Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX) Q4 2025 Earnings Call Transcript

Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX) Q4 2025 Earnings Call Transcript February 25, 2026

Recursion Pharmaceuticals, Inc. beats earnings expectations. Reported EPS is $-0.20528, expectations were $-0.28.

Najat Khan: Good morning, and thank you so much for joining us. I want to start by briefly framing where Recursion Pharmaceuticals, Inc. is today in its journey and evolution. Over the past decade, Recursion Pharmaceuticals, Inc. has built something truly special: a differentiated platform pioneering the integration of large-scale biological data generation, machine learning, and compute to better understand the complexity of biology. We have also deliberately strengthened the foundation in chemistry and AI through the acquisitions of Excientia, Valens, and Cyclica, creating a truly powerful foundation. Today, we are at an important inflection point. We are harnessing everything that we have built to date to do two things. Number one, translating insights into evidence—evidence that this platform, the use of AI end to end, can generate medicines that matter—and we are doing this both across our wholly owned portfolio and through our partnerships.

With strong momentum across both fronts, I am excited to share some of the updates today. In parallel, we are also continuing to advance. Today, we have what I like to call a trifecta that is required to make impactful medicines: AI-driven biology, AI-enabled chemistry, and AI applied to clinical development. We continue to invest to ensure we are defining the standard for how AI is applied across the full life cycle of R&D. As we look across the sector, we are encouraged by the broader momentum in the field—new models, flares, partnerships being announced—but the industry is clearly entering a new phase where value is being defined not only by the models you build and the collaborations that are announced, but by actually translating those into capabilities, into real application, and measurable impact.

The important question now is not only what you build, but what you can unlock, and that is the chapter Recursion Pharmaceuticals, Inc. is in. Our focus is on unlocking that value, using AI end to end consistently to generate better targets, better molecules, and advance programs faster with repeatability. The ultimate goal is to deliver medicines that matter. This quarter reflects that focus. We are making progress across all fronts. First, on the clinical side, with a first positive proof of concept with FAP. On the partnership side, a fifth milestone with Sanofi reflecting our growing joint portfolio tackling highly challenging targets; we are excited to share more about that today. And the continued evolution of our end-to-end AI platform.

Last but certainly not least, disciplined execution, which is something we talked about at JPM, has now extended our cash runway into early 2028. There is a lot to cover today. We will be making some forward-looking statements on this call, so please refer to our filings for more information. We always at Recursion Pharmaceuticals, Inc. start with the end in mind, and for us, as I said before, it is medicines that matter, that are truly differentiated. To do that, you have to use the right data, models, compute, and more. There is a lot of talk about data, but what really matters is data that is high quality and fit for purpose, and at Recursion Pharmaceuticals, Inc., our foundation has been building high-quality data at scale, not just one type of dataset, but multimodal across the board.

This is where pioneering the lab-in-the-loop—pioneering the wet and dry lab—has become incredibly important so that we not only generate data, but then we generate purpose-built models that we test, learn, and improve. We sit in a sweet spot of being able to leverage both public data and our proprietary private data. That is incredibly important to ensure that our models are impactful, insightful, and unique. On top of that, the importance of not just having the ingredients, but actually having a team who knows how to use it well—teams that are bilingual, fluent in science and in AI. I want to add a third lens: it is also important to have reps under your belt, to know what good looks like, and having talented teams that have reps is one of our core differentiators.

Q&A Session

The ultimate secret sauce is how it all comes together—having an integrated end-to-end operation that is a continuous learning loop all the way from novel biology or novel insights through to the clinic. For many of us who have actually made medicines and have focused on this, which is a humbling effort, we all know that improving one decision in R&D is simply not enough. It is the compounded impact of better decisions across molecules and biological insight all the way through the clinic that makes the difference. That is how you truly change not just the outcome, but also the time and cost and how you do things, and that is what we are focused on at Recursion Pharmaceuticals, Inc. What does that result in? First, in our clinical development, we have a diversified portfolio.

We are very encouraged by our first AI-enabled clinical proof of concept with FAP, which has the potential to be a first-in-class therapy for FAP, and we also have additional programs behind that. In our discovery portfolio, we also have another diversified set of programs. Specifically, on the partner piece, we have brought in over $500 million in upfront payments and milestones; we will share some additional updates today. Every single milestone we achieve not only improves the economics, but is also a validation of the platform and a validation that we are learning fast in terms of what works and what does not to make our platform ever more intelligent. Let us talk about the platform. I am going to share this slide every time we have an earnings call because this is so core to what we do.

Number one, being end to end, as I said before, is critical. You have to connect biology and chemistry to ultimately the patient, which is really where the rubber hits the road. That is where we are going. It is important to innovate not just on data generation, but also on your models. We have state-of-the-art foundation models not just in phenomics, but transcriptomics, and we are pulling those together in emerging virtual cell efforts that we are focused on. We are continuing to innovate on additional frontier models in the chemistry space, as well as our newly built clinical development AI platform. Again, it is that integration and how you harness it to unlock value that matters the most. In terms of our strategic pillars, we have three main areas that we are doubling down on in this new chapter.

Number one, tangible proof points—this is so important both from our clinical portfolio as well as our partner programs. Second, in parallel, continuing to invest surgically in our platform grounded in areas that will enable us to have more of those proof points. Third, pairing that bold ambition that we have with disciplined execution: how do we do more with less? One area that is really important for us is we like to track our wins and learnings as we go through each of these pillars. You will get used to seeing that going forward. First, in our first pillar—making progress around the clinical pipeline as well as our partner programs—FAP is really important data for a disease that has no approved therapies to date, with durable and meaningful polyp burden reduction.

Second, we will highlight our Sanofi collaboration. As a reminder, this is where we are tackling challenging targets in I&I and oncology and leveraging our AI component and chemistry component of our platform to design novel compounds. We just achieved our fifth milestone to date. This is an example of the repeatability of our platforms around using AI to develop chemistry molecules and small molecules. Our second pillar is focused on our platform. We look across the portfolio for “green shoots,” proof points where we are seeing that we can do things better and faster. One example is in our AI-enabled chemistry platform. When we look across the portfolio, we are synthesizing 90% fewer compounds than what we see in industry—about 300 versus 2,500 compounds—because we are predicting more and making less.

This is where in silico approaches should be guiding us, and we are seeing that happen. We are doing this two times faster, with an average of 17 months versus 42 months for industry. We will keep pushing on this. In biology, we talk constantly about the amount of unknown biology. We are generating first-in-industry maps of biology—these huge atlases where we are trying to uncover unknown biology. This is in partnership with our great partners at Roche Genentech—two back-to-back maps that were just accepted—and now the team is hard at work translating those maps into novel biological programs. Our third pillar is momentum with discipline. We have a lot of things we want to do, but we have to do it with discipline and good financial stewardship—financially and operationally.

We have seen a 35% reduction in pro forma operating expenses year over year. This comes from multiple areas: sharper focus on our portfolio, optimizing our G&A, and improving our platform efficiency, such as reducing the number of compounds we synthesize and increasing our speed. We are excited to share that we have extended our runway to early 2028. Let us dive into each of these pillars a little more, starting with our wholly owned pipeline. We have a diversified portfolio. I will categorize differentiation in three ways. Number one, programs with novel biological insight from our platform. Number two, programs with emerging biology—interesting biology that is unconquered and not validated yet—where we have developed optimized programs.

Number three, areas with validated biology but where significant unmet need still exists from a patient perspective. We always track which components of our platform we are using across our various programs. Starting with platform-derived novel biological insight, we have two programs in that category. First, FAP—REC4881, or REC-4881. There is significant unmet need as there is nothing approved for these patients. This is a disease hallmarked by hundreds of polyps, each and every one of which is precancerous, with a 100% risk of colorectal cancer by the time you are 40. There are more than 50,000 addressable patients in the US and EU. The Recursion Pharmaceuticals, Inc. differentiation is using the phenomics—the early version of the phenomics platform—to ascertain in an unbiased fashion that MEK1/2 inhibition could work in FAP.

We have completed our Phase 2 study with a positive clinical POC, which we shared in December. Our next steps are on track to initiate FDA engagement on the registration path in 2026. We also have another program with similar elements from a differentiation perspective: RBM39. RBM39 augments what could be potentially important in genomically unstable cancers, impacting a wide patient population. The differentiation for Recursion Pharmaceuticals, Inc. came from uncovering this MOA and the connection it has to CDK12, which is known to be important for DDR modulation. For many decades, it has been challenging to target because of the similar homology with CDK13. Right now, that program is in Phase 1 monotherapy dose escalation, and we expect to share an early Phase 1 update on safety and PK in 2026, later in the year.

Moving to emerging biology that is unconquered, where we can optimize programs: CDK7 and ENPP1. For CDK7, it has been known for a long time to be an important central master regulator of both cell cycle control and transcription, with a wide variety of patient populations that are addressable, given its centrality in oncology. Others have tried this target before, and one of the key challenges has been optimizing the PK/PD and the therapeutic index. That is where we have leveraged our AI chemistry to optimize molecules, especially around gut permeability. We are also leveraging our platform to figure out which patient populations should benefit most from CDK7 inhibition. We finished our Phase 1 monotherapy dose escalation, maximum dose has been selected, and we are in progress on the combination study focused on second-line platinum-resistant ovarian cancer, with more data expected in 2027.

ENPP1 loss or certain mutations lead to challenges with bone mineralization, leading to fractures and pain—another lifelong disease that starts very early. The Recursion Pharmaceuticals, Inc. differentiation is focusing on a molecule that can be oral because what is available today for patients and some investigational agents is enzyme replacement therapy that requires a huge patient burden with subcutaneous injections, sometimes multiple times a week. We designed a molecule for ENPP1 suitable for chronic dosing, especially in hyperphosphatasia, with IND-enabling studies ongoing. We expect to have a go/no-go decision in the second half of this year. The third category focuses on validated biology but significant unmet need still exists. MALT1 is validated from a target perspective in B-cell drivers, but challenges have been around tolerability.

We leveraged our platform to design molecules that could design away from some of the UGT1A1 and other targets that have been seen, which will become increasingly important with combinations with BTK inhibitors and others. We have Phase 1 monotherapy dose escalation ongoing, with an early Phase 1 update on safety and PK in monotherapy expected in the first half of 2027. Another program with a similar theme is LSD1, known to be an epigenetic regulator, preventing or inhibiting some of the differentiation you see in solid tumors such as small cell lung cancer and also AML, with some validated data seen in AML recently. The differentiation here is designing out challenges around tolerability that have led to DLTs and not being able to dose high enough, such as thrombocytopenia.

This Phase 1 monotherapy dose escalation is in startup, and next steps are to have an early Phase 1 update on safety and PK in monotherapy expected in 2027. Another program in late preclinical is our PI3K H1047 mutant selective. PI3K in general is an important oncogenic mutation linked to resistance and relapse. We used our platform to design a molecule that would be much more selective—over 100x selectivity over wild-type PI3K—which leads to fewer tolerability challenges that cause dose interruptions and reductions. This is in IND-enabling studies with a go/no-go decision expected in the second half of this year before we consider a Phase 1 initiation. I would like to double click on REC-481 and our PI3K program. For REC-481, we had our clinical POC late last year.

There are no approved therapies. In our Phase 2, three months on treatment with 4 mg QD of this MEK1/2 inhibitor, we saw significant polyp burden reduction at 43% median, with 75% of patients responding—one of the higher polyp burden reductions to date. AEs were in line with MEK1/2 inhibitors; the majority were Grade 1–2 rash and CPK, with no Grade 4 or 5 to date. When patients were off treatment for three months, we saw continued durable polyp burden reduction, in some cases deepening, with a significant portion of patients responding. We are on track to initiate FDA engagement in 2026 to discuss the registrational study design. We have started enrollment of the 18-and-over cohort; previously we shared data for 55 and over. We are also advancing dose optimization efforts inspired by the durability data.

We expect additional clinical data in 2027. For our PI3K H1047 mutant selective program, PI3K is a very important target across multiple solid tumors. Current PI3K inhibitors have been constrained by hyperglycemia, metabolic toxicity, dose interruptions, dose reductions, and limited treatment duration. Our differentiation and thesis focus on H1047 mutant selectivity with 100x more selectivity over wild type, potentially minimizing risk for AEs. To do that, we designed a molecule with exquisite selectivity. We started with X-ray structures where we had proprietary structural insight and leveraged our MD simulations—this is where compute becomes really important. Our molecular dynamics simulations revealed a novel pocket. We then used our generative 3D modeling efforts and machine learning to design molecules and novel scaffolds for this novel pocket, and used other ML approaches to rapidly design our cycles to achieve exquisite potency and selectivity.

We designed 242 compounds across 13 cycles in 10 months. Compared to industry standards, this is fast. Preclinical data show dose-dependent tumor regression for our compound, with significant regression comparable to Scorpion and much better than Piqray. We also saw synergy with CDK4/6 inhibitors, the standard of care today. Additional data versus other assets such as capivasertib showed improved tumor regression with low dose of our asset versus high dose capivasertib. On tolerability, in naïve wild-type mice we did not see impacts on hyperglycemia markers versus Scorpion and Piqray. In obese diabetic rats, we did not see hyperglycemia or metabolic liability even at supra-efficacious doses for our asset versus Scorpion and Piqray. Clinical validation of improved tolerability is critical to confirm this expansion thesis.

The study is in IND-enabling with a go/no-go decision for Phase 1 in the second half of this year. We will also do more around our partnerships. Proof points come from both our internal portfolio and our partners. To date, we have achieved over $500 million in total cash inflows from our partnerships—both upfronts and milestones—with recent momentum. Each program we are working on has potential for over $300 million in milestones and tiered royalties per small molecule program, with some royalties up to double digits. We are very excited to unveil our joint portfolio with Sanofi. Sanofi has been a fabulous partner. We are showing multiple programs—five—along with multiple early discovery programs. This is a diversified pipeline focused on challenging targets in I&I and oncology, with molecules that have the potential to be first in class and/or best in class, addressing specific unmet needs.

To date, we advanced five lead packages delivered by Recursion Pharmaceuticals, Inc. across five programs and accepted by Sanofi, totaling about $34 million in milestones to date, in addition to the $100 million upfront—$134 million so far. We have important work ahead with later-stage discovery milestones over the next 18 months. Discovery is probabilistic; some will work and some will not. It is the repeatability and the ability for our platform to have multiple shots on goal that is critical. Double clicking on one of these: our platform is not about one data, one model, one asset—it is about a suite of them used for the problem at hand. We start with the problem first, and then we have flexibility and optionality across our models to get to the best outcomes.

Our latest oncology program milestone leveraged both physics-based approaches to understand protein flexibility and identify novel pockets, and machine learning algorithms to rapidly execute design–make–test cycles and find highly potent molecules now progressing to the next stage. None of this can happen without a unique and differentiated platform that is an ever-important work in progress. Our biology insight foundation includes over 50 petabytes of high-quality multimodal data. We build state-of-the-art foundation models across phenomics and transcriptomics, and combine them as fusion models—connecting genetics, transcriptomic, proteomic, phenomic, and patient data. We leverage this to create novel proprietary datasets (biology maps) internally across different therapeutic areas, as well as in neuroscience and GI/oncology with Roche Genentech, fueling our discovery pipeline.

In chemistry, novel small molecules are harder than they look. We use in silico approaches to generate over 100 million molecules, with synthetically aware design. We start with the target product profile and design for what can be a true drug that matters. Ninety percent of these molecules are generated, scored, and prioritized by our models. We increasingly leverage automation and agentic orchestration to get things done better and faster and in a more unbiased approach. Across the portfolio, we synthesize on average 330 compounds versus 2,500 in industry, and we do it in 17 months on average versus 40+ months for industry, going from target to advanced candidate. As a result, we have over 10 development candidates across our internal portfolio and are getting to that line with our internal and partner programs as well.

Our newly built emerging clinical development AI platform begins with a strong data foundation: 300 million-plus real-world lives through internal work and integrated ecosystem data partnerships. Some early results include improving enrollment rates by 1.3x to 1.6x and starting studies faster by up to three months. Our platform can generate a heat map for potential patients across the US, then drill down to state, three-digit ZIP, and site levels, including data around site experience, competing trials, and available patient counts with inclusion/exclusion filters. This is precision operations, starting with the patient in mind. With that, I will now turn the call over to Ben Taylor, our CFO, to go through some of our financials.

Ben Taylor: Thanks, Najat. 2025 was a year of financial transformation for the company. As a part of the integration, we decided to rebuild all of our corporate systems from the ground up. This was really important because we wanted to be able to apply the same level of discipline and rigor to our strategic decision-making that we do to all of our scientific decision-making. We looked at how every dollar in the company goes toward a specific quantifiable outcome, and that is how we were able to achieve the efficiencies that we did over the last year while still advancing a portfolio of five clinical programs, hitting different partners’ milestones, and really investing behind the growth in our platform as well. All of that comes back to focusing on those investments across our pipeline and technology portfolio that have the best risk/return and that are going to give us the most impact for the investment that we are making.

That is how we were able to come back and have a 35% year-over-year reduction from pro forma 2024 to 2025 and even come in 10% below the guidance that we originally provided in May. We ended the year with $754 million in cash. Looking forward, operating expenses—our 2026 cash—are expected to be under $390 million. Cash operating expenses is a non-GAAP measure that we are going to be using to give you guidance. We have a lot of noncash expenses in our P&L, and so we wanted to provide something that showed what our cash profile might look like going forward. This is coming directly off of our cash flow statement. If you look at operational cash flow and then you add back our inflows from partnerships and transaction costs, you will be able to get directly to this guidance number.

In addition, last year it was really exciting to see that we crossed $500 million in cumulative partner inflows. We expect to continue to achieve those going forward. In fact, we hit our first milestone earlier this month already, and so we do include probability weighting of some of those milestones in our cash flow projections going forward. The really exciting part is not only were we able to exceed our efficiency expectations, but that actually means we are extending our cash runway. We are updating our guidance to early 2028 as of now. With that, I will hand it back over to Najat.

Najat Khan: Thank you so much, Ben. We will wrap it up by saying, looking ahead, we have a very broad set of catalysts coming up, and it is going to be a busy next 18 to 24 months. This year, we are on track for our initial engagement with the FDA on REC-481. We are looking forward to that, and also initial data—early safety and PK—for RBM39. We have go/no-go decisions for PI3K and ENPP1, which are both in IND-enabling. We will also have additional data for REC-4881 early next year, and then combo data expected for our CDK7 program, as well as more early safety and PK data from MALT1 and LSD1. Recall for both of those, we designed the assets to be more tolerable, so these are going to be important. Partner catalysts will be very important: our partnerships with Sanofi across multiple programs progressing into more later-stage development candidate and other milestones, and in addition, these maps of novel biology extracting into new programs with Roche Genentech.

We continue to invest and push the boundaries in our platform, defining what industry standard really looks like for making medicines using AI, and as Ben mentioned, pairing that important work with disciplined execution. We have pivoted toward an outcomes-based budget where we test what value creation every dollar can drive—doing more with less. I will close by saying thank you so much for the time. Our focus will always remain on value creation for patients—they are the ones we ultimately serve—and also our shareholders. Thank you again for listening. We will now open for questions. I will also have our CSO, Dave Hallett, joining us as well, in addition to Ben Taylor. We have questions from Sean at Morgan Stanley and Priyanka at JPMorgan, and from Brendan at Cowen.

There are many questions around REC-4881—understanding what potential registrational pathway may look like upon alignment with the FDA, how we are thinking about providing a regulatory update, and the updated patient population. It is a long question; I will break it into pieces. In terms of the regulatory update, as I mentioned, we are on track for that initial engagement with the FDA in 2026. That is going to be really important in terms of the potential design for the registrational study, patient population, and endpoints. We have a very compelling dataset in terms of durability and polyp burden reduction. In addition, we also have natural history data. Coupled together, it is going to be really important for us to have conversations with the FDA.

Second, around the updated patient population: as I mentioned, the 18-and-over arm is already recruiting, and we are also looking at dose optimization schedules given what we saw with our durability data. More data on that is coming in 2027. As we have meaningful updates across both fronts, we have done webinars ad hoc; we like to be real time and transparent. When we have more meaningful outcomes and updates, we will share them with the Street. Next question from Alex at Bank of America: it looks like the cost optimization measures really started to take hold in Q4. Any one-offs that helped in the quarter, or are these levels the expectation for the go forward? Ben, do you want to take that?

Ben Taylor: Sure. Happy to. Yeah. Thanks, Alex. I agree with your data. It is really about efficiency more than cost cuts. We have hit a point where we have gone through all of the integration. I would assume that is all complete. There are no big one-offs on the system side. We come in with the attitude that we want to continue to find ways for every dollar to make more of an impact in the following years and months than it did previously. When you come in with that attitude, you start to find ways to do more with less. That is where we expect to be able to continue growing our pipeline, investing heavily behind our platform, and moving things forward while still hitting those cost targets that we put out there.

Najat Khan: Great. Thank you, Ben. The only thing I will add is the piece around rapid go/no-go decisions and how we are doing that—the mentality and the mindset—and also understanding the variety of areas we are working on and what the value proposition across the different areas is, which evolves as you generate more data. Thinking like an investor is really important—being agile around capital allocation—and that is what we will continue to do, driven by data. Next question: NVIDIA—what was the rationale in terms of the divestment? Do you plan to seek other technology partners? Does NVIDIA now have proprietary insights from the models you have trained, etc.? It is important to decouple two parts. One is the investment from NVIDIA, and one is our collaboration—our technical collaboration—with NVIDIA.

The technical collaboration with NVIDIA continues. Some of you might have just seen we are going to be highlighted in a lightning round for NVIDIA’s upcoming GTC presentation with HyRes, with Recursion Pharmaceuticals, Inc. being a pioneer in how to leverage automation. This wet and dry lab is not just words; this is actually in action. This is how we do millions of experiments a week. Our collaboration with NVIDIA “Runner 2,” one of the fastest supercomputers in life sciences, underpins examples from PI3K—using machine learning and molecular dynamics. Our partnership with NVIDIA could not be any stronger. In terms of the divestment, if you look at the public 13F filings from 2025, it is really a shift in NVIDIA’s investment portfolio to more larger on-strategy supercomputer data center efforts.

It is a portfolio shift, and we were not the only company; other decisions were made as well. It is a collective shift to more on-strategy investments—large, billion-dollar-plus investments. On seeking other technology partners: we have a strategic partnership with Google in cloud compute. We have a partnership with NVIDIA on machine learning, models, and on-prem compute. We have always been one of the pioneers in bridging the world of tech and science, and we will continue to do that. One more question from Georgia: with the recent positive preliminary efficacy data for REC-481 in FAP and the achievement of your fifth milestone with Sanofi, what specific metrics or historical comparison from your current clinical portfolio best demonstrate that Recursion Pharmaceuticals, Inc.

is improving the probability of clinical success or speed of development compared to traditional discovery methods? I am going to hand it over to Dave Hallett to get us started, and I am sure we can add more comments as well.

Dave Hallett: Thank you, Najat, and good morning and good afternoon to those of us in Europe. I will start from the discovery perspective. During the last presentation, Najat highlighted a number of themes: repeatability of delivery, specifically highlighted in the burgeoning Sanofi pipeline we are building together. This is a repeatable platform that is delivering both best-in-class and first-in-class solutions for challenging targets. As we highlighted above at JPMorgan and again in this presentation, the speed of delivery—if you look at the metrics in terms of the numbers of novel compounds that we synthesize and test and the speed that we are getting to these development candidates—further demonstrates the role technology plays in accelerating delivery.

The proof is ultimately in the clinic, and we are very excited for patients in terms of FAP. This is the first example from our platform where we have been able to demonstrate that a compound that came from Recursion Pharmaceuticals, Inc. has shown clinical proof of concept, and the goal over the coming months and years is to show repeatability in that frame as well.

Najat Khan: Thank you, Dave. To add a broader perspective, looking at Recursion Pharmaceuticals, Inc., we have five-plus clinical programs, a diversified portfolio on the clinical side, and a diversified portfolio on the discovery side. It takes time and effort to build a platform. These datasets did not exist; the models did not exist. We are not a one- or two-asset biotech; we are a tech-bio for a reason, which is what Dave mentioned: we are focusing on repeatability and scalability, making this more engineering-focused, using agents or automation to do things better and faster, taking toil out of systems to supercharge our scientists to do the hard work of drug discovery and development. Drug discovery and development is inherently probabilistic—most things do not work.

We have a 90% failure rate. Multiple shots on goal are important. Two worlds—tech and bio—have not really come together before, and we are not just building models that are interesting, but actually applying models that unlock value. We constantly look at metrics and stats—the team knows I call them green shoots—whether it is the number of compounds we synthesize (90% less than industry), the speed, the cost of our INDs. We do the same in the biology platform and in clinical development, where we are seeing improvement in enrollment. There is much work to be done, but this is what gets us excited. It is hard, but incredibly challenging and rewarding work. Thank you to our partners and shareholders, and most importantly to patients who are willing to take a bet on us and our programs and who are waiting.

We are working as hard as possible to forge a new era of how medicines are made for patients who are waiting. Thank you again for joining us today, and we look forward to sharing more.