Recursion Pharmaceuticals, Inc. (RXRX) Earnings Call Transcript & Summary

Good morning. Welcome to the Jefferies London Healthcare Conference. My name is Dennis Ding, biotech analyst here at Jefferies. I have the wonderful pleasure of having Recursion Pharmaceuticals up here with us. We have Chris Gibson and also Najat Khan here with us. So thank you so much for coming.

Thanks for having us.

Thanks for having us.

So before we jump in, maybe just give some opening remarks in terms of some of the transition here, Najat. And just give us an update in terms of what's going on in terms of like the CEO and what kind of new thinking that you would be providing the company as CEO? And any kind of priorities for you over the next 12 to 18 months?

Go ahead.

Yes. No, first of all, thanks for having us. Great to be here. Yes. I mean, I think from a transition perspective, like very, very excited that we get to continue to partner. Chris will be the Chair of the Board. I've been at Recursion for 18 months. So I really had an opportunity to get to know the company joined before we did the Exscientia special combination. From a perspective of going forward, it's going to be doubling down on 2, 3 areas that we've really been focusing on. One is harnessing all of our insights from a platform perspective and showing the proof points that I think the entire sector is looking for in terms of differentiated therapeutics that we can develop, leveraging AI in multiple different approaches across our platform, biology, chemistry, clinical development. I think we're one of the few companies that has that end-to-end integrated AI tech stack. The second piece is going to be we -- the space is moving really fast in the United States. Like I've worked across a lot of modalities and platforms. This is the fastest one that I have seen so far. And so being able to double down in terms of our platform where we can win versus not is going to be another area of huge focus for us and for myself. And then the third thing is pairing that ambition with discipline, right? It's really important for us to have meaningful impact for patients and also demonstrate shareholder value. So as you've seen even in the last year, we have been very disciplined in terms of our runway and also operating costs reduced by 35% without compromising any of our external catalysts, whether it be with internal programs or with our partnerships. So those are some of the 3 areas that we will continue to showcase further. And look, talent is scarce in the space, having the right talent and culture that's, as I like to call it, bilingual, understands AI, understands science, knows how to apply the 2 together. That integrated approach is critical to show those proof points, whether we do it with our wholly owned programs, and we have a readout coming out next month for REC-4881, one of the first programs from our platform and then also through our partnerships, such as with Sanofi and Roche, where we've had really good momentum in terms of some of the milestones that you've seen. But yes, very, very excited, much more to come. But Chris, do you want to share some thoughts?

No, it's just been fantastic building this company over the last 12 years and then being able to handpick a successor. And it took several years to recruit Najat and then the last 18 months partnering has been really fantastic, and I think the company is just in extraordinary hands.

Perfect. And then if we take a step back, just to talk about the platform, right? There are many AI drug discovery platforms out there. So what makes Recursion unique? And maybe talk about Recursion 1.0, talk about the Exscientia acquisition and like how that adds to the platform? And then I guess, talk about Recursion 2.0 now moving forward.

Yes. Maybe I'll kick off just with one of the basic kind of tenets of what we've been building at Recursion over the last more than a decade is one where we believe that biology is extraordinarily complex, chemistry is extraordinarily complex. And that most of the data that's going to give us the answers probably does not exist in the public domain. And so you see this big race today. There are hundreds of companies in our space and thousands of companies around the world who are building AI models based on public data. And what you end up with is convergence and commoditization of those tools because there's no differentiation in the underlying substrate for the tools. I think what Recursion has done quite differently and frankly, our combination with Exscientia last year, we were attracted to them because they had done this similarly for chemistry is build the underlying data set at scale, the positive data, the negative data, all of it is being generated in-house at Recursion. And that's allowing us to build AI models on top of something different, something proprietary. And you see the power of this not just in what we've delivered so far at Recursion with our internal pipeline, but even our partners. We announced last month a $30 million option payment from Roche Genentech. This is our second such option payment on building data sets from which we can discover potential new medicines in neuroscience. And today, we brought in over $0.5 billion from our partners for a pre-commercial biotech company to bring in that kind of revenue, I think, is really, really impressive. And it speaks to this long-term investment of building the data set and the AI stack on top of it. But I know there's other points as well.

No, I think that Chris covered it. The only thing I'd add is like drug discovery and development, you were talking about the various versions. It's a long game, right? Having one point solution that works really well in one area is insufficient. So I think having that end-to-end integrated tech stack takes time, but it's crucial so that at the outcome, you can actually have something that's truly differentiated. And that's what we focus on. Chris mentioned the piece around data. We have about 65 petabytes of data, 40 petabytes of which is proprietary to us. Every day, you will see different models being developed, but they're usually trained on the same data set, public data sets. Where does that differentiation come from? It really comes from that high-quality data sets. We run about a couple of million experiments a week, a 600-person company to have that much data that's been generated and supporting multiple programs wholly owned and with partnerships is difficult to do unless you also automate and have a wet and dry loop where your models get good enough that it actually predicts which experiments you should do. That's where the world for discovery and development is going. So the 2.0 platform that we talk about right now really has that end-to-end tech stack from biology, chemistry, clinical development. chemistry from Exscientia and then clinical development, I just want to point on that a little bit is something that we built from the ground that no other tech bio company has the use of AI end-to-end across clinical development from design to execution, which is recruitment. Nobody else has that. And that is incredibly critical given 70% of the funds to make a drug actually reside in clinical development.

If I can ask a little bit about your interpretation of what AI is, right? Because right now, the industry -- there are so many different variations of what AI is from ChatGPT to other things like that. And when you think back to the last 50 years, AI technically isn't really new, right? Neural networks have been around for many, many years, many decades. So how do you -- like what is AI to you? And how do you use that in your platform?

Look, I think one of the things that's important to note, you mentioned this, right? AI has been around for years. In fact, Yoshua Bengio, who's one of the fathers of deep learning and neural networks is one of our close advisers in Montreal. But there's been this convergence of tools and technologies coming together that are really important. So neural nets have been around for years, but the compute scale to scale neural networks has only been around for a handful of years. Even the storage capacity, right, just storing 65 petabytes of data would have cost $1 billion 2 decades ago, right, per year. So the convergence -- we're at this point in time where it's not just one technology that's coming together, it's neural nets, it's compute, it's storage, it's things like CRISPR that allow us to modulate biology in different ways. And it's at the interface and intersection of all these different technologies where I think we're really starting to see something exciting. Now to your broader question of like what is AI. Unfortunately, that term has become very, very broadly used. And now it basically means anything anyone says that's maybe using a computer that's sophisticated. And obviously, that's not the true definition. But I think it's important for folks in our field to differentiate between companies that are truly leveraging AI as a core piece of what they're building and companies who are using LLMs to put their marketing materials together.

Yes. The only thing I would add to that is interpretability of your models and traceability, data provenance, whether you're doing something more with regulators or even in discovery, that is very different for companies that actually generate their own data and develop models. So we have fantastic AI teams in-house that are building the models and iterating on them time and time again. We have a team called Frontier Labs, which is our 0 to 1 cutting edge, and that's the team that partnered with NVIDIA and MIT and Boltz-2 that we open source that many of you have probably heard about. And then also AI models end-to-end in our platforms. So I think it's also important to look at the talent that's actually working. This is why the point around bilingual team and talent that's very critical, and I'm going to continue doubling down on that commitment to not just have the team but also the culture to go after the hard stuff.

Yes. Okay. And what about the regulatory landscape in terms of the FDA and how receptive they are around some of these AI drug discovery platforms and just preclinical and clinical development, like how are you guys going to capitalize on that?

Yes. I mean, look, we are very, very closely engaged on all of the changes happening, both in the EU and in the U.S. We also have Namandje Bumpus, who was at the FDA on our Board. So fantastic to have her insights. She worked very, very closely with Janet Woodcock and others part of the senior leadership at FDA for years. So there are 3 areas, I think, from a regulatory perspective that's very relevant from Recursion. So number one, we have programs in the rare disease space. So as you've seen for RDEA, RDEP, there's many different evidence and endpoint frameworks and guidance that's been coming out recently. So we're deeply engaged in areas such as FAP, which is a rare disease, 50,000 patients and so forth in terms of some of the progress that's happening in the early engagement, the openness to early engagement with regulators in terms of trial design, endpoints, et cetera. I think the other area is also in oncology. I mean, a lot of you have heard about Project Optimus, Project Roadrunner. There are so many of these programs. What it really means is the evidence bar is going up and also how do you generate that evidence in earlier development around some of the oncology programs. So there, we're leveraging a lot of our multimodal data and also causal AI prediction around which patients will respond to turn a lot of our early development programs from exploratory to more validation. So we're doing that with some of our current programs so forth. And then the third is also around other areas such as the reduced reliance on animal testing. Recursion has invested early on in terms of predictive ADMET models, really critical, especially when you're doing small molecule discovery as well as other approaches such as organoids and of course, a lot of our multimodal data. All of the foresight and early investment is really helping us being able to match some of the guidance that's coming out and also be one of the test beds, frontrunners in terms of leveraging that in our programs. I'll give you a very specific example, just to make it real. We have our FAP REC-4881 program where we should have more data next month. This came from our platform in terms of using an unbiased approach, taking cells that have the biology representation, which is the APC mutation and then going from disease to healthy. We screened a lot of molecules. The allosteric MEK 1/2 inhibitor was #1 on the list. We have shown good data in vivo and promising data in our clinical study that came out in May. Why am I mentioning this? It's a rare disease. Contextualization of open-label studies with the real-world data is incredibly important. And some of the guidance has come both in the EU and FDA, data provenance. How do you actually account for the data that's used to train your models and the limitations of that data. So a lot of that, the team is already captured in, in terms of the evidence generation that we're doing and having the totality of the data for regulatory conversations. That's how you stay at the front edge of a lot of the guidance that's coming up, and it requires a lot of early planning.

Got it. And if we can double-click on the FAP program. Can you just remind us just what that disease is? I feel like not a lot of people are familiar with it. Just the unmet need there and what are you trying to solve?

So familial adenomatous polyposis is FAP. This is a disease that's driven by mutations in the gene APC. And patients with this disease start getting hundreds or thousands of polyps in their gut in their late teens, early 20s and 30s. And ultimately, today, 100% of those patients will get colorectal cancer if left untreated. The treatment today is removing the colon of the patient. So you can imagine if you're a 20-year-old and you have a colectomy, this has a pretty significant effect on your quality of life. And as Najat mentioned, we were able to identify this mechanism that was unexpected in the space using this unbiased approach, take that through animal models. And now in our clinical program with the first 6 patients, we were able to demonstrate that this molecule reduced polyps in these patients by between 30% and 80%. And this is in 5 out of 6 patients. So the median polyp reduction was about 45%. That is about double the highest polyp reduction percentage that's ever been seen in any molecule that's been explored in this space. And what I think is also really important is 5 out of 6 patients is a higher proportion of patients that are responding than the other molecules we've seen in the space. Now the caveat is very small end. And in a few weeks, we'll be able to share more data next month about what the next set of patients in this program looks like. And obviously, Najat and the team will then be able to take that data and if it's promising and continues to look good, potentially go and talk with the agency about how we might find a path to a broader set of patients.

And just maybe a couple of points to note. I mean, this is a rare disease with 50,000 in the U.S. and EU. So a substantial patient population. As Chris was saying, nothing approved to date and surgery is the current standard of care. There's a huge amount of unmet need to your question, in terms of finding alternate approaches to delay surgery to delay the risk of cancer. And most of these patients start pretty early on in their 20s. So we're encouraged by the data that we see, the polyp burden reduction, which is above and beyond what others have seen. We also shared some data around the Spiegelman score, which is a really important staging that physicians use in terms of the risk of colorectal cancer being able to bring that down. And then the other thing is in the trial design, we're looking at both on-treatment and off-treatment. So the data that we just discussed is 3 months of treatment, which is much faster than what others have studied, which is usually around 6 months of treatment, so faster onset. And getting some of the data, hopefully, that we will next month in terms of off-treatment also gives us some flexibility in terms of the scheduling regimen in terms of can you do pulse dosing and so forth. So a lot more to do, but next step is, of course, seeing the data next month.

Yes. Okay. So how many more patients do you think we'll get at the update? And it seems like efficacy would continue to look strong in terms of polyp reduction, maybe we'll get some off-treatment durability as well. But what -- like how are you guys framing that update in December?

Yes, great question. So in May at the DDW, we had NF6 efficacy evaluable patients. The goal would be to try to get to at least 10 by next month. So just in a matter of a few months. 4 milligram QD is the dose, so we should see additional data. We're seeing good activity at that dose. And we should also expect to see a bit more on durability and of course, continue to monitor safety and tolerability.

Okay. In terms of safety and tolerability, is there anything particular with the data set earlier this year? Like when we look at the data, there were some signals, obviously, small end, but of LV ejection fraction depression. So just talk a little bit about that, like talk a little bit about how that could impact the market opportunity for you guys and what you guys can do to navigate that.

Yes, great question. From a safety perspective, the 2 main areas that we see is rash and some LVEF, Grade 2 and grade 0 so far to date. Both of them are on target for MEK1/2 inhibitors. You've seen that with other MEK1/2 inhibitors as well. On the first one around rash, we've been leveraging prophylactic topical steroids, antibiotics and so forth, things that have been used by others, and we'll share a bit more data in terms of the safety profile next month, but it's become more manageable from that perspective versus the earlier management of the disease, which is where some of the data we shared in May. And in terms of the LVEF, look, what we're seeing so far, grade 0, Grade 2, reversible upon stopping the treatment. And again, not different from what we have seen and not unexpected from what we have seen. Now in terms of the scheduling flexibility becomes important. This is a chronic disease. This is why in the study, we're measuring both on and off treatment. We'll learn more with that data and then also explore some of those options as we think about a potential pivotal if the data holds next month.

Yes. At the same time, it is like a risk-benefit equation, right, at the end of the day?

Yes, always. Yes.

How are you thinking about the future Phase III pivotal program for FAP? Like in an ideal scenario, what would that look like for you guys appreciating that maybe the FDA is not okay with just polyp reduction? And maybe they want some event-driven or some kind of clinical outcome endpoint in terms of time to the removal of the colon, et cetera, right? So how are you thinking about the different scenarios in terms of the design?

Yes. I mean, great question. So first of all, for FAP, unlike many rare diseases, there is precedent for pivotal study endpoint, and that's a composite endpoint like a PFS composite endpoint that has elements of polyp burden reduction, which is usually the primary for Phase II, which is also what we are measuring. In addition to that Spiegelman score that I mentioned before, which we're also measuring and other things such as progression to surgery or even death. So it's a large composite endpoint. In our conversations, we'll discuss, again, it all depends on the data and the effect size, et cetera, in terms of alternate versions of a pivotal endpoint that is much more conducive to a chronic disease. The other thing that we're also doing, as I mentioned before, is we'll have a real-world study in natural history, which I think is really important to contextualize what the progression for these chronic disease patients looks like in terms of polyp burden. You mentioned surgery and so forth. So lots of conversations. This is where the early conversations and discussions with the FDA, leveraging and abiding by some of the new guidelines and frameworks that we have an internal team that's working on with Board members and others becomes very critical. So much more to come, but step one is the data next month.

Got it. Okay. And moving on to some of the other assets in the pipeline. CDK7, maybe talk a little bit about that and some of the updates that you guys will be sharing next year.

Yes, sure. Happy to. So CDK7, important target. The goal here is leveraging our platform to design a better therapeutic index and also leveraging our clinical development AI platform to hone in on the right patients and indications to go after. So we just completed our -- a few weeks ago, we mentioned at our earnings call, our monotherapy dose escalation. We have an MTD dose, et cetera, et cetera. From a safety perspective, safety is similar to what we expected for CDK7, but trending lower on some of the GI tox that we see from other published data, which is in line with what the design criteria is. And in addition to that, we did see some early activity, 1 PR, some stable disease, et cetera. As with most CDKs, whether they're transcriptional or cell cycle, we -- our goal is really to look at combinations for potential efficacy. We have initiated our combination study in ovarian cancer, second-line platinum-resistant using a couple of standard of care, one more U.S., one more EU to be comprehensive in terms of what the standard of care is. And the insight to go into ovarian cancer versus breast cancer, which we're also exploring other indications, really came from a confluence of what we're seeing in cell line sensitivity, in vivo CDx models and then also using our multimodal data, our data from our partnerships with Tempus as an example, and our causal AI modeling. So it's really those 3 areas that we harnessed in order to take ovarian cancer as our first indication. Next year, we should just have more rolling data in terms of the patients recruit. We haven't given specific guidance other than the fact that we should have some early combination data in 2027. When in 2027, we'll give more guidance next year as we enroll the patients and learn more about the study.

Okay. What about other indications beyond ovarian?

We are exploring other indications, but we haven't disclosed anything yet. We'll make sure we tell you that as we do.

And then I guess, lastly, just on RBM-319, that's something that was just completely internally developed. So there will be some updates in the first half of next year. So talk a little bit about that and what you are expecting.

Yes. Just -- the only thing I will say that all of our programs, especially CDK7 as well is internally developed. We are developing the molecules, et cetera. I would say for RBM39, again, coming from our platform, the biology part of the platform, we wanted to see what else could we target that's important for DDR modulation and other transcriptional stress sort of related mechanisms that's similar to CDK12, but not -- doesn't hit CDK13. RBM39, novel target, first-in-class. We have a degrader. We've designed the molecule as well. First half of next year, we expect to get some early safety as well as PK data. This is a degrader. So we also want to see engagement overall. And that's happening. We're exploring this in solid tumors. And the last thing I just want to say that we also have multiple milestones coming from our partnerships, as Chris mentioned, we have achieved 4 out of 4 milestones with Sanofi, where we are actually designing the molecules for targets that we co-aligned on, designing both the wet and dry lab. And for Roche/Genentech, a couple of milestones on these maps, $60 million so far in the last year. And I just want to highlight that sometimes we get asked that question, how much wet lab does Recursion do 100 billion microglial cells that we developed and then created these maps of biology and then also for some of the other maps of 1 trillion iPSC-derived neuronal cells. So I just want to highlight that there's a lot of work ongoing with both partners around data, around models and insights that's now translating into programs.

Got it. Well, I think we are out of time, but it sounds like 2026 will be an exciting year, a lot of catalysts.

Thank you so much.

Thank you.

Thank you.