Absci Corporation (ABSI) Earnings Call Transcript & Summary

Good afternoon, everyone, and welcome to Morgan Stanley's Global Healthcare Conference. I'm Sean Laaman, Head of U.S. MidCap biotech equity research. Getting late in the day. But for important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/research disclosures. And if you have any questions, please reach out to your Morgan Stanley sales representative. Right. For this session, we have Absci with the founder CEO and Director, Sean McClain, and CFO and Chief Business Officer, Zach Jonasson. But welcome gentlemen.

And maybe just to kick off proceeding, Sean, contextualize the discussion and give us a bit of a view on Absi.

Yes, definitely. So we're a generative design company really leveraging AI to be able to go after the hardest and most challenging problems that still exist being able to go after hard targets such as ion channels and GPCRs, the undruggable targets. We think of AI as being able to decrease time lines and decrease overall cost, but it also opens up kind of the fourth dimension, which is being able to unlock new novel biology where other technologies are unable to address. And you see that in our partnerships with Merck and AstraZeneca and Almirall, but also in our own internal pipeline as well. We have a portfolio that's focused on I&I. The first asset is in the clinic. We'll have a Phase Ia readout in the coming months. And then additionally, we'll have another asset in the clinic either end of this year, beginning of next. This is ABS 201, and this is for androgenic alopecia targeting the prolactin receptor essentially think of hair regrowth and repigmentation, and we're excited to have a Phase II readout on that the second half of next year. And so I think a really exciting technology paired with, I think, some exciting readouts that we have coming over the next 12 months.

Wonderful. Thank you, Sean. Some macro questions. So with China's rise in biotech innovation, how are you thinking about Absci's competitive position here? And will this influence your R&D and be their strategy?

Yes, it definitely has influenced how we think about our own strategy moving forward. And you have to give China credit, I mean what they've been able to show that they can do over the last 5 years is really remarkable being able to develop high-quality assets in a very cost-effective way. And they're going after targets that are now really kind of taking this soft faller approach and it's really made us rethink how we're wanting to approach our own internal pipeline and how we're approaching it with large pharma and how we stay differentiated and how we continue to stay relevant and innovative. And that's really where we focused on these hard-to-drug targets, such as being able to create agonists towards GPCRs or blocking ion channels again, where others have struggled that's where we want to focus in on. And we're focusing on biology that's been known, but again, hard to drug. And that's really been a driver for us over the last couple of years and kind of shifted our strategy based on what we're seeing in China. And I think that pressure to innovate actually I think, is really important for the U.S. biotech community as a whole. And I think we're actually going to see a lot of benefit come from that.

Wonderful. I am going to ask you this next macro question, but we're asking these questions at all companies. So it's a little circular to ask an AI biotech company, this AI question that as an AI tech enabled by technology company, can you describe the key ways your platform is leveraging AI and thinking about AI's future disruption potential.

Yes. AI is truly transforming how we're doing drug discovery. It's really shortening the time line. So if you look at our TL1A asset, we were able to get that in the clinic and roughly 24 months from starting on that program normally takes 5.5 years. We're seeing a decrease in the overall cost upwards of $30 million to $50 million, traditionally, even upwards of $100 million to get an asset into the clinic, and we are able to get our TL1A asset in the clinic for roughly $15 million and so you're seeing a decrease in both cost and time. But again, as I mentioned, one of the things that we're focused in on and we see as a big differentiator is this fourth dimension being able to unlock new novel biology with AI. I think is where you're going to see a lot of value creation over the next few years. And then an area that I'm really excited about us at Absci aren't necessarily focused on it, but how can you use AI on the clinical development side as well in order to make sure that you're getting the right patient recruitment. You look on the right biomarkers and helping make sure that you're getting the right enrollment into your trials. And so you're already seeing the -- I think some of this early transformation, but we are in the early innings. And one of the things that I can't emphasize enough is the importance of data. At Absci, we've always been a data-first company. We've generated data to train our models and validate it. And this has really created this learning loop that has allowed us to rapidly innovate and ultimately generate the best models, but whether you're generating it in-house or getting a consortium of data, what we've seen for ultimately building the best models is making sure you have the access to the right data.

I'll add 1 point to that, which is I think a lot of people maybe don't appreciate AI is something that's constantly evolving. It's not a static platform at all. And I think what's exciting for us as we look at how our platform has expanded and capability over the last 12 months, even in the last 6 months. So the example Sean was pointing out about these difficult targets that we've been able to address. Those are our last generation of models. We're already on to the next generation. And so for me, what's really exciting as you think about the future, that pace of capability expansion it's not linear. It's nonlinear. And we are definitely on that curve. The ingredients for that are the data that Sean mentioned as well as compute.

Exciting, exciting. What has been the most impact on a sale on the regulatory side, if it's FDA, MFN, tariffs?

Yes. We really haven't been impacted by any MFN or tariffs. We're not a manufacturing company. So -- and we've been doing our Phase I clinical work in Australia, where it's cheaper and faster. So we haven't been impacted by any of those initiatives. The other set of initiatives would refer the administration has been promoting or for advancing more rapid development through FDA. So obviating animal testing. We think that fits really nicely into the kind of things we're doing. I mean we work on poly specificity models and other models that address safety. So we think some of those initiatives are right in line with our objectives.

Wonderful. I guess on to the platform, you've built a proprietary AI drug discovery development platform. So what differentiates your architecture from other AI drug discovery and development systems?

Yes. It's really this what lab-in-the-loop. This ability to generate protein-protein interactions, essentially antibody functionality data that we can use to train our models but also using that same technology to inform how accurate the models are as well. And this has really allowed us to rapidly iterate on what model architectures are best suited for the problems we're looking to solve, what hyper parameters and then particularly, how you curate your data and what data is necessary. We have the 6-week cycle time, and we're constantly trying to push that down to the faster you can create that learning loop, the faster the actual innovation progresses. And I think that, that's been just a very key differentiating feature. And I looked at where we're at with AI almost how the semiconductor industry has gone every 18 to 24 months, you have a new chip coming out and I think that's very similar to what you're seeing at the AI drug discovery space, and it's kind of what Zach was touching on. It's not static at all. it's ever evolving. And so again, the faster that you can iterate, the faster you can stay ahead of the competition. And ultimately, at the end of the day, it's about the product. How can you create differentiated products that ultimately are going to be first-in-class and best-in-class.

Sure. And being at the beginning of that sort of innovation, which is not linear and pace of discovery accelerates and you've got wet lab-in-the-loop. Do you get to a point where you've got enough data points to train a model that you no longer need the wet lab?

Yes, absolutely. I do believe that you're going to get to a point where you have a model that is static for solving a particular problem and you really just relying on the inference and you're on to solving the next problem. And I think the way we look at it at outside you can get to the point where you're able to predict an antibody to bind to an epitope of interest, well, the next step then is, okay, how do you predict the functionality, not just the epitopes. And so you're always kind of going on to the next problem, and that's where kind of this -- again, it goes back to this learning loop and the wet lab-in-the-loop and why it's so important. But yes, you will timely get to the point where you have static models, but again, then you're on to the next problem.

Sure. What are the biggest challenges in designing antibodies, which have historically been undruggable overall challenging to drug targets like ion channels and GPCRs.

Yes, absolutely. It's really the surface exposure. There's not much surface exposure of these ion channels and the GPCRs, which creates a difficult time for let's say, the immune system and an immunization campaign struggles with being able to hit these antibody or to hit these targets because there's just not much surface exposure. But with an AI model, it doesn't really matter how much of surface exposed. You just need some. And if you have an epitope, that is surface exposed, you can then generate an antibody towards that and so being able to block an ion or has always been a struggle. You can now with our models to be able to design deep CDR or long CDR loops that can bind deep within the crevice to block it or in the case of a GPCR, being able to generate an antibody that agonizes the GPCR very similar to how the ligand would. And so these are some of the problems we're trying to solve and why I think some of the traditional approaches have failed.

Sure. And what are some of the smart features you've engineered into antibodies so far, such as pH-dependent binding or agonism antagonism?

Yes, absolutely. So we've been able to show that we can really engineer in a lot of different features. We are able to show on a particular oncology target where we could essentially get differential binding in the tumor microenvironment where it's more acidic but doesn't bind in the healthy tissue at neutral pH. And so that opens up a lot more targets that you can start to go after in oncology when you can have that differential binding. And then, yes, additionally, if you look at some of these GPCRs that are coming up in metabolism, a lot of these are peptides or the original ligand is a peptide and you can essentially mimic how that peptide is binding to the target to agonize it or if you want to block it, you can develop an antagonist towards it. And so these are kind of some of the ways that we're using de novo design to start to engineer in the properties that we want versus kind of this trial and air process that drug discovery has always been.

Sure, sure. Okay. And maybe just to move away from the platform for a second and start running down the pipeline. So on 101, what are the key objectives for the upcoming Phase I interim healthy volunteer data with the readout is expected, I believe, in 2025 for your Tier 1 asset.

Yes. We're just right around the corner. We're really looking forward to the readout. The #1 thing will be safety, obviously, but we'll also be looking for the PK profile. We're expecting to be able to do at least once quarterly. We'll also be looking for PV and the target engagement. This will be in healthy volunteers. And we'll be looking finally to see if we get a good signal around the low AA rate, which is what we expect, given the epitope we selected.

Sure. Thank you. And I guess still on 101, compare it to other TL1A antibodies in terms of potency, durability and patient convenience or what are you hoping to achieve there?

Yes. I mean certainly, we've done head-to-head comparisons against the first-gen molecules. And there, I would say, we see advantages in potency, half-life for sure. Most of those are once every 2-week dosing. We also see some advantages in the ADA profile, which we think is going to be important for the final drug product here. And then I would say 1 thing that is maybe a little bit overlooked is we see really great bioavailability and tissue penetration and we do believe, long term, that could be something that translates to additional efficacy. We also have monomer trimerous binding, which also could get at additional efficacy in certain patient types as well. We think the molecule is very well set up to compete against the first-gen, and we believe it's in a good position to compete against the next-gen molecules as well.

And can you describe your progress in partnership discussions and what you believe may be the value inflection points of the program, where do you foresee partnering to be optimal?

Yes. So we've got a lot of engagement with large pharma as well as Tier 2 pharma companies that are interested in the TL1A program. And we do believe with the breadth of indications that are now under investigation for that mechanism. We do believe there's a large partnership or buyer audience. So we've engaged with them. There are a couple of different points across the time line where we think it would make sense to potentially move into a transaction. One of those is after this interim readout later this year. Another one will be when that Phase Ia/b closes, and we have the final readout there, which would coincide roughly when we'll have a bispecific package at TL1A with a novel arm who has the second arm which we understand from several of our discussions is of high interest to pharma, particularly having a first-in-class bispecific. And then thirdly, we're fully prepared, capitalized and in our forecast is to take this program through a 2a study as well in patients.

Moving on to 201 in androgenetic alopecia. So what makes 201 compelling molecule for the treatment of that disease.

Yes, absolutely. If you just look at the standard of care of minoxidil and finasteride and what you see there is patients being frustrated with having to take those either orally or topically daily so that convenience is a big factor. And patients at the end of the day, a lot of them aren't seeing the overall efficacy that they would like to see. Minoxidil is only efficacious in a certain patient population. And then women really don't have a great option either because they can't take finasteride and minoxidil gives them hair in unwanted areas. And so there really hasn't been a lot of innovation within hair regrowth in the last 20 years. And what we're seeing with the mechanism of 201 going after the prolactin receptor is that if you block this receptor, what we're seeing in both mice as well as nonhuman primates is that you end up shunting the follicle back into the active growth phase and you actually start to get hair regrowth. And we've seen this hair regrowth in stump-tailed macaque and mice. And the stump-tailed macaque is really quite exciting. Essentially, these are monkey but naturally go bold. And when you block the prolactin receptor, they go from their bald gray hair to full head of hair and it being jet black, showing that you can essentially restimulate the follicle growth as well as achieving pigmentation as well. And we actually have seen this translate into the clinic as well. Our Chief Innovation Officer, actually discovered this mechanism when he was CFO at Bayer. And they were actually looking at this particular mechanism in endometriosis. And it was a serendipitous fine that the prolactin receptor was involved in hair regrowth. And essentially, the way they discovered this was the mice that had the drug regrew their hair faster than the control arm, which led them to believe -- led them to discover that this is indeed involved in hair regrowth. And they ended about licensing this molecule to a Chinese company. Hope Biomedicine and they took it into a Phase Ib. And they were able to show that they could get 14 hair per square centimeter and talking with the PI on that study, Dr. Rob Sinclair, he had mentioned to us that they had severely underdosed in that study ultimately getting receptor occupancy well below 90%. And that was also confirmed in our nonhuman primate study that we did with that molecule. And we've engineered the molecule to essentially be able to achieve greater than 90% receptor occupancy and only have to dose 2 to 3 times. And so we do believe that this mechanism has been validated both on the preclinical side as well as the clinical side, and there's really a massive opportunity for this particular market in general. And so we're really excited about this. And I think one of the great things about this upcoming trial is that we will be in the clinic end of this year, beginning of next, and we're going to be doing a combined Phase I/IIa study and second half of next year, we will have a 12-week interim efficacy readout looking at the hair regrowth. And so we're roughly months away from a really exciting pivotal Phase II readout in this program.

Right? So with the molecule, we've got greater receptor occupancy and less frequent dosing with your platform, why couldn't you have done that with a human designing the molecule? What is your platform enabled you to do to come up with that molecule?

Yes, absolutely. Well, I would say humans actually tried to design the first molecule and the first molecule had lower receptor occupancy. And in order to get full receptor occupancy, you'd actually have to dose 24x versus the 2 to 3x that we've engineered. And essentially what we were able to do was increase the overall affinity and potency of this overall molecule. And then we also engineered in an extended half-life the molecule that was originally designed at a half-life of roughly 2 weeks and we were able to engineer ours to at least what it looks like will translate from NHP to humans. I think we're going to roughly have a once quarterly dosing. So 2 to 3 doses over a 6-month period. And this was all done with our AI platform.

And Sean, one other thing I'll mention, too, and this is true of all of our programs, redesigning the developability so you can formulate these molecules quite easily. The first-gen molecule that Bayer developed has got severe formulation conditions. It's not a very stable molecule. So we think that's roughly capped out at about 60 mg per ml, which is very low. We should be at a 200 mg per ml formulation, and we're on track for this trial.

And maybe just to talk about the market opportunity. We wrote something on this very recently. And what do you think the market opportunity is.

We've done some market research. We're doing a little bit more now, and we've also had, I think we've been really fortunate to have some great advisers, including the former Head of Commercial at Allergan as well as the former CEO and when we've looked at this, we've done patient surveys, KOL surveys, we think this is an enormous opportunity in the order of $10 billion, putting aside pigmentation. If we restore pigmentation, then I think that, that market would go up significantly from there. This would be a cash pay market. And when we look at what the consumer needs here, what they really want is they want something it works. So in practice or out in the field, we're seeing that Minoxidil, for example, works in maybe 5% to 10% of patients. And then the results are pretty mediocre. So if we can deliver a robust efficacy with durability, that's what patients are looking for. They don't want daily use. They want something that they could do like once or 2 or 2 to 3 injections is perfectly feasible. And it looks to us like the price point could be quite significant, enabling gross margins, it would be north of 90%.

Wonderful. Wonderful. On the additional pipeline programs. Can you provide an update on earlier-stage programs like 301 and 501. What are the next steps for these programs.

Yes. Absolutely. So 301, we showed some really nice target validation data earlier this year, we are now transitioning into in vivo efficacy studies. And both with 301 as well as 501, we are looking to partner those. We do not want to take these into the clinic ourselves, we think that these are best in the hands of large pharma or pharma that focus in on oncology. And so we do not plan to invest the capital further to take these into the clinic. We do plan to out-license them. And then we have a whole host of other leads that are focused in on I&I and metabolism based targets that are earlier in the pipeline, which we haven't disclosed yet. And we do plan to nominate and disclose a new drug candidate later this year or beginning of next. And again, that is definitely kind of focused on the I&I similar to 101 and 201.

Wonderful. Is it the way to view your business over the longer term. You've got some proprietary pipeline programs ongoing. And for you, it's more about sort of validating molecules to get into the clinic, and then maybe the preference is over the long term, that's where it stops for you and then it's milestones and royalty from that point. But really, what you see your machine as is something that you essentially out-licensed to the pharma where they will approach you to optimize molecules for them?

Yes. I would say, in general, yes, with the exception of 201, we do think that we can take 201 deep into the clinic and even submitting a BLA on our own. This is an indication that is actually pretty cost effective to develop, and we have the domain expertise in-house to be able to run the necessary trials. And so we do think -- by taking this further, we do retain more optionality and ultimately create more shareholder value by keeping it and taking it deeper into the clinic, and it's something that we have very high conviction in.

And can you remind us the status of your partnership with Almirall and what are the next steps for the bispecific ion channel program?

Yes. As you probably saw in end of July, we announced that Almirall had selected a second target to work on with us, which is a bispecific program. And that was really based on our success on the first program, which was successfully designing a highly specific antibody to an ion channel. And so that program and the bispecific -- the new program, I should say, is underway now. And then the ion channel program is currently in optimization. So it's moved to the next phases of development.

And I think this just goes to show once you have success on one challenging target with the large pharma or pharma, they want to move on to the next. And I think that this, I think, really demonstrates the value of the AI platform and being able to go after these hard, challenging targets.

Sure. You've mentioned a potential large pharma partnership this year. What are the key attributes you look for in the partner.

Yes. I think ultimately, we want a partner that complements what we're doing. And if you look at a lot of the partnerships we've had to date, they've focused in areas that we're not focused in on. And this partnership is going to be focused on oncology. And so being able to leverage their therapeutic area expertise, their disease biology expertise in oncology and then leveraging our platform to design first-in-class, best-in-class assets. we see as a great strategy for diversifying our portfolio outside of our therapeutic area of focus.

Got you. Got you. And how do you think about solvency of data? So if you're partnering with a big pharma and you generate some insights at a molecule into the clinic. How do you separate the ownership of the data?

Yes. I mean just to take a step back. Some investors have asked us recently well, why do you even do partnerships? Why don't you just do your own program? And there's a couple of reasons why we do them and I'm going to get one of those is the data but one of the reasons is for diversification because as Sean mentioned, we're typically working with partners that have expertise and indications that we're not focused on. And secondly, obviously, there's nondilutive capital, which is great. But the third reason is, in each of these programs, we generate data in-house, and we're able to keep that data and we keep the results of that data in terms of how we implement it to improve our models. So you can think of it this way, in all these partnerships, our partners are underwriting the development of the platform. And so there's good reason to keep looking to do partnerships in parallel with our own internal programs.

Sure. Wonderful it. With recent capital raises runway into 2028, how are you allocating resources across the various programs?

Yes, why don't you take it?

Yes. It's -- we go through this on a regular basis. So obviously, we are very committed to moving ABS-201 through that Phase I/IIa. We think that, that readout is going to be very pivotal and we're committed to bringing ABS-101 forward to a place where we get a transaction in the range of what we're looking for. And so those are two fundamental core tenets of what we're doing here. And then on top of that, we look at our platform, where we make investments in continuing to build the capabilities. And as we look at where those capabilities are, we then apply them to create new assets. And so as Sean mentioned, if you look at the early programs in our pipeline now, which we haven't announced, they're all these really difficult targets. And that's reflective of the advancements we've made in our models over the last year.

And how do you assess capacity for additional partnerships while scaling internal development?

Yes, this is the thing about AI that is also exciting, right? I mentioned that we're on this nonlinear advancement and capabilities. It's also the case of AI opens up additional capacity as it gets better, getting more efficient. And as an example of that, a year ago, when we do a campaign internally, we might generate 0.5 million or even more designs that we would then test in the wet lab. The models have gotten more and more efficient and precise. So today, we're generally generating 100,000. So almost a factor of 5 reduction in terms of what we need to generate in the wet lab. And so we look at our expanding capacity and then we decide how to allocate that. And so we look at our expanding capacity and then we decide how to allocate that. And that comes down to allocating some of it to partnerships and discovery programs and then the rest to our own internal programs.

I guess generally, biotech has been a tough place this year because of the uncertainty on the regulatory and uncertainty on rates, uncertainty on the M&A environment, which is just the trifecta not fantastic. But I'm wondering if that might have led to some hesitation of potential partners to form partnerships with you. And once these things are lifted, we get more certainty on rates, there's some M&A activity. And it seems that the regulatory picture gets clearer and clearer. So would it be fair to assume that you might expect a greater pace or cadence of inbound?

Yes. I agree with everything you said. I think in this environment, there's a lot of cautiousness and a lot of deep diligence before deciding to embark. But I think about our future, and we're certainly going to continue to do partnerships, but we're going to be much more selective about the partners. because we do want to allocate more and more capacity to building our own programs. And those programs could be programs that we only take to a DC and then do an out-license. But we just see this enormous opportunity in these hard-to-drug targets where we're not worried as much about any competition from China. We truly have a competitive advantage there, and we really want to press the gas pedal all the way.

Fantastic. We're almost out of time. But with that said, is there any question I didn't ask that I should have or any message that you'd like to leave investors with?

Yes. I guess the one piece that just really want to drive home is that we do have runway into the first half of '28. And that allows us to ultimately get through the Phase II readout of ABS-201 and have a year of runway past that allows us to ultimately get through our Phase Ib/IIa and TL1A and get to a transaction there. And then additionally, that runway doesn't take into account the large pharma partnership that we've guided to this year, which we do think will bring in upfront to further extend that runway. And so we feel that we are in a really good position to ultimately execute on the current pipeline, getting us through really important key value inflection points. And additionally, ABS-101 has always " been our lead, but I would say that it's essentially a co-lead with 201, especially since 201 is going to have a Phase II efficacy readout before 101. And so I think kind of thinking of 101 and 201 almost as co-leads, I think, is a really important piece given that we will have a Phase II readout on that next year.

Wonderful. Well, we're perfectly out of time. So thank you, Sean. Thank you, Zach. I appreciate you attending today.

Absolutely. Thank you, Sean.

Okay. Welcome.