that can actually resolve a number of diseases? Now that's a big assumption being put out there. Disease and biology is complex. So can you talk about this scale that needs to happen, the hiccups that can potentially happen? And then how long is this versus 10 years?

So I sit on both sides. Demis Hassabis says we're going to cure all diseases. But he doesn't talk to the FDA. And you can't just go and test these things willy-nilly. So yes, AI might cure some number of diseases, but it's all going to be at the rate at which drugs can be developed.

And the pharma people are much more realistic, but maybe not quite as visionary. So what's going to happen, a lot of it depends on what happens on the regulatory side and on clinical trials. How do we understand the efficacy of these AI designed drugs.

Thank you. Any other questions in the room? I'll keep going. And if you have questions, please do raise your hand. So as a scientist, I guess, I talk to a lot of scientists. And I ask people about protein engineering or any topical loss. You have 3 people out to do it and you get 5 answers, right? I'm always shocked that there's not more standardization, and we're trying to turn into a competitive advantage, but I think, we'll do whatever you want. But what do you think that is that there's not more standardization, like everybody wants to do something different?

Hell if I know. Well, scientists are funny ducks. They like to put their own stamp on it. That's 1 thing. So there might be 1 great method out there. That doesn't mean everybody piles in to do it, especially with a complex problem like protein engineering. Every protein is different. And every landscape, we call them landscape, how you go and explore different sequences is different. How you measure everything is different. There's a great deal of bespoke engineering that goes into it, which really tends to push people into their own methodologies.

That said, I do think, Emily, that there will be a push the button that maybe not be optimal for each problem, but will be optimal across most print protein engineering tasks. And that will involve machine learning, active learning cycles with machine learning. So that project we did with you way back then with the [ optogenetics ]. That was the first time machine learning was applied. And in fact, we developed those methods in 2012, 2013 and did that collaboration with you to demonstrate how you could engineer new properties. And that was well before the ChatGPT here. Yes.

Well, speaking of ChatGPT, maybe I have a personal question. So when you use an LLM, ChatGPT, it's all about the prompt, asking the right question. So obviously, you're massively accomplished. How were you able to ask the right question? How do you think you've got here? Like what made you get here?

I think it was desperation, right? If you're pushed hard enough. I came out of an engineering background, and I jumped into protein engineering when it was a brand-new field. And the people who were doing it, and I'm sure you have had exactly the same experience. The people who were doing a little bit of protein engineering were from structural biology. And their whole mindset was you had to get a crystal structure of this very complex molecule first, which -- often, people couldn't even get that in order to go in and then rationally design everything. And I came in and said, well, I won't get tenure all die first before that happens. And I had to come up with some very different engineering mindset, which is look to the best engineer on the planet and that's called evolution.

So to me, it was totally obvious, but to the field, it was completely not obvious. And I think in DNA synthesis, you experienced the same thing. Everybody was doing at the same terrible way, terrible way and there's no way you could scale that. And you came in as a chemist engineer, and said, no, we have to completely rethink that. Isn't that the case?

Yes. Being a contrarian goes a long way, right? And I would say 1 to the team is that because it's incredibly hard and sometimes it's like, so I'm like good because if it was easy, every idiot will be doing it, right? And then number two, always when there's a plan, okay, let's do this, I always ask why -- why are we -- why this way? And the worst answer for me is like because everybody else is doing it. We're not doing that because everybody else, they have more resource than us. They have more experience, they have better channels, better capital access. And so how can we beat them if we do the same? So total.

But then you also have to have a vision of when you enable this capability what becomes available? And how do you capture that and capture at least some of the value of that.

Yes, totally agree. And that's why I think it's very helpful to be technical. I mean, when I started Twist, I kind of was down playing my PhD because they're like, "Oh, you're on to business." But I think you can learn business easier than learning the technology.

And when you talk to a customer, if you don't understand what they're doing, when you don't even know your product, how can you be effective? And so I think that has been very useful, being able to -- you drop in any account, and I can sell any products that we have.

But what I love is what the technology has enabled that we could not do before. And in my work, some of the same things happened, and that's how I chose problems was what can I demonstrate that you just couldn't do with rational design. A good example is how do you make an enzyme work in the laundry machine. That was a big market for enzymes. How do you make it be happy? What self-respecting natural enzyme wants to work in your laundry machine with bleach and surfactants. There's no way you could design that. It had to become from directed evolution. And you can't go out to nature and find that.

So you had to have some methodology. And you've done much of that, right? You've enabled people -- my methods enable people to do a whole bunch of much more important things than make laundry enzymes. Your technology has also enabled the whole synthetic biology industry, I would say, to do things well beyond what even you could imagine.

Yes. That's true. Any questions in the room? Okay. All right.

Thank you for your time, Dr. Arnold. Curious to know, there is a variety of models that exist today, [ Alphafold, Bolts ] and others that I think are adopted by pharma. I would be curious for your perspective on the utility of those models and drug design today versus what might be required from a new data generation standpoint to ultimately get to the point of -- maybe not quite a good point, but sort of push button, get drug or get got closer to that point rather than the models have been largely built on third-party existing data?

So I'm very excited about the models. I love that Demis and David Baker won the Nobel Prize for understanding protein folding and then design of proteins. But the bottom line is that structure is not function, right? Being able to predict the structure is a game, it's like winning at chess, and you have a good metric for that.

But what we want in the synthetic biology community is something that does something. And the models just can't capture that right now. But I think they will. They'll start beginning to capture that as we get the right kind of data, which we don't have. So even though that problem has not been solved, we are getting close to getting things that can solve it through further experimentation. So this is why it's a beautiful time, right, for making DNA because we're close enough that we just need a whole lot more experiments in order to learn what it really takes to make something that's useful.

Question over there?

To that point, I guess, if a lot of the open source tools are protein structure today and a lot of the drug developers are incentivized to have more siloed data sets in regards to function, how do you see this playing out over the next decade? Is that going to be the path forward that each developer will kind of keep some of that data in-house? Is that going to be sort of a rate limiting factor on the field in general? Or do you think these open source tools will move towards function and more downstream practicality?

I think that's a really good question because we -- and I don't know how to answer that because I don't know how much data it will take. There are those who argue that enough data on your particular system is all you need, right? You don't need a world model that works across all modalities. And to me, as an experimentalist, that makes more sense because I know how bespoke every protein is.

On the other hand, if Demis is right, you'll learn across all proteins and you just have a model that does anything. He doesn't know anything about proteins. So I mean to a first approximation. There's no quotes given here, right? This is house rules. No, I love Demis. But -- so I don't know who's going to be right. But I do know, so I'm on the Board of Generate Biomedicine. So we went public about 6 weeks ago, AI-produced antibodies, we integrate it with a lot of experiments, right, to get to the right developable drugs.

I think there was a question in the front? So -- no? Questions? All right. Maybe the last question for me, going the -- what if you have to start again today? So suspend disbelief, you're back, going to [ new cities ]. If you had to do it all again now with the AI tools with the ability to have the wet lab outsourced, even maybe at Twist, either at [ Caltech ] or not, what -- if you are to start again today, way, what would you work and how work on? And how would you do it?

Two answers. And I want to ask you that question. I love proteins. Protein engineering is not solved. So you could jump in at the place it is and still do really important work, enzymes that are not solved. But if I have a student of evolution -- evolution works at all scales. And so why not apply some of the same ideas to tens of molecules to whole cells, to organisms, to ecosystems. It's the same design process to design anything in biology. And so that means whole new scales of DNA, right. Not just 1 gene, but a whole ecosystem of organisms. And I do know that some of the most visionary people in the community are really thinking about that. How do we use AI, for example, to design whole genomes. That's happening. How do we use AI to design whole ecosystems. That's going to require a hell of a lot of DNA.

I love it.

What would you do?

So I love DNA chemistry. I know to write DNA, read DNA, sell DNA. So I think I was built to build Twist. If we had to start today, frankly, I think it will be very hard because when we started Twist 13 years ago, we knew that we had to raise $1 billion to get to exit velocity. And back then in 2013, you could do it. You could raise the first $600,000 seed round and then a $9 million A round, all the way telling everybody at the end, it's going to take $1 billion. And we could do it because capital was available. And I don't know if we could do it today. I don't know if that...

It's all being sucked up by SpaceX.

And AI. Yes. So I'm glad we did it then, because I think now it will be tough.

That's sad to hear because the Anthropics are raising lots of money, and they're going to be buying all your DNA.

Yes. And we were a work to do it. So it's good. And necessity is the model of invention.

So do you think 1 of those companies will buy Twist?

Well. Now I'm being triggered. So hopefully we'll buy a lot of DNA. But that's not our goal. Our goal is to ramp our revenue. And eventually, we'll get to buy Illumina and Thermo Fisher. That's not long-term guidance, by the way. But this is America, we have forsa every day, if there is enough zeros on the check, I will fly and wash your car.

So on that, thank you again so much. I know you have to catch a plane. I very much appreciate the effort, appreciate the partnership along the years. We can't wait to continue being your DNA protein RNA to enable the great work you do.

And thank you, and thanks to the whole team here. It's just marvelous, what you've created.

Thank you. All right. So next -- sorry, back. Next, we're going to hear from Colby Souders, our Chief Scientific Officer. Colby is our own drug developer. And I believe that at least for drugs that he had his hands in the discovery are in the clinical trial stage and a few of them at Stage 3. So Colby, take it away.

Thank you. Thank you, Emily. Pleasure to be here. Thank you, everybody, for coming and those of you making the trip, hopefully, it's been a great day so far. Fitting to follow-up that fireside chat with the AI topic, which I know is of particular interest for many. But we heard a lot of great comments on that. And so now what we're going to do here is dovetail that conversation into how we approach AI at Twist and how we solve this for other companies and how we enable AI companies to scale and to fulfill their promise to the market that they are making. So I'll dive into this in more detail. And hopefully, by the end of this, you'll understand kind of our position on the market as well as what our solutions provide to solve that.

So this is a slide you saw from Siyuan in the earlier slides. So you can see, first of all, a lot of products that we make touch that AI-enabled drug discovery. Now I'll focus mostly on our IgG and antibody characterization, but also mention in the next few slides, how all those other aspects that are touching that area provide those tools and what differentiates our platform in order to enable the AI drug discovery in IgG and antibody characterization.

So first point to that being, of course, we've heard a lot about DNA. But for AI-enabled companies, it's not just a DNA product that some of them need. Many of them need protein. Most of them need data. They don't necessarily need a physical product from us. They need that characterization data. But to enable that, we need to start from DNA. All biological material starts at that point. And so we've built out that speed, scale and quality to enable that.

Now of course, we've talked about speed. We know with higher speed, we can evaluate more candidates in less time. That really accelerates that design-build test cycle for our AI customers and enables them to develop more therapeutics within the same amount of time. Those are all huge advantages. And of course, with scale, you need these very large data sets. I think what we've seen over the last 2 or 3 years is that folks would start with smaller data sets or they'd start with unstructured public data, and that wasn't good enough. They realized we need to do this at 10x, 100x, 1,000x what we were thinking 2 or 3 years ago.

So we've made that scale. We've enabled not just our DNA but also our protein solutions side, our protein production, our data characterization delivery to match that scale because that's what those AI companies really care about. Now of course, not only does this support more candidates, but it supports other modalities and different target classes as well. So I'll mention this more toward the end. But we think about things in terms of binders and the field has gone from many binders to VHHs to IgGs, but there's a lot of other modalities that these AI companies are thinking about. And so we're just at the very early stages of scratching the surface of all those other capabilities.

And now, of course, you can have speed and scale, but honestly, it means nothing if you don't have quality. Poor data, no matter how much you can make and how fast you can make it, will not be informative for a model, and it certainly will not develop a therapeutic. So you can think of quality in a number of different ways and a lot of the traditional methods that you would measure quality. But honestly, we think about it in a couple of other ways, too. So by providing flexibility in different formats or multiple production systems, now we're enabling our customers to develop their therapeutics in the context that they want.

We heard a little bit on the fireside chat, but 1 of the important things is there's so many different types of biologics. Each of them needs a different system, a different format. If you don't provide customers with that flexibility to order it, the DNA, the protein, the data in the format that you need to fill your model or fill your therapeutic pipeline, then it means nothing.

Again, we've got several different product lines. I'll talk a little bit about how our unique multiplex gene fragment and gene pool systems really enable new library development. And that isn't just for scale, but again, for quality because now you can start making combinations of different products in really unique ways that no other company can enable. And so that's a huge piece, not just of a product offering, but a quality because the design and the flexibility that a company has to make that design and then actually fulfill that is unparalleled with those products.

And then finally, that fit-for-purpose downstream use. Again, I mentioned this, but the endpoint and the starting point is very important for these AI companies. They don't all want to follow this linear gene to protein to data. Maybe they want to come in at the protein, maybe they want to just get data from us. And so we provide that flexibility. So if somebody can start and end on that train, if you want to say, at any point. And so that's very important.

So we've built this foundational system for DNA synthesis and protein production and data collection. Now the important thing, though, is you can't attack it from 1 side. So I'm saying, okay, I can build scale. I can build speed, I can make product really fast, but you also need that expertise. So you need that expertise in the characterization to fulfill the AI/ML dream of data sets.

And so we've attacked it from both angles. So by this, I mean, we have been developing for over a decade now, different in vivo, in vitro antibody drug discovery platforms to fulfill these and to deliver the hundreds, maybe 1,000 therapeutic programs that we've done for partners. We need to establish all of those advanced characterization methods that all these AI/ML companies want on the back end of their data production systems. And we've already built these out by having all the deep expertise in that full end-to-end antibody discovery platform.

And so this has required a number of tools where we have things that support all of these in vivo systems, all the in vitro library discovery that we've done. And so we've developed these tools, some being shown here. And all we have to do is now apply that to the scale and the speed that we've developed on the front end. And so that's been a very seamless process. So now we can deliver with binding evaluation, the affinity measurements, the developability characterization, those functional assays, the things that are fundamental and that these AI/ML companies want, but they want it in high quality, but scalable data.

And so that's really what differentiates us is that many companies try to approach this from 1 side or the other. Maybe a company only has expertise in scaling. Maybe a company only has expertise and deep characterization in antibody drug discovery, but we do both, and we merge those 2 things. So on the 1 side, we've got the deep expertise in antibody characterization, protein binding, things of that nature, but we're able to apply our scalable automation and operational excellence that Siyuan was talking about earlier. So that's how we apply it.

Now I'm going to get into a bit more detail on exactly the different types of workflows that these AI/ML companies are essentially ordering from us and crave in order to fill the pipeline of data that they need. So 2 main modalities here. So 1 being the library-based workflow that is very wide, the other being the clonal sequence workflow that goes very deep. So what do I mean by that? The library-wide workflow means a customer might come to us with tens of thousands or hundreds of thousands or maybe even more individual designs, and they say, I want to test all of these designs. Not sure how well my algorithm does. So I want to test all of those from at least a very basic standpoint. So now I can narrow it down.

Now at the end of that, you can see a lot of those actually feed into that clonal sequence workflow. But what the clonal sequence workflow does is goes very deep. So we apply all of those characterization assays that I was talking about to the proteins that we produce on the scale of not just hundreds, not just thousands but maybe tens of thousands. So now between 1 side or the other, you can tackle a problem from 1 antibody if somebody orders it, all the way up to hundreds of thousands or millions. So being able to utilize each of these workflows, apply them at the right time and in the right sequence is very critical to the success of these AI/ML companies.

Now I'll provide a couple of deeper scientific examples of where we've applied this, first being on the library side, I'll have 2 examples here. One is where we had a customer who actually just wanted the DNA delivered. They want to do the screening in their lab, but they required our library technologies to enable them to even screen that in the first place. And then in the second example, it will be a full end-to-end solution where we not only did the library production, but as I'm showing here, all of that panning and NGS output, the screening, the lead candidate selection.

So here in the first example, going from 1 Nobel laureate to the other. This one is with David Baker and where we collaborated and did some work for his lab. And in this particular example, the challenge was using the algorithms that his lab has developed the protein MPNN tools in the RF diffusion algorithms to design binders, VHHs, nanobodies, however you might know them to 4 different unique binding sites on proteins and wanted to generate 9,000 unique sequences that were targeting those different sites. So we used our multiplex gene fragment technology to make all of those libraries, put them together and then send them to the Baker lab, where he was able to then validate and pick out the leads using cryo-EM and different SPR techniques to measure and validate that those models were working. And so it was very critical for us to be able to use our precise printing library technology and print those libraries, the exact sequence that he wants to be able to deliver that to multiple targets with over 9,000 designs per target.

Now in the second example, this was a collaboration with Amazon Bio Discovery and Memorial Sloan Kettering Cancer Center. And so in this particular challenge, they -- Memorial Sloan Kettering designed over 300,000 unique sequences. And now this was very large because this is actually to an undrugged target, very complex protein, something very complex that had never been targeted before in biology. So we needed to use a very large library to interrogate this and figure out if we had any valuable binders.

So we did all the screening. We looked at 12 different populations, millions, tens of millions of NGS reads. We found hundreds of individual clones that we then selected. But we did all of this work with the library screening, not just the library production, but put them in yeast, did the protein selection, the screening, and we found a number of different very interesting and very valuable targets.

Now the great thing here is that they're coming back. So just like many AI/ML companies, they realize we're probably not going to solve it on the first round. So they're using the data from this first round of output to come back, optimize it and do a second round. Now when we look on the clonal sequence side, there's a number of different applications here. But again, I think people want a lot of data now. We need to go very deep to characterize this to train the models very precisely.

The first example here, being a study where we partnered with Charlotte Deane's lab. Now she is a world-renowned expert as well who designs open source bioinformatic tools that are basically universally across the antibody industry. So very impressive. Now the aim here was to develop a tool to predict nanobody structure and properties. Now her lab had done this for antibodies before and have published those tools and methods, but nobody had ever done this for nanobodies or VHHs before. So we worked with her lab in order to develop the wet lab data that validated and fed back into these algorithms to generate. And we found a number of different really unique properties that were very interesting that hadn't really been realized before for nanobodies. And those have been now incorporated into that model.

Now in the second example here for the clonal workflow, this is, again, with Amazon Bio Discovery, this time with the GrayLab. And so here, what the idea was, is we wanted to characterize all of the different models that were being published in the Amazon Bio Discovery website. What we were able to do was take 5,000 different designs across 50 different targets. It's a lot. It's a big study. We generated 70,000 data points across 7 assays for this particular one. So again, went very, very deep.

This enabled them to learn which of these algorithms were valuable to predict different properties. Not every algorithm was perfect for every property, but now we know which tools to apply to which problem within that platform and we can learn and fine-tune those models now in multiple iterations of that cycle. So very valuable data set, 1 that's being used for benchmarking most of the Amazon Bio Discovery tools and that other folks is open access and people able to use. And so suffice to say, we're fully aligned with Amazon's mission there to build this ecosystem of AI agents and be able to empower these scientific capabilities and make them accessible to many researchers, not just the largest companies that are well funded AI/ML companies that can design their own algorithms, but making them available to all.

Now those were some very detailed specific scientific examples, but I think what might be most valuable here in this setting is talking about the customer journey. In particular, what do most of these companies do when they come to us with a problem when they come to us with an AI/ML workflow that they need to execute on. Now most of these companies start at what we would consider the model building stage. So in this particular case, they might have a model that they've already developed, similar to some of the other examples that I just gave. And they'll say, well, first, we want to do a pilot study with you. We want to know that the data we're getting is going to be valuable and it's going to fit into our models and it's structured the way that we need. So that will usually be on the order of tens to maybe hundreds of sequences, a fairly small study. We'll do all the production and all the characterization and data delivery for those known sequences from the partner where they already maybe have data on that and they're benchmarking us to say, okay, how accurate is your data compared to what we expect. As you usually complete it in just a few weeks and for a matter of $10,000 to $100,000.

Now once we pass that pilot stage, now we go into the first round of training. And so this is where we've got thousands of sequences, maybe tens of thousands of sequences for a single round for a single target. And again, we will go through the full make and test cycle to deliver data. And then this is completed, again, in about the same time frame. Slightly higher costs, but usually less than $1 million to be able to feed into the model. So now this is real world data that they're using in their design algorithm.

But again, now they need to learn. So this will feed back in. Almost never are these models perfect the first time. So usually, you'll see 2 to maybe 5 additional rounds that will provide fine-tuned training for these models. So by that, we mean that it's predicting particular properties of the proteins that the company is interested in. Maybe they're assessing how well it fits also into other parameters. Once we go through this, typically, we've established a really good close long-term relationship with the partner. And more importantly, we become embedded essentially into their make test cycle.

Now the interesting thing is that then most of these companies realize, okay, I need to build more foundational models. So they'll say, "Well, I had this original model I came to you with. I learned a lot from it. But now I want to predict a different property." Or maybe I say, well, that went really well. I need more data. I learned from that process that I need more data to solve additional problems.

And so here is when we get into that library build process. So we have tens of thousands or hundreds of thousands of sequences. And again, those are designed across a number of different proteins now. So we're looking to build generalizable models. So we want to say, okay, I'm not solving a problem for just this 1 property or this 1 protein. Now I'm solving a problem that I can apply to a wide variety.

These are much larger studies, but usually completed still in weeks and for hundreds of thousands to millions of dollars sometimes. Sorry. Then on the validation of this, that's very similar to what we saw. Again, we're going into that clonal sequence process where we're making, testing all of those, and that's so very similar to what we just saw. And again, we go through multiple cycles of that to learn and feed that back in to build these new foundational models. Now the company has a great platform. Now they have foundational models. They fine-tune them. They've tested them. They've learned from them.

And now they say, okay, we're ready for therapeutic discovery. So now we'll apply this to a set of targets, usually not just 1 at a time, usually multiple. And that will be hundreds to thousands of candidates that they want to make and test. So it's not just making tens or dozens like has been published in some -- when you're making a therapeutic, you don't want to take that much risk. So it's better to make hundreds of thousands and overshoot it rather than undershoot it.

And so we'll do the production. We have a second layer of characterization we'll do here. We'll get into functional characterization, so we can really tell if this was an effective therapeutic for that particular application, again, completed within a matter of less than a month for under $1 million, very effective drug discovery campaign. We definitely will take top hits into optimization models. Again, zero-shot discovery of these therapeutic candidates is not where it is today, maybe in the future, but we're still a ways away from that. And personally, I think we'll always want to do some optimization tinkering models whenever you're developing a final therapeutic.

And so again, we'll do hundreds to thousands of these in a similar cycle. And then finally, once we're done with this optimization process, then the partner will nominate that lead candidate. They'll move those into efficacy testing in animal models and tox models at different CDMOs. And then once those tests, then they will enter the partner's therapeutic pipeline.

So the great thing is that these aren't just theoretical. We've been doing this, and we've been doing it for a little while. Just recently, though, we've completed over 200,000 proteins expressed. Over 130,000 of those were assayed. This has generated 7 million data points for dozens of customers. So very impressive scale.

Now what you see here is 1 example of that in the data output. So this is millions of dollars on a slide basically in data. So very impressive throughput and is critical for that training and process. And so the -- thank you. The amount of biophysical data and information that is available for this particular data set has enabled not only model building, but also therapeutic development. And so that's the key here is this is what we're talking about when we say we generate these large data sets not just to help enable them to build more models, but to enable therapeutic development in the future.

And here's another great example of a multimillion dollar project where, again, we're looking at 50 different targets here. So again, binding candidates, 50 different proteins. We're also benchmarking against control antibodies. So those are in red that you see here. And so we're measuring the candidates that we are testing the AI design candidates against those benchmark candidates to find out which ones would be the best properties.

Now we compare all of this data together. So again, it's not just binders, it's not just biophysical properties, but it's all of this combined so that now you can select lead candidates. So here, we see in this green box. You put all of this data together for somebody to again, train a model because of the model will depend on good data and bad data, the data in the yellow and the red. But then when you're selecting the therapeutic candidate, now you can select from that green box. And so that really allows people to use the most out of all of this data that we're delivering.

And the last example that I'll end on here is a really interesting one because this kind of illustrates the way the market goes. So every -- in the biology, every 20 years or so, a new model, a new method will emerge. So here, what we're looking at is actually a campaign where we ran in vivo, in vitro and AI/ML altogether. So in this particular example, we have hits from each method. We took those all the way through functional characterization to find the best hits. And we found that hits from every method were valuable.

So really, the message here is that it's not that 1 method supplants and replaces the last. The traditional drug discovery methods are still very viable and very useful. AI is now just a third additional tool that we can add to the mix. It's a very unique one and the newest in this series of in vivo [ hybridoma ] discovery originally, in vitro phase display discovery, and now AI/ML.

The interesting thing from this one is actually, the partner has told us that they've nominated the lead, they're coming back for more optimization on that, but the lead is actually from the AI/ML library, interestingly. So that was their lead candidate, it came from that design. It passed very well through the animal efficacy studies, very useful. So we know this is working. We know this is a tool that people are going to continue to use, and we're very pleased to see that, that all 3 of these methods can work together in concert to find lead candidates.

And then again, kind of teased it a little bit at the beginning, but it doesn't end here. This is -- so far, we talked a lot about monoclonal discovery, single candidates. But we see a wide future of different modalities, one of those being bispecifics. So we put a lot of effort into this recently as well. So you can see here, we don't just end at the multiplex gene fragments like I showed for the Baker lab study, but we also had the gene pools.

So now being able to make a single gene of that length means now you can really uniquely pair different bispecifics together. It's a very unique thing to be able to do so that you can start, again, AI designing how they should come together. And now we can do that in that library method. But then the question is, okay, now when you want to go deep, when you want to have the characterization workflow, how do you do that and high throughput? Because bispecifics are traditionally very hard to work with.

And that's where the B-Body platform comes in. So we had the licensing of the B-Body platform from [ Invenra ]. And this works extremely well in that high throughput method to make hundreds or thousands of candidates very quickly and then characterize them very deeply and keep them in that format for your downstream manufacturing in CMC. So this is where we think the AI/ML market is going next or at least 1 of the ways that it's going, 1 of many that we will continue to support. And so we've been very proud to support it and provide the data, the genes, the protein wherever somebody needs to start and stop along the way. And we're very excited about the future of the industry as well and the different modalities that it's going to enable.

And with that, I'll turn it back over to Emily.

Thank you, Colby. So what does that mean in terms of business? Thank you. So in terms of business, we mentioned that we were able to deliver a number of -- a large number of antibodies that assay, less number of data points.

And to help illustrate, we want to share the revenues that we got from 5 different customers. So it's not an exhaustive list. There is a mix of a frontier AI lab, a large pharma, a native biotech, a dry lab biotech. And what you can see is that we have to meet customers where they are. They all have different colors because we have a full [ money ] of product. And at different times, even the same company needs different product. And that has been our strategy to become a leader in drug discovery is we had to, one, have the full menu. If you want to hybrid, we'll do a hybrid. If you want a single-cell workflow. We have that. And so on and so on.

So not only we have the full menu, but we have the best implementation of that full menu, and that is being seen here. And AI is turbocharging this. So you can see, generally, it's up into the right, and customers are doing things differently. And that is back to the key that we -- of what we provide is industrialization of what you want, very high throughput, but we're going to be flexible, and we're going to customize our solution to exactly what you want.

In terms of the market, what does that mean? So we are updating what our belief in terms of where the market size are going to be. And please note that this is for 2030. And so we think that the DNA market is going to be flat at the -- remain at $2 billion. We're seeing that the antibody drug discovery is going to grow. And on top of it, in antibody discovery service we have an additional $0.5 billion that is driven by AI-driven drug discovery. And then we think that the protein expression slide is [ earth low ] putting expression, market is also going to grow. And so we think there is a big opportunity.

Of note, the assumption that we have here for AI is that there's not 1 dominant model. This is an assumption that many models are going to win. And then the second assumption is that AI drug discovery is going to work. And we heard from Colby, we think it's going to work. We have example of it's going to work. But if it didn't work then, then the market will not be as big.

And last, we are very happy to share that the growth between FY '25 and FY '26 so far is in the triple-digit order growth. So we've talked about AI in the context of drug discovery. But as a reminder, AI is broader than just drug discovery. And here are just 3 examples of work that has been published by customers on the left. We have using AI and [ citic DNA ] to discover, engineer develop CRISPR tools in the middle, the same leveraging AI for mRNA expression in cell and gene therapy. In say with mRNA as a modality, the promoter, the enhancers, the terminal regions are very, very important. And you have to engineer those regions. And nothing better than the combination of AI plus with DNA to be able to engineer that.

And then last on the right. We can leverage AI for protein engineering. We heard from Frances that she's doing it. Others are doing it. And you'll hear from Paddy this afternoon, even our own experience in ensuring our own enzymes and protein leveraging AI. So we absolutely love AI-driven drug discovery. We think it's going to grow our markets. It's going to be a great catalyst for Twist. But -- or and we should not forget that actually, the AI, the tool is useful in a much broader fashion.

So with that now, we're going to hear from our customers, and I will let Angela introduce those customers. We have a number of customers, I think you're going to like it.

Fantastic. Thank you, Emily. We've had some great discussion about our internal platform. And now we are going to hear from directly from customers. We have some prerecorded videos, and we have some customers here with us to present live and in person.

Our first is a video coming from Josh Meier, who is the co-founder of Chai Discovery, where he leads the development of AI-driven technologies to accelerate drug discovery and molecular engineering. If we could queue that video, that would be great.

Hello, everyone. I'm Joshua Meier, Co-Founder and CEO of Chai Discovery. And today, I'm excited to tell you a little bit about what we're working on at Chai and how we're leveraging experimental capabilities at Twist in order to fuel our journey.

A bit of background about myself. I started my career at OpenAI back on -- in the early team and the nonprofit days. We worked on GPT1 and GPT2 back then, showed some of the early scaling laws. And I realized that if the models were able to learn how to speak English and speak French, why wouldn't they someday be able to learn how to speak DNA and proteins? So I went to Facebook of all places where I trained the first language models, transformer language models for protein sequences. And then before starting Chai, was Chief AI Officer at a company called Absci, another antibody pipeline company, where we also did a lot of great work with Twist.

Another fun fact is I was actually 1 of the Twist beta users back in my academic days. I was working at the Broad Institute working on gene editing, and we actually tested some of the first oligo pools coming out of Twist. So I've been working with the Twist family for a very long time and very fortunate that we're a continued partner of them now at Chai.

At Chai Discovery, we're building a computer-aided design suite for molecules. And the big vision of what we're trying to do in this space is to generate antibodies that are ready to go as therapeutics. If you think about the process of making a drug, there is many, many steps that go into the process even all the way from the preclinical stage. You need to get a molecule that binds to a target efficiently. So for instance, what you can see here is we might take a drug target in purple, we'll have a specific portion on that drug target where we need the antibody to bind.

And then actually, what our models are doing here is these are diffusion models that are placing the atoms in 3D space so that they actually bind the targets effectively. So we need to do this in a specific way. We need to do this in a high-affinity way. But even if we can get a binder to these targets, there's a big difference between making a binder and making a drug. So these molecules also need to have a whole host of drug developability properties.

When we produce the antibody, we need it to be specific to the target. We need it to be expressed with high yield. And we need to have low viscosity, so we can't have the antibodies self interacting and binding to 1 another. They just need to bind the target. And then we also need them to be stable.

Even if we can make antibodies that bind these targets and have these drug developability properties, we also need them to actually have therapeutic function. So 1 of the really nice things about these models is that they reason at the atomic layer. These aren't like the NLP models that I was working on at OpenAI that reads it in English or reads it in French, these models actually reads it in atoms. And that's allowed us to come up with very specific designs.

So for instance, you can see here a peptide MHC complex, where we would like to get a design that is specific to a single point mutant on cancer. And it turns out that now the models are able to do things like this. And if we can bring all these challenges together, then the outcome will be generating drug-like antibodies including to think to hard targets, which are difficult to go after with traditional methods. So really opening up the surface area for the kinds of molecules that we can make and the kinds of targets where we can apply them.

So how does Twist fit into this picture? Well, if we look at how the field has evolved over literally the past year, the success rates of AI methods in molecular design have really gone through the roof. So literally a year ago, back in May of 2025, the state-of-the-art for antibody design was a 0.1% success rate, meaning 1 in 1,000 of the antibodies that you would make would actually bind in the lab.

Well, 1 of the really exciting things that we've seen with Chai is that if you look at the Chai [ t model ] that we published in June of last year, we were able to go from that 0.1% success rate to about a 16% success rate, meaning if I make 1,000 antibodies, now 160 of them are going to bind. So really an over 100-fold improvement in the number of designs that are binding the target.

In order to make this happen, there is both a ton of compute power that we need in order to train our models, but then, of course, also the data sets in order to train the models as well. So this is where a company like Twist can really come in. I think Twist really has this potential to provide a ton of the data need it in this area. The technology is, again, a great fit where you can start from the beginning of actually synthesizing the DNA. If you think about what happens when we're building out a training data set at Chai, you might have a certain sequence that you want to design. We might use our model, for instance, to design an antibody molecule. We can take that protein, and we can think about what the DNA is.

So Twist, of course, great technology and DNA synthesis. We can either make gene fragments. So each 1 of those sequences can actually be -- we can order those independently. Or we can actually even bring this together into larger oligo pools and then synthesize many, many molecules at the same time. So we can scale data sets that way.

The other thing that we would like to do is if you look at the way that antibodies are conventionally discovered, you might take a target and put it into an animal to immunize it and basically have the model create antibody -- the animal create antibodies against the target, which we would then extract from the animal. Or we might run a phage or a yeast display library, where we might have a fixed library, so a bunch of random sequences that have been fixed that we try to latch on to a target.

But what's exciting about AI is actually now, if I want to cut down those time lines and also go after those harder targets, I might take a target generate those molecules with the AI model and then go straight into the lab and order DNA for each of these. So whereas before, I might have used the same library each of the times or I might not have even used a library at all, you're now for every target actually having AI-driven repertoires or AI-driven sequences that were going straight to testing in the lab. So I can imagine that this is, again, another place where we have a close partnership with Twist, where we can very quickly send sequences to Twist on a Monday, for instance and then a couple of days later, actually have DNA synthesized so that we can go and confirm whether these designs actually work in the lab.

So to give you an example of a case study of how we might use a method with these success rates in order to either create data sets or validate our models, let's talk about that drug developability challenge. So 1 of the exciting things we're seeing with the latest models is that we can generate an antibody that doesn't just find the target, like I showed on the last slide, but actually has drug developability properties like thermal stability, poly reactivity, self-interaction, hydrophobicity as well as a host of manufacturing properties.

And now again, let's think about how we actually build this data set. We would take a bunch of targets for this benchmark. We generate antibodies against each of them. And now we have to, again, we go to Twist for instance, and we generate those sequences. And then we turn them into antibodies, and we measure developability properties. And 1 of the great things about working with a company like Twist is that there's actually expertise in all these areas. So you could actually run this entire study at Twist, right? So everything from the gene fragment synthesis, to the antibody production, to the measure in the developability properties, that can all be done over here.

And 1 of the things that we've loved about the partnership with Twist is just how responsive Twist is to some of this feedback from a customer like ours. And they have been since the early days of working together. And I think that's really important if you think about where this industry is headed. The workflows that we are running today in the lab look pretty different than they were even just a year ago, right? Because if you're going from testing a system where 1 in 1,000 of your antibodies bind versus where 100,000 or 200,000 bind, it really changes the kind of experiments that you might want to run and the feedback loops that you'd like to run.

So if it only takes now 24 hours to design an antibody in the computer, the next bottleneck is actually how fast can actually synthesize that molecule and then run these downstream experiments. So we're really happy that Twist is continuing to invest in this and continue to make those time lines faster because that's something that can directly benefit the feedback loops through which we can validate our models and then also continue to build their training sets.

Maybe lastly, the thing I'll say is that this is a really exciting area to be building in right now. I think both the advancement and experimental methods and how fast we can turn things around. And then also just the pace at which the models are developing really creates an interesting flywheel where as the models get better, there's more DNA that we need to order. And then as we can synthesize DNA faster, and we can run these experiments faster with that models update faster. So this whole thing goes into a really nice flywheel.

And as a company like Chai, we were -- we've skipped over this slide earlier, the company has raised about $0.25 billion of capital in order to go and kind of push the frontier in these models and then take those models and deploy them in some of the largest pharma companies in the world. But in order to do all of that, that quickly, we've need it to work with partners like Twist who've been able to iterate really quickly in our needs, able to push the balance on how many of these antibodies we can actually produce, the amount of sequences we can measure.

And this has been extremely productive for us because it means that we haven't had to go and develop all these things in-house. We can instead rely on a partner like Twist who has the expertise to really deliver on the scale that we need in order to push forward the bounds of AI. Thank you very much for having us, and hope the rest of the Investor Day goes great.

All right. Fantastic. So Chai Discovery, they do not have a wet lab, right? You heard from them directly as to how they leverage our services, and they use a wide range of services. We've worked with Joshua for many years in different capacities. And so it was a great example of a very satisfied customer who continues to push the bounds of research.

Now I'm pleased to introduce a real live person in the room, Dillon Flood, who is Co-Founder and Scientific Director of Elsie Bio, a wholly owned subsidiary of GSK, where he leads the development in next-generation oligonucleotide therapeutics and RNA engineering technologies. Our objective is to show a lot of different a lot of different customers doing a lot of different things.

So Dillon, over to you. Thanks so much for being here.

Thank you. Yes. Thanks for having me today, guys, and I'm excited to show you a little bit about what we're doing at Elsie Bio. Like Angela mentioned, we were a small scrappy San Diego biotech company that started in about 2021. And in 2024, it became a part of a much larger company called GSK. It's been a wild ride. It's been super fun.

But I'll tell you today about our really interesting technology that we built that allows us to really increase the efficiency of oligonucleotide screening and selection to make better drugs. We did that in collab -- with a lot of collaboration with Siyuan and his chemistry team here. It's been -- as myself as a trained chemist, it's been wonderful to be able to ask to pitch these crazy types of requests to Siyuan and their team and have them come back and say, "You know what, that's wild, but in my work, let's go for it."

So with that, we've opened up some really big doors. And instead of using this technology to look at -- to train models for antibody type drug discovery, I'll cut to the chase. We're now using it to look at oligo nucleotide drug discovery. So kind of a different bend, but it all comes back to DNA writing.

So what are oligonucleotide therapeutics? These are short synthetic pieces of RNA or DNA that are used to target RNA or DNA in the cell. What we typically think of when we talk about RNA therapeutics are things called antisense oligonucleotides and short interfering RNAs. I'm sure you guys have heard of them. They're fantastic drug modalities. And for a long time, folks thought these things were extremely programmable.

So when we started the company about 5 years ago, there was a lot of dogma in the field and people thought that these things we're super programmable, you could use [ Watson Crick Franklin ] base pairing to program your sequence, you slap on a few patterns and you're good to go. That might have worked for some of the early targets, but now that these things are being going from rare diseases to common diseases, right, we need better therapeutics.

And 1 of the things that makes us hard is therapeutic sequence prediction. There is a lot of -- you can very easily predict short-range sequence, shape and folding on kind of shorter RNA. But once you get to full target length, it gets really hard. And that's because these things are not a one-dimensional string of [ ATG and Cs ], right? They're a dynamic folded structure that has secondary structure, Tertiary structure and a really kind of under defined protein RNA [ interactome ].

So what we did is develop a technology that we call ROSALIND. It's a DNA-encoded library based technology that allows us to rapidly screen massive amounts of chemical diversity through our platform. And this allows us to perform our selection techniques in a system that natively recapitulates the RNA structure and hopefully that binding interactome as well.

So why did we develop ROSALIND? It was really to get at this, eliminating the -- what we call the oligo design question for any 1 target. There's tens of thousands of sequences you can make to that target. There's tens of thousands of patterns of chemical modifications. You can pattern on that sequence, and there's tens of thousands of ways to stitch these things together.

I'm not a mathematician, but I'm trusting that people who put that on the slide. There's a lot of ways to put these things together. So what we tried to do was come up with a way that we can take a much larger chunk of the pie to really look at the chemical diversity that we're seeing in this space. And that's because most folks, if you look at any -- a lot of the drugs in the market now, were discovered after focused, kind of dogmatic type drug discovery efforts where people looked at 100 to maybe 500 different constructs. And we think there's just so much more out there to explore that we can find better molecules out there even in crowded spaces.

So what we did is we develop our ROSALIND platform. Again, it's a DNA coated discovery engine. That's all super fun and great. But what it actually allows us to do is increase the amount of screening and selection we can do at these various stages by orders of magnitude. So when we look for our sequence to any target, we're now instead of screening a couple of hundred, we are screening 10,000 to 100,000 constructs in a single tube all at once. And this is how we define our selectivity and our activity of our constructs.

We then take 1 of those winner sequences, and we start to pattern on modifications to these things. DNA is a great format for storing information and doing all sorts of stuff, but it's not a really good drug. So we need to modify it. We take our best -- our best sequence and we start patterning in our modification patterns. We can do this on the scale of 10,000 to 50,000 constructs at a time, and this really helps us fine-tune our toxicity in our PK/PD profile. And the last bar yet is not yet DNA-encoded, but what we can do is fine-tune those properties through optimizing the linkages that link all these nucleotides together.

So I'm going to show you some data. It's a little embarrassing because it's all from 5 years ago. It's the oldest data we have. It's all the lawyers let out the door. But even the first time we ran a oligo discovery campaign with our ROSALIND platform, that was only -- we were only able to do that because Siyuan picked up my phone call and said, "Sure, I think we can put degenerative basis in there." And that was the start of a great partnership.

But what we did here is we took a highly studied transcript -- target transcript. This is for TTR. It was the benchmark case for all the RNA companies out there on [ Ilan, Riona ], so on and so forth. And we said we're going to go find a better sequence to target this gene. What most of these companies have done is they've all landed in a similar locus, which is kind of by the 5 prime -- or the 3 prime UTR. What we did is ran our exhaustive screen or exhaustive search of this transcript. And we found constructs that were distal to that area that were about 20x more potent. So we thought that was proof positive and increased screening would give better results.

What we did then next is take our single sequence. We threw away the dogmatic [ gapmer ] type approach and sort of patterning in modified nucleotides into these things. What we knew about that was that, that would -- that would help us modulate the protein binding effects of these strains of nucleic acids. And what we can see here is that we could modulate our -- or we could keep our high activity constructs where really modulating our toxicity profiles.

What we can then do is take that kind of second-generation lead, and we can pattern in with some really cool chemistry that my other co-founder at Elsie developed, we could fine-tune the properties of these constructs. I won't spend too much time on that because I want to get to what I think is even cooler, which is where we are now. Back in the day, we were producing libraries with Twist with all DNA constructs. We are then brute forcing our Gen 2 constructs with classical column-based synthesis approaches. But thanks to another phone call that Siyuan graciously picked up. We've now been able to incorporate all sorts of interesting modified sugars into this -- into our synthesis process.

And what this allows us to do is really change the activity and protein binding impact -- or profiles of these types of molecules. So I'll talk kind of about these ones on the right here. It's 2 prime MOE and 2 prime LNA. These are kind of the base kind of state of play for antisense oligo nucleotides out there as well as the PS bond. But after much back and forth with Siyuan and his great team, we were able to get some really cool methods that could allow us to incorporate PS bonds into these constructs at high scale. I'm not talking just in a couple of sequences here and there. This is across the entire chip with complete control, with as good a control as you get with regular DNA.

We were then able to incorporate 2 things, the [ MOE and LNA ], like I mentioned. And really interestingly, we were able to incorporate [ MOE ] at very high fidelities. This is notorious for being a hard to incorporate base in oligo chemistry. So the fact that we're able to do it on the silicon chip was amazing to us as chemists, super exciting as a scientist, but really started to enable what we're doing next.

And what that is, is building fit-for-purpose data to train our own AI/ML models to power our kind of the entire foundation of our oligo discovery platform now. Right now, we are using this data like today, we're using this data to start triaging compounds that come out of our selection and screening processes for possible tox effects. This is great. This is a wonderful use of AI. It takes our screening from hundreds of compounds down to maybe 800, just a huge win for us in time and money.

That said, where I see this going is as we continue to increase this data corpus that we have, we're going to be able to start using this in a generative fashion to predesign the constructs we want to move forward before we even get to the screening phase. And with that, this doesn't happen alone. Our small little team of 20 is now a part of a massive team of about 20,000 researchers. So it's been a great -- it's been great to integrate into GSK. Very different than the Elsie vibe, but it's fun. But with that, that is the Elsie story, and I'm happy to answer any questions if anybody has any. Just shout it out.

Back to mic, and we'll run the mic back to you.

Thanks very much. The predictive toxicology and PK/PD, it seems like that's a huge potential maybe for AI, but the models haven't made a ton of progress yet. And given most failures are kind of like the Phase II tox phase, I'm just curious, as someone that's working [indiscernible], what your perspective is on the progress we've made on productive toxicology? And if you think there's potential to improve approval rates over time, if that's an area that can contribute there?

Yes. I think in a therapeutic modality like this where there are strong class effects, class tox effects, and you can boil that down to interaction with a protein or a class of proteins, there was actually a lot of promise in being able to predict that, right? Because what we're functionally doing here is taking constructs that will fold into 3-dimensional shapes, expose under these proteins, seeing how they're binding those proteins and ultra-high-throughput and using that to make predictions. And if you can basically boil down what is causing the tox and it's something you can address, I think we're going to be able to build the data to actually predict on it. But some of these other larger multisystem tox effects might be harder.

Okay. Can you hear me?

Well enough.

I'll try it again. Okay. I'm kind of loud, but you probably want people to hear. You can hear? Okay. All right. Thank you for doing this. In your presentation and in the other presentations, it's been really exciting to hear how quickly AI is leading to development of a higher number of well-profiled drug candidates in much shorter duration.

I'm going to kind of actually ask a little bit of a financial modeling question, which I know is a bit unfair. But you're generating a lot of data. You're probably better than me, so don't worry about it. So you're generating a lot of data upfront. Is there a point where -- using you as an example of an important customer for Twist, where you've essentially generated enough data recognizing how much you have, your staffing constraints, your capital constraints where there's kind of like a bolus of activity with Twist and then it drops? Or is it the opposite where there's kind of a consistent build of demand for Twist? Because I think a lot of us in this room are really excited about this. But I think we're trying to figure out like our customers like you going to spend a lot on Twist upfront and then kind of pull back for a while? Or does it actually amplify over time?

Yes. I think that's a really great question, something that I have to talk to our higher ups about all the time. So what I see is that kind of far off future where kind of the screening dies off is something that's in the far unforeseeable feature just yet. That's when AI takes all of our jobs and we don't work anymore, right?

But in the near term, what I see is that we have just started to produce models that are useful and exciting to the internal stakeholders that we're working with. We've been able to deploy these on programs not only to just reduce tox and triage, but also go the other way and tuning polypharmacology that are interesting and elusive and hard to get at. So what I see in the next 5 to -- 3 to 5 to 6 to 7 years is a steady ramp-up of needing to build data. And this platform relies on ultra-high throughput paralyze synthesis to do that data build.

I think there could be a time in the next short to medium term where for a single modality, we have enough data that we can be predictive. But as a large pharma, you have -- we have a handful of modalities that we want to go after that we can't even start to think about modeling it because we don't have the data to do it, right? If we're putting our flag in the ground in ASOs and siRNAs. This is a rapidly expanding field. There are self-amplifying RNAs. There are upregulating RNAs. There are RNAs that interact with the reg RNAs. This is a whole field of emerging RNA biology that we're still going to go after, [ 8Rs ], all these things. So I see while I'm still around, I don't see that dying out anytime soon.

Super helpful. Thank you.

Thank you so much. I am continuously inspired by our customers and the endless creativity for the next problem, the harder problem, the deeper they go each and every time. And you just heard it directly from Dillon. It's not a problem that's going to be solved in our lifetimes.

So with that, we're going to change directions a little bit. We're going to move away from drug discovery, and we're going to focus on enzyme engineering for sustainability. So we're really going to change it up. Our next presenter is also joining us via video because she is in Australia. Vanessa Vongsouthi is Research Founder and Head of Bioengineering and Discovery Research for Samsara Eco. She leads development of AI designed enzymes that enable infinite recycling of plastics and textiles through advanced circular biomanufacturing. Vanessa?

Hello, everyone. Thank you for having me today as part of the Twist Bioscience Investor Day. My name is Vanessa Vongsouthi. I'm 1 of the research founders and Head of Bioengineering and Discovery Research here at Samsara Eco. Twist is 1 of our longest collaborators and enablers in our discovery and scale-up ecosystem here at Samsara. So it's a real pleasure to be here today, and I'm really looking forward to taking you through what we're building.

So at Samsara, our mission is to create circularity for the world's most valuable materials. And we started this mission in 2021 by targeting a group of materials that is arguably 1 of the hardest to ignore, and that's plastic. The world has produced over 10 billion tons of virgin plastic from fossil fuels to date, and this really isn't just a problem of waste. So every kilogram or ton of virgin plastic begins its life as oil or gas, extracted, refined, and then moved through a supply chain that is long, carbon intensive and increasingly exposed to geopolitical risks.

So it's no surprise then that the production of virgin plastic currently contributes to over 3% of global greenhouse gas emissions. And this is expected to reach 15% by 2050 if we stay on this trajectory. Despite this, only 10% of all the plastics we produce globally get recycled today. And this rate is as low as 0.3% when it comes to textiles to textile recycling.

The reality is that no matter how meticulously we sought the plastic waste at home, most of the things that we consume actually don't make the cut for traditional mechanical recycling. Usually, they're too contaminated, contain dyes or a mix with other plastics. And textiles have an even slimmer chance of making it through. In practice, it's really only the cleanest, clearest plastics that enter the mechanical recycling waste stream, where they're melted and extruded into recycled plastic. But with each of these cycles, the plastic also loses some of its material quality and strength until it eventually ends up in landfill or has to be blended with increasing amounts of virgin materials.

And so this is really the problem that Samsara was founded to solve. And it's what brought us to leveraging enzymes to deliver material circularity. So this slide gives you an overview of our technology platform. It's an integrated system that takes end-of-life products and turns them into virgin identical circular materials. At the heart of our platform is a machine learning-driven enzyme design engine. The designs we generate are brought to life using Twist's clonal genes. They're delivered to our labs ready to experimentally screen in the 96-well plate format. And so rather than cloning and sequencing genes ourselves, we received them sequence-confirmed and ready to slot into our enzyme screening workflows. This means we can move really quickly from a computational design sequence to an experimental data point at a pace and scale that just really wouldn't be possible otherwise.

From screening our engineered enzymes, we take the most promising candidates forward through characterization and process integration until they ultimately feed into our chemo endomatic recycling process that you see here on the right. As input to our process, we can take colored, mixed or degraded plastics in textiles, and then our enzymes get to work, breaking them down into their original chemical building blocks, also known as monomers. These monomers are equivalent to what we have to extract from petrochemicals today, which means we can purify and repolymerize them into virgin quality materials that can be manufactured into brand-new products.

Importantly, this really enables infinite recycling. And so we see no loss in the quality or the integrity of the material no matter how many cycles it goes through. So at the core of everything we do are our enzymes. And specifically, these are new to nature enzymes engineered to break down plastics at speed and scale. These aren't just enzymes borrowed directly from nature and dropped into an industrial process. Whilst our naturally occurring enzymes can degrade plastics, they rarely meet the demands of the real production environment. Often, they're too slow, too unstable or unable to withstand the operating conditions that we require. And so we use natural enzymes only as a starting point and a foundation that we can then build on to create proteins that are optimized for speed, stability and precision at scale.

What makes this challenging but also exciting is the sheer scale of the protein design problem. So to give you a sense of this challenge, a typical enzyme with just under 300 amino assets has more possible sequence combinations than there are items in the observable universe. And so the question we face in enzyme design is how do you search that vast space efficiently to find the very few sequences with the properties you actually need for an industrial process? As you can imagine, searching through by random trial and error would be near impossible. And so we need smarter, more principled ways to navigate our search.

And this is what our platform is built to do. It does turn out that 1 of the most powerful approaches we can take is to learn not just from the enzymes that exist in nature today, but actually from their entire evolutionary history. Like species, enzymes have a history that stretches back billions of years. And along the way, countless enzyme variants have existed and disappeared. And so using a technique known as ancestral sequence reconstruction, we can actually infer all of those earlier sequences and bring them back into view. This matters enormously through our understanding of how different protein families work that also gives us very rich training data. So rather than only having a few hundred natural sequences that are relevant, leveraging this method gives us tens of thousands of ancestral sequences that allow our deep learning models to identify the patterns that better link things like protein sequence to protein function, activity and stability.

And so a really great illustration of this is our Nylon 6,6 [ hydro lease ], which is estimated to sit at about 10 to 82 possible sequences away from the closest naturally occurring enzyme. And to our knowledge, it was the first-ever enzyme that was characterized to be capable of degrading Nylon 6,6. And so this is a breakthrough that was really only reachable because of the richness of this evolutionary data.

And so if we compare mechanical, chemical and enzymatic recycling approaches side-by-side, the advantages are clear. So our process enables a true closed-loop circularity, returning plastics all the way back to their original monomers with no loss of the material quality. It handles the mixed plastics and composites that other technologies reject and it operates at a low carbon footprint relative to virgin plastic production. And critically, we've demonstrated that the monomers we produce can be repolymerized into virgin identical end products that look, feel and perform exactly the same way that fossil derived materials do.

Our milestones to date reflects the real-world traction that our technology is generating. On the product side, with our partner, Lululemon, we produced the world's first Nylon 66 enzymatically recycled Nylon 66 garment and launched a full retail collection with them. We've also produced a clear recycled PET bottle. These are consumer-facing products that prove our materials are virgin identical in every sense that matters.

The industry response has also been equally strong. So Lululemon has committed to sourcing 20% of their fiber portfolio from Samsara over the next 10 years. And LSKD, another athleisure brand founded here in Australia, have also followed with a long-term agreement. And we've also announced a polyurethane collaboration with the LYCRA Company. Our first facility is now open here in Australia, as you can see in the top right, and our commercial plant is on track for 2028. We've raised $107 million to get there. And so the technology works, the market is ready, and we now have the backing to build this. The plastics are really just the beginning.

At Samsara, we believe biology scaled into industrial processes is 1 of the most powerful tools we have for addressing the material challenges of our time. Our platform is built on the marriage of protein design, process chemistry and engineering. And it's that combination that makes what we do differentiated. We built this platform for plastics, but it's designed to go much further.

The same approach of co-designing proteins and processes that scale applies equally to other polymers, screen chemicals, critical minerals and even carbon capture. We're not building a single product company. We're building the infrastructure to scale biology into industry.

So thank you so much for your time and for the opportunity to present at the Twist Bioscience Investor Day. On a personal note, I've been working in this field for nearly a decade now, and the difference that Twist's technology has made to the pace and discovery of innovation is something that we genuinely feel firsthand at Samsara every single day, and it's just fundamentally changed what's possible for companies like ours. So if you'd like to continue the conversation, please don't hesitate to reach out, and you can also follow Samsara Eco on LinkedIn. Thank you.

[Break]

So for those on the webcast, we are coming back from lunch. And for the second part of the day for the afternoon, we are going to start by a focus on NGS strategy. That focus will be in 2 parts, and we will start first with our SVP of Product and Marketing and imagine they get away.

All right. I might be measured by [indiscernible] how many of you I put to sleep. So stay away from me. I'll take off this afternoon, starting with NGS portfolio, and then I will dive into MRD specifically.

Coming back to the slide, I'm part of the commercial execution, and I'm also part of the NTI machine at Twist. So my focus, like I said, is NGS and plus at RD. We are a $200-plus million NGS business. And in the last 5 years, we grew on average, about 30% per year.

For those of you who follow the industry, post dynamics, the industry grew low single visit, right? So how do we win and get to the 200-plus new in today is through all of these things on street, quality, workflow, the asset customization, not going to bore you reading through every light. A full portfolio of products. But I want to pick up where he want last off that I mentioned, right, on the bottom left corner, we sequence 20,000 samples per day. for our team production process using NGS.

And you do the math, 6 to 7 event samples per year. So we know and we know what we're doing here. And that's all you need to know the left. Where do we sit in our portfolio?

three sequencing we are compatible with all sequencers out there. We are specializing from sample to that library ready to load on the sequencer, and that's where we play.

Our bread and butter starts from target capture. What is that? That is you sequence only the things you're interested in, and we enable our customers to do that.

Over the years, Twist has built a very strong reputation this panel and [indiscernible] high uniformity and low sequencing costs. And more importantly often, we enable our customers to fail less of their samples because of lower coverage. And for those who lab operations, lower failure rates makes the world of different maybe can attest to that.

So you may ask, why is this even relevant? How genome sequencing is taking over at target capture. So let's do that math together. So in 2021, per gig day of sequencing cost $6 the highest input.

Today, the number is 1 to 2. So as a sequencing dropped to sixfold. In the last 5 years. Now before we say everything goes to whole genome sequencing, you look at a typical cancer panel on the left in the top chart -- from a typical panel to go to [indiscernible], it's 100x step-up of sequencing data quantity required from exome genome is another 100 fold of sequencing data required.

And so if you take sixfold of reduction in sequencing costs and you say a moving panel from panel to genome, you are going to pay 10x more in sequencing costs than you paid in 2021 and not at that's not that.

Now that is not to say genome sequencing. By the way, WGS stands for 4 genome sequencing. Sorry, I should point out that last slide. I shouldn't say [indiscernible] Hogan sequencing is not relevant. It is very relevant. And therefore, we are fully embracing it. In the past 5 years, we have built up a full library preparation portfolio that enables us to capture that whole genome sequencing market. In fact, this portfolio specifically currently grow 56% year-over-year for us.

So why does that enable us to win? If all you got is a chip, why library? Well, looking to what semis buffer, it's DNA and a time in suffer this water in sales, everybody has it. But now I don't have to say DNA, and I don't even have to say enzymes and enzyme engineering because speakers in the morning has told all the stories.

Now you realize we have every reason to play and win in this space, $1 billion market, a lot of room for us to continue to grow game and win. Whole genome sequencing, we offer the highest throughput. [indiscernible] put 1,500 samples in a single flow cell but a way to run whole genome sequencing on it.

Going beyond 4-genome sequencing, you can put 3,000 samples again, industry highest on a single flow cell or a lane on it and to really enable super high throughput sequencing. We did something that's not done by anyone before. and that is putting 1,100 samples in a single 96 or plate.

What is that? Previously, you will need 12 liquid handlers filling up the entire lab. With this technology, you can go away because we put 12 samples into a single well in the very first step of lab processing. What you have to do, 2 things you have to do in that 1 step, adding DNA barcode or tagging, so you know where the DNA is coming from when you do sequencing.

Two, you normalize those 2 different samples to even so that when you're sequencing them, those soft apps get reasonably equivalent coverage. And that is very difficult to do and is enabled by what we call a normalization [indiscernible] Everything is done in 1 step, therefore, 100 samples in the single 96 well play.

Patty is going to talk about our end of engineering and how that enables high performance, right? Just again, this is an exciting part of NGS portfolio for us. As if you haven't heard enough about this, I'm going to go yet again. Largest -- a lot of the largest clinical last really trust us. Why is that? Do we just do better? Maybe. But [indiscernible] or glad to say, the reason come back to the chip.

So bottom left, up to 1 million oligos, many thousands of clusters or we call clusters, what we call it, you can understand as well if you may. And in each well, 121 unique different oligos in right? Our competition, what do they do? They use 96 or 384 wells to do the same thing. So I'll use a typical common scenario.

You want to build a panel of 50,000 trips again, cyclical. How do you do it in the [indiscernible] or 384-well plate or at a time. It required 130 synthesizer machine or less machines but different batches, adding up to 130 different production runs. What do you hear when you hear that, they're variation, variation, variation.

What it is for us, you carve a small corner out of that chip, the entire 50,000 in 1 machine on 1 ship in 1 back all that together. No variation, no variation, no variation. And that's not the end of it. If you do it, 50,000 wells and 384 plates, just how do they come together and you're going to pipe 500 times to bring those 50,000 different well together.

For us, 121 unique oligos are already in one well, 120x less tieing. What do you hear? Consistency, consistency, consistency from lot and that's what our customers counting on trust to deliver over and over again, right?

That is the unfair advantage is very, very difficult to overcome. So it's not just all about technologies. I want to go deeper into applications. We use liquid bonds as a field. As many of you know, 3 key applications in local -- and again, many of you know, we are a leading supplier in early cancer detection through our methylation detection system.

We are a leading supplier in therapy selection for the [indiscernible] biopsy through our custom panels for concrete has genomic profile. And we are a key supplier not leaving in Amart. And so how we play in this space, in liquid biopsy, many companies, many last in this field, right?

We are, first and foremost, making clear to our customers, we do not compete with them. We are not in the course race. And we don't bet on a single course. But we work very, very hard for all the horses running on our platform for them to have a better chance to win because when they win even. And that's how we operate in this field.

Going into MRD from this point, clearly, there is work for us to do in MRD space, as you saw from the pre slide. I want to step back from the technology from the application even just talk through the patient's angle for MRD. Linda , 63 years old, we've hired 2 years ago after a close to 40 years of teaching. 2.5 months ago, diagnosed with colon sensor. A month ago, had surgery removed confirmed stage II colon cancer. 1 month post surgery today, she goes into the oncology office oncologist office and ask the question stock with now.

And the oncologist will wait for an MRD results to sell Linda, whether you can go home, all done, or more chemotherapy is needed. And the #1 utility of MRD, which is a treatment decision at that point. My mother went through kidney cancer and breast cancer 2 years ago, surgery, all done fully recovered.

But I can tell you that week 5, you go into the doctor's office, you try to find offer surgery, are used on or more things are coming every day matters. You can't sleep. And for this first utility, you are leasing against and it's not easy even though if you think there's 4 weeks of window for you to take action for Linda to know what's next because these tissue samples is time to be put on the glass, shift session doing axonal genome sequence, do the analysis through the panel design or the MRD tunnels, process in text thing now, the window closing fickly and then they'll stop.

Were waiting, right? And that rate to speed is a fundamental need for MRD. So the second, which is good news, Linda's good. The oncologist that you can go home, just come back every 3 or every 6 months to monitor making sure it does not come back. So once she comes back, she does another test -- this is the second utility of MRD to really monitor disease recurrence.

In this study is no longer above speed. In fact, has nothing to do with speed anymore. It is all about sensitivity because different sites metastasize require a different level of sensitivity to detect and you want that test to detect every way of recurrence.

And so with that patient angle in mind, we'll look into MRD Express from Twist. So 2 dates prior to this, it takes us a week to get [indiscernible] tunnel produced and shipped to our customers. I probed that we just went through with Linda. That's not good enough. And therefore, we completely redid how we make MRD panels.

Pull just about everything back to the single thing we're really, really good at -- that is the ship. The entire MRD panel production now happens on the chip. And that enables us to do it in a single day. Without any compromise on sensitivity, we're known now to enable ultrasensitivity without losing that with no compromise on single day.

We solve that first problem. Now moving on to the second problem. Many of you follow the space, there are many different tests out there. There's something about fragments to analyze that using AI to analyze full genome sequencing results and they have 6 channels, methylation, multi-omic markers to the tech MRD.

In the personalized space, you have digital PCR, try to measure structural variations. You have amplicon panels trying to do the same thing. And you even have technology that tried to enrich patients specific variant trucks, so many technologies. I am so confused which one do we choose. Right?

This is all about sensitivity. And we want to have you see the way we see it. People often equate local biopsy I'm already included to looking for a needle in the haystack isn't exactly accurate, right? Because patients and their normal DNA in blood 99% of time, they are identical, which means you are actually looking for a needle in the sack needles.

And so the first question, for get of all those technology, you ask yourself to say, 2 scenarios. I tell you, there's a 1 million needles. There's one that's a different [indiscernible] It's not going to be easy for you. The second, I tell you, same 1 million stack of needles. Look for a needle that's 5x bigger and part of that needle is black. You now have a much better chance of finding a needle because you know exactly what you're looking for.

And that is the difference between tumor informed and tumor-agnostic period. And so now you know we're trying to secure that. Now how do you further enhance sensitivity. Now I'll give you a scenario #2. There are 3 stack of needles. In stack, number one, 1 million there are 16 special needles that look different. In a second step, there's 64 of them that look different. In the third stack, there are 2,000 that look different.

Now all you have to do is go up, choose 1 pile and look find 2 needles a lot different, which tiles you go to Pole 3, where there are 2,000 I only have find to Congress, you just figure it out the size of the panel truly matters because that gives you the auto sensitivity and twist technology enable you to look for that 2,000 needles in that spec needles.

So sensitivity and speed, as you hear heard, is taken care of by our platform. I purposely skipped the other element that's very important which is cost. But cost is a soft problem for Twist. At the same time, the cost factor itself has very little to do with Lind. Has a lot to do with clinical labs, maybe margin of operations.

We care about that too, but speed and sensitivity really change Linda's life. And so we're a great hopeful you're committed, but we want to take a step back to say this is not about Twist and for MRD field in general.

Right now, as we say in this period, currently about 1 million MRD test is done per year, just about and it's growing about 50% year-over-year. So in 2 years, someone does do the math I did it for you, 2.3 million MRD tests.

In an ideal world, every patient gets for test potentially. But right now, it's just around 2 or slightly over 2. So that is 1.15 billion or more than 1 million MRD panels that needs to be produced. For us, every panel is how many targets, 2,000 probes. And you do the math, we have to produce 4.6 billion all oligos just for the MRD field if we were to meet all MRD needs in the field in 2 years, 4.6 billion.

[indiscernible] C1 says, no problem. All I need is for writers. You've seen quite a few there. That's all that is for us. Now if you look the standard oligo production way, which also is playing in this field, same math.

They are not doing 2,000. We say they can do 10x less. So only 200 for them to do, which translated into instead of 4.6 billion, they only have to do 460 million oligos. 1/10 of us, easy to do, right? But if you do the math, that translates into still 1.5 million oligos per day.

What is a typical vendor for primer oligo synthesis capacity 150,000 oligos. We can't do it. They can't do it until they grow 10x. And that end picture is super important. Right? We have a future-proof capacity figured out as of today right now, and that's what we have. And for the oligo vendor, we'll lend them a hand.

We'll make sure this capacity issue for them in a couple of years, they don't have to worry about it. All right. Thank you, everybody .

They won't ask to worry about it because we'll take it on. So next, thank you, [indiscernible] It's great. Next, we're going to hear from Pat is going to continue our strategic talk in NGS Diagnostics with some details around our enzyme engineering and our nitric acid therapy

I'm going to new technology here. Right. Really excited to present for you guys. So I'm a unique chemist by training. Number of different jobs we've had over time and are somewhat grizzled veteran commercial leader educated in the school of life. I'm going to talk to you about what's quantum engineering, what's cool and sequencing, what can be coming in new click at and therapeutics.

And for those in the room that are going to understand this really well. I'm going to talk to you about commercial execution. This is my favorite slide deck ever and outside of my family. This is my favorite stuff to talk about. Second take is accent in the company. And all I can advise you to do is listen super fast because there's a lot of content coming enzyme engineering for NGS. Why do we care? -- right?

We're a [indiscernible] company. But I think you've heard enough coming through today about why downstream at the chip matters. And I simply can't talk with a level of intelligence that our customers have demonstrated around what's coming with the we start to look at things like what our customers also do with our product, for example, in target richen sequencing workflows or how we use enzymes internally on the tour, I'm sure you would have seen some enzymes being used to produce product.

It caters important that we have a best-in-class enzyme portfolio. It matters for our customers, and I'm going to explain why for the kits that we produce. It matters for us internally, control the supply chain matters, improved economics. My guess is not everybody in our competitive field loves our emergence in the space. So being in control of supply chain is incredibly important.

We're fast, but also by being in control, it allows us to have a global strategy for commercializing product, make their own staff that economics are more favorable. It allows you to sell all across the world through different distribution channels and allows us to truly enable the global community to get onto our platform.

And the reason we like this space is we have an advantage -- and I think you've seen it many, many times coming through customer presentations. We can make a lot of DNA. We make it in parallel. It's incredibly high quality. The economics are enabling. And when you partner with that with our application expertise, and I'm sure you saw in the core, we do have a lot of sequencing.

Tens and tens of thousands of samples. Every single day, we sequence to make sure we're in good shape to go in the right tube to go to the right customer on the right day so we've got this incredible throughput, incredible speed, the ability to make enzymes ourselves and screen into an application that we understand is incredibly enabling.

And it's so important that we keep all of those pieces rolling together to drive great product out to the market and deliver good infrastructure for our own internal services. If you go one step further as we go through a cycle of design build test learn launch, we just continue to improve. We get faster, we get more effective, our knowledge continues to develop. We launched really interesting products.

And if you look at the workflow, you've seen it before, we're going to start with an enzyme target. We're going to use 0 shop design. We're going to lead off into the Twist Gene Sensus platform. I'm sure you've got deja vu over again around this slide. We're going to crank out the sequences of interest. We're going to go into a high-throughput protein expression purification into a screen where we know our application expertise is incredibly enabling.

We're going to pick out the enzymes of interest with features that we like. We're probably going to turn the crank on this a few times because this does have some runway. And so we're going to really optimize what we're building to create super products that when we found the features that we like in screening, we're going to hang on. We're going to actually drive into making kits.

And if you think about our overall, how do we launch products, how do we drive our NPI machine? I'm sure you've seen so far of you've just had Jimmy present who's full of ideas. Dr. Chen, wherever he is, is just an idea a [indiscernible] and our tone at the top.

We know what our customers are doing. We have another chemist that leads a company that has intimate knowledge of what's going on out in the field. We're constantly ideating -- we're into our enzyme engineering platform to try and build out the enzymes of interest that then go into our product development pipeline, driven hard through NPI and ultimately out into launch products.

So pretty straightforward, right? You can see the molecular advantage we have and how we drive that into a new product pipeline. So I'll take just a couple of case studies.

So for anyone that's a close follower of the sequencing world, there's a couple of important enzymes. We'll start with ligase. Now I'm going to be a little bit honest. I'm kind of pleased that Dr. Arnold has gone talking about enzyme engineering in front of I was kind of nervous, afraid and excited all rolled into one, so whatever that is. But ligase matters in the sequencing workflow. You have to get the template that you want to analyze, modify, you have to ligate on adapters to get it to stick on a sequencer to allow you to characterize. Ligase is a crucial component of that workflow.

And so you can see the methodologies we used up above, and we challenged C1 to say, right, let's make a ligase that's best-in-class. Let's get something that works with low input templates, something that's good for cell-free, something that really drives conversion efficiency and the way I think about it is no molecule left behind.

There's nothing gets left in the tube. It all goes on to the sequencer. And also an important point here, buffer sensitivity. Customers do the strangest things with your products.So building a system that's robust and could tolerate the variability in behavior is very, very important. And so again, LLM-based design or basically high-through expression screening and why we find a really genuinely interesting ligase.

And I'll save this for the exam that's coming at the end, so everyone will be paying attention. But the most important point here is we just draw in the bottom left corner when you look at conversion. The twist is a big green bar. Big is good, okay?

What that means is the conversion rate of template to something that can be sequenced, that is lights out versus the competition. And then also importantly, if you go to the bottom right corner, you just look at how does the enzyme perform in a broad range of salt conditions, i.e., when the customer has got some pretty sketchy samples going into a workflow, does it behave well? And again, conversion matters.

No molecule gets left behind. Look at the behavior of the green line versus what's on the market today. That means you're going to yield a very, very competent, capable product. Flush with success and not wanting to let Dr. Chen rest on his laurel, we thought, okay, ligase is important. High fidelity polymerase is next. It's pretty obvious, right? Even I could build that product portfolio out.

So we set off for the same challenge. GC bias is a problem. We know that. We use polymeries to assemble genes in the factory here, so on the other side of the business, but we also need to QC genes to say, okay, we can ship this and collect revenue. So similar strategy, get out there, let's get in there and use our LLMs to sort of teach us what makes a good polymeries, high-throughput synthesis, expression, characterization or assay to learn how it behaves in application, space filling design of experiments to optimize the buffer.

Again, same thing, broad range of input you need something that works in the customers' hands and lo and behold, outcomes and exceptional polymeries. And I'll draw your attention to just a couple of points. If you just look up in the top left corner, you can green for twist or go or yes, purchase order, I guess, please. You can see improved performance at the edges of GC content, both sides. You can see an error rate that's incredibly tight, incredibly high fidelity.

And in general, for high fidelity polymerases, it's hard to get them to behave in a product. You have to get goldilocks, you have to get it just right. And that is a beautiful performance from polymerase. And then most importantly, comparing to Polymerase, and I'm very pleased to be anonymized for that polymerase is, but any enzyme fans out there may know, you can see slight improvement. And by slight improvement, I mean when we use that in our gene factory, I take that top result there, we can't ship that product. Right?

That's the wrong gene. So I've spent a bunch of money and I can't ship the product. So for those of you that haven't worked with Emily, that's a bad spot to be in. It's really bad. If you use the twist polymerase, what it does do is it allows us to read through the complex sequence. So then when we QC and say, yes, the product is there. So now I can ship. And I'll still get the phone call where revenues, but at least we ship this product. If we just pause for a second, it looks incremental, okay?

I don't feel anybody getting the purchase orders out to buy the polymeries. But let's just hold off and think that one through a little bit. So we moved approximately 1 million genes roughly last year, ballpark, plus or minus a little bit. I think a 5% improvement in terms of polymerase performance and acceptable performance. And yes, this is good to ship because we've got a polymerase that works and reads through difficult stuff, allow me to QC and release product. That's quite useful.

Not to mention the fact that we're also vertical now in supply. I don't need polymerase Q or polymerase K. This is our polymerase, and that is a very robust position to be in. I'm going to segue over to the other side of the screen here, and we're going to look at how our polymerase performs in the sequencing experiments for customers. It allows them to amplify and access parts of the genome where others polymerase will stutter, okay? That's a problem.

You start to get constrained in terms of how good your whole genome sequencing experiment is. And that's something we're working hard to improve upon and core enzymeology, novel differentiated features are fundamental to driving that behavior into those products. So I still don't feel like a soldier anything. So let's go to the next slide, and we took these core enzymes. And built these beautiful kits around them, the TRUAP library prep kit and the PCR-free whole genome sequencing library prep kit. Really elegant products, best-in-class ligase, high fidelity polymeries.

You can see the incremental improvements. Now if I think about the customer and what does it mean for them, think about what it costs to press the start button on your sequencer. It doesn't matter which sequencer, press the start button. It's a well-finished BMW, right?

So utilizing that sequencing real estate matters. If I'm in a commercial setting, independent of which workflow I'm using here, I get a 5% improvement in the number of samples I can sequence or a 10% improvement in the number of samples actually on to the sequencer, the impact to my business, the impact to my research has greatly improved. And that's a bit that matters, right?

At scale, these incremental improvements lined up give you absolute success in your lab. And again, you probably heard that a few times today, we'll meet the customer where they're at. We don't make kings in Queens, but we're going to enable each of the segments to do a good job.

So again, PCR-free. And so by definition, no polymerase or our library prep kit that utilizes the polymerase to kind of give you the best of both worlds, lower input, but still getting incredibly good data out of that -- out of that workflow.

That's C1 2 for 2. So now he's resting on these laurels. And so now what's coming is what other enzymes do we need to build out into the portfolio. And methylation is an important marker in the oncology space, there are some constraints on workflows. By sulfide treatment, it's been around for quite a long time.

And it's a very, very useful workflow, but to industrialize that, put that into a facility that's running hundreds of thousands of samples. It's a little bit of an art form and it struggles with low input quantity of template. It destroys the template basically.

And then you've got the emergence of enzymatic methods, which are good, but have room for improvement, and you want to really think hard about supply chain. So now we've had -- can and the team working hard on a cytosine deaminase that makes the difference or detect the difference between methyl C and C, again, important methylation marker.

Same idea that I've gone through before around the utilization of AI for design. We've come up with an enzyme that's 42% of the residues are changed from wild type.

Now I do like myself, a good dose of evolution in our platforms, right? And there's no better advocate for evolutionary methods than Dr. Arnold that we seen earlier. And imagine that, 32 million oligonucleotides every single day, the ability to screen for features of interest because we know our application.

And think about being able to play in that space and start to deliver enzymes and reagents with features that are coming from remarkable primary amino acid sequences. That's true enablement and there's differentiation here and more differentiation to come in the future as we build this portfolio out.

So just in case you haven't noticed, quite excited about the enzyme portfolio and how we can utilize that internally. So a bunch of enzymes is fine, and you've seen how good we are at making DNA.

The quality system that stands behind making sure the right nucleic acid, right tube, right customer at the right time. But you also have to have an NPI machine that's sustained by a quality system that scales. And we have a unique challenge. You have the Wild West of the gene synthesis community.

They need a molecularly pure product. But if you're a researcher, my guess is you're not too worried about ISO-13485. Then on the other side of our business, when you go all the way through to a clinical lab or even as you start to think about nucleic acid therapeutics, the quality system has to scale up.

So you've got sample extraction, library preparation, target capture sequencing on any platform, the quality system to wrap around the products that you see and some examples up at the back there has to stand behind that customer base, something that's very understated in the company.

James gave a beautiful presentation showing the fine details of how we move a piece of DNA around the building, that underpins a quality system that supports our diagnostic customers in perpetuity. We audit incredibly well, and it's actually become a weapon of offense to help support, sustain and help our customers grow.

Now if I just pause for a minute and start to build, I'll segue over, Jimmy's presentation was beautiful early. It's very hard to follow Jimmy from a presentation standpoint, and also personalize the topic. But if you look at the continuum of cancer care and research, and Jimmy is far more articulate than I around that continuum.

But you've obviously -- you've got your screening, early cancer detection, you have a bad outcome. Potentially into surgery, you've got your molecular residual disease test, therapeutic intervention if required and then continued monitoring. And this is what excites me about Twist Beyond belief, and we're just at the very beginning of this on the NGS application side.

From a screening standpoint, the product portfolio is incredibly strong. As we go through early detection, this efficient use of your sequencer, really efficient and effective target enrichment is enabling whether it's [indiscernible] methylation markers, liquid biopsy or comprehensive profiling of a tumor.

Jimmy talked about MRD and our capacity and the emerging trend of sensitivity and the impact it's having to earlier detection of recurrence of disease, massively impactful and a growing body of clinical evidence saying that's good for the patient. And then through into obviously, treatment response and monitoring.

And just walking along the bottom here, that has an exquisite collection of products. And if you think about the future of precision medicine, this platform is incredibly well positioned today around the sequencing side, the diagnostic side, what we're going to enable in MRD.

And then ultimately, you have how the pieces come together. On the DSPS side in drug discovery, the platform is incredible. Most new therapeutics are biological. And so at the risk of turning into a seminar, where the biological molecules start. Piece of DNA doesn't matter the moat. It starts with a piece of DNA. And if you want true precision medicine, you're going to need a lot of different DNA sequences.

So we're excited about the therapeutic pathway, [indiscernible] our antibody discovery capability, NG characterization, shortening the time of drug discovery against the most complex of disease. That's a very, very powerful offering and not to mention what we can do in the mRNA space. So you can see we're well positioned through that continuum of care where the technology and the products enable the community to address some very, very difficult challenges.

I'm going to dream just a little bit, just a [indiscernible] Maybe AI won't get that like a Milligan on smidge. I'm going to dream just a little bit. But having spoken to the lab of Onco earlier on, not really dreaming that much. But just imagine the situation where we'll talk about nucleic acid therapeutics.

It's one of many potential new growth frontiers for Twist. So let's pause for a second. The infrastructure that's been built out in CRO, CDMOs is built up. So you can spend approximately $1 billion to build out the infrastructure to make a nucleic acid therapeutic at scale. If you need to make kilograms of nucleic acid, that's what it takes to make it happen.

But we think that there's an opportunity in building out. As precision medicine matures, it's no longer about -- it's not just about the kg or the swimming pool scale manufacturing. It's about building out across different sequences at smaller quantities to help challenge a well-characterized disease.

And so that's -- the need is capacity, scale and economics to enable needle-to-needle success, needle for liquid biopsy to start you into the standard of care, you go through the journey that Jimmy had described earlier, and I tried to copy my slide back to a needle in the arm with your personalized therapeutic to attack your form of cancer. That needs built out. And so just to capture a picture, what I was trying to say, we talked about the workflow. We talked about speed. We talked about economics. We talked about really the impact that, that's going to have the patients. And so the question is, what's that going to take? And I'm not about to claim that the problem is solved. But it's going to take affordable price. A therapeutic that costs $1 million for each individual. That's not going to happen.

It's got to be high quality. I'd like to have the right sequence shown to my arm at the right level of purity, please? It's got to have scale. So WHO is predicting, I think I might be off by a couple of million, but it's about 22 million, 23 million cancer cases by 2029. We've got ever-improving diagnostic tests, disease monitoring that patient population is going to continue to increase.

So you need scale to get hundreds of thousands, millions of doses out to the global community. It can't just be a medication for a few people. You need speed that I'm horribly underqualified to talk about neoantigen escape. And I'll leave that to some of our customers to talk about that.

But the point stands, the longer you wait to get a therapeutic into an arm of a patient, the disease is changing. And ultimately, you end up treating something where you're looking in the rearview mirror rather than treating the disease they've got at the time of therapeutic injection.

And then complexity. I'm sure you heard we launched a complex product just recently, expanding the sequence space, the number of sequences we can accept and deliver to support an even broader operation. And that product portfolio sits quite well.

So if you can think of a company that can make millions of genes, doses per year, that has formed for putting the right nucleic acid, the right concentration in the right tube shipped to the right customer on the right day at economics that are truly enabling okay? Then I would challenge that I'm not saying that the biological challenge is fixed. But if you think about distribution, if you think about molecular quality, that puts a massive dent into the challenges facing companies in this space today. And so we're writing the future of nucleic acid therapeutics -- stuff was brilliant. I cannot tell you how much I enjoyed that from about 100 years ago, learning about antisense oligonucleotides as a postgraduate -- or sorry, as a PhD student, it's brilliant to see what you're doing. It's absolutely incredible. If you remember the key criteria to deliver on this promise. It has to. It has to go through here because there's no one else with distribution or scale to make it happen.

My next favorite topic commercial, execution. And we said it very eloquently this morning. Twist, on June North, we are in the business of delighting our customers. And Paul is going to talk about culture in a little while. It's one of these things to me that really matters when we started this -- the commercial side of the business together, what every single twister to care about our customers' scientific success. That is absolutely no compromise. If you don't care, you don't belong here. I don't have to explain the economics of a business with high customer retention versus not. I think Bain Capital describes that better than I ever could.

Every twist of cares. Our tone at the top, and we mean this. We're going to play in markets where there's going to be a #1 and there's going to be #2. Old-school methods have made the contribution. They've been super. But now with speed, economics throughput, they're holding the community back. It's time for this platform to be worldwide.

From a sales standpoint, our philosophy, our commercial execution. There's one way to go. That's up into the right. We've had 13 sequential quarters of let of rip. And that matters not just for the business results, but from a culture standpoint, do people care day by day, hour by hour when they execute? And the answer is yes, okay? That's why you're here, you're drawn to this company because we know the importance of our technology to the global community.

Philosophically, we have an OEM strategy. We sell product to people we see in the field. We'll sell to our competition. Now there's 2 things that go into that. First of all, the economics matter. And secondly, it's Twist a sales team. So I expect the to Twist sales team to win every deal. So our team has a chance to use our platform, that's great. We'll sell the product and expect the Twist sales team to outperform anybody in fact.

At the risk of being complementary, I would say we put that team up against anybody else, and I would expect a very favorable result. As a salesperson, you are or your numbers say you are, you are what your numbers say you are. If you're behind, we're going to muscle win to help you. If you're ahead, guess what, we're going to muscle win to help you, and we are going over the top together.

As a commercial leader, if you don't address your commercial problem, you become the problem, and I will manage that. But on the other side of that, we reward performance and reward performance well if you're not performing, it's much better that you work for the competition. And when we look at our channel strategy, it's kind of omnichannel, okay?

With direct sales is key account management, as our accounts have grown in size, the needs change. It's no longer about selling a product. It's truly the product, product quality, scaling, supply chain, procurement, quality, regulatory audit, it's a much, much bigger challenge that takes a certain skill set to drive key account management. And then we've got a strong team out in academic and government sales taking the word Twist out all across the community. We have scaled growth channels for certain areas we can't afford to send a sales rep.

So you've got inside sales, an exclusively well-educated team, great farm for potential future account and key account managers. And we have digital channels that continue to improve e-commerce punch out API-based ordering to make it easy to interact with Twist.

June North, we are in the business of delighting customers. And then we have our OEM channel, which expands our reach into areas where we may not be strong, which is very complementary to what we do. But the multichannel model allows Twist to serve everyone global community as soon as reasonably possible. And this is where we're excited about the business. It's diversified. It's robust.

If you look at some of the simple things, your customer types, that is a broad range of customers. Large pharma biotech big tech academia, government AI native. I don't need to read the slide diagnostics, and we see what's happening in the cancer space where you see the flow of cash into the area. Our demand drivers, it's an all-you-can-eat DNA buffet, the applications that continue to develop when you partner with the community, the makes great ideas that our customer base is using on the platform. It's incredible. It's fast, and it needs our scale.

Then obviously, our funding pools. It's not too difficult to see the flow of cash from the AI-driven companies into our space. So we like the way the funding environment is developing. And just as a reminder, the word resilience, we've survived and thrived in the hardest biotech funding environment for quite a long time. So I hope it's clear, and you can see we're making good progress as a business across many applications, many markets. And because of that, the resilience in our business is incredibly strong. So I think with that, that's probably enough for me, and it's back over to you, Angela.

And so for the webcast, we will see you in 20 minutes. We are going to cut the webcast for 2 customer presentations. You will then join us for the last 3.

[Break]

The last 3 customer talks are amazing. So and they are with us in the room today. It's my pleasure to introduce John Overton, Chief Sequencing Officer of Regeneron Genetics Center, where he oversees large-scale genomic sequencing initiatives that advance precision medicine and human genetics research. I saw John present at a sales meeting for Twist. And when we are talking about who to bring in for customer stories, I request John. He graciously accepted. Over to you, John.

Thank you very much. Yes, and thank you for having me here today. I'm going to take a slightly different angle than the first couple of talks. And I'm going to give you a story. And I'm going to talk to you about why in this world of rapidly decreasing whole genome sequencing cost, I don't think that's the best approach that we use for our large-scale drug discovery research, especially at a place like Regeneron.

So the RGC has been around since 2013, and it was founded with the individual goal. We were going to use the power of the human genome to find the individual differences between 2 people that make you either susceptible to or resistant to developing disease. Now I wasn't in the talk through most of the day. So I'm going to take one step back here and just talk about DNA for a second. All right, DNA, 4 pieces of information, ATG&C. It's those patterns over and over again make each one of us unique.

But the genome is big. It's 3 billion pieces of information, actually $6 billion because you have 1 copy from your mom and you have one copy from your debt. So we're looking for these individual variants that make each person different.

And now at Regeneron, what we do is we sequence people. We sequence through DNA. We have access to their medical records. We compare them and drives our drug discovery process. To date, we've done that for over 3.5 million people. It's one of the world's largest database to do this research. It drives our drug discovery and clinical trials. We do this through 2 different types of technology. One is genotyping. The other one is whole exome sequencing, which twist is an expert at.

So what are these approaches? Genotyping, it's been around for a very long time. A couple of decades. It's an array-based approach, you have a probe, each one of those probes detects a variant in the genome tells you what variants that position. it's kind of low throughput. They're spread throughout the genome. It's not high resolution. There are tools that we use. They have a couple of hundred thousand probes in them. You look at common variation. And what you do is you create something called an imputed genome. And the reason we can impute your genome or guess at it is because when we inherited our DNA from our ancestors, you didn't get on ATGC at the time. You got a whole bunch of them. You've got 1 million. You've got 5 million, you got 10 million, you here them in big chunks.

And so I can look at your genome. And if you have a variant here, here and here and the reference has the same variance here, here and here, everything else in the middle is probably the same. We can assume that. So we do that genotyping. We combine it with something called whole exome sequencing. We do this with Twist, where we get very fine resolution of the coding portions of the genome. That 1% of the genome that makes the proteins. We can do that very quickly. It's about 20,000 genes in the genome.

We take these 2 things, we combine them together, and we get a very, very good imputed genome. But the question I get asked all the time, probably at least once a week, why not just sequence the whole genome? Costs are going down, just sequence the whole genome. If I sequence the whole genome, I get a lot more variance. It's a lot more than if I do an imputed genome. It's about 3.6 million variants on average in a person, but this comes at a significant cost in a significant amount of time.

Most of the variants you detect in the genome, they're incredibly rare. They won't make each one of us unique, but they're not seen very many times. They don't have a great impact on the research that we like to do. It's also a lot more expensive. And so I'm going to take you through some data that hopefully convince you of that.

So if you haven't heard the U.K. [ Biobank ] before, this is an incredibly interesting cohort. It's 0.5 million people in the U.K. They've agreed to be part of a research cohort. They signed up a couple of decades ago. We sequenced their DNA and they have folded them longitudinally, so we can use them research. These 0.5 million people, incredibly unique because they've been genotyped, they've been excellent sequenced and they've been whole genome sequenced. This is unprecedented. This is never going to happen again. This is not a cost efficient thing to do.

But it's been -- it's moved as the progression of the technology has moved, but it allows us to compare how each one of these technologies performs in the drug discovery and in the research world. And so first, just going to take you through the number of variants that are detected. It's kind of small here, but in the top box, whole exome sequencing that was done at Regeneron, you detect about 17 million variants when you do that.

The genotyping and the imputation, it's about 110 million, 111 million variants combine those together, you get about 126 million variants and 150,000 people that we studied. Whole genome sequencing, nearly 600 million variants, 5x more than that imputed genome. It's a lot more data.

But what does that really mean? If you look in here and you compare the number of variants in the coding sequences, the coding sequences, the 1% of the genome is the exome, these make your proteins. These are the things that we can make drugs against, whether you do an imputed genome or you do a real genome, you get the exact same numbers. It's about 6.7 total million variants that you get out of there and the overlap between those, it's 97%.

So whether you do either one of these assays, you're getting about the same exact number of variance. If you look at the individual level in the chart that I've just highlighted, per person, it's about 20,000 variants, a coding region, the genome, regardless which assay you look at, but the really important part is right here.

In the right-hand side of this panel, what I'm showing you is the number of observations in an imputed genome or in a sequenced genome of each variant that we find. So there's 475 million additional variants in a whole genome -- over 300 million of those, they're only seen 1 person. Just 1 person that is not going to contribute to research. And almost all of them are seen in less than 3 people. It's not worth it. These are not going to be strong enough to stand up to the type of statistics that we need to drive these processes.

But if you don't believe me, I can show you because we have medical information on these people. We plugged 100 traits out of this data set. It's 80 quantitative traits. These are things like height and obesity, they have a distribution, 20 binary traits is like, do you have the disease or don't you? Do I have endometriosis, don't I have it? And we did analysis with the data and we looked for new discoveries. When you run this analysis, you get about the exact same number of discoveries, regardless of which data set you use.

If you look on the right-hand side of that panel, there's less than a 1% difference in the discoveries that you make if you run an imputed genome or you run a whole exome. It's about 3,500 of those when you do the intersection, again, 97% of the discoveries they're exactly the same.

When you look at the 3% that are different, most of these are not reaching strong statistical significance. And if we run another 150,000 samples, they don't replicate. These are artifacts that probably aren't going to show up in a larger data set. And so this is an incredibly efficient way to do this. But this actually -- this is a ridiculous experiment because it's matched on the same number of people sequence, 150,000.

But we know targeted sequencing, it's a lot cheaper than doing a whole genome. So normally, when you're doing these projects, they're on a fixed budget, not on a fixed number of samples. So if we do this again and we fix the budgets, we made a guess when we did this type of analysis that the average lab can probably produce exome and genotyping data about 3x cheaper than a whole genome. It's a fair assumption. We have the data here.

If you look at 48,000 whole genomes or you look at 150-or-so thousand imputed genomes, data is right here, it's 3x more samples, you get about 5x more results. So on the same budget, you can stretch that and get a lot more information. It's also replicated here. So a whole genomes, if we have 150,000 of them or nearly 500,000 imputed genomes, again, 3x more samples, almost 5x more results again. Now if you're really efficient, and we've worked with Twist over the years to really drive that cost down, if you can be 10x cheaper than a whole genome, data is still here, 50,000 whole genomes, 0.5 million imputed genomes, it's 20x more results. So on the same budget going to an imputed genome, you can generate many, many more results here.

And as the cost of whole genomes continue to go down, targeted sequencing costs are going to go down 2. It's just going to happen. And so we should be easy for us to keep pushing these numbers higher and higher, especially the company like Regeneron, we have ambitions to do millions of samples. It's going to be driven by that cost we can get.

Now one more point here though, I keep talking about imputed genomes, the arrays that we use that it's an antiquated technology. I talked with Emily years ago, I wanted to make arrays obsolete just as much as she did, and we've worked on these over the years because the problem was I would do whole exome sequencing and I would do a raise, I couldn't keep up. That array technology, it's decades old, 2 decades old. It hasn't changed over time. And I wanted something new because I couldn't get these data sets done at the same time. And as we scale the millions I wanted to be able to do exome sequencing, Twist is already an expert at and genotyping at the exact same time. And so I brought this to the Twist team, and this was an incredibly ambitious project.

There was no one else that could do this. I needed 600,000 probes, and I wanted a couple of little pools. The other technology providers at the time, they didn't even want to talk to me. Trust said, let's do it. And so we wanted to replace those arrays. I needed something that didn't exist. They were on board with that. We created something called the Twist Snip Diversity Panel. We did this in about 2019, 2020. We went through, we selected regions of common variation in the genome, ended up being about 600,000 probes.

Now when we capture a sample, there's over 1 million probes in that tube when we are capturing the DNA for these samples. It's about 600,000 probes, but it actually gets a lot more veins than an array. And array, 1 probe gets 1 variant. When we capture things, we get pieces of DNA at a time on average each one of those pieces of DNA has about 2.5 variants in it. So we get about 1.5 million variance.

When we combine these together, we have an incredibly powerful tool where I can get my exome sequencing in my arrays at the same time, incredibly low cost and take advantage of the power of sequencing, so I can fly through samples. One more very important part, many of the research to date, it's highly, highly focused on people of European ancestry -- when we designed this, we made sure that it would work in all ancestries equally, and we've been able to show that over and over again, that imports incredibly well across all continental ancestries.

So I hope that -- be able to show you that even though the whole -- the cost of whole genome sequencing is plummeting, targeted sequencing is going to go away. It is the tool that we're going to have to use to create databases of millions and millions of samples and the data shows that that's the case. So I want to thank you again. I love to talk about this more and just keep working with the Twist on this. Thank you.

Thank you so much, John. I think you can see why -- some of you in this room have asked me that question. With whole genome sequencing is an exome sequencing going away. So I can say no all day but it's so much better for John. He's so much more eloquent than me in how he delivers the message and how he proves all the things that they're doing at the RGC and the amazing discovery that they're doing.

All right. So our next customer presentation is from Josh Clevenger, who is an expert in plant breeding and genomics and a faculty investigator HudsonAlpha Institute for biotechnology. We're going to change directions. And we're going to go to the plant side of the business. Josh?

Thank you. Thank you. Actually, I was looking at this and wondering how I can advance slides, but it's a big green arrow. So you can't be -- you can't screw that up.

So I just want to say real quickly, it was amazing listening to all the incredible sort of health care applications. And one thing I like to talk about when I'm talking about representing those of us who work in food security is that there hasn't been a single human born that can exist without eating food.

And so the work that we do across the globe that helps secure that global food supply chain is incredibly important. And what I'm going to do today is talk to you about really how Twist has changed the game and giving us the ability to do things that we never could have thought possible before.

So in my lab, what I'm focusing on is how do we translate genomics into applied crop improvement. And I work with companies, large and small, USDA, private and public, to help provide them with the ability to actually take the food that they're interested in and make the improvements that they need to make.

And I want you to think about it as we talk about this. When you go in the grocery store and you look at just the foods in the produce department. Every single one of those foods has a different genome that's incredibly complex and different than the one next to it. And so we have to have the ability to understand and make improvements and insights into all of those genomes at once. Otherwise, the panoply of food that you enjoy, you wouldn't have access to.

And so my lab has really been built on Twist's library prep. And there's very simple reasons why that is. And I'll just go very quickly through those because the other things I want to talk about are more important. The first thing we heard about is ligase and ligation and the normalization by ligation. So every week, I'm extracting DNA from a dozen different plant and animal species. I have to get DNA from the clippings of lizards of their tails, from soil, from blood, from shaving of seeds, from seeds themselves, and they're coming from all over the world. I don't want to have to worry about normalizing all of that DNA. And with Twist FlexPrep, I actually never quantify the DNA. I extract it from all these things, and I go straight into library prep. And that is incredibly impactful. That drives the speed and scale of the things that we can do.

And the second thing is the complexity. So we talked about enrichment panels and enrichment panel sequencing and the ability of Twist to print these probes at speed and scale at a cost that makes sense. And so again, we're developing probes that target genes and pine trees or the genes and horses or the genes in a peanut plant or blueberries or raspberries or any of the crops that we're interested in. And so we really want for those panels to have that complexity.

So these graphs really just show the dots at the top is better. And so the complexity of the library prep and the ability to do that enrichment at scale allows us to, again, go straight from DNA from lots of different sources into that prep and make those insights in a better way.

Okay. So the way I wanted to basically talk about the work is we have a lot of work going on in a lot of different ways. So I'm not going to show data. Instead, I'm going to show you the young scientists who are actually using Twist library prep and Twist technology to dream the dreams that they want to dream and then to go on and make a difference in the world.

And so from left to right, we have Zach Myers. Zach Myers is actually looking at how to take hundreds of thousands of these enrichment panel results or whole genome sequencing of plants in a library and actually do what we call the single experiment. So every time that we have a disease that is affecting agriculture, every day that we don't have a tool to combat that disease, there are farmers and there are real people that are suffering. And so we want to be able to get at those tools faster than ever before. So Zach is working on that.

Holly Wright is extremely passionate about a wonderful crop called finger millet. It was actually -- is very tolerant to high heat and drought stress. And so being able to layer on genomic data onto that crop and which hasn't been done before is helping with breeders and farmers in areas of the world where they rely on that crop.

And Ash Meida is working on how to layer on AI into selection modules. So we can actually just read the DNA of tens of thousands of plants and make that selection first without having to put out in the field and select for phenotypically.

I have young scientists in my group that actually are working from Southern Alabama and supporting breeding programs all over the world. So we just get slivers of seeds that come in. We sequence that DNA and then we make selections for them in Argentina and Brazil and Africa and even China.

In India, Justin Vaughan is a good friend of mine, who is working on how do we utilize all the sequencing data on pan-genome graphs. So we actually are one of the only groups right now that's able to take low coverage whole genome sequencing and call genotypes and impute them on the graph to actually have access to all the variation. So if you've heard about pan-genome graphs are incredibly powerful, but it's still really hard to access that information.

Ethan Thompson on the bottom right is actually a young scientist that is changing supply chains with the discovery he makes by the ability to actually sequence populations at scale and ask these questions.

And then [ Embryo Right, ] I'm very proud of. She was an intern in my lab and now is an expert at getting DNA from any kind of tissue that comes in from any parts of the world. And again, working in the place where we work, where we have to deal with all these complex tissues, that is extremely important, and there's not a lot of groups that can do that.

Okay. So some more targeted sort of examples of what we work on and how genomics protects our food supply. So quite simply, if you have a global pathogen that's affecting global supply chains, this is peanut, which we worked on with the USDA and Mars. It only affected peanuts in Argentina, but they export all of their peanuts for Europe and the rest of the world. And so that pathogen actually affected complete global supply chains. So we worked with them. And again, the ability to understand the gene that conferred resistance to that trait, we had to be able to sequence at scale. And we had to be able to sequence those things from tissue that came from another country and it had to be fast and accurate. And then we use that information to map the resistance to layer on pan genomes to identify those genes.

But then what we did was we worked with 5 different breeding programs in the U.S. and South America to make rapid selection. And in 3 years, I was able to show Mr. Frank Mars, I said, Mr. Mars, this is the peanut I developed for you that's resistant to [indiscernible] and it's high [indiscernible] and it's going to be great for your global M&M supply chain. And that is an incredible achievement that we could only have done if we had the ability to do that sequencing work.

So this is kind of a fun example, too. Kendall Lee is actually a co-founder of a company I founded to scale long-read sequencing, and she did a post doc on blueberry. So blueberries in Southeast Georgia are having problems with late-season freezes. So actually, they would flower, it would freeze and then you wouldn't get blueberries, and that was really a disaster. And blueberry is of the top 3 -- I'm sorry, Georgia is one of the top 3 blueberry-producing states in the country. So she looked at the populations in the breeding program and identified 2 variants where she can make selections so that the blueberry would flower later. So it would get out of the window of that late season freeze and then it would produce the fruit sooner, so faster. So you're actually getting blueberry production in the same window and you're moving it in a way that it actually offsets that risk. So that's really incredible result.

And then sort of a less sort of scientifically satisfying but incredibly important thing that we're able to do with Twist enrichment panels and the FlexPrep technology is we're starting to actually move into the germplasm collections. So germplasm collections of crops are one of our most treasured resources. It's way more valuable than gold and diamonds and silver and all of those things because it's the only source of genetics to solve the problems of agriculture that we have. And once it's gone, it's gone because the places we collected them from are now parking lots and gas stations and hotels and McDonald's and other things. And so we don't have access to them again. Because of working with Twist, and actually, they've been a great partner with us, we're starting to move into the germplasm collections and create what's called digital backups. So that even if that seed does not survive, we know the genome of all of those.

And I'm showing you sorghum because sorghum is really interesting. There's 45,000 sessions of sorghum in the world left and there is no curator. That means there's nobody that goes in and make sure that those seeds survive. So being able to do this is a really big deal. That's our first project that we're working on.

And then in the end, I have a project that is really fun in which we actually do the breeding in high schools. We're in 15 high schools in Southern Alabama. I go into those high schools. They each have a plant they sequence. They extract the DNA of that plant, and then we use Twist library prep, sequence it and then analyze the data with the students and then they tell me which plants they want to move on to the breeding program. And what's interesting is that, that area of Alabama is basically the center of peanut breeding in the United States and peanut production. And so a lot of these kids are learning about how to use sequencing to improve the crops that are really driving the economy in their region. And we could not be able to do that without the ability to actually have these incredible preps.

So again, thank you. I hope that I convinced you that this ability really is driving food security globally in a really big way. So thank you very much.

[p id=703517233 name=Angela Bitting type=E ]

Thank you so much, Josh. You truly are saving the world one plant at a time. It is really impressive what you are doing and all the people you are training under your tutelage. Really, your impact is so much greater because you're training the next generation. So thank you for all your work. Thank you for letting us be a part of it.

Our final presentation from a customer today is from OncoDNA. And we have 2 presenters, not just 1. You get a twofer. We have Koenraad Eycken, who is CPO, Sales and Business Development for OncoDNA; and Christophe Van Huffel, Co-Founder and CFO of OncoRNA.

Welcome, gentlemen. Please come tell us all the cool things you're doing.

[p id=1905934051 name=Koenraad Eycken type=D ]

Thank you for the invitation to speak here about OncoDNA. So what we do at OncoDNA is really linked to the patients and the oncologists. So what we see is we talked about a lot of details about sequencing. But actually, the oncologist, what they need is just something very simple, what can I do with my patient. And this is what we do as OncoDNA. We make the tumor profiles actionable for the oncologist. So what it means is we take a tumor sample or a blood sample and then we create a report for the oncologist that is really very clear where they find out which variants or which biomarkers are available in the sample and what they can do with it for the patient.

And as OncoDNA, this is what we've been doing for the last 13, 14 years already since it's founded in 2012, we've tested already more than 100,000-plus samples. And we are working in a global setting. So we have 45 distributors in different countries. We have customers sending in samples or we are sending in reagents to different geographical regions.

And how we started was really in a centralized setting. So meaning that all the samples were sent to OncoDNA in Belgium because as you can hear from the accent, we are not a U.S.-based company. So the samples were sent to Belgium. We did all the wet lab work, the bioIT and the reporting, and we did it based on an amplicon-based solution.

But we found out that there were some limitations because there were new -- more and more information available into the market. So we needed to go to a newer version of our solution. And at a certain moment, we needed -- we faced some challenges with an amplicon-based solution to go for a bigger panel to do CMP detection to do complex biomarkers.

So this is when we moved to Twist when we came in contact with Twist, and we tested their solution. And we really saw a difference with the capture-based technology. So we saw a higher quality of the sequencing, and we saw that the uniformity and other performance parameters were really outperforming the amplicon-based. And therefore, it was easier for us because a good quality sample means that the interpretation is going to be easier. If you have a good uniformity, it means you can run more samples on the same sequencing flow cells. So it really means more patients that can be analyzed and also more information can be found in the tumor.

That was interesting, but what was also interesting. So we opened a new business line, thanks to Twist, because in Europe, it's not common to send samples to a central lab. So now that we were with Twist, they were also -- we could also offer our solution on Illumina sequencer or Element Bioscience NGI that are now coming up. And we really changed our business model. So instead of sending samples into the lab, we now send reagents to the labs in Europe, and we do the bioIT and reporting still in our cloud solution. So this really made us an extra business line next to the centralized business.

And it has been an accelerator for OncoDNA because when you see here in the graph of the number of samples we've tested over the last years, the green is the decentralized model, and you see that it appears and then keeps on increasing in the time.

And it's not -- we're not going to stop here. So what we did in 2019, we started collaborating with Twist. We did our centralized solution. In '21, we launched our decentralized solution. In '24, we launched our liquid biopsy panel. In '25, we are going this year -- last year, we launched the large RNA panel. And in this year, we're going to launch a large RNA panel as well. And what we hope before 2030 that we can also launch a personalized vaccine into the market.

Before we talk about the vaccine that Christophe will talk about it, maybe I just want to show 1 or 2 slides extra. So the OncoSelect Gene Panel, it's a liquid biopsy panel, which is really the newest -- the hype in the market. So we really need to go into the liquid biopsies as well. And together with Twist, we've been able to make a test that performs very well with a good performance characteristics.

We're also working together with them with the personalized MRD panel where we will do the variant selection. Twist will make the panel and then our customers can run it in their own lab and do the interpretation in our software platform.

And actually, with all of those different tests, we are -- we can, as OncoDNA, be present at different time points when the patient needs to be tested, and all the data can be shown in our own platform.

Unfortunately, this is not enough because even with all of those testings, there are still a lot of patients who cannot receive a good treatment and who are still sick, and that's why we have OncoRNA that has created a solution.

[p id=D88 name=Christophe Van Huffel type=D ]

Thank you, Koenraad. Thank you, Twist, also for having us here today. It was a great day and a nice presentation.

So about those patients, so about half of our patients when we apply all our panels and whole genome analysis, we don't find always actionable mutations. Actually, half of them have many mutations that are not characteristics of associated threats that we can identify.

So the approach here is those mutations on some antigens at the surface of the tumor. They are specific, unique to the patient. It's a set of markers there that can trigger the immune system. We can't predict that. And of course, this was revised. The RNA-based approach was revised with the pandemic with Moderna and BioNTech being involved in that approach, in fact, before switching to the pandemic vaccines. And they used what we discussed earlier this morning or this afternoon, also, cloning.

So they would clone and the limitations of cloning is that you can only fit a certain number of those markers within a plasmid. When it's too long, too large, you're limited. And they had to select up to 30, 34 neoantigens. And we believe that OncoDNA that, in fact, this limitation is a selection that's based on bioinformatic approaches, but it's only not reflecting the particular situation of the immune system of the patient.

So what we discussed then and at that time also with the revival of RNA approaches was also concomitant with actually the pool technology from Twist that we're around 2021 there. The pool technology of Twist was enabling to synthesize oligonucleotide pools of 300 base pairs in length. And we needed about 500 base pair to really design individual RNAs, each containing 1 neoantigen.

And those pools, we've seen that during the day, I mean, have features with 121 neoantigens. So I approached Siyuan and it took only 15 minutes over a Team's call across the pond to convince him. Two weeks later, we had already -- he had made DNA, but also RNA out of that. At the same time, I had a call with 1 of the 2 manufacturers of the lipid nanoparticles, Aquitas, which was the one used by Pfizer BioNTech. And it took also 15 minutes just on a Teams call and said, let's do it. And 1.5 months later, we had actually injected a set of mice to prove that we were able to trigger the immune system in those animals.

So we have now something that's quite unique and only enabled by Twist, which is typically in a tumor, you have about 100 to 400 neoantigens in any individual situation. So that really fits well with the format of feature of 121 if you try to select the best out of it. So -- but that's already important to be able to put more than 30 as, let's say, Moderna is doing. We put 120 or 100 or eventually all the neoantigens we find to, instead of selecting bioinformatically those antigens, we put them back in the patient and let the patient's immune system do the selection because those bioinformatic tools are based on selection process of in vitro studies and not in a natural environment.

So then, of course, the length of the oligos was critical. And there's really no limit to the pool diversity. We could go above 120 if needed. And in some melanoma tumors, we reached 400, 500 neoantigens, and we could use that. However, we believe it's important to have a large number because a tumor is only a sample that you take. You may have multiclonal situations. And you want to be able to lock as many degrees of freedom for those tumors to evolve and escape from their immune control and also to be able, as we've shown, to have even mutations that code for genes that not necessarily are activated yet in the tumor for which RNA is not expressed, but may be expressed later on during a relapse situation in a metastase because there's drifting of those neoantigens and the tumor composition over time.

So typically, so what Twist chip technology makes possible is really to be able also to shrink that time to -- our goal is to be able from needle to needle to go in 3 weeks with the delivery to the patient. By doing so, we sequence with the Twist whole exome sequencing panel. We do a neoantigen design then. And the Twist chip synthesis' 5 days turnaround time. We then go for that process, which is processed in a single tube where the pool of 121 antigens is found is encapsulated and injectable.

It's important to be short because, for instance, we collaborate with the doctor who is also involved in the BioNTech pancreatic cancer situation. So he recruited 30 patients [ for by now ] and only 5 were, in the end, treated. Although 30 were eligible, but it takes more than a month, if not 2 months to make. And by the time the drug or the therapy is available, the patient has evolved in a situation where it's no more the point to treat him.

So that's really [indiscernible] and shrinking that time is really critical as we think in this situation. That's something that Twist really enables and can make a big difference. No other competition from Twist is able to have both that speed, the right format and the price point that matches the needs for this therapy.

So in summary, we have more than 100 neoantigens. We believe it's important to have more. The 3 weeks turnaround time, we've shown that. And surprisingly that 90% when you -- instead of concatenate the neoantigen as a long insert, treat them individually, 1 RNA, 1 neoantigen as a pool, they trigger at 90%, each of them a CD8 cytotoxic immune response.

We've been able to have a nondilutive grant from the EU also of EUR 31 million. And with that, we hope to enter into Phase II. Finance also a dedicated production facility where we can -- because it's synthetic based, so we can, in the same facility, produce for many patients in parallel. And for thousands and thousands of patients. So it's only waiting for growth here that we can order more Twist panels and also more pools from Twist, which has been really the enabler of this switch from technology to something that maybe is going to deliver something fantastic for the patients. Thank you very much for...

[p id=703517233 name=Angela Bitting type=E ]

Thank you so much, Christophe. Finishing very strong with OncoDNA. And we now have a break for you. We have some local Portland fair as we have all day and all evening. We have Poplandia popcorn. It's quite a thing here. So please join us for a little short break, and we will return at 4:05 or 7:05 Eastern. Thank you.

[Break]

[p id=242921299 name=Emily Leproust type=E ]

Thank you very much. Appreciate it. Very often, when we do investor engagements, we get the question, okay, you're going to be profitable. And now we delivered, which is a great improvement compared to the past. And often, we get the question and then what And so finally, we're going to hear from Adam what we will do after we turn profitable.

[p id=1867872015 name=Adam Laponis type=E ]

Thank you, Emily. All right. Well, thank you all for spending the day with us. I'm Adam Laponis. I am not a DNA chemist. I'm the CFO at Twist. And I have the honor of working with this team.

What you've heard today is a lot from our customers. You've heard from the team, you've heard about the strategy. But I will also tell you, it is the culture, it is the mission and it is this team that makes Twist unique.

I'm excited to share with you the financial outlook for Twist. We're at an inflection point, and we have a strong opportunity for growth and growth of profitability. But before we go there, I want to spend a little time grounding us in some of the recent financial performance.

Okay. In 2023, we opened the Factory of the Future we're standing in right now, and Twist has been in the platform build-out stage up until today. In 2023, we had $245 million of revenue, and 13 sequential quarters later of growth, we're now forecasting $442 million to $447 million in fiscal 2026, a compound annual growth rate of 22% over the last 3 years.

Now let's keep in mind, the backdrop we did this in was not exactly easy. The life science and tools space has been struggling to have single-digit growth in many, many of the peers that we compete with.

And we also did this while executing with financial discipline. The gross margin of the business has grown over 15 points in the last 3 years from 37% in 2023 to above 50% in fiscal '25, now guiding to be above 52% in fiscal 2026. And during this time, we've held operating expenses relatively flat for the last 3 years, improving adjusted EBITDA from negative 60% of revenue in fiscal '23 to forecasting to be breakeven 130 days from now in fiscal Q4 of 2026. That's financial discipline in action.

This next slide is probably the most important slide in my presentation, so I'll spend some time on it. We are at the inflection point of profitability. And as Emily pointed out, the question is, where do we go from here We have a strong growth trajectory to more than double revenue from organic growth from 2027 to 2031.

Now let me be clear, that's a floor, not a ceiling from which we believe we have many opportunities for upside, many of which you heard about in the conversations that we had today with our customers and our team.

We're forecasting to be adjusted EBITDA positive in Q4 and make continued progress in '27 and beyond, improving profitability sequentially. As we cross the cash flow -- free cash flow point, we plan to continue to reinvest our profits into driving the NPI machine and the continuous process improvements that you heard about today. We also have a line of sight to a long-term gross margin of above 60%, and we'll spend some time talking about how we get there as the business continues to grow and mature over time. And finally, we see significant leverage for opportunity for operating leverage in the business and a long runway in our current facilities, and we're charting a course to be above $1 billion of annual revenue capacity by 2030.

So let's dive into each one of these parts. Let's talk about growth. And where it starts, as we've hit on all day today, is on the chip. It feeds our NPI machine. We leverage it for our operational excellence, and we drive commercial execution across the business. I think this is best -- the growth opportunity is best described through our available market size. We talked about in 2020, Twist has an available market of about $2 billion. And through the NPIs and the new product we've launched as well as the category growth we've seen in our existing markets, we're at over $7 billion today and growing. And by 2030, we expect to be at $13 billion of SAM.

And that's because of the NPI machine as well as the markets we participate in growing.

Looking over the DSPS side of the business, how do we grow It's doing what we've been doing time and time again, continuing to broaden the workflows, adding new capabilities, expanding our menu and fundamentally offering customization at scale for our customers. That's what we do. You heard about it today. You heard about it from our customers. We'll continue to do that time and time again. And ultimately, what we're driving towards is helping our customers accelerate their flywheel of design, build, test and learn, where we can participate in more elements and expand our offering. But ultimately, what we're doing is we're expanding our wallet share with our customers over time.

On the NGS application side of the business, we're the key supplier today for liquid biopsy and a number of other large growing markets, such as in oncology diagnostics, rare disease, and it's because of our unique product quality and rapid onboarding processes that allow us to be successful.

We heard about how we're in the early innings of the diagnostics revolution in oncology. With up to 20 million people having cancer by 2029, we see the opportunity for sticky, recurring revenue streams from this area. And more broadly, as cost of sequencing continues to decline, it opens up new markets, new geographies and new opportunities to use NGS that allows us to grow with our customers. Twist isn't just competing in these markets. We're creating and enabling them as we go. And it's that type of growth that will allow us to continue to see the path towards more than doubling our business organically over the next 5 years.

Now let's talk about gross margin. When I first joined Twist, I was excited to see how the team operates and understand that it's not just revenue growth, but automation and continuous process improvements as a priority to enabling gross margin expansion.

Growing from 2023 at 37% to above 52% in fiscal 2026 was enabled on a number of the capabilities. The #1 foundation of that was our fixed cost. It's like flying the airplane. The more seats you put on the plane, the more profit you drive. So we were able to leverage over the last 3 years, relatively stable fixed cost as the revenue grow. This is best exemplified in the following way. Our revenue grew 8x faster than the people in manufacturing teams across Twist. And you can see that in all of our processes.

You heard about process reengineering today as well as automation of the automation. And where that's taking 4x the capacity in the writers by shortening that cycle time. And you think about what that was, that wasn't just a process improvement. That was a capacity expansion, a quality improvement, a reagents reduction and the ability to offer direct synthesis of up to 500 base pairs on the chip, allowing us to introduce new products, all without any capital investment.

Looking at the other elements of the business, you spent time today on the tour you saw GeneLab 1, where we make our fragments. We've been able to take that process with automated equipment and automate that automation, taking the footprint of that space and dropping it by 80% to double the capacity of manufacturing in the clonal fragments, the non-clonal fragments. And ultimately, we target all of our products to have a gross margin above 75% of incremental revenue dropping to the gross margin line, particularly as we scale, drive automation and enable continuous process improvements on everything we do. That is what we're defining as operational excellence directly driving gross margin at Twist, and we'll continue to do it time and time again.

Let's talk about our facility and our capacity. We're standing in the Wilsonville facility here where we have approximately 210,000 square feet of available space today. And we've got confidence and we're charting a course to having over $1 billion of revenue capacity online by 2030 in these existing facilities.

We're here with our -- where our primary manufacturing location for DNA synthesis and Protein Solutions is. We have 4 riders online today. And on the tour, you saw that rider room has capacity to have 16 riders in the room today. And you also saw we have over 65,000 square feet of available expansion space in this facility that we have not yet touched.

In South San Francisco, our primary location for NGS applications manufacturing as well as our corporate headquarters, we have the 4 riders online today, more on order. And we -- as we've announced earlier this year, we signed an expansion to take over the third floor of our building, and we did it without adding any cost, but also getting a bunch of TIs to enable that build-out. And finally, as part of that lease expansion, we retained an option to expand further into the fourth floor of our facility, taking over the whole building in the future as we continue to grow, should we need to.

So kind of putting the math all together and how do you think about this. We have 8 riders today running across the business. They're running at about 50% capacity. We have 4 more riders on order, and we have the capacity space today to have 22 riders with more room for expansion should we need it. That means we have roughly 3x the rider capacity available in our current facilities than we have online today. Now granted, riders are not the only thing that will drive our capacity needs, but they are the founding principle because everything starts on the chip.

So with that, go to the next slide, we'll talk a little bit about our capital discipline and capital allocation strategy. Twist has been an organic growth engine, driven by our NPI machine. That trajectory will not change as we go forward. We're continuing to focus on organic growth, and we plan to self-fund our innovation by investing into our NPI machine as long as the returns justify it. We'll continue to stay close to our customers, understanding the science that they need to help enable them to make new products, enter new markets and drive new capabilities. And we also believe we can expand our footprint and continue to expand our offering using a sustaining level of CapEx investment generally in line with depreciation today. That's been our criteria, and that will continue to be our criteria for how we think about driving growth and investment.

We've also used M&A opportunistically, focusing on bolt-on technology opportunities that are accretive to our business. The best example of this is recently is in Invenra, where you heard about the B-Body technology platform we licensed earlier this year that's giving us access to capabilities and bispecifics we didn't have before. And we're committed to making investments in agreements to support our long-term free cash flow trajectory and margin profile.

In short, our central criteria is fairly straightforward. First, will this bring more volume onto our silicon platform And second, do we believe we can improve the unit economics over time with it. Ultimately, we're not looking to buy companies. We look for opportunities to bring more volume onto our platform that can accelerate growth and deliver improved unit economics.

Okay. Now let's bring it all together. Medium- and long-term financial summary. Revenue growth, more than doubling our revenue from 2027 to 2031 organically. And again, this is the floor -- a floor, not a ceiling for growth. It's going to be driven by the NPIs, our market share growth and the category expansions we have in our existing markets. We see multiple opportunities for upside, whether it be AI drug discovery, MRD, mRNA technology and therapeutics and nucleic acid therapeutics and other areas as well. And we expect our long-term growth to be driven both by DSPS and NGS.

Looking at adjusted EBITDA, we've committed to being profitable adjusted EBITDA positive in Q4 of this fiscal year. And once we cross that, we plan to expand that in 2027 and beyond. And we have a clear path to free cash flow positive with our available capital on hand. Our gross margin trajectory is strong, having improved over 15 points in the last 3 years. And we're driving towards a path with revenue expansion that will get us to over 60% gross margin as we continue to grow and mature. And of course, this is all based on our ability to continue to automate and drive the continuous process improvements we've seen in the past, and we know we can continue to apply to every step in our process.

Our capital allocation and CapEx strategy has been consistent and clear and is to self-fund our growth and our organic growth through driving our NPI machine and CPIs. And we have a clear path to over $1 billion of revenue capacity with sustaining levels of capital investment.

In summary, now that we've built the platform, Twist is entering a new phase of profitable growth, where every dollar of growth is more profitable than the one that came before. Our platform and scale will continue to drive robust profit, top line growth with multiple opportunities for upside.

Thank you for the time. I look forward to the Q&A later today. I'm going to hand the mic back to Emily.

[p id=242921299 name=Emily Leproust type=E ]

Thank you very much, Adam. We have one more presentation before we go into Q&A. You've seen we have great technology. We have great software, great patents, great products, amazing customer, and we have people -- business of people. And Paula is going to tell us about our culture advantage.

[p id=382973279 name=Paula Green type=E ]

Thank you, Emily. So I stand between Q&A and wine. So I'll get the show on the road. Thank you so much for being here today. I think I have one of the luckiest jobs in the world. I'm going to go back one slide. And just let you know, I am Paula Green, as this picture actually shows. I've been with Twist for 10 years, and I am responsible not only for HR, but also for the facilities here at Twist.

You guys got a flavor of our culture today from all of my colleagues who spoke about that. But I'm going to spend a little bit more time speaking in depth because really, for us, our culture is our moat, and it is an extreme advantage to us. Without it, we are a great -- still a great company, but we are an even better company with it.

So our mission and our vision are really -- I went the wrong way, so sorry. Our mission and our vision are simple, yet extremely powerful. So we make synthetic DNA to improve health and sustainability. And really, we see our customers as our heroes who are changing the world for the better. But it's our mission that really attracts highly driven people who really want to make a difference on solving the world's problems. So we all know that purpose only matters if you really operationalize it. And so it's how we succeed through that mission because employees are deeply connected on the execution of the day-to-day responsibilities that they have across the company.

So when we go back to Twist's history and the foundation, what we stand on is not just the scientific innovation. So from the beginning, when Emily and the 2 bills founded Twist, they were willing to rethink how DNA could be written, manufactured and commercialized. And that mindset still defines us to today. So we move quickly, we iterate quickly, and we are comfortable making bold decisions in pursuit of our long-term advantage.

We work like a sports team, not a family. And that distinction really matters. We are really focused on execution, delivery and pushing the envelope forward. Our competition is outside of the company. And that means we have 0 interest in the political games, in the backstabbing, in doing anything other than making sure we are successful and executing with kindness. So that means we have 0 tolerance for brilliant jerks. We just don't have time for it. We demand solid performance. It is our guiding principles that actually drive our execution. So these are not just symbolic words. They actually are operational for us.

Our 4 guiding principles are grit, impact, service and trust. Most importantly, they are not posters on a wall. They directly shape how decisions get made within our organization. So for us, grit means resilience and accountability. Impact means focusing on outcomes, not just activity. And service means deep connection and customer obsession, both internally and externally. And for us, trust means transparency and integrity and accountability at scale. So we live and breathe these principles, which enable us to create an organization that moves fast without creating chaos. And that balance continues to be important for our global enterprise.

So here's where we get our competitive advantage. It's the combination of our cross-functional collaboration with speed and enterprise discipline. Most companies are good at one or another, but we believe we've built the ability to do both, maintain the precision and the quality and the scalability while still moving with urgency. We will always maintain customer centricity. We teach our teams to forge and create long-term scientific and commercial partnerships with our customers. And you saw that today. So the mindset is really how we create those deeper relationships, so we build stronger retention and better insight into emerging opportunities.

So this is one of my favorite, favorite slides, and this is the people philosophy. We demand a lot from our employees. And in return, we give them a lot. It is very common to hear our employees say, this is the toughest job they've ever, ever had, but it is the most rewarding job for them.

So our people philosophy is quite simple. We hire exceptional people. We build high-performing teams. We create that accountability through transparency and we reward impact. So as we've grown to over 1,000 employees, maintaining this culture becomes increasingly intentional. We remain highly focused and disciplined on preserving this culture as we continue to scale.

Commercial execution with kindness. It's one of the clearest reflections of how Twist competes. So as Paddy shared earlier today, we are highly ambitious commercially. We believe the best long-term outcomes, they really come from combining urgency with trust. So our teams are encouraged to listen intently to our customers' needs, bring them back into the organization, and then we work cross-functionally to solve the problems quickly.

Most importantly, we go beyond simply selling products. It is really our desire to continuously implement, troubleshoot, scale and succeed. So that creates a different kind of customer relationship. What we're asking for is one that's built around partnership and reliability rather than a transactional relationship.

Customer centricity is at the heart of everything that we do. So for us, the customer sits front and center. We listen first, we serve them proactively. We focus relentlessly on consistency, quality and speed. And we really work side-by-side with them in their growth and therefore, ours.

Here's my last slide. We have the ability to maintain precision, quality and scalability all the while moving with urgency. And at the same time, scaling successfully for us requires discipline. So one of our core beliefs is say what you're going to do and do what you say.

That commitment drives accountability across the entire organization. So automation and software infrastructure, operational efficiency and cost discipline are really embedded into how we build products and processes. We pair that discipline with start-up velocity. So we believe in launching, learning, iterating and then improving rapidly, and this is continuous. We fail fast. We fix fast. We understand that because of this, when our markets are evolving really quickly, speed of learning becomes a major advantage for us.

So in closing, when we talk about Twist's future, we're not only talking about technology platforms or product pipelines. We're really an organization that's designed to execute.

We're built around high performance, customer obsession and most importantly, operational rigor and speed. So ultimately, we believe that our culture is one of the most durable competitive advantages to continuing to scale Twist in the future. Thank you very much.

Thank you very much, Paula. We are at the time for Q&A. We have people online, and we will take questions both from the room where we have microphones and runners as well as online. Angela is our moderator, and then I will send the question either to myself or to a member of the management team.

Indeed. All right. I'll start on this side of the room and head to that.

Excellent day, Emily. First one for you on -- given the focus on of the day, AI, obviously, front and center, a key question on AI is really the growth velocity here in the near to midterm. You're showing triple-digit order growth fiscal '26 versus '25. I mean, is that fair to assume that you'll do $50 million in orders this year? And if that's the floor? And maybe could you talk about maybe the ceiling, what's the upper bound to that? And can you -- how should we think about the revenue conversion there from those orders? Should it imply a triple-digit revenue growth as well for the AI business?

Thank you. That's a great question. Maybe I'll start -- I'll take this one. I love when you talk about ceiling because we talk about ceiling with our customers, and we tell our customers, back up the truck. You can't break up. You're going to run out of money before you're going to break us, right? And so go out, bankrupt yourself. We are here for it. So there's no problem for the ceiling. Shatter that.

And then in terms of the floor, right, it's trying to achieve 2 things at the same time. One is absolutely crushing the next quarter and delivering continuous growth, the 14th quarter and 15th and so on. At the same time, we are not into the flash in the pan building. We are here to build a durable business. And so that's why we are, investing in the technology, in the product lineup and listening to customers. As the science evolves, as things change, we'll be able to adjust and adapt because we have the nimbleness and the platform enables that flexibility.

In terms of yes, order growth, orders were $25 million last year for AI-driven drug discovery. We think we're going to get triple-digit growth this year, and that could be more than $100 million. And in terms of the revenue capture, you heard from Colby. A lot of those experiments is 20, 30 days. That is the whole point of AI is that it's faster.

And that's why AI is going to become the first path for the discovery broadly, we believe, because in vivo in vitro are fantastic. You can find amazing drugs, but they are slower. So that's the great thing for us is that the to book the revenue is very quick. And last year, we mentioned the orders because the order actually came kind of late in the fiscal year.

So I'm going to take one -- Subu, you're next. I'm going to take one from the webcast and then you're next, Subu. All right. From the webcast, fiscal '27 to '31 revenue doubling. Does it mean fiscal '27 revs to double by 31%, meaning rev CAGR is 19%. If I use 26% as new rev base, it implies 16% CAGR. What's being assumed for NGS versus DSPS segment growth relative to the 19% CAGR for overall Twist?

Adam, do you want to take this one?

Thank you, Emily. In terms of the revenue numbers, yes, if you look at the exit number for 2026, $442 million to $447 million. We plan to double that by -- or more by 2031 fiscal. And so you look at the growth rates on that. You say that's the floor, not the ceiling, and that we have a number of areas of opportunity for upside.

looking at the growth rates across the 2 areas of the business, in my comments, we talked about essentially having the ability to see growth contributing roughly equally from both sides of the business. That won't necessarily be the case in every cycle or every quarter. But over the course of time, we like to say we love our babies equally, and we see the opportunity for long-term growth in both sides of the business.

Thank you. And that's a very important point is, again, we're very diversified. It all comes back to the silicon chip, whether it's a DSPS or NGS, but we have opportunity for broad-based. And I'm going to say the same thing with the different it will depend on the cycle. Some quarters will be more one and some others will be the other. And that's what we like about I think thousands of SKUs, hundreds of applications and many, many customers.

Actually, can I ask a follow-up to that question. If you actually do meaningfully better than that, Emily, is it likely via a source we talked about today or any other adjacencies that won't showcase today?

That's a very good question. So we all heard how C1 and Colby are amazing at launching new products. We can do 2026 without any new products. We probably can do 2027 without many substantial new products, but we are an API machine, right? The products that are going to sustain us in 2030, they are not launched yet. We have an idea, however, have ideas about what we want to do.

Actually, we have more ideas than we have time and capital to do. And so we have to force rank our opportunities. We routinely force rank and then force rank by the biggest return on investment, so biggest opportunity with the least amount of effort. And then we make deliberate decisions that we're going to do the top 2 or the top 5, and we do nothing on the rest. And that discipline is very important. But we are nowhere near running out of ideas.

And a quick one. Are there scenarios, especially as you work with earlier-stage AI-focused companies where you would lower upfront cost for -- in exchange for downstream economic participation?

Maybe I'll send this one to Paddy.

Yes. I mean it's a great answer. It depends. I mean the goal here is to make an incredibly low barrier to interact with Twist. And we don't make kings or queens. We're here to enable the entire community. So for the moment, the deals we're seeing just get on the platform, get the crank turning. And for the moment, it will be business as usual. And there's plenty of opportunity to grow into.

Okay. And we'll take a question from the room. Any...

All right. So no surprise. Well, actually, before I ask my question, thanks to the team. Really fantastic day. I appreciate the effort and the time. So I too want to talk about the AI opportunity. And again, I'm trying to get at how to model this. You did a great job today with the customer presentations. I certainly have a better understanding of the role that Twist plays with a variety of customers. But again, I'm still struggling with how to frame the opportunity.

So given it's an AI question, I used AI and looked up a few data points. And there's over 500 companies globally working on antibody research, development and manufacturing. Close to 200 of them are active in developing mabs in their drug pipelines. But of those, only about a dozen are considered largely dominant and hugely and broadly active.

So when you look at those numbers, -- what's your real target customer size over the next few years? That's the first question. I know you're going to say all of them. So the second question is how do we define success given that, that does seem to be the customer base? And maybe this is an Adam question. I'm not sure, but like what's the revenue potential realistically on a customer-by-customer basis?

Yes. Sumit, maybe I'll start, and it's great that you can do your own question and answer. And you're correct. We want all of them with a caveat. And the caveat is not all companies want to do AI drug discovery. We have an internal joke with each other, which is how many top 20 pharmas are there? There are 20. I mean it's 100 pharmas out there, 100.

Do we have all of them doing AI drug discovery? And the answer is no. It's not because we haven't knocked on the door. It's because they don't all believe in AI drug discovery. There is even customers we go visit them, going the whole way you go right to talk to the AI drug discovery team, and they love it, they buy from us, you exit, you go left to the traditional drug discovery and they tell you that AI drug discovery does not work, right? So even in the same company, different groups differ.

And so we are not here to tell customers how to do science. We are here to have a full menu of products. We are here to meet them where they are. And if they want to do AI drug discovery, we're going to provide them with the best tools possible. If they want to do in vivo in vitro, we are going to do the same.

And so ultimately, probably everybody infinitely will get to AI drug discovery, but not all of them are ready to do so. At the same time, back, I think, to your second question in terms of the contribution of AI drug discovery to the revenue growth.

We given a guidance for the year. We just gave you what we think will happen in terms of revenue growth for the next 5 years. We think that AI drug discovery will be an important part of it. But by no means, it would be the only thing. I think one of -- hopefully, one of the things that you get out of the day is all the many levers that we have and make a guess that if tomorrow AI discovery was forbidden, out loud, can't be done, I think we still find a way to make it happen because there's many other things to do.

There was a table in C1's presentation about all the different format of DNA that are needed that we could get to. Just right there, there's a lot of white space that we're not in yet.

Maybe to get at that another way, maybe Colby maybe help. You gave the customer hearing example. and sort of the iterations working through a model to ultimately get to a lead. And you gave a variety of numbers. I couldn't quite do the math in my head. But just how would that compare to your typical engagement with a non-AI program in terms of the work that you do to get them to a lead?

Colby, do you want to come on stage here? So Colby, our CSO, is our best salesperson because he is a scientist, so he designs everybody. So you can tell us how much more can you extract from an AI company than from an in vivo in vitro project?

Yes. It's a good one because it is true that we have multiple platforms for a reason. So a number -- my favorite example is the last one I gave, which was this was a can't fail campaign for that customer. This was -- they have a very small pipeline because they're a smaller company, but they don't mind spending because it's they can't fail. So they had to do all 3 techniques. They said, I want to do in vivo, I want to do in vitro, I want to do AI. So we have a number of companies that come to us to do that because we can offer each of those platforms. So those do end up being 3x the campaign size, both in terms of amount of work and dollars.

Now the other campaigns, you'll go to a company like you're saying, you turn left in the hallway, you turn right. Some people like in vivo because of their background. Their background is an in vivo immunizations, and so they choose to go down that path because that's the data they're comfortable with.

And so those campaigns, I'd say, amount of effort, you say amount of effort, amount of dollars spent, they're all fairly similar. It's just what method are people comfortable with or people have a background. And now we have this emerging contingent of AI/ML companies that weren't there 3 or 4 years ago. So that's just one additional group that's emerging from these contingents for us to now serve.

So again, similar amount of effort overall, just faster time line for them, maybe some different demands, larger volumes. But no matter how you slice it, it's adding another piece to the pie for these companies. And to your point, I think every company eventually will do AI. It's just how long does it take all of them to get into it? And what's that long tail of how many companies emerge from that.

Yes. And that's why when we showed the market that we project that we will be serving in 2030, we showed that there was growth in antibody discovery service and growth in protein expression as well as on top, putting $1 billion of AI-based drug discovery. So we see both the industry growing and adding extra dollar on top, thanks to AI because there's an extra flux of capital going into those AI companies.

Emily, congrats to the broader team on a great day. In terms of the AI order book, how do you actually construct it? Is this -- do you have visibility from customers directly saying we're going to place x amount of orders in x amount of days? Or are you doing sort of an estimation based on the expected scope of the project and how much has already been ordered? What's the process there?

And then in the same breath, that order book is really one, the speed is a huge differentiator for you guys. So is it fair to say that, that order book in and of itself is likely to be converted into revenues within either the same quarter or the next?

We need to hear it from the person who's actually whipping that order book. Paddy?

Treatment Inspirational environment would be a better rephrase. If you remember the slide on commercial Execution. If you're going to work at Twist, I expect you to have tip the spear activities. You need to know your customer. You need to know what they're doing, you need to know when they're doing. You absolutely need to know how much. And the unit of mass I like there is dollar bill. And then to help Mark and the team out from a sales and ops standpoint, we really do need to understand the timing.

So as far as I'm concerned for the commercial team that put up against anybody else in the industry, those are table stakes in terms of how we function. So we've got good eyes on what's going on. We obsess about that. It's a little bit -- I think the word intensity was maybe missing from the slide, but just don't get between either of us in a purchase order. That's a very dangerous place.

In terms of NGS and your regulated clinical customers, how do I think about the tail of being spec-ed in on a revenue basis? And given, I guess, you're earlier in adoption, would you expect your growth to exceed, I guess, liquid biopsy combined with rare disease?

Paddy?

Yes. So I mean the platform resonates for any of the segments, okay? Super fast design, build, test, learn iterations, super cost effective at small scale and absolutely scales up beautifully to when you're successful. If you remember the picture of research, development, verification, validation, it's a very, very powerful platform.

I think the things that maybe kept us away from 100.0% market share essentially is entrenched, right? You've got maybe got an assay that's validated or under some sort of control. But all I can tell you is we're through the door, okay? Because if you're not on the Twist platform, you're economically compromised in your assay. So we're just getting going.

This is Alex [indiscernible] from Canaccord Genuity here for Kyle Mikson. Congratulations on the successful event. So you noted you're looking for platform accretive technologies and potential bolt-on offerings that could support your free cash flow and margin aspirations. Can you just dive a bit more deeply into that? What aspects of the portfolio do you feel would be best served by bolt-on offerings at this juncture versus perhaps things you believe you could end up developing in-house?

Maybe I can start and if you want to add anything. We love all of our applications. As has been mentioned a few times, we don't make kings or Queens. We don't force, we think our technology view on the market, except maybe in the area of tumor-informed MRD, where we don't tell people how to do science.

So actually, we don't necessarily have deep plans that are preconceived there. I think what we want to do is to follow the market. We want to wait for the phone calls from customers saying, can you do this? And that is -- definitely informs our product road map, informs our future.

And the answer sometimes is yes and sometimes the answer is no, which means not yet, that eventually, we'll get there. So we -- in some ways, we are very opportunistic to see how the market is evolving and then take advantage of our technology. The best example is AI-driven drug discovery.

18 months ago, we were selling some protein. We are not selling characterization high throughput. We got a call and we realized that, that was the beginning of a new trend. That was not on our road map. We adjusted the road map very quickly. We serve that customer really well on the second one, the third one, just reinstant repeat. So that's how we're going to move forward. Anything you want to add?

Adam, can I ask a cleanup question? We know how to do math, but by 2031, is the revenue supposed to be $900 million? Or is it supposed to be $1 billion? Literally, I'm getting so many e-mails on it.

Yes. It is doubling to $900 million.

$900 million by 2031.

By 2031. That's the floor, and that's organic growth only, and that's by 2031.

Emily, Puneet Souda, Leerink Partners. So just you have a scalable platform. You have a miniaturized silicon platform that has enabled all these applications. And from the early days of the company, you've been -- you found these markets first in kits, then NGS diagnostics, now AI, you're scaling into that. The question is what's next? And I think Paddy alluded to that -- started to allude to that. And then I think there was some incination towards therapeutics oligos. Obviously, there is a large competitor that was acquired for, I think, $9 billion plus in that market. So maybe just educate us what are the new applications and the real next one is therapeutic oligos.

Yes. I mean the nucleic acid therapeutics, we see it as a definite growth opportunity for us, both in enabling the discovery of antisense oligonucleotide as well as we are being dragged by customers saying, we are getting DNA from you and every one sequence is going to 1 patient or 1 cluster going to one patient. We heard it from onco RNA today.

So we think that the drive towards personalized medicine is inevitable. And that is one definitely big, big growth opportunity for us. There's others, but that definitely is one of them that we are following closely, and we think as legs. Anything you want to add, Paddy?

So over the years, you've done a great job reducing a key component of the bill of materials in a lot of different areas, clinical as well as others. That said, you're only one, maybe a little bit more than one in some instances, but you're only, we'll say, one component of a customer's bill of materials. You can build efficiencies there, you can lower cost, but you can't control the cost of others.

So where I'm going with this is, are there instances where you would consider forward integrating to make sure that opportunities that could be great for Twist and markets that should be open but aren't open because of cost constraints. Essentially, would you forward integrate to make it happen because others are holding you back?

Take the first part, Paddy?

Good one. It's a good question. I don't want to get my enzyme slides out again, but that's a second component of workflow that we've helped them improve efficiencies on. I think our role in the community, the customers the hero or Batman to our Robin to borrow your joke, it's very good. And I think for the foreseeable future, that's the right place for us to play. And I think it's just really that relentless focus and attention on serving the customer most effectively. If there's product we can add to help with that enablement, that's certainly something as long as it leverages our advantage in DNA synthesis, I think that's something we would look at.

Paddy, can I ask a quick follow-up? Have you demonstrated with your new methylation enzyme, some of the recent issues where they compared bisulfite and enzymatic conversion? Does your enzyme actually address that error issue?

We are cautiously optimistic that Dr. Chen's good luck in making enzymes continues. So we're in early access. I trust him implicitly when he judges the molecule, but we're out there sensing with the customer how well we're doing. But the way that enzyme is designed and developed, we have a cautious sense of optimism at this point, if it's okay to leave it there.

Okay. We'll take two more questions. I have one spoken for. So if you have our last question, get ready. In addition, before we take Robbie's question, don't forget to take your swag. It's on either side. There are some super cool little pockets for all your little chargers. It will be very useful for all of you who travel a lot, which is all of you. All right. Robbie, you up.

Yes. Just wondering about the headcount plan right now, you're about 1,000 employees with all the efficiencies that you are -- have been talking about. wondering what assumptions are baked in through 2030, 2031.

Adam?

Thank you for the question. And if you kind of look at where we are today in terms of OpEx, if you kind of fast forward to kind of what we projected out by the end of this year, Q4, we'll be growing revenue probably about double the growth rate of OpEx year-on-year.

And so looking forward in terms of headcount, not all headcount are created equal, a $25 an hour operator versus a PhD scientist of 30 years' experience has a slight difference. And so we take that into consideration. So really, we're optimizing for the OpEx line. But I think what we've seen is we can continue to drive investments in the business where they matter and at the same time, drive efficiencies in the business to offset that.

And so whether that be through automation or that be through process improvements inside manufacturing and out, we see opportunities to be balanced such that we can see that continuous leverage into the P&L all the way through. And so we're not so much targeting a specific headcount number as much as we're targeting that continued OpEx leverage.

Okay. Who's going to close this out? I know I could count on you.

All right. No, I just want to say congrats to the team. I know it's been several years of hard work. I mean there were times,, I won't want to say what the gross margin was at that time, but a remarkable success getting here and good luck ahead.

Thank you so much. Much appreciated. And I will close. I just have a very few slides before we go to some wine testing. Coming back to this slide, which is the evolution of our product road map from 2021 to 2026. This is key to what we do.

You heard from C1 on oligo synthesis on clonal gene ultra complex gene, our MRD 10K. You heard from Colby on our in vivo in vitro IgG antibody characterization. You heard from our customers about oligo pools. You heard from LC about something that is not on the chart, right? So remind -- remember that, that if you need DNA that's not there, call C1. If you need protein or data that's not there, call Colby, and we'll work on it. We heard about clonal gene customer, where -- from Jimi about our UDIs, about our MRD Express, from Paddy around our enzyme from John about our exome, from Alpha about our Flex prep and from OncoDNA about our custom DNA and RNA panels as well as our library prep, right? And so we did a kind of a full view or we did a view, but there's a lot of products we haven't touched on, right?

And so that's something to remember is this was not by any means an exhaustive list. You will be very, very tired of us if we went through everything. And that road map is really key to what's important to us and what's important to us is the durable top line growth. We're very happy with what we've accomplished over the last few years, but it's all about what have you done for me lately.

And so it's all about the future. That future is going to come through revenue growth from different products. At JPMorgan, we share the growth of a few products. Here, we are showing the growth of different products, but it really shows that, that growth is multifaceted. It's not one lever. It's many, many levers.

In terms of our market, those are updated market size that we believe will be in 2030. On the NGS application on the left, most of the growth is coming from our oncology diagnostic that's going to grow much faster than the entire market. And that is why we are singularly -- not singular, but we are very focused on that market. We are very focused on providing solutions that are differentiated and that going to enable the revenue growth.

And then on the right, with DNA synthesis and protein [indiscernible]. We've already gone through the market for DNA synthesis antibody discovery service and protein expression. And now we are adding one more SAM for us to go after with nucleic acid therapeutics. So overall, not only we have a history of building an NPM machine on top of our silicon chip, but also that NPM machine enables us to increase our SAM.

So last slide, I think, for me, kind of the take-home message. We have built an industrialized platform with extremely precise measuring prowess that thanks to our automation, our software, and we're leveraging that into -- by building multiple monetization layers to build a diversified list of customers that are leveraging our product portfolio.

We want -- soon we'll have profitable growth, and we want to turn that profitable growth into durable growth. And at the end of the day, what enables us to do that is the unique culture that we have built and that we're going to leverage in the future.

So with that, I want to thank everybody for -- on the East Coast that have waited late in the day. I want to thank the entire Twist team. I want to thank the customers that made the effort to come here and thank the investors and analysts that also have done the trip. And for those of you that have done the trip, you'll have a special reward of getting on a bus to a winery.

So with that, thank you so much.

Welcome to Twist Biosciences 2026 Second Quarter Financial Results Conference Call. [Operator Instructions] Also note, this call is being recorded.

I would now like to turn the call over to Angela Bitting, SVP of Corporate Affairs. Please go ahead.

Thank you, operator. Good morning, everyone. I would like to thank you for joining us for Twist Biosciences conference call to review our fiscal 2026 second quarter financial results and business progress. We issued our financial results press release before the market, and it is available at our website at www.twistbiocience.com.

With me on the call today tare Dr. Emily Leproust, CEO and Co-Founder of Twist. Adam Laponis, CFO of Twist; and Dr. Patrick Finn, President and COO of Twist. Today, we will discuss our business progress, financial and operational performance as well as growth opportunities. We will then open the call for questions. We ask that you limit your questions to only one, and then requeue as a courtesy to others on the call. This call is being recorded. The audio portion will be archived in the Investors section of our website and will be available for 2 weeks.

During today's presentation, we will make forward-looking statements in the meaning of the U.S. federal securities laws. Forward-looking statements generally relate to future events or future financial or operating performance. Our expectations and beliefs regarding these matters may not materialize, and actual results and financial periods are subject to risks and uncertainties that could cause actual results to differ materially from those projected. These risks include those set forth in the press release we issued earlier today as well as those more fully described in our filings with the Securities and Exchange Commission. The forward-looking statements in this presentation are based on information available as of the date hereof, and we disclaim any obligation to update any forward-looking statements, except as required by law.

We'll also discuss adjusted EBITDA, a financial measure that does not conform with generally accepted accounting principles. Information may be calculated differently than similar not update presented by other companies. When reported, a reconciliation between GAAP and non-GAAP financial measures will be included in our earnings documents, which can be found on the Investors section of our website.

With that, I will now turn the call over to our CEO and Co-Founder, Emily Leproust.

Thank you, Angela, and good morning, everyone. Twist delivered another strong quarter and extended our track record of consistent execution, posting our 13th quarter of sequential revenue growth. We have outperformed the broader license tools market with a model that scales efficiently and drive increasing value creation. Twist core technology advantage is a semiconductor-based D&A platform that provides a structural advantage in cost, scale and speed that feeds into every product and service we offer. This same platform also enables a highly efficient new product introduction engine, allowing us to rapidly translate customer demand into scalable offerings and continuously expand our portfolio. .

As we increase volume on the silicon chip, we expand our wallet share, accelerate product innovation and further strengthen our competitive advantage. The model works exactly as designed. We have delivered sustained revenue growth, expanded margin above 50%, invested strategically to drive continued return on that investment, and we remain firmly on track to achieve adjusted EBITDA breakeven in the fourth quarter of fiscal 2026.

Focusing our results for the second quarter of fiscal 2026, we grew total revenue to $110.7 million, up more than 19% year-over-year. DNA synthesis and Protein Solutions grew 28%, powered by continued strength in AI-enabled drug discovery, NGS applications grew 12% year-over-year and 9% sequentially. Diving deeper into DNA synthesis and Protein Solutions, we continue to see robust growth. Last month, Amazon Web Services announced Twist as a wet lab partner for Amazon BioDiscovery it's AI-powered drug discovery application. This is an exciting validation of our DNA synthesis, protein solutions and biologics capabilities.

In advance of the launch, Twist has been working with AWS team for several months providing web lab services for the applications scientific launch partners, including Memorial Sloan Kettering Cancer Center and the GRAIL Lab adjuncts of Kim Senior City. The objective for researchers using Amazon BioDiscovery is to deploy AI models to design and optimize antibody candidates faster. We are here to support them with products and services that accelerate that pathway. I think in close contact with our customers. We identified this emerging category of AI near early and invested ahead of the market acceleration with increasing adoption across pharma, drylab and big tech companies.

Importantly and on balance, the growth of AI enabled discoveries complements our work with customers pursuing traditional discovery, which remains a robust area of our business. With our lesser approach, our customers for DNA synthesis and Protein solutions are all working through the same fundamental design build test learn cycle. What differs is how they execute against that framework. There is no 1 side fold model. Edge program is tailored to the customer scientific resources and stage of discovery, but remains constant across every engagement is the foundation Twist silicon platform, which enables cost-effective synthesis of hundreds of thousands of unique sequences in parallel. That unique and [indiscernible] capability is what makes speed at scale possible, no matter where the customer enters the workflow. No 2 orders are identical, so we see consistent patterns in how these campaigns are structured.

On Slide 6, to give you some context. One example is our work with Memorial Sloan Ketterings and Amazon, where the team ordered approximately 100,000 specific DNA sequences as a pool labor. This approach is highly efficient and precisely because of how our platform is built. [indiscernible] DNA can be manufactured electively rather than individually clone and processed, driving on cost of sequence dramatically. And Twist is a unique provider, we can deliver hundreds of thousands of specific sequences pooled at speed and scale. Once there is a pool DNA, either twist out a customer then screens that labor to identify promising candidates selects the most relevant sequences and advance those into individual synthesis, protein expression and characterization. Through interative cycles, this process yields a validated antibody leads.

The second model involves customers ordering hundreds to thousands of fragrance and executing downstream workflow internally. Here again, Twist platform delivers an edge in the ability to synthesize diverse sequence sets quickly and at accessible costs, meaning customer connect for broader design spaces. In these cases, customers [

indiscernible] fragment to clinogenes, express proteins and perform characterization assays within their own laboratories. Others choose to start further downstream, purchasing clonal genes or antibodies and binding proteins, such as ITG SCIB, DHH and others to focus their internal efforts on functional characterization and validation.

Even at this entry point, the advantage Twist can stream, the parallel synthesis capabilities underpinning our platform and show the sequences they receive reflect the speed at scale that alternative cannot match. And we have a growing segment of customers we reliant with as an end-to-end partner. In these engagements, we entered the DNA synthesis cloning construction, protein expression and characterization. Our platform's ability to run large complex sequence sets in paralell, accelerates every stage of that workflow, and we deliver high-quality extent data that enables customers to focus on critical analysis, decision-making and iterative design.

We also have a number of customers who give us a biological target and ask us to do all of the work through in vivo, in vitro and our AI/ML discovery approaches. Across all of these models, cost scales with the scope and complexity of the workflow, ranging from smaller exploratory programs to multimillion dollar discovery efforts. Our role is to provide flexibility across the spectrum. Because our platform was purpose-built for parallel census is at scale, we can make customers where they are, whether they need a pool libraries of hundreds of thousands of sequences or a fully managed core program. We support them their research advances.

On Slide 7, you'll see our portfolio for DNA Synthesis and Protein Solutions, serving customers across the vertical continuum. Building on our success in serving therapeutic disco customers in February, we licensed the body-byspecific platform to expand our capabilities in this rapidly growing modality. We will enable high super discovery in bispecifics, an area that is strictly been limited by scale. We have already received our first orders for this platform with a robust funnel looking forward.

Moving to Slide 8 and NGS. Growth reaccelerated in the second quarter. Our NGS Trus business remains a durable and growing part of the portfolio with particular offense in oncology diagnostics. We operate at a critical part in the workflow between the sample and the sequencer, where our products support precision and customization as cap. Our target enrichment and be preparation solutions delivered the uniformity and on-target performance required for high-sensitivity applications. This is especially relevent in the continuum of cancer care on Slide 9 where we are seeing increasing adoption in commercial diagnostic tests, including issuance molecular or minimal residual disease, or MRD. The applications demand extremely high accuracy and possibility, faster times and our chemistry is well aligned to these requirements.

Specifically, on Slide 10, has MRD testing transitions from early clinical adoption into scale deployment across oncology diagnostics the technical and operational requirements become significantly more demanding. These assays are pushing the limit of sensitivity, also requiring detection of Viant at extremely low allele frequencies. That places a premium on panel design as well as the entire flow to ensure uniform coverage and reposability across run. In this environment, success is on the ability to deliver highly customized target regional panels library preparation enabled by novel and lines as well as the buffer BCDRUMIs and other components optimized for specific indications and evolving clinical needs. Equally important is speed.

As these steps move into broader clinical workflows, laboratories and diagnostic developers this rapid or on panel design synthesis enrollment to support asset development panels and commercial scale up. At West, we combine high throughput DNA synthesis with precision, prop design and manufacturing at scale, enabling fast real delivery of customer panels with consistent on car statistics. That allows our customers to move quickly from development realization commercialization without compromise on data quite and importantly, securing and future-proofing the supply chain.

For bespoke, our tumor-informed MRD panels, like all of our NGS panels, this is a consumable-driven workflow that scales with stale volume, supporting recurring revenue as these applications extend. This time, I'd like to turn the call over to Paddy to expand further on our growth initiatives around the product offering.

Thank you, Emily. Happy to our fiscal year, the results are strong, and we believe the road ahead is stronger. Everything we do in protein solutions and AI-enabled discovery runs on 1 foundation, our DNA synthesis platform. It's a structural advantage for cost, scale and speed, full stop. And we continue to advance and strengthen this platform to enhance customer experience even more. Today, we accept the vast majority of daily sequences as we know we can manufacture them.

We have an algorithm embedded in our e-commerce system to inform a customer immediately if they have uploaded a sequence that may be difficult to manufacture. Dissipation improves the user experience for customers as some sequences present manufacturing challenges, repeat regions, Herpen extreme GC content. Three years ago, we accepted about 96% of clonal genes. I could manufacture about 97.5% of clonal genes and about 98% of DNA sequences more broadly including oligo pools, DNA libraries, gene fragments. Today, we accept about 97% of clonal genes and can manufacture approximately 98.5% of clonal genes and about 99% of DNA requests more broadly.

That's not theoretical capabilities. That's production reality at scale. We routinely deliver clonal genes and fragments up to 5,000 base fares, all the gaps up to 300 basis, novelex-gene fronts up to 500 base payers across a wide range of formats. If a customer can design it, we are increasingly able to make it. Even as the acceptance rate for DNA sequences remains very high, we recognize that in a single sequence in a larger set does not meet acceptance criteria, customers may choose to route the full set elsewhere. This dynamic highlights a clear opportunity. Continued improvements in acceptance rates can unlock incremental share gains and expand total order capture.

On Slide 11, you'll see that we announced this morning that we will soon take a full range of sequences across length and complexity, driving towards accepting approximately 99.5% of chloro genes and 99.9% of all D&A products more broadly. With constant drive to improve sets us apart and importantly, more sequences accepted means more orders won and we intend to win them. And that matters because this is not a standardized market. Every customer is different. Every order is different. -- breadth drives share gain. It's that simple. In contrast to Twist, the competitive landscape has a pattern, niche players narrow offerings limited reach. That is not how customers operate and it is not how this market has won. We employ a different approach. We anchored our strategy around end applications where performance, scale and execution are the only metrics that matter.

Customers do not want to stitch together multiple vendors. They want 1 partner who can deliver consistently across the workflow. That is where Twist is differentiated. We offer both depth and breadth across the biological continuum from perasynthesis through proteins, biologics and NGS. We support customers increasingly across the entire life cycle of their programs. is how we continue to take share, expand wallet and reinforce our position to leading platforms, serving therapeutics, diagnostics, industrial, academic and government markets.

With that, I'll turn it over to Adam to discuss the financials for the quarter.

Thank you, . Turning to Slide 12. Q2 is another quarter of consistent execution against the financial model we've laid out. Revenue grew 19.3% year-over-year to $110.7 million, our 13th consecutive quarter of sequential growth. Gross margin expanded to 51.6% versus the prior year, an improvement of approximately 200 basis points, and we remain firmly on track for adjusted EBITDA breakeven in Q4. Let me walk you through the details.

On Slide 13, you'll see DNA synthesis and Protein Solutions revenue increased to $53.3 million, growth of 28% year-over-year. On Slide 14, we show NGS applications revenue for the second quarter grew to approximately $57.4 million compared to $51.1 million in the second quarter of fiscal 2025, an increase of 12% year-over-year and up 9% sequentially, driven by growth in top accounts. For the quarter, revenue from our top 10 NGS applications customers accounted for approximately 39% and of NGS applications revenue. We serve 627 NGS applications customers in the quarter with 174 having adopted our products.

Looking geographically on Slide 15. Americas revenue increased to approximately $64.3 million in the second quarter compared to $55.2 million in the same period of fiscal 2025. Growth of 17% year-over-year. EMEA revenue rose to $37.3 million in the second quarter versus $30.6 million in the same period of fiscal 2025, growth of 22% year-over-year. APAC revenue increased to $9.1 million in the second quarter compared to $7 million in the same period of fiscal '25, an increase of 30% year-over-year. APAC accounted for 8% of our revenue in the second quarter. China continues to be a relatively small portion of our revenue at approximately 1% of total revenue for the second quarter of fiscal 2020.

Looking at revenue by industry on Slide 16. I Therapeutics revenue rose to $40.8 million for the second quarter of 2026 compared to $26.3 million in the same period of fiscal '25, growth of 55% and reflecting the increased uptake of our products by large pharma and biotech customers in their efforts on therapeutic discovery and including AI-enabled discovery. Diagnostics revenue was $40 million for the second quarter of '26, compared to $35 million in the same period of fiscal 2025, an increase of 14%. Diagnostics revenue grew 13% versus Q1 of fiscal '26 based on strong growth from top accounts. Industry and applied revenue was $5.8 million in the second quarter '26 compared to $7 million in the same period of fiscal 2025.

Academic research and government revenue was $12.8 million for the second quarter of '26 compared to $12.5 million in the same period of fiscal '25, an increase of 3%. And Sequential growth was 5% versus prior quarter, driven by strength in U.S. accounts. Global Supply Partner revenue was $11.4 million in the second quarter of 2026, compared to $12 million in the same period of fiscal 2025, primarily due to order timing.

Moving down the P&L on Slide 15. Our gross margin for the second quarter increased to 51.6%, an improvement of 2 margin points versus the same period of fiscal '25. Market expansion was driven by strong revenue growth and moderated sequentially as we continue to invest in new product offerings and manufacturing capacity that we expect will result in future margin gains as we accelerate growth and implement continuous process improvement.

Operating expenses, excluding cost of revenues and litigation settlement costs were $95.8 million for the quarter compared to $87.6 million in the prior year. The increase reflects deliberate investment in our commercial organization and digital infrastructure to support the growth trajectory we are delivering, particularly the 55% growth in therapeutics. These are revenue-generating investments with a clear line of sight to return. We are managing these investments with discipline. In April, we reduced 36 positions to reallocate resources to our highest return opportunities. Combined with additional cost initiatives underway, we expect these actions to contribute to sequential OpEx improvement of $6 million in Q4 of fiscal '26.

Looking at our progress on our path to profitability. For the second quarter of fiscal 2026, adjusted EBITDA was a loss of approximately $13.3 million, an improvement of approximately $1.5 million versus the second quarter of fiscal '25. We have dramatically narrowed that loss through a combination of revenue growth, gross margin expansion and operating expense discipline. We expect the actions we've taken, combined with continued revenue momentum to fully deliver on our targets for Q4. We reached an agreement in principle regarding the securities class action for approximately $17.1 million.

In fiscal Q2, we booked $7.2 million for litigation settlement costs net recoveries as we expect the additional costs to be covered by our insurance. We view this as a positive resolution allowing management to remain fully focused on execution. We ended Q2 with $171.7 million in cash, cash equivalents and short-term investments versus $197.9 million as of December 31, 2025. The Sequential change reflects $17.6 million in operating cash usage, $7.9 million in CapEx as we continue to invest in manufacturing automation and $5 million in cash for the Invenra license and equity event.

On Slide 18, turning to guidance. For fiscal 2026, we expect total revenue of $442 million to $447 million, growth of approximately 17% to 19%. We -- for Q3 of fiscal 2016, we expect total revenue of $114 million to $115 million, growth of approximately 19% year-over-year at the midpoint. As previously discussed, we expect NGS to be the driver of sequential growth in H2 and return to 20% by Q4. We remain confident in our trajectory and continue to forecast reaching adjusted EBITDA breakeven for the fourth quarter of fiscal 2026.

With that, I'll turn the call back over to Emily.

Thank you, Ed. On Slide 19, as we look ahead, we remain focused on delivering consistent measurable growth designed to scale over time. We see strong momentum across the port fill with continued growth in denseness and protein solutions, increasing adoption in AI-enabled discovery and a return to growth in NGS. We serve large and expanding markets where our platform is increasingly relevant. At the same time, our operating model continues to perform as expected.

We have delivered 13th consecutive quarters of sequential revenue growth, expanding gross margins above 50% and maintain a clear path to adjusted EBITDA breakeven in fiscal 2026. What opens this performance is the stability of our platform. As volume increases, we expand or improve efficiency and generate operating leverage across the business. Our ability to serve a wide range of customer workflows from early discovery through clinical and diagnostic applications provides both resilience and opportunity to capture more value over time. Across the business, we invest with discipline we high return opportunities allocate capital deliberately underlying investments with clear growth drivers. We execute with focus and urgency to drive durable growth and build the company with increasing strategic resent and long-term value creation.

With that, we're happy to take your questions. Operator?

[Operator Instructions] Our first question comes from Mac Etoch with Stephens.

Maybe just to start, could you just discuss how AI-driven workflows performed in the quarter relative to your internal expectations? And how the change in the outlook for both of the segments is really contributing to the change in the updated fiscal guidance from here? .

Yes. Thanks for the question. Obviously, we're very excited with the performance of the SPS growing 28% year-over-year. And for the therapics category, we cracked the $40 million for the quarter. A lot of companies in the drug discovery field, they tap out at $50 million a year. And now we're way past that. We're almost there every quarter. So obviously, there is the trend throughout the menu. What we see is that customers don't want 1 thing -- and so the NPI engine that we built that creates a lot of options for people to enter as it's been a great driver. And obviously, AI-driven drug discovery has been a big help in that area. It just increases the number of sequences that people want to look at.

If we look sequence before, now with AI, they can get thousands. And so it just increases the overall value of the deals. And a lot of companies don't have the capacity to analyze that number of antibodies. And so it enables us to upscale upsell to data and cell coradiation. So overall, the entire menu is doing well, but hydro discovery has definitely been great sales in -- great wins in our sales.

Our next question comes from Vijay Kumar with Evercore ISI. .

Congrats on a nice friend. Maybe on the prior question rate related to -- when I look at the up 55% in the second quarter. That's an acceleration from Q1 growth levels. How much of this acceleration was driven by AI-related programs. I think in the past, memory called data characterization genes versus traditional biopharma and when you look at back half, is this 50% kind of growth sustainable when you look at your order book and backlog?

That's a great question. Definitely, AI has been a source of strength. Again, the nice thing as people develop more sequences, which have levels of DNA, we can make if they want a pool library of EHS, we can make it if they want. Even for biospecific now we can improve. We can make the flavor of the SG&A on pool. So that's -- so the entry point of whatever they want that broad menu, they're useful. -- but with AI, as I mentioned earlier, there is more need for data characterization.

And what has happened is maybe in 2025, there was excitement definitely on our side. But that excitement came from very few number of accounts and now that we are many quarters into this, now it's dozens of accounts that are driving the growth, right? It's not just a few. And so we can see that is repeatable with the existing account, but we're able to bring more and more people into the fold. And then sometimes, we can enter through AI-driven with discovery, but a lot of those companies are doing both AI-driven drug discovery and traditional drug discovery and the fact that we have a full menu enables us to grow in all areas.

So overall, is broad-based. Going forward, we are not guiding per product groups. I think there's very good growth potential within the business. And we doubled, the raise is double the beat, right? So obviously, there's a lot of confidence. But that confidence is broad-based. And we also share some strong confidence for our NGS business. So the business is reaping. The sales team is confident customers are happy. So we just have to do it again.

Our next question comes from Doug Schenkel with Wolf Research.

I want to just cover 2 topics, the academic and government end market and then gross margin. So in A&G, what are you seeing? Are things stabilizing or improving? Just want to get a sense for how things are trending. And are you still running the academic promotion on express genes and kind of building off of that, when do we start to lap the headwinds on price per gene from express gene fragments. I think that's this summer? So I just want to be mindful of that as we're updating our models.

And then a quick 1 on gross margin. Gross margin was down nominally sequentially and a little bit light of our model and street models. That just may be in the noise, but wanted to see if there's anything to call out there.

Okay. Yes. So maybe I'll take the first question and Adam will cover the second one. Yes, on academic, definitely that end market is suffering from funding pressures. Our approach is to take market share. And so academic the -- when funding is a pressure, they are very cautious because they are dollars. And so especially, that market is basically shrinking right now. So the fact that we are growing and growing sequentially is definitely a good thing. It shows that our product offering resonates more than the competition.

And so right now, we are very happy to extend the premium discount for what that enables economic people to get [indiscernible] genes at the price of standard lens. And so they get a great value on the DNA they're seeing from us. They're getting great speed. It enables them to go faster and get better at their next brand and knowing that those brands are very competitive. So I think our discount is very well received by those customers. And definitely, the growth there is smaller than for industry segment. but we are taking market share.

Adam, you want to cover the gross margin question?

No, absolutely. Welcome, everyone. And Q2 definitely reflects a deliberate investment, specifically for IgG and characterization for a discovery projects. as well as our digital capabilities. We remain confident in our 52% or better guide for the year. And this investment is really around adding capacity of people to support the accelerated demand. We see a huge ROI on it. And we also see the path to continuously making improvements on those efforts in new products and returning to a 75% to 80% average drop through on incremental revenue to gross margin, as we move forward and automate workflows.

Our next question comes from Luke Sergott with Barclays.

Just a couple here. I want to follow up on Doug's question there. When you -- Adam, when you're talking about the gross margin improvement and the kind of the automated workflows, like what -- you guys have just built out the new facility. It's pretty state-of-the-art from what you had previously. So talk about further investment or how much more you guys can continue to push that automated workflow.

And then I wanted to follow up and ask more on the data characterization of the AI projects. You guys talked about $25 million in bookings in 4Q coming from some of these AI projects. How much of the revenue in the first 6 months of the year has been converted from that? Or like is that still a majority on the comp?

Thanks for the question. So maybe I start with the first one, and you guys are really good to squeezing 2 questions into 1, but [indiscernible]. So on the first one, as you [indiscernible] at the front end and the back end, right? So the front end is a shift. That's where we make the oligos, -- that's where we get the massive advantage. And then after the vials are made, depending on the flavor, it goes to different back-end. So in general, the back end for our GSPS is a different back end for NGS. And then in the [indiscernible] solution, we first make a fragment and then some stop there, they get shipped. Then we make clonal genes, some stop there, make a chip, and then we make IgG expressions and stop the bigger chip and then some of the agility gets characterized.

And so as you can imagine, as we add flavor, we have to add a little bit of automation to add capacity for that new flavor. So it's not a tremendous amount of CapEx. I mean it's significant, but compared to building a new fab from scratch, it's not comparable. And so that's the strategy we've been using is showing the demand from customers and adding a little bit of automation on the back end on the branch that's needed. So that's one.

And then the other thing we've done is we've been automating the automation. And so for those of you that come to our Investor Day you'll see that the giant room, we are now in that 1 room, we've been able to automate the automation and now we we'll be able to have multiple times the capacity that we used to have in that room. In terms of your second question around the $25 million of orders that we had last year, those have all been shipped now -- most of that was finished by Q1 and very little impact of that in Q2.

As you remember, one thing that that's a key differentiation from us is the speed at which we deliver data. And so some data -- most of the data is delivered 15 to 20 days after we receive the sequence. It's a quick turnaround to book the reps.

Our next question comes from Subbu Nambi, Guggenheim.

You increased your full year revenue guidance by more than the magnitude of the Q2 beat. Any specific area or areas which drove the increase, essentially, I'm asking for coverage.

Yes. No. What we talked about in the back half, while sequentially, most of the improvements are going to come from NGS -- if you look at the full year guide, you can see that, that implies that the DSPS is really the strength of the raise in the back half. So if you are modeling it out, sequentially, we don't expect anything to go backwards in terms of DSPS. We only expect continued increases in the overall revenue, but at a more modest rate. And again, it really is dependent upon the pace of us getting new customers into the DSS side and the ability to potentially exceed those expectations will -- is an area of opportunity for us. .

Our next question comes from Matt Larew with William Blair.

I wanted to ask on the complex DNA offering. So what's the time line at which you expect to deliver products sort of in line with the capabilities you described today, what lengths do you expect to be able to get up to in terms of manufacturing? And do you have any sense or could you give us a sense or what the missed opportunity has been a story. You referenced, I think in some cases, losing a whole order because you couldn't make a sequence. So what kind of additional opportunity does this unlock by adding these new capabilities?

Matt, it's Paddy here. Thanks for the question. We're pretty excited about the product. I think you could hear from my comments, retinal we accept the vast majority of sequences that come our way across all of the portfolio. And there is a few percentage business or a few percentage points in sequence we don't take, which we see some instances where the customer wants to take that total order somewhere else. The trends that emerging nucleic acid therapeutics and plant engineering and, of course, AI, they have some special needs or special requirements arriving then the sequence complexity. .

And so true to auction, we're focused on the customer experience. Our goal is to create a one-stop shop when you leverage our DNA synthesis platform that can print really high-quality DNA. And just as a reminder, up to approximately $1 billion per ,that gives us super quality, speed and economics of scale, including for complex sequence. And so now with a little bit more optimization throughout the entire workflow, it's going to deliver products best-in-class. So broad sequence acceptance really strong, predictable and transparent performance when you're on the Twist platform to its reliability.

Our platforms industry aligned, it's faster, cheaper, strong best-in-class customer us-- sorry, e-comm user experience, then you've got Twist customer service and support, so that's trained scientists to understand the customer's experiment there to help the customer through any of their challenges. So there's a lot to like with the offering. And we're looking forward to scaling in the coming few quarters. So we're talking about early access start with a sort of classic to make a few customers happy and learn as we grow and you'll continue to see the capability of ramp up as we go through the quarters.

Our next question comes from Catherine Schulte with Baird.

Maybe on the margin side, still on target to hit adjusted EBITDA breakeven in the fourth quarter. you've been very prescriptive about gross margin incrementals. I guess as you hit that milestone, how should we think about leverage beyond that point and maybe EBITDA margin incrementals going forward? .

Thank you for the question. In terms of where we are today or laser-focused on making sure we cross the adjusted EBITDA positive here while also, at the same time, ensuring the maximum acceleration of revenue growth. So it's really titrating that investment in such a way that we maximize growth rates. As we go forward, we have optionality. We talked about that before. We'll talk about it more in the future. But we're steward to the market, and we understand that our continued path of not going backwards is very important to us and also ensuring that we continue to sustain the accelerated levels of growth. So those will be the 2 focus areas. And I think we'll spend some more time talking about that at our Investor Day here coming up.

Our next question comes from Puneet Souda with Leerink Partners.

You have Michael on for Puneet. Congrats on the quarter. I was hoping to get some color on the jeans. So I saw a strong growth in the physical gene shift. Last quarter, you talked about 58% for characterization. I was wondering if you could offer any color on the contribution of gene per productization this quarter? And if you could offer any insight to us on how much of this ad demand is driven by more model building versus the incorporation of AI into your ongoing drug discovery, lead generation work at pharma?

Thank you. Great question. We did not share the numbers today. We were trying to straddle the fine line between being as transparent to indisposable and not keeping the competition too much. I'm sure some of them are on the line today. Now what we can say is that it's more, right? So there definitely growth in many genes where internally to generate data that was sold. So definitely, to be definitely the growth there. And the second -- there was a second question. .

Yes. It was around the ability to how much of the growth is coming from making the model .

I'm sorry, on the number. Yes, very interesting, actually, -- we're seeing a lot of customers that have shifted while the beginning was bidding model. I think by now, most of them or a lot of them are now turning the crank. And so when they turn to crank, what we see is that the orders maybe each order may be a little bit smaller. So billion model is a big bolus upfront, it's a little bit smaller, but it's more of it. And so we are very pleased to see that people are returning -- and yes, it's working as expected. It's working well. .

Our next question comes from Brendan Smith with TD Cowen.

Maybe just to put a pin in the AI or just questioning here fully. I guess, looking at that $25 million from last year, do you have a sense or can you tell us what kind of run rate you're looking at now for like '26, maybe just with the first half on your belt. And I guess related to that, and within those revenues, I guess, can you give us a sense what the relative breakdown between revs from oligos versus IgG versus analytical data? Just kind of wondering really how much is the area mostly done, kind of further down that funnel or really where they're shaking out?

I can start with a little bit of color around how to think about where we're going. If you think about the therapeutics segment as a whole, obviously, the AI discovery is falling in there. And if you look at the outsized growth versus the average of the business, that delta is predominantly AI discovery work. I will say, at times, it's hard to know exactly whether it's a discovery or class of work. We don't always know exactly the number as well as whether it's training or building the models. But pretty strong confidence that the vast majority of at growth in therapeutic advance scope. I want to hand it to a second half.

Our next question comes from Tom DeBourcy with Nephron Research.

Diagnostics as a whole and the double-digit growth and also double-digit sequential growth as sort of you had, I guess, projected before. Just as you think about the rest of the year, do you see incremental sequential growth through Q3, Q4? Just thinking about diagnostics customers and their contribution to NGS.

Thank you, Tom. Yes, definitely, we are seeing growth. I think we in Q4, we're experiencing at least 1% growth in NGS alone. And it's a good reminder. We talked a lot on the call of our DNA synthesis and protein solution and AI discovery, we love it all. At the same time, it's a good reminder that dollar growth in NGS or dollar growth in DNA synthesis and Protein solution is very similar to us. And so what we are optimizing for as a management team. for revenue growth for the entire business. gross margin above 50% and getting to adjusted EBITDA breakeven.

And so -- and the portfolio of panels that is the library prep that we built in the NGS application group enables us to sustain great growth. We are liquid biopsy customers, our MRD customers ramping and adopting. And so we are very definitely very confident in that part of the business. And we are very much looking forward to return growth -- they were not so long ago, it was flipped where we had 28-ish percent growth in NGS and 12% growth in DNA synthesis and protein solutions, it's going to flip-flop back and forth. But at the end of the day, what matters to us is the entire business out growth in float into the life science tools industry hopefully, we are unique in the kind of growth that we are being able to post, not just this quarter, but by now certain quarters in a row. So [ $110.7 million ] this quarter and not so long ago, we had $19 million for the entire year, right? So we'll keep doing it. And I think the future is very bright.

Thank you. There are no further questions. At this time, I'd like to turn the call back over to Emily Leproust for closing remarks.

As we wrap up, we look forward to continuing the competition in person at our Investor Day on May 21 in Oregon. This will be an incredible opportunity to go deeper into the drivers behind our performance cleared directly from our customer in Twist across therapy discovery NGS workload and participate in a tool that brings our platform to life. We will also have the chance to engage with members of our management team as we discuss how we are scaling the platform, expanding into new applications and driving long-term value creation. See you there. Thank you.

Thank you for your participation. You may now disconnect. Good day.

All right. Good afternoon, everybody. I'm Luke Sergott. I cover life science tools and diagnostics for Barclays. With me, I have Emily Leproust, CEO of Twist; and Adam Laponis, CFO.

Long time we've been doing this. So again, appreciate for making it down in Miami, in March, as we were saying, is not too bad.

But I guess from the jump, let's talk about the resegmentation into the SynBio plus the Protein Solutions and how you guys are an AI winner. And this has kind of like been a big theme across the space of why -- because you're not -- there's not a Claude plug-in for you guys? Like how are you guys the AI winner? And walk through the kind of the resegmentation, how that fits in with that.

Yes. Thanks for having us. Thanks for the question. It's definitely one area where we have a lot of momentum. The DNA Synthesis and Protein Solutions grew 27% in Q1 year-over-year.

And so our traditional drug discovery is done in vivo/in vitro. People inoculate the mouse with the target and then you extract the DNA. You screen for the novel antibodies or you take a large library of more than 10 billion antibodies, and then you pan them through phage display. And in both cases, you get 10 to 100 antibodies that you have to characterize. And so the outcome of what we used to sell to customers when they give us a target was up to 100 antibodies that were characterized.

We are also serving customers who are doing their own discovery in-house, by selling them DNA and/or protein. We still do that. And last year, our therapeutic drug discovery was about $111 million, growing more than 25%. And so we cannot achieve scale in drug discovery because it's a very fragmented market. A few companies get more than $50 million. So that's the traditional before AI.

And then when AI came in, what people did was use compute to come up with thousands of sequences of antibodies. So instead of using in vivo/in vitro, the computer spits out thousands of sequencing -- sequences. But then they need the data, they need the characterization. What is the affinity, the functionality, the epitope binding, the developability, thermostability for those thousands of sequences? And so they came to us because we could make the DNA, we could make the protein, we could characterize. And what they really wanted was speed and scale. And really, we think we are the best in the business where, if you give us thousands of sequences, we'll be able to give you data in 15 days.

And it's data for the full menu. And so what we are seeing is customers build their model either in one or several shots, so again, thousands of sequences, and then once they have the model, they go into the drug discovery process where they declare a target and, through their model, come out with a bunch of sequences.

We do the building and the testing, give them data. And the benefit of using AI versus in vivo and in vitro is in vivo/in vitro is 6 weeks to get to a hit. And with AI, it's 2 weeks. So you just can do 3 rounds of discovery and engineering, while you could do only 1 round with in vivo and in vitro. So it's been a great momentum for us and it's contributed to our growth.

So when a pharma company or a biotech, like you just have like -- or got the Invenra collaboration. But in the past, it was you'd layer out and you'd get these press releases you just partnered with XYZ biotech and we're going to provide the antibody optimization platform with them. How has that moved further downstream or upstream, I guess it is, for the SynBio side? And now that you're doing this as one whole business where you were doing the DNA, the synthesis piece and now you're doing like the protein optimization or the antibody optimization together, like how was -- like what's the next step here for you guys as you're thinking about this workflow?

Yes. And so our next step for us is we want to have a full menu, right? Again, when we compete with people in the drug discovery space, and there's a lot of companies and they all have a niche. There is a company that they only do mouse and there's another that they only do humanized mouse, and another one, they do a need, that kind of phage display, someone else does yeast display. Everybody has their own little niche. And what we want to do is provide the full menu and meet the customers where they are.

And so we are very happy to sell data, but there are still customers, they don't want data. They want to buy DNA from us and they want to do the work in their lab. We are very happy with that. But at the same time, for the new AI drug discovery companies that don't even have a wet lab, we also want to be their partner.

So our approach is to not tell people how to discover drugs. Our approach is to, again, meet them where they are, provide whatever science they want to do. We are going to have the tools for them and enable them to go faster with the highest quality and the best price so that their budget can go further.

And at some point, you want to talk about Invenra, I don't know if it's the right time, but it fits in the strategy of, in that menu, one area where we were not good -- or actually, I should say, we were equally bad with everybody else, was bispecific.

Bispecific, obviously, really hard to make. To make an antibody, you need 2 vectors, right, 2 plasmids put together. For bispecific, it's 4. And if you don't have the right ratios for those 4 coming together, when you express, you get something that's not clean and you have to purify to get the one bispecific that you want. And that makes it really hard to process in high-throughput.

Invenra, they've come up with a system, a platform where it's almost auto-purification where it doesn't matter what the ratio is, it comes out clean. And so therefore, now we have what we think is the only platform in high-throughput to express and purify bispecific and, therefore, enable the discovery of bispecific molecule in a high-throughput.

And so when you marry that with AI, not only can be used by traditional companies that want bispecific, but for people that we want to build a bispecific model through AI, which is very difficult to do now, it will be possible. So it fits that strategy of just adding all the tools that someone may need such that we are the one-stop shop.

How is -- like from the throughput on the bispecific, how is that being -- that was being done by the pharma company themselves. They have to build out that group?

Yes. So bispecific, the concept of bispecific is simple. In practice, there is more than 100 different flavors of bispecific, but they all have the same issues around expressing them at high purity, which they don't. So any flavor out there, you have to express and then there's a heavy step of purification. So at Twist, we are able to make all the flavors, but we don't have an advantage to make them pure. But with Invenra, we do.

And then the other thing with the Invenra platform that's very smart is the engineered linker that they have is very small, is very human-like and is in the clinic. And so you don't take immunogenicity risk with the Invenra platform. And so again, it's -- any customers, they say, "No, we want our own bispecific," we're happy to do that. Like anybody else, we won't really have a high-throughput advantage for [ antibody ] approaches.

Yes. And on the protein side, you guys talked about being more choosy in what projects you're taking as you're thinking about this as a more holistic across the SynBio and the protein synthesis space -- or Protein Solutions. How much of that is because of the capacity? And this is kind of what I was talking about before where you take on almost any project from a biotech. And now like some of these projects are starting to scale and you're bumping up into like ability to deliver, which goes against the DNA of your company, is like you want the fastest turnaround time. So the balance there of how you choose these projects, like profitability, things like that, about as you mature into this market?

Yes. And so our approach, I would say our North Star, is always have customers back of the truck, right? Don't worry about capacity, it's our problem, we've got you. And then the second is that we don't want -- frankly, we don't want to subsidize our customers' R&D. We want to be paid a fair value. And so it's a lot easier to do that now that we're established as a high-quality and fast and a great price.

So it's much easier now than it used to be 5 or 6 years ago when we were known for being a DNA-centric company, and then we'll tell customers, "Give us a target, we'll give you a drug." It was harder sell then. Now I think we are recognized as a very high-quality, fast providers of products, DNA and protein, as well as services where, end-to-end, you give us a target, we give you a drug, such that now our job is to make sure that we stay ahead of the demand with capacity.

They are part of the process where we have ample demand. For instance, on the DNA side, I think we've said that we have capacity for 3 million genes a year. Last quarter we shipped 271,000 genes, plus we used 50,000 genes in-house to make proteins and characterize and sell data. So you can see that we are still far from -- near -- or far from capacity.

There are other product levels where we're probably closer to capacity. It's ramping very fast. Data characterization grew more than 200% last year. And so there, we are adding capacity ahead of the demand. But yes, our goal is we never want to say no if it's -- if the price is right.

Yes. I mean that's commercially violent.

That's commercial violence right there.

Speaking of that, so let's shift gears to the NGS side. I guess, the timing issues from the commercial customers switching from translational to the clinical side, like that seems to be behind us. Talk about how you guys are thinking about that ramp from a test perspective, but also just kind of an overall update on the NGS side from the liquid biopsy piece of where you guys are playing, like where you're seeing a lot of the momentum in the business coming from?

You want to start?

Yes, absolutely. Great to be here. And no, I mean, we're really excited what we're seeing in NGS. I mean a lot of the end markets continue to show significant growth, could be liquid biopsy, MRD and then many of the other diagnostics. We also see longer-term opportunities in new markets, ag being one of those markets that we know is ripe for NGS opportunities. And we're just getting started in that.

When we look closer in to 2026, what we said is, in Q1, excluding that 1 customer, we were about 18% growth. And overall the business that customer ramps back in, we see a path to being a 20% growth in our NGS business by Q4. And I'm happy to say that, yes, we're seeing the customer order, things are going great and the relationship is quite strong. So we're feeling very good about where things are going.

Okay. And then as you think about -- you guys just came out with the TrueAmp, right, for the library prep. And this is, I guess, more of an existential question where one of the bear arguments against you guys were, one, SynBio market is not as big as we thought it was. And then two, it's going to be, on the NGS piece as you provide enrichment tools for panels and the market's moving towards genomes or panels to exomes, right? And now the market is moving to genomes, it's like you have a terminal value problem as that market shrinks.

So I feel like the TrueAmp is there to get into the library prep for whole genome. But talk about where you're -- is that right? Is there a risk to the NGS business as you go from whole exome to whole genome?

Yes. It reminds me of AGBT in 2009 -- 2009 when someone stood up and said -- I won't name -- I won't say who, but said that panels were a flash in the pan. And so we are clearly, 17 years later, debating whether there's life for panel. And absolutely, there is life and we're very bullish about the growth.

It is true that in some applications, for instance, rare disease, that rare disease in the U.S. is going to go from exome to genome as the price goes down. But for rare disease outside of the U.S. where the reimbursement is nowhere near what's available in the U.S., actually people will start doing exome. So even in rare disease ex US, there's great growth.

And then for cancer, I would argue that the lower price of whole genome is really good for our business because it's enabling more tumor-informed MRD. Because to do tumor-informed, you have to have a whole gene sequencing upfront. You can do exome, but it's probably more powerful to do genome.

And so our view is that the lower cost -- our view today might be the same as it was in 2009, which is as you lower the cost of sequencing for whole genome, you're going to enable more applications of panels. And we are seeing that now in AgBio, where AgBio, they're currently using microarrays, so it's a market that's extremely price-sensitive. And now that the price of the whole genome sequencing is there, Twist for sequencing is going to make -- is going to be cheaper than running a microarray for all the AgBio applications.

And so in general, I disagree with the premise that the whole genome sequencing is going to be bad for us. At the same time, what we sell is workflows. And what we realize is that the performance of the enzyme is somewhat limiting the use case of what can be done. And so that's why a couple of years ago, we engineered a best-in-class ligase to this day, which lowers the limited detection for rare mutants, so that ligase is great for increasing sensitivity of the assay. And then we next put our focus on the polymerase, which is what we just launched with TrueAmp.

But what we find is that people, they like the PCR-free protocol because it's convenient and there's no bias. But if you could do a PCR, it's just easier because you have more material to work with. And so with the TrueAmp, what we did is we provided the same performance as a PCR-free but with the convenience of a PCR. And we did that by engineering a polymerase that has extremely low bias at high or low GC, and an enzyme parameter that was able to go through repeat regions without stalling or skipping.

So we think it's the best of both worlds. Again, PCR-free-like performance, but with the PCR convenience. And it's the kind of thing that we are showing that Twist is not only great at making DNA, but it's also great at engineering enzymes that are key to a particular workflow. And so for whole genome application, we now have what we think a terrific library prep that's going to enable us to go into new markets that we were not in, such as, for instance, academia, for NGS.

Okay. And then with the capturing the workflow and becoming that solutions provider, is that the genesis behind MRD Express as well?

Yes. For MRD Express, the genesis was -- I see it's 5 years ago at AGBT, we launched MRD, we saw that tumor-informed MRD was going to be the winner. And way back then, there was a lot of bear thesis against that as well. Now it's clear that tumor-informed is the way to go.

But what we're seeing is that the medical need is going to be for high-sensitivity MRD. When you say "No, you don't have cancer," well, it's better that you really don't have cancer. And if there is going to be a recurrence, you want to find it as early as possible. So high sensitivity is going to be key. So that's our MRD.

We get 100 -- 500 probes for the price of 16, or we can get thousands of probes. But what we also heard is, "Yes, it's great to get 10,000 probes in 5 days. I mean it's amazing. But you know, it would be better if you could do it in 1 day." And so that was the genesis of MRD Express, is actually a customer request saying those 5 days actually, those can -- it can limit the window. And so that was the customer demand, and we responded. And now we have MRD Express. And so we -- sometimes we are seeding the market with technology, that's what happened with MRD. But in the case of MRD Express, we also listened to what customers need and we rise to the challenge.

On that tumor-informed versus tumor-naive, but like as you think about where you guys are playing within your liquid biopsy customers, in general, there are a few that still have not adopted from your technology. I guess like, what's the -- when you go out to those big players that still haven't adopted, like what's their pushback? What's the reasoning why they won't come to you guys? Because it seems like you're the only ones with this 1-day turnaround and accuracy and cost and everything else.

It's like you're listening to my sales call review every Friday, it's like we are doing well, but why aren't we doing better? Why don't we have 100.0% of the market?

There's inertia, right? Victory is certain. Timing, still to be determined. What we know is when we get into a pilot, head-to-head, we win the panels, we win on the workflow, we win on the enzymes, we win on the supply chain, we win on IVDR. And I think right now, nobody is getting fired for choosing Twist.

And we're seeing the sentiment channels. They were our customers before, but they were customers that were saying, "I picked someone else before you are in business and I won't switch." And now we are seeing more and more those people calling us saying that they need to switch because the supply chain confidence that they are getting from our competition is not where it needs to be at the scale, the volume they are now. So we are playing to win. We are playing to win it all. And I think we -- it's a question of time.

Got you. In the last 55 seconds, Adam, let's talk about the margins. The gross margin trends here, towards 55% plus, it's been -- it seems like that's -- you guys are building good momentum there. Targeting breakeven -- EBITDA breakeven at the 4Q. Talk about where you think -- and I know you're not going to put numbers around where you think like margin is going to be. But when you think about the incremental margin opportunity built within the business, where it is now and where you think it could go, like walk us through that.

So look, if you look at the journey over the last 3 years, we've seen our margin growth, over 20 points of margin growth. Obviously, some of that is through the continuous process improvements. But the vast majority of that is the fact that for every dollar of revenue growth, whether it be on the DS/PS -- the DNA Synthesis and Protein Solutions side or it be on the NGS side, we're targeting about 75% to 80% of that dropping through to the gross margin line, the revenue growth dropping through. And we're seeing that pretty consistently as we scale up any of our areas of the business.

I think that trend will continue. And so what's going to accelerate or decelerate the pace of the margin expansion is how much incremental effort you put on driving out costs versus introducing new products. And so what we've said is now that we're above the 50% gross margin line, there's no going backwards ever. And we're going to continue to see progress as we move forward because we know we can drive that revenue. We know we can do it. But we want to make sure we're also investing in the capabilities. So the pace at which we grow at is to be determined.

Got you. And then when you're thinking about from a guidance perspective, you guys typically would -- you guide conservatively and you tend to beat on the numbers from a quarter or for a year. But at what point do you start leveling out on that margin piece? Because again, you got to take on -- do it on the investments.

And then does the change in kind of the segmentation and the ramp of MRD, the ramp of all the NGS stuff, does that set you up for like significant upside much more so than you ever saw in the past? Like typically, you beat by like a few percentage points here and there because they're longer-term contracts or longer-term projects. Is the business starting to change now where you can get a lot more juice, I guess, on the quarter than what we've seen?

I guess you'll have to wait, is the answer.

All I needed to hear. Amazing.

See you in May.

On the Investor Day, any sneak peek there or?

Look forward to it in May.

Okay. All right. I appreciate it. Thanks again.

Thank you so much.

Yes. This has been really good.

Thanks, Luke.

Good day. Thank you for standing by. Welcome to Twist Biosciences conference call to discuss the license agreement for the B-Body bispecific technology platform. [Operator Instructions] Please note that today's conference is being recorded.

I would now like to turn the call over to Angela Bitting, SVP of Corporate Affairs. Please go ahead.

Thank you, operator. Good morning, everyone. I would like to thank you for joining us for Twist Biosciences conference call to discuss our license of the B-Body bispecific technology. We issued a press release early this morning and is available at our website at www.twistbioscience.com.

With me on the call today is Dr. Emily Leproust, CEO and Co-Founder of Twist; Adam Laponis, CFO; Dr. Patrick Finn, President and COO; and Dr. Colby Souders, CSO, will be available in the Q&A session after our prepared remarks. We ask that during Q&A you limit your questions to only one and then requeue as a courtesy to others on the call. The call is being recorded, and the audio portion will be archived in the Investors section of our website for 2 weeks.

During today's presentation, we will make forward-looking statements within the meaning of the U.S. federal securities laws. Forward-looking statements generally relate to future events or future financial or operating performance. Our expectations and beliefs regarding these matters may not materialize, and actual results in financial periods are subject to risks and uncertainties that could cause actual results to differ materially from those projected. These risks include those set forth in the press release we issued earlier today as well as those more fully described in our filings with the Securities and Exchange Commission.

The forward-looking statements in this presentation are based on information available to us as of the date hereof, and we disclaim any obligation to update any forward-looking statements, except as required by law. We'll also discuss adjusted EBITDA, a financial measure that does not conform with generally accepted accounting principles. Information may be calculated differently than similar non-GAAP data presented by other companies. If available, a reconciliation between GAAP and non-GAAP financial measures will be included in our earnings documents, which can be found on the Investors section of our website.

With that, I will now turn the call over to our CEO and Co-Founder, Dr. Emily Leproust.

Thank you, Angela, and good morning, everyone. This morning, we announced that Twist will be the co-exclusive provider of the B-Body bispecific technology platform together with Invenra. This license builds upon our momentum in AI-enabled drug discovery to serve the rapidly growing market opportunity as it fits very well with our high-throughput automated platform and drive more volume to our proprietary silicon chip. Through the license to this innovative technology, we complement and extend our DNA synthesis and Protein Solutions group with a patent-protected clinically validated technology.

Taking a step back, a bispecific antibody combines the binding arms of 2 unique antibody proteins, each with independent mechanisms of actions, into one molecule, potentially enabling a single therapy to engage 2 disease targets at the same time. This dual action has the possibility of improving effectiveness by, for example, directing immune cell more precisely, blocking multiple disease pathways at once or enhancing drug delivery to disease-affected tissues.

Of the hundreds of commercially available antibody therapies today, bispecifics represent a small but rapidly growing and very promising drug class. However, bispecifics have been famously difficult to develop as they have 2 different binding domains bound by an engineered linker that is, by definition, unnatural. This creates challenges in automating discovery, producing many high-purity variants, testing those variants and scaling up manufacturing for clinical and commercial use to treat patients.

On Slide 4, we have detailed the differences between monospecific antibodies and the challenges bispecifics present. First, they are more complex than standard antibodies. A commercial antibody is built from 2 identical halves. These halves naturally find each other and assemble efficiently within a cell, like 2 pieces of a puzzle and are then expressed as a single molecule.

Bispecific antibodies combine 2 different binding arms connected by unnatural protein engineering, which increases the chances of [indiscernible] during assembly. Cells may put the wrong pieces together, creating imbalance production and an overall reduction in yields or unusable product.

Second, the protein must fold correctly. Proteins are like origami at the molecular scale. Bispecific antibodies contain more moving parts and unnaturally engineered geometries, making correct folding harder. The cell, which is used to express the bispecific protein, often degrades incorrectly folded antibody proteins, resulting in a significant decrease in the amount of antibody to characterize and validate for further development.

Third, building on the first 2 points, folding and assembly challenges create stress within a cell used for expression. Stressed unhealthy cells slow down growth, production and robustness, resulting in significant manufacturing challenges.

Fourth, once a potential therapeutic antibody completes discovery and preclinical studies, human clinical development presents an additional challenge. Once [ that lands ] into humans for bispecific antibody to work safely and effectively, the molecule must closely resemble antibody structures that the body already recognizes at cells. By their very nature, bispecific antibodies include man-made engineered changes. If any portion of bispecific antibody, including the engineered portion that links the 2 domains together, appears artificial offering, the immune system may treat the antibody as a threat, reducing its effectiveness or causing safety issues. Because of this, the linker must be carefully designed and tested to ensure minimal or no activation of the human immune system to the engineered bispecific antibody.

Finally, standard manufacturing platforms experience repeated challenges as many bispecific formats require multiple purification steps to yield high-quality material and also require specialized production system that are not widely used. Further, some require complex processing conditions to make a bispecific out of 2 different monospecific antibodies and then [indiscernible] the fraught with risk and increased cost.

Turning to Slide 5. At Twist, we built a semiconductor-based platform to write DNA sequences at scale. The DNA powers the creation of antibody proteins with unparalleled speed, throughput, quality and cost advantages. As a reminder, when developing therapeutics, our customers are engaged in the design, build, test, learn cycle, as shown on the slide.

The customer design the sequences, we build the proteins, and we then conduct a series of analytical measurements to test the proteins. Once we deliver the data or product, the customer learns from the information and optimizes the cycle for the next iteration. To date, when we work on bispecific antibody discovery, we will work on each individual arm separately. Those customers will then take the individual optimized antibodies and conduct bispecific reformatting and optimization in their labs or with a specialized provider with expertise in bispecific expression and purification.

Moving to Slide 7. Today, we have a patent-protected clinically validated platforms to support automated bispecific discovery, reformatting, expression and analysis, significantly expanded our expertise and meaningfully enhancing our ability to compete and capture value in this high-growth area, transforming market access into competitive participation. Essentially, it gives us more short-term goal to expand our wallet share within existing accounts and access to new customers who are solely focused on bispecific therapeutic discovery.

We reviewed a number of bispecific platform and selected the B-Body technology as it fits very well with our robust semiconductor-based synthesis, high throughput platform and extreme automation for speed, scale and throughput. In addition, the B-Body platform solves the manufacturing challenges to enable safety and stability, reducing Chemistry, Manufacturing, and Controls, also known as CMC risk.

On Slide 8, you will see the B-Body specific platform is a plug-n-play system designed so that the 2 binding functions are connected using antibody structures the body already recognizes as noninvasive. Rather than using unknown artificial connectors, the B-Body format uses native human antibody building blocks with very minor changes to the linker itself, making them easier to express, purify and characterize.

On Slide 9, you'll see data showing that B-Bodies have high purity and homogeneity that makes them compatible with our existing high-throughput automation technology. Critically, the B-Body bispecifics are not recognized as foreign by the human immune system.

Moving to Slide 10. While bispecific antibodies were previously within our capability and part of our serviceable market, we now have the technology to allow us to successfully navigate the challenges, automate onto our platform and scale bispecific discovery in a way that has not been previously possible.

To give you a sense of the opportunity, antibody campaigns today may typically result in approximately 12 antibody variants generated as leads for each arm to test further. Ideally, to pursue bispecific antibody discovery, these variants will then be combined in all variations and configurations, resulting in 12x12x2 or 288 different bispecific antibodies. With legacy bispecific technology, expressing, purifying and characterizing 288 bispecific antibodies is impractical and is not done routinely. With the plug-n-play ability of the B-Body technology, together with the Twist platform, we will be able to conduct such testing in high throughput easily and cost effectively.

We know that bispecific makes up an increasing portion of clinically approved antibodies. And further, as of December 2025, there were more than 180 bispecific antibodies in clinical studies for a wide range of applications. The number of bispecifics entering clinical trials has doubled since 2019, confirming the opportunity and the acceleration of interest in this area of therapeutics. Importantly, this meaningfully enhances our ability to compete and capture value in this high-growth area, accelerating market participation through execution and commercial violence.

On Slide 11, you will see overarching product groups of Twist, DNA synthesis and Protein Solutions, including several of our applications and our bispecific fits directly within our robust and expanding portfolio of products and services. With bispecific representing a growing therapeutic modality, building bispecific large language models has been exceptionally difficult as the generation of robust data set at scale has been impossible to date. Specifically, it has been impossible to produce purify and test in an automated fashion.

We believe that combining the B-Body technology with a high throughput production and characterization platform opens up AI-enabled discovery to bispecific. Large technology customers may build new LLM models, pharma customers may augment existing models and importantly, it expands AI-enabled bispecific discovery capabilities to small organizations that can benefit from our platform.

Turning to the financials. On Slide 12, Invenra will receive $5 million in cash and approximately $15 million in Twist common stock for the 7-year co-exclusive license. Concurrently, in a secondary transaction with Invenra stockholders, Twist also acquired preferred shares representing approximately 6% ownership in Invenra, along with rise to one seat of the Invenra Board of Directors for $13.8 million in Twist common stock. We also expect Invenra to transfer technology to us to enable future success.

Twist will receive all revenue generated from bispecific antibody discovery programs we conduct. In addition, customers developing B-Body bispecifics need a commercial license at the time they enter a human clinical trials. Whether this license is generated through customers working with Invenra or Twist, Twist will receive all of that revenue and subsequently pay Invenra a 20% royalty.

To summarize, we believe this is a compelling strategic and financial opportunity. We are gaining access to an innovative patent-protected clinically validated technology at an attractive valuation relative to its long-term potential. We expect this capability to benefit significantly from automation and optimization with our proprietary platform and NPI engine, further strengthening our competitive position and expanding our revenue base. We expect the agreement to be accretive to revenue and adjusted EBITDA in fiscal 2027 and beyond.

This is part of the turbo engine we discussed on our earnings call. We are layering capabilities on top of our platform this year to expand our funnel and top line growth next year. We expect to transfer and automate this capability within our existing operating expense structure. Importantly, we remain committed to achieving adjusted EBITDA breakeven for the fourth quarter of fiscal 2026.

At this time, we will open the call for questions. Operator?

[Operator Instructions] Our first question coming from the line of Subbu Nambi with Guggenheim.

You'll be paying Invenra a 20% royalty on all license revenue. Is that an impact on the 2026 margin expectations and breakeven in 4Q -- fiscal 4Q '26?

Thank you, Subbu. Your line was a bit muffled for me, but I think you were asking about our gross margin in Q4 and our adjusted EBITDA breakeven in Q4. There's no change. We are very, very committed to the guidance we've provided at the last earnings call. And we're excited about this deal, but it's not changing our commitment to adjusted EBITDA breakeven in Q4 of this fiscal year.

And our next question coming from the line of Puneet Souda with Leerink Partners.

So really, bispecifics have been around for some time, and you have had the biopharma antibody discovery program as well. And if we recall, some of those programs have started and also shut down. Again, this is -- antibody discovery is a multiyear endeavor, 7 or more years to really see the results and see if the products can actually get into clinic and eventually to market.

Maybe could you talk about what agreements Invenra has and what are those royalties are at today and the number of those agreements? And how should we think about this more near term, what it could do for Twist beyond bringing the bispecific platform technology and adding to your already existing mAb technologies that were part of your biopharma program?

Yes. Yes. Thank you, Puneet, and you touched a very, very important question. And you're asking what value vectors are we going to see at Twist. First and foremost and most important, we think that it's going to increase the volume of discovery that happens at Twist. We were -- we report the numbers every quarter. We're doing really well. The revenue that we're getting from antibody discovery is ramping. It's one of our fastest product line.

At the same time, we also recognize that we can compete really, really effectively in monospecific. In bispecific, we can't leverage the full advantage of automation because the other bispecific technologies that are out there that we can use are very hard to automate for us and for everybody else. So we like the B-Body because now we can very easily automate the production, the verification and the characterization. And so we think that, first and foremost, is going to drive revenue through discovery.

In addition, there is licensing opportunities as a second vector of value to us. Those will happen as customers want to go into the clinic. So it's not royalties in the sense of when the drug finally sells in the future. Those licenses have to be taken before going to the clinic. So it will happen a bit sooner than 7 years, but we anticipate that the main value creation for us will be that the volume it's driving discovery. And then last but not least, we're also a small -- now a small investor in Invenra and so this will create some value in the future as well.

And our next question coming from the line of Doug Schenkel with Wolfe Research.

A couple of questions really on the customer profile. So really, what I'm trying to get at is when you think about what you were hearing from the customer base and when you looked at it, how much overlap is there with your existing customer base? So that's the first question.

And kind of building off of that, is this a capability or technology that those customers, whether it was Invenra's, whether it was your core customers, is this something that they were looking for? And kind of by extension, was this a hole you felt you had in your offerings? I guess what we're trying to get at is, one, how much synergistic fit is there with your customer base and what you're selling today as we try to really better understand the impetus for the deal?

Thank you, Doug. That's a great question. So if you take AI out of the equation, the largest top 20 pharma type customers, frankly, they probably do not need this. Most of the big pharma have developed their own proprietary technologies for bispecific. And so if you were to just take AI out, maybe it does not -- it's not greatly appealing to large pharma. However, again, taking AI out, any new biotech that wants to go into bispecific, they need access to a platform, and they need a platform that they can trust, a platform that is already clinically validated because you don't want to take a risk on a new platform that once you go into the clinic a few years down the line, you have a toxicity product. And so for smaller and starting biotech and drug discovery companies that this becomes really appealing, especially now that it will be on the Twist platform, we'll be able to enable high throughput. And so it's a way for us to keep reaching the full market.

Now going back to -- and I'll say actually, the same is true for kind of Magnificent 7 that are going into AI. They all need a bispecific platform that they can get to. And now going back to the top 20 pharma, now adding AI in the equation. AI so far has been -- in drug discovery has been applied to monospecific. And to the extent that those big pharmas are going to want to go into AI-driven bispecific discovery. Now you need to be able to build large language model. And to be a large language model, you need to build a lot of bispecific and making those bispecific, if you -- if they are not -- the technology is not intrinsically enable to high throughput expression, purification, characterization becomes really hard. And so what we think is combining our high throughput platform with AI could open bispecific AI-driven drug discovery for top pharma.

So very much appreciate the question because that's how we think about market access as well. And so we think that for us, it has a broad access to smaller biotech companies, large pharmas and the Magnificent 7 focused on drug discovery.

Our next question in queue coming from the line of Matt Larew with Stephens Inc.

Maybe just a follow-up on the last point. How differentiated is B-Body in generating these high-throughput data sets versus existing bispecific formats? And maybe just to follow up on Doug's question as well. Does this open up the opportunity for new logos, too?

Sorry, what was the last question? Does it open opportunity for new?

New business partners, new logos.

Yes. No, absolutely. So our view is that it fits really well with our current customer base and it expands it in 2 ways. One is we can upsell the current customer base where we were not doing a lot of bispecific business. Again, we didn't have a differentiated technology, and now we do because it's highly automatable. And then the second is it expands the customer base to new customers that now we can win because we have a bispecific. And so it's something that -- again, we have been doing a little bit of bispecific, but now we can really lean into it because we have access to a best-in-class technology.

In terms of your initial question of how differentiated it is, again, there are hundreds of technologies out there. But as far as what we could see that if clinically validated and available, we believe this is the absolute best in terms of being able to put it in high throughput, number one.

And then number two, there is another aspect that we haven't really touched on quite yet on this call -- on the Q&A is the ability to plug-n-play. So if you have, let's say, a dozen antibody for one arm and a dozen antibody for the other arm, it's actually really hard to combine them with existing technology. Whereas with B-Bodies, that plug-n-play ability, it's very easy to combine -- to make the combinations and test them. And so again, this being so compatible with our platform that is highly automated, we believe it's going to give us a leg up and it's going to enable growth, again, growth within existing accounts and growth with new accounts.

Our next question in queue coming from the line of Brendan Smith with TD Cowen.

I wanted to actually ask a bit more about the technology itself. And I appreciate this might be getting a bit ahead of where it is now, but kind of piggyback on the last few questions. I guess, do you expect the B-Body platform could, over time, allow design of other biologics, trispecific or even ADCs? Or is this really intended to kind of just maximize share within bispecifics alone with maybe expansion into other modalities something you might explore separately down the line? Just kind of trying to contextualize the land and expand strategy here.

Thank you, Brendan. Very, very great question. This is specifically for bispecific. It is not a trispecific license and not an ADC license either. So you're correct, there are other modalities out there. Right now, bispecific has a high growth rate. And we think that some of our customers are going to be able to realize that the legacy bispecific format that they have been using will be able to be replaced with our new and improved format like the B-Body. But it's -- right now, it is fully focused on bispecific.

And our next question in queue coming from the line of Matt Larew with William Blair.

Maybe just following up on that point, Emily, you mentioned that many larger pharma customers may have in-house capabilities. But as you've spoken with other customers, just trying to get a sense for what those customers are doing today, if this is something that they've been coming to Twist and asking about? So sort of getting a sense for the push versus the pull and perhaps tied to that, you referenced it being accretive to revenue and EBITDA in 2027. Could you just give us a sense for what that looks like today in terms of where the company is at?

Colby, do you want to take your first crack at the question, and I'll circle back with the financial comments?

Yes, absolutely. Thanks, Emily. Great question. And what we've seen a shift in the landscape over the last 2 years or so has been companies that have been interested in replacing some of the legacy platforms. So like Emily was saying in response to the last question, we've seen folks ask if we have a proprietary technology or bispecific format that could be used. So this will directly address that market so that we can capture that market share and meet those customers. So that's been our goal there.

And in terms of financial, it's going to drive revenue in 2026. And at the same time, we are, again, very, very committed to adjusted EBITDA breakeven in Q4 in 2026. And we believe that adding this capability will enable us to accelerate the growth as much as we can in 2027 and beyond. So we are adding this bispecific into our NPI engine, and we'll be able to provide a fully automated end-to-end solution to our customers that's going to drive revenue with existing customers and new customers.

And I'm showing no further questions in the queue at this time. I will now turn the call back over to Dr. Emily Leproust for any closing remarks.

Thank you for joining us today. Supported by growing clinical success, favorable regulatory pathways and strong industry partnerships, bispecific antibodies are rapidly becoming a cornerstone of precision medicine and a key growth driver in biopharmaceutical innovation. We believe that incorporating the B-Body technology platform in our DNA synthesis and Protein Solution groups will position us to continue to lead at the forefront of next-generation antibody innovation and drive shareholder value. Thank you.

Ladies and gentlemen, this concludes today's conference call. Thank you for your participation. You may now disconnect.

Ladies and gentlemen, thank you for standing by. Welcome to Twist Bioscience Fiscal 2026 First Quarter Financial Results Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would like now to turn the conference over to Angela Bitting, Senior Vice President of Corporate Affairs. Please go ahead.

Thank you, operator. Good morning, everyone. I would like to thank you for joining us for Twist Biosciences conference call to review our fiscal 2026 first quarter financial results and business progress. We issued our financial results press release before the market, and it is available at our website at www.twistbioscience.com.

With me on the call today are Dr. Emily Leproust, CEO and Co-Founder of Twist; Adam Laponis, CFO of Twist; and Dr. Patrick Finn, President and COO of Twist. Today, we will discuss our business progress, financial and operational performance as well as growth opportunities. We will then open the call for questions. We ask that you limit your questions to only one, and then requeue as a courtesy to others on the call. This call is being recorded. The audio portion will be archived in the Investors section of our website and will be available for 2 weeks.

During today's presentation, we will make forward-looking statements within the meaning of the U.S. federal securities laws. Forward-looking statements generally relate to future events or future financial or operating performance. Our expectations and beliefs regarding these matters may not materialize, and actual results in financial periods are subject to risks and uncertainties that could cause actual results to differ materially from those projected.

These risks include those set forth in the press release we issued earlier today as well as those more fully described in our filings with the Securities and Exchange Commission. The forward-looking statements in this presentation are based on the information available to us as of the date hereof, and we disclaim any obligation to update any forward-looking statements, except as required by law.

We'll also discuss adjusted EBITDA, a financial measure that does not conform with generally accepted accounting principles. Information may be calculated differently than similar non-GAAP data presented by other companies. When reported, a reconciliation between GAAP and non-GAAP financial measures will be included in our earnings documents, which can be found on the Investors section of our website.

With that, I will now turn the call over to our CEO and Co-Founder, Emily Leproust.

Thank you, Angela, and good morning, everyone. Q1 provided a strong start to fiscal 2026 and [indiscernible] pattern of consistent execution of marking our 12th consecutive quarter of revenue growth. This performance builds directly on the operating momentum established in fiscal 2025 and reflects trends that remain intact as we move through the year.

Notably, over the last 3 years, we have delivered a revenue CAGR of 24% and increased margin by 20 percentage points on relatively flat OpEx, materially outpacing growth across [indiscernible] industry. We believe the [indiscernible] of sustained growth and meaningful operational progress, clearly differentiates Twist within our peer group.

Going back to basics, Atlas, our strategy is simple and very deliberate. We built a semiconductor-based [indiscernible] platform that gives us a technology advantage that translates into speed, scale, quality and affordability for our customers. Everything we do build on this differentiated informational platform. As we [indiscernible], serving more customers with more products, we improve our financial performance, strengthen our competitive position and extend our addressable markets.

This quarter's results reflected our model is working as intended and we are building for the opportunities we see that leverage our advantage to accelerate growth. Over the last several years, we have transformed Twist into [indiscernible] as an NPI machine. We consistently launch new products that sit on top of the same core manufacturing infrastructure, allowing us to expand into new applications and customer workflows without adding risk or complexity. As a result, our estimated serviceable market has expanded from approximately $2 billion in 2020 to roughly $7 billion today, driven by our current portfolio of products and services.

Based on our market growth and customer adoption patterns, we continue to see a clear case to more than $12 billion of [indiscernible] market by 2030 with additional growth opportunities as we launch new products. Importantly, the [indiscernible] expansion is occurring while serving some of the most sophisticated customers in life sciences, across therapeutics, dynastic and applied markets. These customers choose Twist because we future-proof their supply chains and innovation to enable them to move faster at scale with confidence.

Paddy will dive deeper into one of the opportunities in the DNA Synthesis & Protein Solutions Group to detail how we are playing to win in the AI-enabled discovery market as [indiscernible] and scales in real time. For our energy applications group, we see a serviceable market of over $3 billion, with about 10% market share today. Looking at our serviceable markets within this group, we expect a blended CAGR for the industry of approximately 14% across oncology and rare disease diagnostics, [indiscernible], biopharma R&D and academic markets. Keep in mind, our revenue for NGS comes both through direct sales and [indiscernible] partners who sell our [indiscernible] under their brand.

Importantly, we expect our growth to outpace industry levels as we leverage our NPI engine and commercial intensity to outperform our peers. We expect to drive growth in NGS Applications by expanding volumes with existing [indiscernible] customers as they are testing skills, particularly in oncology, where recurring testing support sustained demand. In addition, we expect to add new customers in the diagnostic space.

Twist will also pursue market share gains by converting legacy macro workflows, sequencing [indiscernible]. In biopharma R&D and academic research, growth will be driven by increasing adoption of Twist [indiscernible] portfolio and by expanding product offerings to support new applications and workflows.

The key to our ongoing success is that over the last decade, we have built deep long-standing customer relationships that gives us clear insight into unmet needs and emerging demand. With [indiscernible] customer insight with our proprietary platform to consistently deliver a strong product road map and a disciplined cadence of commercial launches.

About a year ago, we recognize the early formation of a new category in AI-enabled therapeutic discovery, a market that was effectively nonexistent in 2024. In fiscal 2025, we had bought more than $25 million in orders, specifically tied to AI discovery. This exemplifies Twist's ability to help define new categories by listening closely to customers, adapting our road map ahead of market inflection points and investing early to establish leadership. This was done with flat operating expenses through the fourth quarter of last year. Going forward, we see meaningful growth ahead as this category continues to develop.

In the first quarter, we made targeted deliberate transient and structural investments to expand our advantages for all the opportunities we see across the business. Some of these investments are in the commercial team to amplify [indiscernible] in the market, although [indiscernible] and operations to support the scale of the full portfolio. Importantly, we made these investments while remaining focused on our core financial priorities, including revenue growth gross margin and adjusted EBITDA breakeven. To be clear, we are committed to adjusted EBITDA breakeven in the fourth quarter of fiscal 2026.

On top of this, we see an opportunity to increase our growth rate and we have [indiscernible] our operating expenses up by about $10 million per quarter without putting adjusted EBITDA breakeven at risk. As you know, investment in growth is like a turbo and engine. There is a lag between pushing the gas pedal and the acceleration. On an ongoing basis, we expect approximately 75% to 80% of our all incremental revenue growth across all product lines to drop to the gross margin line. We have worked hard to get to this point, and we'll continue to tune the machine.

As a team, we are focused on 3 key performance metrics: revenue, gross margin and adjusted EBITDA breakeven. We measure many other things within the company and the business, but ultimately, these 3 metrics drive our future growth. Our management team and every employee at Twist are measured and incentivized on these 3 metrics. Overall, we are managing the business, keeping an eye on the gas and the growth like [indiscernible]. And we expect to become an even more formable force in the coming years as we sustain growth through disciplined reinvestment of our profits.

At this time, I'd like to turn the call over to Paddy to further expand on our growth initiatives around AI-enabled discovery.

Thank you, Emily. At Twist, we're constantly engaged with our customers and key opinion leaders. Going back to December 2024 within a relatively narrow time window, we were in dialogue with several customers, bringing forward new ideas to the use of our platform technology, expanding beyond DNA synthesis and deeply into protein expression and antibody characterization for thousands of sequences.

As you may remember from biology class, DNA includes a sequence for protein, which can then be expressed in a cell. The proteins expressed in a cell can then be purified and run through specific assays to determine the protein's characteristics, including stability, developability and more. Historically, we made DNA. We've expanded to also expressing proteins from the DNA, opening up a $700 million market to Twist.

Because our semiconductor-based platform writes DNA sequences at scale, AI presents a fabulous use case that incorporates our unparalleled throughput speed, quality and cost advantages. All customers are engaged in a design-build test land cycle. The customer designs the sequences, we build the proteins and then we conduct a series of assays to test the proteins. Once we deliver the products or data, the customer learns from the information and optimizes the cycle to the next iteration.

These customers fall into 3 different buckets. First, we are currently working with large pharma companies who are building robust, large language models and preparing training sets. We received thousands of sequences to [indiscernible], but ultimately, this customer type doesn't want the DNA strands. They want Twist to conduct protein expression and characterize the protein, delivering only a data file with the result of the assays. These customers have a wet lab, but cannot handle the volume of sequences we want to test and may not be new to Twist with this being an expansion of our work with them.

Our second customer group includes [indiscernible] tech companies focused on creating or expanding their presence in life sciences. They do not have a wet lab and operate as a so-called dry lab company. This means they rely on Twist to conduct all research experiments, and we deliver the data to them for evaluation and next steps. These customers are new to Twist.

A third customer group is well-funded biotech companies that need thousands of sequences and data, essentially pursuing similar paths to large pharma. Across all customers, the work is customer calling to their requirements, with the work streams and margin profiles are similar across customer types, and we have the capacity to serve all.

When we launch a new product or service and the work in AI-enabled discoveries a series of bots, we completely worked using the best tools, spending no expense to ensure the customer receives what they needs. Once we know the product resonates with customers, we optimize processes, automating proceeds through a series of improvements to rapidly bring the margin profile in line with the rest of the business for 75% to 80% of incremental revenue dropped to the gross margin line.

We've done this time and time again with a repeatable process. In fact, we continued to strive in the first quarter with 74% of incremental revenue dropping through to gross margin. What began as exploratory work in early 2025, leveraging our platform to AI-enabled discovery market, is now transitioning into repeat production-level workflows with customers increasingly focused on generating high-quality data at scale. As models mature, the constraint is no longer algorithm development with the speed, quality and economics of experimental data generation. That shift is driving demand toward platforms that can reliably deliver large volumes of data quickly and cost effectively and Twist fits that needs very well.

Our platform is uniquely suited to this customer need, allowing customers to move from design to data in days, not months. We see this as a durable and expanding opportunity that aligns directly with Twist's core strength of customization at scale, with an immediately serviceable market of $1.5 billion for customers of antibody discovery services and $700 million for protein expression.

With that, I'll turn it over to Adam to discuss the financials for the quarter.

Thank you, Paddy. Revenue for the first quarter increased to $103.7 million, growth of 17% year-over-year and approximately 5% sequentially. Gross margin came in higher than expected at 52.0% for the first quarter of fiscal 2026, an increase of approximately 4 margin points over the first quarter of fiscal 2025, supported by increasing revenue and our continuous process improvement efforts.

DNA Synthesis & Protein Solutions revenue increased to $51.1 million, growth of 27% year-over-year driven by strength from customers pursuing AI-enabled discovery, whether building models or testing molecules. NGS Applications revenue for the first quarter grew to approximately $52.6 million, excluding one large customer, NGS grew 18% year-over-year. For the quarter, revenue from our top 10 NGS Applications customers accounted for approximately 36% of NGS revenue.

Looking geographically. Americas revenue increased to approximately $58.4 million for the first quarter compared to $53.7 million for the same period of fiscal 2025, growth of 9% year-over-year. EMEA revenue rose to approximately $38.4 million in the first quarter versus $28.3 million in the same period of fiscal 2025, growth of 36% year-over-year. APAC revenue increased to approximately $7 million in the first quarter compared to $6.7 million in the same period of fiscal 2025.

Looking at revenue by industry. Therapeutics revenue rose to approximately $37.2 million for the first quarter of 2026 compared to $26.8 million in the same period of fiscal '25, an increase of 39%, reflecting the increased uptake of our products by large pharma and biotech customers in their efforts on therapeutics discovery and including AI-enabled discovery.

Diagnostics revenue was approximately $35.3 million for the first quarter of 2026, substantially equivalent to $35.5 million in the same period of fiscal 2025. Excluding one customer, Diagnostics was up 12% year-on-year. Adding to diagnostics, recall about 75% of our global supply partners revenue is OEM partners selling Twist products for NGS Applications. Although we do not always know our OEM partners and customers, we believe the vast majority of their revenue is focused on diagnostics.

Industry and Applied revenue were $6.1 million in the first quarter of 2026 compared to $5.5 million in the same period of fiscal '25, an increase of 11%. Academic research and government revenue was approximately $12.2 million, relatively equivalent with $12.4 million in the same period of fiscal 2025. We saw less large-scale projects compared to the same period of last year, but a large expansion in the number of customers purchasing from us based on our MPI and academic commercialization efforts. We see the academic market returning to growth in Q2 with increased confidence in NIH funding for 2026.

Global Supply Partner revenue was $12.8 million in the first quarter of 2026 compared to $8.5 million in the same period of fiscal 2025, an increase of 50%, driven by 3 factors: a significant new partner for NGS coming online, substantive growth in our diagnostics OEM partners and growth for our distributors in APAC.

Moving down the P&L. Our gross margin for the first quarter increased to 52.0%, an improvement of approximately 4 margin points versus the same period of fiscal 2025, reflecting our strong revenue growth and customer base as well as continuous process improvements. Operating expenses, excluding cost of revenues for the first quarter was $86.9 million compared with $77.5 million in the same period of 2025. The increase in operating expenses was driven by investment in our commercial groups to drive additional revenue growth as well as digital capabilities.

Looking at our progress and our path to profitability. For the first quarter of fiscal 2026, adjusted EBITDA was a loss of approximately $13.4 million, an improvement of approximately $2.8 million versus the first quarter of fiscal '25. This improvement demonstrates our ability to scale efficiently even as we front-load strategic investments in commercial and digital capabilities. These investments are expected to remain stable or moderate slightly in the second half of the fiscal year.

For the first quarter of fiscal 2026, net cash used in operating activities was $24.8 million. Capital expenditures in the first quarter of fiscal 2026 were $10 million. We ended the quarter with $197.9 million in cash, cash equivalents and short-term investments.

Turning to guidance. For fiscal 2026, we expect total revenue of $435 million to $440 million, growth of approximately 16% at the midpoint. We expect the revenue increase versus prior guidance to be generally balanced across DSPS and NGS. For Q2 of fiscal 2026, we expect total revenue of $107 million to $108 million, growth of approximately 16% year-over-year at the midpoint. For NGS, we expect strong sequential growth in Q2 behind growth in key accounts with sequential growth throughout the year, as previously discussed. We remain confident in our trajectory and continue to forecast reaching adjusted EBITDA breakeven for the fourth quarter of fiscal 2026.

With that, I'll turn the call back to Emily.

Thank you, Adam. As we step back and look across the business, what stands out is how consistently the pieces are coming together. At Twist, our growth is being driven by a repeatable model. We're expanding our addressable markets through disciplined product innovation, [indiscernible] more volume onto the same silicon-based platform and converting that scale into improving financial performance. This is not dependent on a single product, customer or market. It is the result of an NPI engine that continues to deliver paired with operational execution that scales efficiently.

We continue to introduce new products across both DNA Synthesis & Protein Solutions, and NGS Applications with different adoption dynamics with the same underlying outcome, more customers, more applications and more volume flowing through the manufacturing infrastructure. This is what allows us to support growth while maintaining margin discipline and capital efficiency.

Importantly, we have built this platform with significant capacity already in place. We're able to support continued demand without introducing meaningful execution risk. As revenue scales, the economics of the model become increasingly favorable, reinforcing our confidence in the path ahead. This is why we remain very confident reiterating our expectation to reach adjusted EBITDA breakeven in Q4 of fiscal 2026.

The drivers of that outcome are already visible in the business today: consistent revenue growth, gross margin above 50%, disciplined investment in operating expenses to accelerate growth and a stable cost structure. More broadly, Twist is increasingly positioned as an enabling infrastructure provider across the biological continuum from early discovery through diagnostics and into therapeutics development. Whether it is enabling AI-driven discovery, supporting precision diagnostics or scaling production for applied markets, customers choose Twist because our platform allows them to move faster, reduce risk and operate at scale.

To help investors engage more deeply with our strategy, platform and long-term opportunities, we plan to host an Investor Day in May. At that time, we will provide a deeper look at how our customers use our products, our product road map, market expansion opportunities and financial framework as we continue to scale the business beyond adjusted EBITDA breakeven. We expect to provide more event details in the coming weeks.

In closing, Twist entered the remainder of fiscal 2026 with a differentiated platform, expanding markets, consistent execution and a clear line of sight to profitability. We are focused on doing what we have consistently done, launching products, serving customers exceptionally well and scaling the business with discipline.

With that, we are happy to take your questions. Operator?

[Operator Instructions] And our first question is going to come from Matt Larew with William Blair.

I want to follow up on demand you referenced in terms of AI trend discovery. Emily, you talked about this being a durable opportunity, but when you think about customers trying to build out a foundation model and [indiscernible] models, is that -- does this demand something you think that's measured in months or years? Or is this that you referenced just a new part of the drug discovery ecosystem, will be the first part.

And the second part of that was you called out over 50,000 genes manufactured for data characterization in the quarter. Curious what that number was in the prior period and kind of how the economics of delivering data versus delivering DNA work for Twist?

Thanks, Matt. Great question. Yes, so we're very excited about what AI is doing for Twist by pulling volume onto our chips and enabling us to ramp revenue and definitely, this quarter, 27% growth in DNA Synthesis & Protein Solutions is very much driven by AI. In terms of durability, so it's still early days, but what we are seeing is customers that had big orders over the last few quarters are coming back with other big orders. And so it doesn't seem to be letting down. And at the same time, we're adding more and more of the top 20 pharma to our platform as well as the [indiscernible] as well as the startups that are very well funded, but we were -- we don't have full penetration. So we see that there's a lot of growth coming.

And then long term, what we think is that AI is going to become the first path in the -- right now -- in the path, in vivo or in vitro was the question you had to ask yourself and we think AI will be the first path, it will be about probably the same cost of about $250,000 to discover an antibody. But it would be -- the data will be delivered in 2 weeks instead of 6 weeks with in vivo or in vitro and then in vivo and in vitro are still going to be important, but as a second path.

As far as the 50,000 genes that we use internally for characterization, I don't want to call it quite [indiscernible], but it is back loaded into our Q4 and Q1. And in the first quarter, we felt we had to share the number because 271,000 genes in the quarter looks good, but actually, it -- it's a small number compared to the actual number because we had more than 50,000 genes. So the trend looks good.

In terms of cost or price for the data, it kind of depends. But in general, it's $50 for a fragment, $100 for clonal genes, $200 plus for an antibody, can be $300 to $400 of the data depending on what kind of data customers want. So definitely, as we sell the customer to data, we get a great benefit to the top line as well.

And our next question will come from Subbu Nambi with Guggenheim.

You raised guidance by more than the fiscal 1Q '26 beat. Could you speak to where the increased confidence specifically is coming for both DNA Synthesis and NGS? And as we move past the single customer that created the fiscal 4Q, fiscal 1Q air pocket, are there any other single customer dynamics that you're carefully monitoring and your outlook for the rest of this fiscal year?

Yes. Thanks, Subbu. Well, I think the -- you're correct. We raised guidance by more than double the -- at the midpoint. This comes from all across the board confidence. The one customer dynamic that we mentioned in [indiscernible] application, it's passed. That customer is back. The orders are in. So we think we are set up really well in NGS, just not that one customer, but overall, when that customer -- now that the customer is back. We think there's a great setup as we look at more and more data coming from bespoke MRB enabled by Twist at super high resolution, high sensitivity, bespoke [indiscernible]. We think there's lots of [indiscernible] there.

And then in DNA Synthesis & Protein Solutions, we have a great -- we had a great platform with DNA head-to-head. We win pilots and with great growth, but now that we've added protein and data on top of it, it's really meeting the moment. And we see those big customers coming back for more. So we overall the strategy that we've had, which was to add great products that all feed onto the same [indiscernible] platform is working.

And so we'll feed the NP engine, we'll do it again. We'll deploy commercial balance. We have a lot of head count open for salespeople when our competitions that are laying off people and laying out salespeople, is just where the [indiscernible] in the field. And we don't expect to have other one customer dynamics in the future.

And our next question will come from Doug Schenkel with Wolfe Research.

My first is a follow-up on the over 50,000 genes manufactured for data characterization. I just want to make sure that I'm thinking about it right, when I think of that as being almost synonymous in pharma volume. So that's one. And then kind of the follow-up there is I just want to make sure we understand what's in guidance for the balance of the year. So that's one topic. Sort of related to that, the second is, the 271,000 genes shipped in the quarter was quite robust. That grew over 30% year-over-year. Can you just talk about what you're seeing competitively? And I'll leave it there.

Paddy, do you want to take that one? .

Yes. Yes. Thanks for the question, Doug. Yes. I mean the growth you're seeing is primarily driven by the pharma segment and the interest in AI. I think we're pretty clear in those numbers. And that the scale of the platform is resonating incredibly well with that customer base. I think I said in my words that when we're being approached the typical experimental size is a few thousand genes, a few thousand antibodies and a few thousand characterizations run in parallel, and that's where the power of our platform with scale and speed and quality and economics is very, very enabling. So that's a good start. We're starting to understand the reordering pattern of the customers. So just to echo Emily's comment, it's early, but we can see what's coming next is good.

Then from a competitive standpoint, we have a healthy paranoia and obsession with what's going on out in the market. We're starting to see the early benchmarking studies when customers in this segment are going to other vendors. And the data really shows how strong Twist is compared to our competitors, we're weeks faster than the competition as it stands to be.

And then just in general, on the competitive landscape, we've got our eyes on our competition. Where they are, we know what they're working on, we know where -- we know where they're laying off. We know where they're resizing, restructuring. We know them at a very, very intimate level in the hand-to-hand combat of selling. But really for us, we are just relentlessly focused on our own game, and we're looking to really scale and accelerate into this opportunity.

And our next question will come from Catherine Schulte with Baird.

This is Josh on for Catherine. I was wondering if you could walk through gross margin expectations for 2Q, how that should progress through the rest of the year? And then I was also wondering, should we still bake in sequential improvements for the rest of fiscal '26?

Thank you. Adam, do you want to take that one?

Thank you, Josh, for the question. We really see Q1 and the 52% performance is proof that our manufacturing engine is working as intended. Our decision to hold the full year guidance at above 52% reflects really deliberate choice in accelerating our top line growth rather than maximizing short-term margin expansion. We do see continued improvements throughout the year coming in terms of the gross margin.

But what we're doing now is we're both hiring the operators, adding the additional automation to ensure we can handle the higher capacity and throughput for things like AI drug discovery that Paddy talked about in the call. We've also introduced several new characterizations of rapid clip and other new technologies. When we launch these products, we do it with manual process [indiscernible] plating them to make sure we meet the customers' need staff. We're now automating those over time, and this investment temporarily moderates the margin expansion. That's really the right trade-off for long-term revenue growth.

Most importantly, the core engine of our business hasn't changed. We continue to see 75% to 80% of incremental revenue dropped through to the gross margin line over time. So maintaining our above 52% on gross margins, about giving ourselves the flexibility to aggressively fund the growth we see right in front of us, and we always say it, but we'd much rather build a multibillion-dollar business at a 50-plus percent gross margin than the $500 million business [indiscernible]. So thank you for the question.

And our next question is going to come from Vijay Kumar with Evercore.

This is Mackenzie on for Vijay. You talked a little bit about the strategic investments you made in the quarter. And I was just wondering if you could talk a little bit more about these investments why now and where specifically were the investments made? And then the second question is you disclosed that the AI-driven orders were $25 million in fiscal '25. How much of this came in fourth quarter? And what did you see for orders in the first quarter?

Yes. Thanks for the question. The $25 million of order growth that we saw last year were back loaded. So some came in Q4, but some also came in Q1 meaning that the order came in, in Q4, but they shipped in Q1. So definitely, some of the Q1 performance comes from the -- that order growth that we've seen. And those customers are coming back. So there's definitely a high confidence. It's not a onetime thing. It's not a flash in the pan. It's coming back.

In terms of the investment, we think of the investment in 2 ways. One, a more structural investment and the other are transient investment. So to give you a flavor, the structural investments are mostly in hiring of sales and commercial people. We just -- we've always had the strategy of hiring a little bit ahead knowing of what the business will need. It takes one or 2 quarters for a salesperson to ramp rapidly. And so we definitely want to do that. And we see a lot of good growth coming our way. And so we want to make sure that we're able to capitalize on it and that we don't have -- we're not short on salespeople. So those are structural investments that are here to stay.

And then there's some transient investment to help improve the business, mostly around our digital infrastructure. So to give you a flavor for what that is, just last week, actually, we launched our first e-commerce for NGS Application business. In the past, we've always had a very, very strong e-commerce presence for the DNA Synthesis & Protein Solutions and a very large fraction of our revenue for that product group comes from e-commerce. However, we did not have any e-commerce for NGS Applications. So literally 0% of our revenues.

But we hired some contractors, and we made a what we call a transient investment in our digital infrastructure to launch a new channel for e-commerce. And we anticipate that, that channel will be a catalyst and complementary to the sales people we are hiring. And over time, we have the ability to taper off that investment in our digital infrastructure. Again, a contract or -- we decide when to turn them on enough. There's still more work to do on our e-commerce platform. So it's not totally finished yet, but we're excited in the first phase of the launch. So hopefully, that gives you a flavor for what we call the structural investments and the transient investment.

But at the end of the day, Q4 [indiscernible] breakeven is a given for us now. So now it's all about how much growth can we exit in Q4. So it's all about we have that very high confidence in growth and hopefully, the guide reflects that confidence.

And our next question will come from Brendan Smith with TD Cowen.

Great. May just another one on NGS from us. Emily, I think you referenced plans to sign additional partnerships within the diagnostics space moving forward. So I guess, first from us, I mean, do you all have kind of a target number of deals you expect to confirm this year? And maybe more importantly, how material do you feel kind of new partnerships will be to growth of NGS and maybe hitting your own internal revenue expectations over the next, just say, 1 to 2 years versus really just core execution and advancement of the existing partnerships you already have?

Yes. Thank you. That's a really important question and something that we spent a lot of time on, actually Paddy is head of opening those doors for new partners. And so we think about it kind of in 2 ways. The first is 2026 growth in NGS is going to come from existing partners. Those deals are already signed, the pilots are done, the validation of [indiscernible], it's done. And so now it's just being there for them as the volume ramp for our current investors -- current partners, make sure that our supply chain is there for them.

And -- however, when we look at the growth [indiscernible] in 2027 and 2028, we do need new partners for that. And we have a very sustained effort. And actually, you may remember that at JPMorgan, we split where [indiscernible] took investor calls and Paddy, our CTO, [indiscernible] the customer's call. And so we have a dual track where we're working actively to get new partners on board for diagnostics. We don't have all of the volume. But the performance that we have is very, very, very strong. The supply chain excellence that we bring, we're able to future proof the supply chain of our partners. And so we are working on bringing more on board is going well. But we don't need them for 2026, but we're counting on them for sustained growth in 2027 and beyond.

And our next question comes from Mack [indiscernible] with Stephens.

Apologies if you already answered this question, my connection is a little spotty. But just looking at the performance in 1Q and the continuation of the 52% gross margin guide, how are you thinking about the cadence for the rest of the year, just given the level of investments that you're making in the business?

Thank you. Adam, do you want to cover that question?

No, no. Thank you, Matt -- Mack, we did have a chance to address that question. It really reflects our confidence in the business and our ability to drive growth and the choice to invest into that. And so while we do see continued improvement in gross margin throughout the year, it will be at a more moderated clip as we continue to invest into the CapEx and the infrastructure and the people to accelerate the growth. And we are maintaining, giving ourselves that flexibility really would rather -- I said it before, build the multibillion-dollar business at a 50% plus gross margin and a $500 million business at a 60% gross margin. So we are excited. We are looking forward to moving forward and there's no looking back an as Emily and Paddy have also mentioned is, within line of sight, we are absolutely committed and focused on making sure that any scenario where adjusted EBITDA breakeven by Q4 of this year.

I am showing no further questions at this time. I would now like to turn the call back over to Emily for closing remarks.

Thank you for joining us today. Twist's growth is built on a repeatable scalable model. Our innovative technology, increased platform volume, operational rigor and commercial prowess are translating into improving financial performance. This is not driven by one product customer or market, but by a durable NPI engine and execution that scales efficiently. We remain confident in our ability to drive sustained growth. Thank you.

This concludes today's conference call. Thank you for participating, and you may now disconnect.

Good afternoon, everyone. My name is Ryan Rice, and I'm an associate with the JPMorgan Healthcare Investment Banking team. Welcome to the session for Twist Bioscience. Presenting today, we have the Twist Bioscience CEO and Co-Founder, Dr. Emily Leproust; joined by the CFO, Adam Laponis. The presentation today will be about 20 minutes, followed by roughly 20 minutes of Q&A. So we please just ask you to hold your questions until the end. I'll go ahead and turn it over to Emily to get started. Thank you.

Thank you very much for the invitation and the introduction. I'll start by saying that I'll be making some forward-looking statements today. So at Twist, we are an emerging leader in life science tools, we are global, and we outperform in the multibillion-dollar markets that we serve.

And the key technology for us is a semiconductor approach to DNA synthesis. Our products are actually very diverse. We serve many applications. One of them is DNA synthesis and protein solutions that you may know formerly as SynBio and Biopharma. The other is NGS application formerly NGS. And the key applications we serve are the therapeutic drug discovery, diagnostics, the chemical production -- the production of chemicals through yeast, algae and E coli in a way that's more sustainable and enabling our customers to make sure that our food security is there through the engineering of traits in plants and animals.

We serve the top of the top institutions in multiple markets. We have more than almost 4,000 customers. And the key for us is about innovation. We enable our customers to accelerate discovery to improve their performance, to reduce their supply chain risk at attractive economics. And we -- the way that resonates really well with our customer is that we future-proof their supply chain.

I mentioned that the key technologies are silicon chip. From our silicon chip, we derive an unfair advantage in making oligonucleotides. Oligonucleotides are small pieces of DNA. And we have a very simple strategy, and that is to load more on the chip, very similar to an airline that wants to fly full planes, we want full chips. And so therefore, we've developed a wide variety of products that serve many applications, many customers, many markets, but it all comes down to the same chip.

And here, I'm showing you -- so the bottom left is the chip. We make oligonucleotides. And I'm showing you the product lineups that we had in 2021. And with the next animation, which we're showing is how that product lineup has evolved over the last 5 years. And so you can see that we have way more products. And so we are an NPI machine that builds on top of that oligo unfair advantage. And now that product lineup is a moat, and I'll discuss in a few slides, it also enables us to expand our SAM.

And as you can see here, this is the markets that we serve. DNA synthesis and protein Solutions, more than $4 billion of SAM; NGS application, more than $3 billion of SAM. And as you can see at the bottom, it's quite important messages. In 2020, our SAM was $2 billion. In 2025, it's $7 billion. So what happened was, number one, the market grew. But more importantly, we added new products through our NPI machine that enable us to go into new markets. And we estimate that in 2030, our SAM will be more than $12 billion.

Thanks to that product introduction, we have delivered revenue growth. Here, I'm showing some of the bright spots in our product lineups that are growing really fast, and that enabled us to deliver more than 20% year-over-year growth last year. And there are no other life science tools company that do that. And our ambition is to make sure that we have durable top line growth going forward. We do that in 2 ways.

Number one, going back to innovation. We enable innovation at scale, both in the solutions we provide to the customers, in the value that we provide and the impact that we enable them to have on their business. So innovation is our first driver. Second innovation is execution. We have a very powerful advantage in the superior solutions that we launch, the exceptional customer service that our customers get from us, the operational excellence that we provide.

And what we do is customize biological reagent at scale. We are leveraging automation and that operational excellence is key to our success. And last but not least, is our financial discipline, and we are reiterating today that we will be adjusted EBITDA breakeven in Q4 2026. This is a key milestone for the company, and we're doing the things on the left to do that. And after that, we want to drive durable, profitable growth.

In terms of financial perspective -- financial performance. You can see here the revenue growth for the last few years. Since 2023, we've made a key effort in ramping gross margin. Last year, we got gross margin above 50%. We're not going back. But now we'll focus more on revenue growth rather than gross margin growth. You can see that our OpEx has been fairly flat. And so as we ramp revenue, as we ramp gross margin and our discipline on OpEx, our adjusted EBITDA loss is shrinking, and that's why we think that -- we really think that in Q4 this year, we'll be adjusted EBITDA breakeven.

Double-clicking on quarter performance now. We preannounced today our Q1 numbers. Q1 ends in December for us. And now we have 12th consecutive quarter of revenue growth at a CAGR of 24% over 12 quarters. So really, really good, very unique in life science tools industry. We are changing the way we are reporting the industry group. And at the bottom, we are showing the new groups. Diagnostics is the biggest group, therapeutics coming second, both more than 25% growth.

Academic is small. We are underexposed, but 13% growth is better than our competitors [ in the funding ] environment last year. Industry and applied, if we remove the one customer that we discussed previously, was actually 23% growth. And we're introducing a new category of global supply partners. And those are on the left, 18 companies that are reselling Twist DNA or Twist products under their brand. And then on the right, our network of distributor, it's a great way for us to ramp revenue to load more orders on the chip in a way that's very profitable.

And now mixing the 2, our industry mix with our product mix, you can see that the therapeutics is mostly DNA synthesis and protein Solutions, diagnostics is mostly NGS application, and we're showing the numbers for the other industry groups. We're not going to share these numbers every quarter or maybe not every year, but we thought that for the first time, it was a useful addition.

So now diving a little bit deeper into the 2 product groups, first, DNA synthesis and protein solutions. On the right, here, you can see the products and the services that we sell. So it's a very extensive menu. We win in products because of our speed, cost, scale, quality and frictionless e-commerce. Usually, you may have to pick 2 out of 5, we deliver 5 out of 5. And then in the antibody service area, we win because we are the one-stop shop. You come to us, you give us a target, we give you a drug. And we can use AI, in vivo, in vitro. We have the full suite.

Our strategy in growing there is to expand wallet share once we've landed a customer. On the right, you can see the value chain. Sometimes we land customer with a gene fragment or DNA prep or IgG and then we upsell them, mostly in the area of therapeutics. And to try to help illustrate the dynamics, we are sharing actual revenue number from 2 different customers. Both of them do drug discovery. You can see that the top one, they use gene fragment; the bottom one, they use clonal genes. So different customers do science differently.

In Q1 2024, as we launched express gene, you can see the top customers were able to expand our revenue into different part of the organization and the revenue overall grew. And then the bottom customers, you can see that when we launched high-throughput IgG, that's something that was well matched for what they were looking for. So hopefully, it shows the different dynamics that happen with different customers that may be doing the same thing.

What we -- the [indiscernible] says that if you put 5 drug discoverers together, you'll get 6 opinions on how to discover drugs, and we meet them where they are. A big growth opportunity for Twist last year has been AI drug discovery. On the left, you have traditional drug companies that need more data to feed and build their model. And on the right, you have AI and tech companies that don't even have a wet lab. They just have a dry lab. And those companies now come to us where instead of selling them DNA, we can now sell them data.

And we -- our platform is uniquely positioned for this AI moment because people need a lot of data points. They need them fast, they need them high quality at a great economics. And this will turn into the killer application that really drives massive volume to the Twist platform. In practice, how it works. At the bottom, the customers, they do the engineering principle of design, build, test, learn. They design the sequence, it comes to us. We do the building in days. We do the testing in days, and they get delivered data between 5 to 10 days depending on the details of what they want to do.

They can either characterize a few hundred sequence as they used to or they can build large language models with thousands, if not tens of thousands of data points. $25 million of our $66 million order growth in FY 2025 came from selling data and AI. So we believe this is here to stay and very useful to us.

Moving to our NGS application. There, the products that we sell are on the right side as well as our services. So we have the full suite of panels, library prep kit, other products as well as services. We win in products because mostly of our quality. When we serve diagnostic customers, quality is paramount and with high quality of our products, what happens is they have to sequence less. And as they sequence less, they get better margins.

So it's a great win-win. And then on the service side, we win because we ourselves sequence tens of thousands of samples every day in the previous product group I mentioned. And therefore, we have experience for massive high throughput sequencing of samples. The samples we sequence are small. It's 2 megabases genomes, but it's applicable to other genomes and our service partners benefit from that experience.

The growth strategy there is to scale with our customers. And so as we land MRD customer, liquid biopsy customers, rare disease, NIPT, agrigenomics, we either win through a service lab or an R&D pilot. Once they're happy, they go through validation and verification, clinical study, commercial ramp and through those steps, revenue ramps. To help illustrate that trend, we are showing the revenue data from 4 different customers. Starting on the top left, for tumor-informed MRD customers, the revenue is at the beginning in R&D phase. It grows in the validation and verification period and then it keeps growing in the clinical period and the trend is very smooth because when a patient comes in, a panel gets ordered, Twist gets paid.

For an oncology customers that has multiple tests on the top right, you can see that quarter to quarter, revenue goes up and down. And it doesn't mean that their volume is not ramping. It is ramping, but R&D projects start and stop and the company may decide the inventory level that they want. And so in some quarters, they may build inventory; in other quarter, they may remove inventory.

Another key aspect is we are known for the DNA, and that's in dark blue and light -- sorry, dark green and light green. Those are the panels we sell. And this is a big part of our revenue, but we also get substantial revenue from our kits, reagents, library preps and other. And one thing that's a little bit underappreciated is that Twist powers the continuum of cancer care research.

The bottom left, you see all the products that we sell for diagnostics. And then on the right, you see that we also have exposure on the therapeutics part. And so when you think of the continuum of cancer care, the needle to needle, the first needle that is the screening that may find cancer through a blood test to the last needle that provides a personalized therapy, personalized neoantigen therapy, both will come from Twist and everything in between will come from Twist as well. So it's unfortunate that people get cancer. However, as advancement in science is turning cancer into a chronic disease, Twist is powering that full continuum of care.

As I wrap up the last few slides, I want to come back on one key aspect of what I discussed, and that is the NPI machine. Remember, the evolution from 2021 to 2026 of our NPI. Those are the NPIs we launched in 2025, very robust, key to our growth. On the left side, in DNA synthesis and protein solution, I won't go through the details, but typically, it's a very rapid uptake. We launched it. We get an immediate boost to our sales. The total market opportunity for each of those launches may not be very big, but they come in quickly.

And it's a different dynamic in NGS Solution. In NGS Solution, we launch a kit, it takes a long time. It takes many quarters, sometimes a few years to grow. But once it grows, it grows into really big markets that are very sticky. And so different dynamics, but we benefit from both of them.

So looking ahead, we are going to keep that NPI machine going and that will mean more products, more applications that are sold to more customers and serving more markets. I won't go through the details. Happy to answer in Q&A. But again, that feeds into our strategy of loading more on the chip. As we load more on the chip, the financial performance gets better and better.

So it's a virtuous circle of more products means more revenue, better margins and profitability for us. So one question that sometimes they get is, as you're successful, do you have the capacity to capture the revenue opportunity? And the answer is yes. We leverage automation. Right now, we have about 50% of our -- a bit less than 50% of our existing capacity that is utilized. And we will continue to ramp revenue and add capacity ahead of demand.

And one thing I'll mention is last year, 90% of our revenue growth dropped to the gross margin line. This is fantastic. This is not a long-term trend. We think that the longer-term trend is more towards 75% to 80% of revenue growth that will [ drive ] to the gross margin line. And that is very healthy, and it's a benefit of our semiconductor based technology. Once we have absorbed the fixed cost, the variable cost is very small.

So to conclude, before we go into the Q&A, one thing to remember is that we have a differentiated platform to write DNA, it's based on semiconductor technology. We are serving growing market. We are expanding our serviceable market. We are working with the top companies. We're landing more. We are expanding. We have shown that we have durable revenue growth and that now that we have gross margin over 50%, we'll keep focusing on that revenue growth. We have a line of sight for adjusted EBITDA breakeven in Q4 of this fiscal year, which we reiterated. And at the end of the day, we are serving those big markets and what differentiates us from our competition is that we are an NPI machine. We have great operational execution, and we deploy commercial violence. So I think there is a very compelling upside to come from Twist. Thank you very much.

Thank you, Emily. So now we're going to begin the Q&A session, and we have a few questions here to start off. So Twist issued a press release this morning announcing fiscal year Q1 revenue ahead of guidance, including roughly $53 million of NGS application revenue and roughly $52 million of DNA synthesis and protein solutions revenue. Can you walk through the performance in fiscal year Q1 versus expectations?

Yes. Thank you for the question. Yes, it was a great quarter for us. The growth in our DNA synthesis and protein solutions product group was 27%. So it's really good. The growth in our NGS application group was 8%. But you may recall that we had one customer that is transitioning from a clinical study to a commercial. And when you exclude that customer, actually, it was 18% growth for NGS application. It was one risk that was identified during our earnings call.

We have very close relationship with that one customer. We knew it was coming. We told our investor base to make sure that there was no surprise. But at the end of the day, you're only as good as the numbers that you are, and we are showing that now that risk has been retired and so all in all, 17% growth for year-over-year. So just overall, a very fantastic quarter, and we keep executing, and this is our 12th quarter of consecutive revenue growth.

Amazing. So kind of building off of that with the NGS application revenue of the roughly $53 million, much of the recent conversation regarding the NGS business has been centered around a large customer transitioning from commercial -- or transitioning to a commercial launch, really creating a near-term air pocket. How does this specific customer trend versus expectations?

I'm happy to take it. Thank you. And great to be here, everyone. No, when we guided, we said we knew we had an air pocket of -- in Q4 fiscal and Q1 fiscal about $5 million each. When we reported the Q4 actuals, it came in a little bit better. And as we're getting here into Q1, it looks like it's going to be the same, similar, but a little bit better than initially expected.

Got it. Okay. And so fiscal year '25 growth in DNA synthesis and protein solutions was very strong relative to peers. What are you seeing the most sustainable share gains in the business are here? And what are the main factors that customers consider when switching to Twist?

Yes. That's a great question. And it's all of the above. The -- what we try to do is leverage our unfair advantage from the silicon technology to, one, offer a very, very broad menu of products. So that's key. And then second is when we offer those products, typically, you have to choose. Do you want fast and cheap or fast and high quality but expensive. And for us, the value that we provide is all of the above. It's fast, it's high quality and it's great value.

And what we find is customers have a fixed budget in mind. And by providing this great value, we're able to grab the entire budget and then enable customers to really innovate the way they do things. And so if you're not using Twist, you're using an inferior solution. And then -- so that's kind of like almost the base case. And then on top of that, there is this new AI drug discovery opportunity that is coming that maybe a year ago, we had not anticipated and maybe a big -- a large company will not have been able to capitalize on it, but we saw the opportunity and we launched that data generation product line very quickly, and it met the moment.

And so that's the combination of not only a great technology, but the ability as a management team to recognize opportunity and push it through our NPI engine to launch products that makes a difference for our customers. It sounds simple, right, make a difference for our customers, and that's what we are doing.

Amazing. And that's a great way to transition into the AI-enabled drug discovery. So in fiscal year '25, AI-enabled drug discovery contributed to growth, driving roughly $25 million of orders. What has unlocked this market as a future growth driver?

Yes. And so the way we look at it -- so it's a great number, $25 million order growth out of $66 million. At the same time, it's still concentrated into a few opportunities. In general, there's 3 buckets of potential customers. One are the large pharma. Second are AI companies that are very well funded. And then third are the Magnificent 7 that are leaning into AI and drug discovery.

And so we have successes in each of those 3 buckets. But we don't have 20 pharma companies yet. But they are top 20. We need all 20. We don't have all Magnificent 7 in -- that our customers yet. And so now that we know that it works, now that we know that it brings value, we know that we have a full menu, our objective is somewhat simple is go get the 20 plus 7 customers plus all the AI companies.

So it's a matter of execution. It's a matter of commercial violence, and that's what we've been doing. That's what we are good at and look forward to our future earnings call to update. But really, again, I think this AI is a perfect match for the platform that we have. So it's no accident that there is a significant revenue growth. What those AI models and that AI way of doing drug discovery needs is high throughput, high speed, economics that are affordable and great quality, and we have all 4.

Thank you. So I'd like to open up questions to the room. Would anybody have any questions for Emily or Adam? If not, we can continue with questions from the webcast. All right. Seeing no questions. We'll continue with questions from the webcast here. So Emily, how should investors view the relationship between commercial test volume growth and Twist NGS revenue? Is this a one-to-one relationship?

You probably -- you mean for NGS.

Yes, for NGS.

So just to step back and give context to the question, in NGS, the vast majority of our revenue come from diagnostic companies. Those diagnostic companies, they find a patient, they use reagents from Twist to prepare the sample, sequence it and provide a clinical report. And so if diagnostic company revenue grows 40%, should the Twist percent grow 40%. And I think that's the question. And the answer is, in general, yes, with maybe a couple of caveats.

The first caveat is in our contracts, we do have tier pricing. And so it could be that -- I mean we are very clear at Twist, the more you buy, the more you save. If you want to commit to 100 million patients a year, we'll be able to provide it to you for $10 per patient at a great margin to us.

So there is tier pricing. And as our customers go from one tier to the next, there is some discount that is applied to them as it should be. So that's the first caveat. The second caveat is that for a test that is tumor naive, it's -- every patient gets the same panel. And so what that means is that our customers, they decide when to add inventory and when to burn inventory. And therefore, there may be up and down from one quarter to the next, depending on how they want to manage their balance sheet.

And so that's why sometimes when we report our quarterly results, we share our top 10s who were the top 10 -- the percent value of our revenue that was from the top 10 customers. And from one quarter to the next, top 10s may change because one customer may buy more in a quarter or not. So in some way, it's -- in the short term, it's untied. The volume is untied to our revenue.

But in the long term, it is, of course, tied. There's one exception to that, and that exception is in tumor-informed MRD. With tumor-informed MRD because it's tumor informed, our customer does not know what the patient will need. So they have to wait for the patient to come in to know what panel to order. And so there, we see a much tighter tie-in between the volume from our MRD customers and our revenue because it happens very fast.

So the patient comes in, the order comes in the next day, 5 days later, the panel is delivered. And at the same time, the invoice is sent. And so as our revenue percentage gets bigger and bigger for our tumor-informed MRD, we should see a tighter tie-in from a time point of view between our customers' volume and our revenue.

Great to hear. How would you direct investors to evaluate price versus volume across the DNA synthesis and Protein Solutions agreement? And is this increase -- is increasing ASP a good metric to use for success?

I'm happy to take that one. No, it's -- so I know there's been a lot of attention over the years looking at the number of genes we ship and then looking at the revenue and trying to do that in the simple division. There's actually 3 or 4 different elements that drive that. And so it can be kind of confusing. I think the #1 element that we talk about is the length of a sequence is the primary basis for what we charge, whether you're buying a -- whatever you're buying 1,000 kb gene or a 5,000 kb gene, it's going to be priced based on the length of the gene. So that length is actually the primary driver.

The secondary driver that's becoming more of an influence is whether a customer is buying a gene fragment and that might be at $50 or a clonal gene that might be $100 or an IgG that might be a couple of hundred dollars or a data report that might be a couple hundred dollars more than that is -- that's all counted as one element. So there's the big variability in price depending on what you're buying. And then the third is, of course, the ASP.

And so whether it be through the Express or regular or various contracted pricing. But I'd say the sizing of that variability is kind of left to right from the first to the third. And so it's probably not the best metric in terms of how to look at the business, and we laugh about it a lot is we don't study that internally. We're really looking at what is the relative revenue and profitability by each of those lines, and we're confident that, that 75% to 80%, whether you're looking at one element or another that 75% to 80% of revenue growth dropping to the gross profit line is actually what we're targeting across the board.

So what does the typical revenue progression look like as the diagnostics customers progress from verification to clinical validation and eventual commercial launch?

Yes, that's a great question. And in some ways, it depends on the quality of the test. It could be that the test has not the best sensitivity. But in general, what we see is what I shared on the slide deck, where if you remember, I think it was Page 28, where we are sharing the revenue growth from an MRD to more informed customer. You can see that there is a very significant growth.

It almost looks like an exponential growth from the R&D to the validation verification to the clinical work. And so we benefit from that. At that point, it's still R&D work. And then sometimes there can be an awkward quarter 2 when the clinical work ends and the commercialization starts. And so they can be -- there could be a gap. But in general, what we find is that our customers are able to develop highly sensitive tests that have great margin.

And so it's really a benefit to the patient. It's a benefit to the company without breaking the bang for the insurance. So it's a great overall outcome. But that's what we typically see is it straight up into the right. There can be a gap and then the commercialization comes back. So that gap can be awkward because there is -- in some ways, there's a lump that is missing. And so what we tell our sales team at Twist is that if you have a lump problem, find more lumps. And so we did have an air pocket in Q4 and Q1 from one customer. This was actually quite unusual. It's the first time it happened over -- since we launched in 2017. But in general, we feel very, very confident on the trajectory of that business.

So kind of bringing the conversation back to the NGS applications. What do you think is the most underappreciated aspect of the NGS applications business? And what do you think investors may be missing here?

Yes, I think there's one key aspect is -- there's some question sometimes from the cost of whole genome sequencing. What happens to our business when whole genome sequencing becomes affordable. And the answer depends on the application. So if you think about rare disease, for sure, there's a long-term direction where rare disease is going away from exome on to whole genome sequencing.

So that's a long-term trend we can't stop, and we're not even trying to stop. But even there, we still have the opportunity to add the library prep business, which is not negligible. Even there, maybe the U.S. can afford the whole genome sequencing, but maybe that enables exome to be more affordable in other markets like the Middle East or Europe.

And so even there, I think in the rare disease space, it's not absolutely the end of the world. And more importantly, for us, our exposure is mostly in cancer care. And in cancer care, lower cost of sequencing is very beneficial because it lowers the cost of the test and it lowers the cost of the test. It makes that test available more broadly. And so we think that all in all, lower cost of sequencing is good for our business.

We are very much pro sequencing company who's doing that. And then there is a last dynamic where right now, some tests are just 1 or 2 genes. Some genetic tests has to be $100. And so you will never do it with whole genome sequencing. And right now, you can't do it with the exome either. But as the cost of sequencing goes down, those tests that are looking at a very small number of genomic location, they are not done with us because they are made with amplicon sequencing, which we don't do.

And as the cost of sequencing goes down, we have an opportunity to upsell those tests away from maybe 2 biomarkers to 5, 10, 15, 30. And so that creates yet another growth opportunity for us. So I think that's one key misunderstood maybe -- question is the impact of lower sequencing costs to our business. And overall, we are all for it. We are encouraging all the sequencing companies to do so. And we are sequencer agnostic. And we think it's overall a great thing for the industry, a great thing for Twist and a great thing for patients.

That's amazing. So kind of zooming out here and coming to a close, could you provide a "state" of the union for the company as we enter 2026?

Yes. So I think the state of the union is the good -- I'll start with the good. I mean the business is absolutely reaping 20% growth last year, 24% CAGR growth over the last 12 quarters. So that's fantastic. Very happy customers. We're an NPI machine, just launching product after product after product. Our gross margin, we passed the 50% gross margin, which was key to us.

The math of getting adjusted EBITDA breakeven did not math without getting 50% gross margin. So we did it. We have adjusted EBITDA breakeven in our line of sight. And again, we are reiterating for Q4 this year. So from that point of view, I think it's all system go, all green, very happy about it. I think, frankly, we are still a little bit disappointed with our multiple.

I think when we compare the consistent execution, innovation that we've delivered compared to life science tools competitors, I think we are unmatched. And after 10 years of commercial selling, we are probably about to become an overnight success. And hopefully, we'll see the multiple more in line with what we think we deserve. But at the end of the day, it's very simple, keep launching products, expand our SAM, ramp our revenue, expand our gross margin, load the chip, add happy customers and do it again.

All right. So if nothing further from the audience, I think we can wrap the presentation up, and thank you, everyone, for coming.

Thank you very much.

Welcome to Twist Biosciences 2025 Fourth Quarter Financial Results Conference Call. [Operator Instructions] Please note, this conference is being recorded.

I would now like to turn the call over to Angela Bitting, Senior Vice President of Corporate Affairs. Please go ahead.

Thank you, operator. Good morning, everyone. I would like to thank you for joining us for Twist Bioscience conference call to review our fiscal 2025 4th quarter and full year financial results and business progress. We issued our financial results press release before the market, and it is available at our website at www.twistbioscience.com.

With me on the call today are Dr. Emily Leproust, CEO and Co-Founder of Twist; Adam Laponis, CFO of Twist; and Dr. Patrick Finn, President and COO of Twist. Today, we will discuss our business progress, financial and operational performance as well as growth opportunities. We will then open the call for questions. We ask that you limit your questions to only one, and then requeue as a courtesy to others on the call. This call is being recorded. The audio portion will be archived in the Investors section of our website and will be available for 2 weeks.

During today's presentation, we will make forward-looking statements within the meaning of the U.S. federal securities laws. Forward-looking statements generally relate to future events or future financial or operating performance. Our expectations and beliefs regarding these matters may not materialize and actual results and financial periods are subject to risks and uncertainties that could cause actual results to differ materially from those projected.

These risks include those set forth in our press release we issued earlier today as well as more fully described in our filings with the Securities and Exchange Commission. The forward-looking statements in this presentation are based on information available to us as of the date hereof, and we disclaim any obligation to update any forward-looking statements, except as required by law.

We'll also discuss adjusted EBITDA, a financial measure that does not conform with generally accepted accounting principles. Information may be calculated differently than similar non-GAAP data presented by other companies. When reported, a reconciliation between GAAP and non-GAAP financial measures will be included in our earnings documents, which can be found on the Investors section of our website.

With that, I will now turn the call over to our CEO and Co-Founder, Dr. Emily Leproust.

Thank you, Angela, and good morning, everyone. Today, our team delivered a record quarter with [indiscernible] million in revenue, exceeding our guidance. This represents an increase of 17% year-over-year and our 11th quarter of consecutive growth. For the year, we reported $376.6 million in revenue, growth of 20% over fiscal 2024. Gross margin for the quarter came in at 51.3%. For the year, gross margin was 50.7% compared to 42.6% for fiscal 2024, demonstrating the leverage of fixed cost with higher volume and reflecting our focus over the last year on continuous margin improvement. .

I'd like to underscore that over the course of fiscal 2025, we grew our business 20%, leveraging our proprietary silicon deep-base technology platform to deliver high-quality products and services rapidly to our growing customer base. Importantly, through the addition of new products and solutions, we expanded our market share with an eye towards addressing new supermarket in the year ahead. Our commitment to commercial excellence continues to ensure we meet and exceed our customers' expectations. Today, with our differentiated manufacturing technology, our innovative R&D for the continuous introduction of new products, our base of more than 3,800 customers across multiple industries, our hundreds of SKUs having a wide range of levers applications and an increasing market share in multiple markets, we're operating with incredible execution and financial discipline.

I know adjusted EBITDA breakeven within our reach by the end of fiscal 2026. This year, we'll focus on setting the stage for future growth acceleration. Turning to our results. [indiscernible] revenue came in at $39.5 million, up 17% year-over-year. Our growth in Tinviocontinues to be led by the EFS portfolio, which remains best-in-class in terms of price trantime and scalability. 2 years after launch, our customers have come to depend on the rapid on-time, high-quality and exceptional experience they receive from Twist as their new normal and what they expect regularly.

We have decreased the turn on time for gene fragment, clonogen, high-throughput DNA preps and high social proteins, and we now run assets and provide antibody characterization data as part of our offering for many customers. One area of substantial growth or [indiscernible] offerings came from customer [indiscernible] their therapeutics discovery initiatives both traditional drug discovery and AI-enabled discovery because our platform delivers precision, scale and speed at enabling economics.

While traditional discovery continues to be a focus of many customers, the rapid expansion of AI-enabled drugs core create powerful new opportunities and amplifies the value of our technology. Recently, this AI-driven discovery fueled significant growth at in fiscal 2025. orders from customers working on AI discovery projects for more than $25 million versus fiscal 2024. These projects primarily fall into the Simba and biopharma bucket today, and a customer pursuing AI-enabled discovery delivered our single largest purchase order to date. And the emergence of dozens of the organizations across pharma, biotech and big tech, pursuing new discovery approaches expands the market opportunity for Hindi and biopharma groups today.

Relatedly, as we have moved further up the value chain from fragment to genes to prep to protein to delivering characterization data and beyond the strategic connection between our [indiscernible] Biopharma groups tightened. More customers now leverage both products and services to accelerate discovery and identify breakthrough therapeutics. This growing convergence highlights the power of our integrated platforms and we enforce to unique position to serve the full spectrum of innovation and discovery with more products and services to facilitate deploying opportunity coming in the months ahead.

Over the last several years, our product introductions are focused on pharma and biotech customers pursuing therapeutic discovery as well as academic research. As we analyze the future market opportunities, we believe this continues to be a rape area of focus for additional tools and services. Moving forward, we have a robust road map and planned product introductions to augment our portfolio that we believe will continue to drive revenue growth in 2026 and in the future.

Turning to NGS. We reported revenues of $53 million, growth of 16% year-over-year, driven largely by continued commercial success from our diagnostic customers, clinical assays. Our NGS products are an integral component within many commercial dynastic workflows. Recall that we provide tools for customers offering test for therapy selection, liquid biopsy, a comprehensive generic profiling, rare disease, noninvasive print testing and preparation genetics. In addition, we continue to support minimal residual disease customers with several of these groups targeting commercial launch in late 2026 and planning commercial scaling into 2027. And we are beginning to see conversion of the macro to flex plat pre-sequencing workflow. Introduced about a year ago, we believe this product provides significant potential growth opportunities, both for population genetics and [indiscernible] applications. During the quarter, we had 2 significant operations genetic wins with the final growing in serviceable opportunity for the $500 million market that uses a [indiscernible] technology today. Customers in both segments run millions of samples. So once converted, the business is bulky. We have maintained our sequencing agnostic strategy throughout our NGS product portfolio with all sequencing platforms, while the majority of our customer continues to use the Lima platform, and we have an active OEM agreement with [indiscernible]. We also see growing interest in other platforms.

To this end, we announced an advancement of our agreement with Element Biofene last month, that enables us to gain exclusive access to Element's new [indiscernible] freestyle workflow, facilitating in the use of Twist full lineup of library preps for the [indiscernible] system. Together, Element and Twist shortens the workflow from sample to sequencer from more than 20 hours down to 5 hours a true time savings.

In addition, we are powering the gene Biggin preparation genetic test that runs on the ultimate genomics sequencing platform. This complements our work with PacBio Oona and others. We continue to see traction, building for RNC workflows having customers who offer diagnosis test as well as labs offering clinical services with growth expected across all areas of NG's portfolio in 2026.

Looking at Biopharma Services. We reported $6.4 million in revenue, an increase of 22% year-over-year. Importantly, orders of approximately $11.5 million for the fourth quarter of fiscal '25 reflects a large order that spans both in bay and biopharma from a key account that we do not expect to repeat every quarter. More customers now partner with Swiss across the full design, build, test and learn cycle for developability assays and characterization data. This trend continues to grow, especially among AI-driven do distort companies. Many of these customers operate without a wet lab and rely on this to execute the critical experiments that bring their designs to life.

We help them move fast, generate robust data and advance programs with great confidence. We see much more integration between our [indiscernible] and biopharma businesses as customers increasingly use both our products and services to power their discovery pipelines to capture this opportunity. we aligned our sales organization to deepen collaboration and fully leverage the synergies between the 2. We also received valuable feedback for investors that are tiny and biopharma names for revenue grouping more clear. Reflecting this progress and feedback, we will combine banana revenue for reporting going forward under the term DNA synthesis and Protein solutions indicating synthesis and refracturing of sequences for DNA, RNA protein and data for customers going through design build as lean cycle.

DNA synthesis and protein solutions, more accurately represents our customer base and we intend to provide additional insight into industry groupings that better reflects how we serve a broad range of customers. Our NGS tools will now be called NGS applications as its products and services facilitate needing sequencing workflows. Beginning in the first quarter of fiscal '26, we will be breaking out industry groupings into therapeutics, diagnostics, industry and applied markets as well as academics and government.

In addition, something that is under appreciate about resist number of organizations that buy products from Twist and then resell them under a different brand name. As such, we will also share global supply partner revenue encompassing distributor and OEM partners as part of our industry breakdown. These new groupings, enhanced transparency and better aligned with how our business operates, providing investor insight into our strong growth engine.

I would now like to turn the call over to Paddy for commentary on our growth initiative for 2026.

Thanks, Emily. As we close fiscal 2025, it's remarkable what we have achieved in the last year, and I'm even more excited about what is to come. While my comments during earnings throughout the last year focused on margin initiatives, we have now crossed the important threshold of 50% margin, almost 20 margin points increase over the last 2 years. We expect to continue to remain above 50% margin moving forward. And this year, my remarks will focus on our growth plan. Today, I'd like to talk about remarkable and differentiated product introduction for our NGS product line aimed at empowering our customers in an area of increasing importance for cancer diagnosis, monitoring and treatment.

I'm pleased to share that we're in the final stages of optimizing an express product for minimal residual disease, or MRD, which we expect to introduce commercially in early calendar 2026. As you know, MRD for therapy selection cancer monitoring and early treatment of recurrence offers tremendous promise. We already work with many MRD customers providing library prep and target enrichment panels for tumor-informed and tumor-naive panels as well as whole genome sequencing approaches. And we continue to hear from customers developing tumor-informed assays that we gain better sensitivity and specificity using hundreds or thousands of sequences specific to a patient's tumor.

The data presented at recent medical meetings back up these beliefs. Reportedly, recent studies also show that physicians desire the capability to sequence the cancers present and have a test in hand for a patient with cancer within a 4-week window. While we currently manufacture enrichment panels within about 5 business days, we see the desire for delivery of a tumor-informed panel as fast as 12 hours. Using our proprietary DNA systhesis platform, we developed a process to do just that. Manufacturing ship an individualized panel as fast as 12 hours after receiving the sequence data. Our MRD Express solution provides a speeden simplest of a tumor naive test while maintaining the precision and sensitivity of a tumor-informed as something not possible using any other method of DNA synthesis.

Taking a step back and looking at the broader implications, we all move family and friends and maybe many of you personally impacted by cancer diagnosis. In the midst of the storm, turnaround time is critically important, both to determine treatment I create a personalized panel to monitor recurrence of disease. At Twist, we believe it's our responsibility to respond rapidly, potentially offering a path to enable reduced treatment time or pursue therapy at an earlier stage of disease. This higher calling motivates all of our twisters to go above and beyond for our customers to play a role in transforming cancer into a manageable chronic condition.

On the business side, we believe Twist MRD expressed has the ability to support our customers in changing the diagnostic and treatment paradigm, lowering the operational barrier of entry for personalized MRP. We've been able to shift through our synthesis platform along with automation, delivering personalized panels and a time line equivalent to a tumor naive workflow. We believe our connection to the customer, our ability to turn a customized panel is used 12 hours, all underpinned by our proprietary platform will enable increasing availability of tumor-informed cancer acids.

On top of this, we have the capacity today to serve these markets, future-proofing customer supply chain constraints and vulnerabilities. With that, I'll turn the call over to Adam to discuss our financials.

Thank you, Paddy. Revenue for the fourth quarter increased to $99 million, growth of 17% year-over-year and approximately 3% sequentially. For fiscal 2025, revenue increased to $376.6 million, growth of 20% year-over-year. Gross margin came in at 51.3% for the fourth quarter of fiscal 2025 with the margin for full year of 5.7% and an increase of 8 margin points versus fiscal 2024, with approximately 90% of revenue growth in FY '25 dropping to the gross margin line, supported by our continuous process improvement efforts.

Taking a deeper dive in our revenue. Synbio revenue increased to $39.5 million, growth of 17% year-over-year. For the full year, Synbio revenue increased to $145 million compared to $123.5 million in fiscal 2024, an increase of 17%. The NGS revenue for the fourth quarter grew approximately $53 million compared to $45.5 million in the fourth quarter of fiscal 2024, an increase of 16% year-over-year.

For the quarter, revenue from our top 10 NGS customers accounted for approximately 39% of NGS revenue. For fiscal 2025, NGS revenue increased to $208.1 million, growth of 23% year-over-year. We served 588 NGS customers in the quarter with 159 having adopted our products. For biopharma, revenue was $6.4 million for the quarter, a growth of 22% over the same period in fiscal '24, with orders of $11.5 million. We had 84 active programs as of the end of September 2025 and and we started 47 new programs during the quarter.

Compared to last quarter, these programs are more substantive as we see a shift to AI discovery-driven projects. For fiscal 2025, revenue was $23.5 million, growth of 15%. Looking geographically. Americas revenue increased to approximately $57.3 million in the fourth quarter compared to $52.7 million in the same period of fiscal 2024, growth of 9% year-over-year.

For the fiscal year, the Americas accounted for 60% of revenue. EMEA revenue rose to $34.6 million in the fourth quarter versus $25.5 million in the same period of fiscal 2024, exceptional growth of 35% year-over-year. For the fiscal year, EMEA represented 33% of revenue. APAC revenue increased to $7.2 million in the fourth quarter compared to $6.5 million in the same period of fiscal '24, an increase of 9% year-over-year. APAC accounted for 7% of our revenue in fiscal 2025.

China continues to be a relatively small portion of our revenue at approximately 1% of total revenue for fiscal 2025. Moving down the P&L. Operating expenses, excluding cost of revenues for the fourth quarter were $80.8 million compared with $74.3 million in the same period of 2024. Operating expenses, excluding cost of revenues for fiscal 2025 were $327.3 million, which marks our third consecutive year of relatively flat operating expenses, excluding cost of revenues.

Looking at our progress and our path to profitability. For the fourth quarter of fiscal 2025, adjusted EBITDA was a loss of approximately $7.8 million, an improvement of $9.2 million versus the fourth quarter of fiscal '24. For fiscal '25, adjusted EBITDA was a loss of approximately $46.9 million, an improvement of approximately $46.6 million versus fiscal 2024. Cash flow from operating activities continued to improve, and we are driving to breakeven. For the 12 months ended September 30, 2025, net cash used in operating activities was $47.6 million. compared to $64.1 million for the equivalent 12-month period in 2024.

Capital expenditures in fiscal 2025 were $28 million, reflecting our investment in growth for fiscal 2026 and beyond. We ended the fiscal year with cash, cash equivalents and short-term investments of approximately $232.4 million. As Emily mentioned, beginning next quarter, we will provide new revenue by industry for the following categories with increased clarity around our key customer groups and transparency on how we are progressing as follows. Therapeutics customers, which includes both large pharma and early-stage biotech. Diagnostics customers who use our products to deliver a clinical report for a patient, industrial and applied customers, including agricultural bio academic research and government customers. Global supply partners, which will include distributor and OEM partners servicing customers across a variety of industry.

We believe these new categories will provide added color and metrics for investors to track our progress in reaching different end markets and customer segments. We do intend to share a retrospective view on the new industry group performance in our fiscal first quarter reporting.

Turning to guidance for fiscal 2026. We expect total revenues of $425 million to $435 million, growth of approximately 13% to 15.5% year-over-year. For our DNA synthesis and Protein Solutions Group, we expect revenue of $194 million to $199 million, growth of 15% to 18% over fiscal 2025, reflecting strong demand from our AI discovery customers.

For NGS Applications Group, we expect revenue of $231 million to $236 million, growth of 11% to 13.5% over fiscal 2025. We see a path back to 20% growth year-over-year by Q4, as we expect the large diagnostic customer will begin ramping their commercial volume in the second quarter. As added color, our NGS forecast assumes approximately 1 to 2 points of growth for MRD in fiscal '26. With the ramp for this particular product group coming in late '26, into '27. We expect gross margin to be above 52% for fiscal 2026, and we expect to exit fiscal '26 with our fourth quarter achieving adjusted EBITDA breakeven.

For the first quarter of fiscal 2026, we expect revenue of $100 million to $101 million, growth of 13% to 14% compared to the first quarter of fiscal 2025. Our guidance includes the expectation that our Q1 revenue will be impacted by a large cancer diagnostics customer who is transitioning their assay from research to commercial with a reacceleration of purchasing in the second quarter of fiscal 2026. We also see significant revenue from the record AI drug discovery order since that our 2 product groups will be relatively equivalent to the first quarter.

With that, I'll turn the call back to Emily

Thank you, Adam. Our team executed exceptionally well through 2025, delivering strong results and building the foundation for webcams next. At West, we [indiscernible] strong finish, we go again. We see substantial opportunity ahead across all our markets. Staying close to our customers continues to be our quite competitive advantage. It allows us to anticipate emerging needs and identified the next set of products that will move the needle for growth. Like our customers, we have an unidentified idea and a disciplined approach to prioritization.

Over the past 2 years, we deliberately focused on gross margin expansion and with growth branches now above 50%, we have successfully positioned the business for continued profitable growth. As we reallocate R&D resources to odors, we're investing in innovation that we believe will drive sustained top line acceleration. Our map remains robust and well sequenced to deliver growth over the next several years.

Looking forward, we expect balanced growth across our [indiscernible] synthesis and protein solutions and NGS applications with some normal quarterly variation. We're advancing new products that support customers leveraging high and drug discovery as well as those using traditional therapeutic development methods. Fiscal 2026 is about translating our margin strength into durable revenue growth. We know where we need to go, and we are already on our way. With that, let's open the call for questions. Operator?

[Operator Instructions] One moment for our first question, and it comes from Catherine Schulte with Baird.

Maybe first on gross margins. Guidance for the fiscal year implies incremental margins off of 25%. So it would be in that 75% to 80% range if we did it off of '24. But I think the expectation was you'd flow more of the '25 upside through. So I guess the question is, is this pricing driven? Do you have some manufacturing investments that you're making? And when do we get back to the kind of 75% to 80% incrementals.

Catherine, this is Adam. Thanks for the question. Very much encouraged by the progress of the team over the last 2 years. The 20% growth in gross margin has been extraordinary. While we expect to continue to see the 75% to 80% on average, we are lapping some tough comps, particularly given what we saw in Q3 of this year. For the last quarter, we dropped, I think it was over 80% of gross revenue growth drops to gross margin line. And generally, I'd expect that to continue to hold in the future. And if you look at that 2-year metric, it absolutely will, but there will be some noise. And I'd say it is more around the specific customer mix that we see in any given quarter that drives it more than anything else. But we expect it to continue to expand -- that said, we will continue to focus on revenue growth as well as gross margin and optimize for the gross profit.

Okay. Great. And then for NGS, I think that guide came in a little bit below Street for fiscal '26. Can you just talk through the drivers there and maybe get a little more granular on the expectations for that customer ramping moving into production. And I think the guide implies 11% to 13% or 14% growth for NGS. How should we think about long-term growth for that business? Is this kind of the new baseline that we should be thinking about? .

I'm happy to take that one. In terms of growth for NGS, we're very excited about the prospects. We mentioned it on the call last quarter. That we have a customer transitioning from their verification and validation that there would be an air pocket in Q4, and that would continue through Q1. We expect that, that customer will ramp as well as our customers. A couple of points of commentary and color that we provided, we expect to be back to 20% growth by fourth quarter in the NGS business as well as we expect to continue to see growth from MRD and other new product introductions and we've assumed about 1% to 2% of overall growth from the MRD business products in 2026.

Our next question comes from the line of Puneet Souda with Leerink Partners.

Wondering if you can -- yes, thanks. So wondering if you can provide a view in into Instant Bio and with the new segmenting, can you elaborate a bit on the biopharma order -- what -- obviously, that's driven by AI, but just trying to understand how sustainable this is how much of a momentum, what are you hearing from the customer development teams? How meaningful the AI contribution could be in fiscal year '26. .

Puneet, thanks for the question. We've been talking for a few quarters strategically how important the biopharma business is and the close ties to the SymBio product offering and I think you're starting to see real validation of that with the order described in our comments today. It leverages everything that we're good at. Our knowledge from a single gene all the way through to discovery what we're seeing with the AI potential, it's our throughput and scale that really enables and supports that offering. So we continue to be very optimistic in that space and see a fantastic lineup of the total twist offering, all the way through from one gen all the way through to full discovery basically puts us in a good spot for our future opportunities.

One moment for our next question. It comes from Brandon Smith with TD Cowen.

Great. I also wanted to ask just a little bit more about guidance into next year. Maybe just quickly for -- I know you're not guiding to GM during Q1, but can you give us a sense of how you're thinking about gross margin sequentially from Q4 and then over the course of next year to really get to that 52% plus on the full year 2026.

And then maybe just quickly on the NGS portfolio. Anything that you're hearing specifically from customers really that kind of driving some of your assumptions to get to maybe the upper versus lower bound of the guide there?

Brendan, happy to take the question. In terms of gross margin in the guide, we do see improvements throughout the year. I'd say it is going to parallel with revenue growth being the primary driver of our gross margin expansion as we are continuing to see our efforts from continuous process improvements play out, but we're also continuing to invest in new capabilities across the business to drive our new product initiatives and growth. And as mentioned in the call, a lot of focus on the AI drug discovery and capabilities to support those customers.

In terms of the exit point and also where to go from here, we are -- we do see a path to continued gross margin expansion, not just in 2026, but well into 2027 and beyond, but again, optimizing for that gross profit dollar, not necessarily just the gross margin now that we're above 50% and not looking backwards.

Our next question comes from Vijay Kumar with Evercore ISI.

Had a 2-part question on NGS. NGS I think your Q1 guidance, it looks like it's going to be down sequentially, maybe revenue is up mid-singles. And I understand had Q4 had the customer transition impact rate. So why would Q1 growth be below Q4? Is there some additional timing elements on Q1 NGS and sort of later on the MRD Express. Did I hear you correctly that sensitivity on the tumor [indiscernible] would be as good as tumor informed and -- is there any data that you've highlighted, what kind of interest are you seeing in this product.

All right. Well, maybe I can start with the the NGF guidance, and I'll let Paddy talk to the MRD elements of it here. In terms of NG's guidance, thank you for the question on -- we gave the update back in Q3 call that we had a customer transitioning from commercial and validation, the commercial ramp. -- and that impacted Q1 and it's going to continue to impact Q4. And we expect to see a sequential growth from that point for for the MDS business starting -- so we will continue to see that air pocket continue in Q1 and then in Q2 and beyond, we'll see the sequential growth such that by the time we get to Q4, we're expecting to be back at 20% year-over-year growth in the NGS business. I'll let Paddy talk the MRD question first.

Thanks for the question. I think when we look at recent medical conferences, I think you've seen the tumor-informed approach is leading to increased sensitivity in the assay. And that's got us excited about potential and the clinical endpoint and for the patients that are going through a tough time -- so we see sensitivity enhancements from tumor informed. And again, our scale and speed, we think, is really going to help enable the segment of the market that's focused on that approach. .

One moment for our next question, please. And is from the line of Subbu Nambi with Guggenheim.

Paddy, just a follow up on that MRD. MRD Express is an exciting launch next year. Could you speak to who the end user is. Is it almost sounded in your description electric is executing the MRD assay for the physician or delivering the talent hospital to run in-house? Or is it the same customer as your NGS diagnostics?

So a great question, and thank you for the opportunity to clarify. -- twist role in the community is an enabler, okay? We don't run the test. We supply our customers and our partners to enable them to drive their assets to the clinic. So again, our role will be to supply and enable them. .

Perfect. So how will you approach pricing for the MRD Express? What are the expected margins here? And I'll hop back in the queue. .

Yes, good question. So pricing has not been set at this point. It will go from our basic principle, Subbu, which is we're here to enable our customers at scale to truly drive their product to market. And we think with this product, in particular, truly impact the impact of MRD to health care. We've listened closer to the customer base. I mean we understand the value to this market segment of speed in this 12-hour turnaround time. And I think our operating scale and quite frankly, derisking any vulnerabilities and supply chain is good value. we'll share that value with our customers as we go forward and enable them to drive best-in-class differentiated assays out to the market. .

One moment for our next question. that comes from Matthew Larew with William Blair.

You reiterated the expectation to hit EBITDA breakeven in the fiscal fourth quarter. But obviously, you're starting a little bit lower on the top line. Given growth is contingent on the expectation of that NGS customer ramp and MRD contribution how much rating room do you expect to have in the fiscal fourth quarter? And I guess how airtight are you going to hold yourself to hit in that mark? I guess that's the first question. And the second is, Adam, just what does the guide include in terms of the macro picture, given we've seen perhaps some recent positive updates relative to your pharma and biotech customers -- and perhaps there may be some more certainty for our academic customers coming over the next few weeks or months.

Thanks, Matt, and I say I never want to predict the macro environment. So we always will be on the air on the side of caution and that assuming things don't improve from where we are today. So we've got -- we obviously left our sales assumption that the environment stays relatively stable. And in terms of the growth opportunities, we do assume that acceleration of the commercial customer, the customer is ramping its commercial product today. We also have only assumed 1 to 2 points of growth for the year from our MRD products. We know that, that ramp is going to come. We're extremely excited about it. The difficulty is always in timing that we want to make sure we're on the right side of that timing, but we are extremely excited for that ramp and the opportunity it brings to the business. not just in '26, but in many years to come. .

Our next question comes from the line of Doug Schenkel with Wolfe Research.

I got a few questions. So thanks for, I guess, getting us in. So First, on Synbio, you had an academic promotion that removed the express gene pricing premium for academic customers in response to funding pressure. Is that promotion still in place? And if so, how much longer do you plan on running it and then building off of that how should we be thinking about price per gene '26 versus '25. So that's the first topic. The second is there's obviously been a lot of focus on Q1 guidance. specific to NGS and the sequential step down. As you have pointed out to others, you have repeatedly noted over the course of the last several months, a patient dynamic within NGS -- is the guidance simply a function of that? Or is there any change in underlying trends or demand. The third topic, which I think is a pretty important one, and I'm not sure this is the right forum. And if it's not, we can certainly come back to this at our conference next week. But I believe one of the challenges that investors have with Twist is defining the market opportunity. In the newly named DNA synthesis segment, what is the size of the market opportunity and how penetrated are you? And on the NGS side, specific to MRD and MSA, what is the market size? How penetrated are you? And what is average twist revenue per assay. I think that would make this -- those are questions that I think if people have the answers to, it would help with modeling and frankly, help people basically develop some more conviction in the long-term growth trajectory of the business.

Thanks, Doug. Great question. This is Emily Leproust and good job, squeezing 3 questions in one. So maybe briefly on Synbio, you're correct that we had an academic promotion where we got customers expressed for the price of standard that has been widely successful. We're in the new year, and the price has not changed. It is just working for us commercially. And you can see in the number of gene that the growth in engines from in Q4 was really strong, thanks to that. So we are not announcing whether or not it will close. But as long as it's working, we'll keep doing it and it is working. .

As far as the Q1 guide, yes, it's purely a pacing dynamic in Q1. There's a lot of excitement and we are winning on many fronts. Of course, we've been very, very strong in liquid biopsy and the MRD bespoke that we're enabling now with adding 12 to 24-hour express delivery. I think that will be a long-term catalyst. Our FlexPrep launch is starting to to work well for the biomarket and the preparation generic market. So that's another source of longer -- long-term strength.

We worked really hard to integrate into a number of sequences, the workflow of Twist and the elements Abitireally shortens the the time between the sample and being on the sequencer. And since you're doing all the washes after the capture on the sequence, you can be on the sequence as low as 5 [indiscernible]. So there's lots of good things that are happening in NGS. And we definitely don't see a lower demand is just that is the law of big numbers is a big customer that has an air pocket. And we we signaled some very good growth coming back in Q4 for NGS.

And then the last question around defining market. We totally hear you. Now is not the right forum. But I think we are looking at ways to help our investor base be as excited as we are about the market. We are very far from penetration and we have differentiated product. And so to us, that means that we have a lot of growth coming. It is true that we are a tools company. Right now, diagnostic company at a moment they deserve it. They worked on their business models, their reimbursement is really good. And so the in comparison right now, maybe our growth compared to the [indiscernible] is a little bit lower. But compared to tools company, I think we were -- I don't want to sound arrogent, but I think we're doing really, really well against our our competitors. And I don't say that we are wiping the floor with them, but we are doing really well.

And we hear you on finding a metric, like you said, the average test revenue per patient in NGS. We're looking at metrics. Part of the issue, as you may appreciate is some of the test, people pay more than average and some of the people pay less than average. And that depends on the test complexity. If you have a test that you did 3 million oligos, you're going to have to pay more than if you're using 50,000 [indiscernible]. The primary if we have a price per test that's public every quarter. We may have half of our customers being really unhappy even though the value we provide is fair.

So thinking about it and not the forum, but we understand that we want to articulate our market sizing better to help our investors.

Our next question comes from the line of Luke Sergott with Barclays.

This is Sam on for Luke. Could you talk a little bit about the new DNA synthesis and Protein Solutions segment and the rough split between biopharma and synthetic genes in the '26 guide. The combined segment guide came in above Street expectations. And I'm just wondering where that's coming from and if it's driven by that large AI program.

Thanks for the question. I think the impetus was twofold. One, there was, I think, some confusion with our customer base around SymBio and maybe underappreciation of how much we are doing in therapeutic discovery and development. So that's number one. Number two, more and more as our sales team go talk to customers, there was in practice very little separation from someone who buys a piece of DNA, so the incentive or someone who wants to protein all the characterization. And so as it's one continuous workflow. Some customers stop at [indiscernible], some stoppage, some stop at protein, some want the full characterization [indiscernible] made sense to put them together.

And as far as the numbers, yes, maybe we're getting a little bit ahead of what people thought. I think it's just a reflection of this business is doing is doing quite well. And there are a lot of synergies between the [indiscernible] piece and the protein piece a few years ago, some people were asking us when you spin off biopharma. And we knew it had strategic benefit. And we're seeing it now. It's not the 1 plus 1 equals 2. It's 1 plus 1 equals 3.

As far as your question around where is the growth coming? Is it coming from DNA and protein. Frankly, the reason we're putting it together is because we -- one, we don't know and two it kind of doesn't matter. What's important is that growth is there, and we meet customers where they are. And customers may change from quarter-to-quarter, some quarters they buy the DNA and some quarters by the protein, and we'll meet them where they are and we provide great value. Our products are differentiated. We win no matter where they want to be, and we're looking forward to capturing that growth.

Our next question comes from the line of Mark [indiscernible] with Stephens.

Maybe just a few quick ones from me. Just given this 1 to 2 points of growth from MRD, is it possible to frame up the proportion of MRD revenues in -- fiscal 2025. And I'll stop there and follow up.

Happy, Matt. Great to hear you, and happy to share -- what we've said in the past is our MRD business is relatively small, it's a lot of small numbers and that we are growing significantly faster than the overall business. kind of applying that rule, it's a relatively small percentage of our overall NGS business in 2025, but it is growing much faster than the overall NGS business, and we expect that trend to continue, not just in 2026, but well beyond.

Our next question is from Puneet Souda with Leerink Partners.

Thanks for the follow-up again. I appreciate you providing a lot of input. But I just want to boil down to a key question. What is the real NGS underlying growth ex this large customer in the first quarter and the fiscal year '26?

Puneet, if you step back a bit and look at where we were in 2025, the overall growth of NGS being around 23% neutralizing for the growth from the one customer, it would be closer to 20%. And if you go into 2026, I'd say the same general dynamic applies as well.

And we have a question from the line of Vijay Kumar with Evercore ISI.

Sorry, on this Q1 NGS question. The -- is the customer headwind? I know you've had the customer headwind in the back half, right? Is that worsening in Q1? Is that what's driving the Indus assumption? And because we already had the headwind in Q4, right? Like why would it worsen sequentially? .

Vijay, I think you're asking the right question. The air pocket from Q4 is continuing into Q1, but then it will significantly reverse as we expect the ramp to begin starting in Q2 of 2026.

And ladies and gentlemen, this concludes our Q&A session. I will pass it back to Emily Leproust for final comments.

Thank you for your questions and for your continued support. With our strong execution in 2025 and a clear path to profitable growth in 2026, we remain focused on delivering differentiated products and services for our customers. and sustained value for our shareholders. Thank you.

And this concludes our conference. Thank you for participating. You may now disconnect.