Good afternoon and welcome to the 43rd Annual J.P. Morgan Healthcare Conference. My name is Dave Praharaj, and I'm part of the Healthcare Investment Banking team here at J.P. Morgan. Today, I have the pleasure of introducing our speaker, Sean McClain, Founder and CEO of Absci. In terms of logistics, please reserve any questions for after the presentation. For those in the audience, the mic will be passed around, and for those viewing on the web, please submit your questions online, and I'll be able to view it through the iPad on the stage. With that, take it away, Sean.
Thank you. I'm Sean McClain, the founder and CEO of Absci, and I have three important updates to go over with you today. First is our partnership with AMD and how we're working with AMD to scale our compute to get better price performance and better training resolution. Second, we'll be going over ABS-101, our potential best-in-class TL1A antibody, and some really exciting new data that we have on that. And additionally, we're going to be going over ABS-201, a really new, exciting breakthrough potential therapy in the category of hair regrowth. Now, before diving into these exciting updates, let's look at last year. What did we accomplish?
We were able to launch a new, exciting de novo model where we're able to now take a target of interest that has no known binder and generate antibodies to that particular target of interest, being able to go after hard, challenging, complex targets such as the HIV CDR3 region. We've been able to successfully apply this model to the execution of partnerships with AstraZeneca and Almirall, looking at GPCRs and ion channels. We've been able to enter into strategic partnerships with AMD, where they've made a $20 million investment in Absci. We've been able to meet our guidance for 2024, entering into four new partnerships, which I'll talk about on subsequent slides. We've been able to advance our pipeline. ABS-101 is a potential best-in-class TL1A antibody, which will be in the clinic early this year with a phase one interim readout in the second half.
Additionally, we have ABS-201, a new potential therapy in the category of hair regrowth, going after androgenic alopecia, targeting the prolactin receptor. Additionally, we have ABS- 301, a potential novel IO target, and ABS- 501, a potential best-in-class HER2 antibody. And what's exciting about this particular antibody is for trastuzumab-resistant cell lines, we see efficacy. Now, over the last five years, we've been taking our platform, our data generation engine, and applying it to AI, this merging of tech and biology. And what's allowed us to have the success that we've had in reflection, it's four key ingredients. It's data, it's the models, it's compute, and the multilingual expertise. This is what's led to Absci's leadership position in the de novo design of AI with our AI models. Now, let's talk about the data. We've always been a data-first company.
The company was founded building synthetic biology technology that allowed us to scale protein-protein interactions, how antibodies interact with targets of interest. We were able to go from screening tens of thousands of antibodies to being able to screen millions, and this was right around the time deep learning was taking off, Transformers in 2018, and it was this idea if you could take this data with these Transformers, you could really go from this paradigm where you're searching for a needle in the haystack to actually being able to create the needle, in our case, an antibody, and this led to the development of a lab-in-the-loop, this iterative process where we're able to go from data in the wet lab to training our models to then being able to validate them, and we do this in a very rapid time period, in six weeks.
This allows us to rapidly iterate on our model designs and architectures and has allowed us to advance our models at a very rapid pace. And it's the reason we're here today. Now, let's talk about these leading AI models. We have two. We have our de novo design model, where we're able to design antibodies from scratch. You have a target of interest, you specify the epitope you want the antibody to bind to, and the model's able to design the CDRs that can bind to that particular target and epitope of interest. Now, once you have a binder, we have our lead optimization model. This is based on state-of-the-art protein language models.
And this is where we're able to co-optimize simultaneously different parameters, such as developability, manufacturability, or what I like to call smart features, such as pH dependency, binding in the tumor microenvironment, but not binding in healthy tissues. These leading AI models are used to create novel and differentiated therapeutics. We don't want to just use AI to make things faster and cheaper. We want to use AI to go after the hardest problems that still exist in our industry, being able to go after GPCRs, ion channels, these undruggable targets, and being able to then apply engineering principles to introduce precise control over the designs so we can enable smart biologics such as pH dependency, being able to increase or enhance potency in MOA. That's why we're building these models to solve the problems that still exist within our industry and to create these novel and differentiated therapeutics.
Now, let's dive into two case studies that illustrate this. We went over these case studies at our R&D Day in December, and so I encourage you all to click the QR code if you are interested in diving deeper into these, but I'll mention them at a high level. First, on the de novo design, we partnered with Caltech and the Bill and Melinda Gates Foundation to go after HIV. The goal was to design an antibody that could be a neutralizing vaccine for HIV, and the researchers at Caltech, Steve Mayo and Pamela Bjorkman, discovered the co-receptor region, this highly conserved region in HIV. Now, the issue with drugging this region is that it's a deep crevice, and no traditional technologies have been able to generate antibodies that can bind to this highly conserved region.
And we were able to use our AI models, our de novo design model, to generate a very long CDR3 antibody that could bind in that deep crevice. Now, this is the first time anybody has ever been able to drug the co-receptor region. And this is really a great example of how we've been able to apply our models to solve these challenging problems. And not only that, this could be a potential neutralizing vaccine for all different clades or variants of HIV. So if we move to the right-hand side, we were able to show with our lead optimization model that we can start to engineer in these smart features. We showed that we can design molecules that have pH dependency. They bind in the tumor microenvironment that is more acidic, but don't bind in healthy tissues.
These are two case studies that really illustrate how we are utilizing AI to solve these challenging problems in this industry. As three JPMs ago, we released a pivotal manuscript on de novo design of antibodies. This is where we were able to, for the first time ever, actually anybody in the space was able to design the HCDR3 of an antibody to bind to a particular target of interest. This was HER2. That's the most, the HCDR3 is the most variable region in the antibody. And since then, we were able to increase that to three CDRs. And now, with our latest models, we're to the point where we can design to a target of interest that has no known binder, and not only that, go after challenging and hard targets. I like to see that this very much reminds me of how AGI is progressing.
are different levels, and we're going to continue to progress just like AGI in de novo design of antibodies. We're really just getting started. As we start to see these early wins on the board, what we're seeing operationally is that we are spending less money in the wet lab and spending much more money on compute. That really got us thinking. We need to figure out how to scale compute more effectively. Just last year, we doubled our compute capacity. That's the reason we decided to partner with AMD, was to get better price performance on these chips, being able to scale much more effectively, and additionally being able to get better training resolution.
I was able to sit down with the CTO of AMD, Mark Papermaster, last week after we closed the deal with AMD to talk a little bit about why we decided to partner with AMD and why they wanted to make us a Lighthouse account. With that, I'll turn the video on.
I'm Sean McClain, the founder and CEO of Absci, and I'm here with Mark Papermaster, the CTO of AMD. We're here at AMD's headquarters in Santa Clara. Mark, thanks so much for having us here today. Sean, I've been so looking forward to this discussion.
So before we dive into the exciting collaboration that we have together, why don't you talk a little bit about your vision for healthcare and specifically your vision for drug discovery?
We're all about high performance. When you think about high performance computing and where AI can play, it's about acceleration of innovation and new invention. Where can that have an impact that really helps society? It's exactly, Sean, where you and Absci are focused. I think you guys have already demonstrated that you can make an impact and can speed that impact through the use of AI and high performance. That really motivates us at AMD to collaborate with you. It drove this investment, and we're really excited about what's to come.
Yeah, I couldn't agree with you more. The industry, in particular in AI drug discovery, is progressing so fast. Yes, it's at its early stages, but it has so much potential. And we're already getting early wins on the board. If you look at what we've been able to do, we've been able to use our de novo design model to design antibodies from scratch. And not only that, we're getting drugs into the clinic just this year that were designed with AI. We're getting those early wins on the board. And now what we're seeing is the shift in capital going from spending in the wet lab to actually spending it on compute, on these AI models. And we're seeing this new paradigm where we're starting to actually have a lab on a chip. And this is the future.
And that's why we're partnering with you, because we realize that compute is so critically important to solve the challenges we want to solve with AI in drug discovery. We want to get better efficiencies on our training and inference. We want to be able to solve new complex biological applications that we see your chips being able to provide with, like higher memory. And so the vision that we have is very much aligned with your vision as well. And so why don't we dive into Absci's collaboration with you? Why did you decide to make a $20 million investment in Absci?
You actually hit part of the answer just in your very comments, because to do the kind of discovery you are accomplishing with this data and analytic and AI-driven analysis, you need a ton of computation. And you just said it. You need it to be efficient. That's what we do. We're very focused on really changing the paradigms. There's not been competition in this industry. We're bringing competition. We're bringing that high performance, but we have architected to bring more memory to bear. It's going to drive a much more efficient inference and a very competitive training. And that's exactly what you and Absci's team have taken advantage of.
And so that's what drove our investment, is we wanted healthcare and societal impact to be one of our first vertically focused investments that we make in AI so that we can make a difference together and we can make a difference in society.
Yeah, absolutely. So we've been working together for three months or so. And over that three months, we have been able to accomplish a lot. We've been able to take our models, three models actually, and be able to show that we can run them on AMD chips. We've been able to show that we can get great performance on inference. And we're also able to train on your chips as well. And we were astonished by how quickly we were able to spin up our models on your chips. And our team got really excited. And I think that's what really drove some of this collaboration that we have is this innovative mindset. Can you speak a little bit about how you approach innovation and how you approach collaborations to make them successful, like the one here with Absci?
It's really in the way that we operate. So the DNA of the company, we're built around collaboration and innovation to drive high performance. And so if you look at our history, so we have a broad portfolio with CPUs, GPUs, and embedded computing in gaming. So we have decades of experience on GPUs, which is what you're primarily banking on for this acceleration. And so what we found is that you can run a generic application and get excellent performance. You take advantage of the parallelism of the GPU. But the toughest problems need a collaboration to go deeper than that. They need to really understand the root of your algorithms. Where might they be bottlenecks that if we co-innovate together, we can eliminate them and push even further the boundaries of the computation capabilities?
In three months, I think we've demonstrated that the teams can readily work together. They've already been pushing the performance forward. Sean, I think the future is really bright. There's lots more that we're going to do together here.
Yeah, no, absolutely. I feel like you guys have the entrepreneurial spirit. You're hungry. You're tenacious. You're willing to solve hard, challenging problems. And that's what we look for in partners. And that's why we believe that this partnership is going to be so successful. And if I summarize what we're trying to do, at the end of the day, we're trying to get better drugs to patients faster. And we're switching from the way of doing things in the wet lab to ultimately designing drugs on a chip, on these AI models. And we need AMD to ultimately help us achieve that, being able to get better training efficiencies, being able to get inference costs down, and helping us solve these complex problems that still need to be solved within biotech and biology. And so, Mark, we're really excited about this partnership. And the best is yet to come.
With that, do you have any final thoughts?
I do. I'm so excited that we're working together to not only advance the science, advance the innovation, but to give you a better economics in doing so, to speed the delivery of new solutions. I mean, it's really a superintelligence you're applying with AI that's going to speed new antibody development. It's in our DNA to co-innovate and to push the boundaries. And so I'm hoping that DNA creates future DNA and antibody solutions in the future through our collaboration.
Absolutely. Well, thank you so much, Mark. It's been a pleasure and really excited to ultimately collaborate to get better drugs to patients. Thank you.
Thank you, Sean.
I mentioned that we worked on transferring over three workloads or three different models. And what we saw from that was pretty remarkable. And it really gets back to why we chose AMD. First was the unmatched training resolution. With protein design, these protein design models, when you have lower memory capacity, you have to do cropping. You're not able to get the full protein trained on your model. And so you have less context going into the training, which means that your models aren't going to have as much information and therefore not be as accurate. But if you can have increased memory, you can get more biological context. There's no longer cropping of these proteins. And that means that you can actually get higher and more accurate models. And that's exactly what these AMD chips provide. They provide us with industry-leading memory capacity.
They have the best memory capacity of any chips in the industry. And so that delivers point number one, unmatched training resolution. The second is accelerated throughput. Through batch processing, we are able to significantly scale the in silico design and evaluation of our antibodies, which dramatically reduces our R&D time and overall costs. And we're able to do this batch processing again through the higher memory on the chips. And so this is giving us better price performance as well as better training resolution. Those are the reasons why we decided to go with AMD. And as we continue to scale as an AI drug discovery company, we believe that this is going to be extremely important. Because remember, we're seeing costs go from the wet lab to compute.
We really need to make sure that we can figure out how to get the best training and how to get the best price performance. Just like AMD, we partner across the board with industry-leading companies. We do this to ultimately get better drugs to patients because we believe that it's not just one company that gets better drugs to patients. It's the whole industry. We do it together. It's exciting to be able to partner with industry-leading partners like the ones that you see on the slide here. Now, the fourth key ingredient to success is this multilingual team. I'd mentioned that over the last five years, we've been integrating biology and AI together. What I've seen is that your team has to understand each discipline extremely well.
Your AI scientists not only have to know how to design leading AI models, they actually have to know the problems that they're solving, which means that they have to be experts in protein engineering. They have to be incredible drug hunters. And the same is true of the wet lab scientists. And we've assembled an incredible team of Unlimiters here at Absci that do exactly that. And they're able to take what seems to be impossible and make it a reality every single day. And it's a true honor to be able to work with this team that we've been assembling over the last five plus years or so. Now, we're taking these leading AI models and applying it to our own internal pipeline that's focused on IO and oncology. We have ABS-101, which is a potential best-in-class TL1A antibody going after IBD.
We have ABS-201, which is a new, exciting potential therapy in hair regrowth, essentially common baldness or androgenic alopecia going after the prolactin receptor. We have ABS- 301, which is a novel IO target, which will be disclosing the in vivo efficacy data earlier this year. And we have ABS- 501, which is a potential best-in-class HER2 antibody, where we've been able to show superior efficacy in trastuzumab-resistant cell lines. Now, let's dive into the latest data that we have on our TL1A antibody. We've been able to show a really compelling profile in this PD study that we did. We were able to show and confirm target engagement. We were able to show a dose dependency as we increased the dose. We ultimately hit a nice ceiling. And third, at the same dose as the competitor molecules, we see improved target engagement.
We say sustained target engagement as well through day 60. We see this as a very encouraging PD profile and target engagement. Especially as this program enters the clinic, we're really excited to see the phase one interim readout on that in the second half of this year. ABS-201. I think a lot of investors and a lot of people thought that we were going after atopic dermatitis. No, we are going after hair regrowth. We're really excited about the opportunity here. It's a massive market. There's huge unmet medical need. The target itself is a validated target on both efficacy and safety. Additionally, the development path compared to other indications is relatively fast and cheap. Now, let's look at the unmet medical need. 80-90 million Americans suffer from androgenic alopecia. Again, just common baldness.
The last therapy that's been approved in androgenic alopecia was in the '90s, 25 years ago. Patients and clinicians are looking for better treatment options. They don't want just slowing of hair loss. They want hair regrowth. They want safe and minimal side effects. They want a durable and lasting effect and a convenient way to administer the drug. That's exactly what ABS-201 is hopefully going to deliver on. If we look at the mechanism behind the hair growth of ABS-201, it's built on the prolactin receptor. If you look at the hair growth cycle, you start off with the anagen stage. This is the active growth stage. This is where you have new hair growth. This lasts anywhere from two to six years based on your genetics.
And then prolactin within the scalp pushes the phase from the anagen to the catagen phase, where you start to see apoptosis. And ultimately, your hair falls out and you start to see the hairline recession. And now, by blocking the prolactin receptor, what we see is that the catagen phase gets shunted back into the anagen phase. And you start to get active hair growth again. And I'll show you on the next slide here that once you're in the active phase, you stay in it for that two to six years based on your genetics. And so this is a really exciting new mechanism for hair regrowth. And not only are we seeing hair regrowth, but data suggest that we can actually restore potentially hair pigmentation, essentially going from your gray hair to your normal hair color. And we see this as an exciting additional upside.
Now, let's take a look at the translational model that validates the prolactin receptor target. So there is a monkey study that was recently done, stump-tailed macaque. And this is a population of monkeys that naturally go bald. And so what you see on the screen here, the images, are the tops of the monkey's head. They look pretty bald in the baseline photo here. And as they go on treatment, as they go on this anti-prolactin receptor antibody, you start to see the hair regrowth. And treatment stops after 28 weeks. And the hair continues to grow. And you see hair growth. And you see hair durability sustained all the way through four years.
Not only are you getting the hair growth during the treatment, post-treatment, you're able to see this sustained and durable hair growth, which again shows that the prolactin antibody is again pushing the hair follicle back into that anagen phase where you're getting the active hair growth. Now, how does ABS-201 compare to minoxidil? We performed a study. This is a mouse study where we shaved the mice. We dosed at two different doses, ABS-201 and compared it to minoxidil. You can see from the images as well as the hair score on the right-hand side that we see with ABS-201 superior efficacy versus 5% topical minoxidil after 21 days. The hair grows much faster. Again, seen superior efficacy for ABS-201. Now, let's dive into the market. ABS-201 represents a massive market opportunity.
As I had mentioned, 80-90 million Americans suffer from androgenic alopecia. From talking with KOLs, there is a strong willingness to self-pay. And the market size, we estimate conservatively, is $14 billion. And that's assuming an 11%-12% conversion rate that you see with Botox. But we think that this could be even much larger. We think that that conversion rate could be two to three times that, making this market size obviously a gigantic market opportunity. And that doesn't even include repigmentation, which would be additional upside. 2025 is going to be a really exciting and pivotal year for Absci. We have some exciting catalysts that are coming up. We have ABS-101, as I mentioned, is going to be entering the clinic here shortly with a phase one interim readout the second half. We have ABS-201, which we've just nominated a drug candidate.
We're in IND enabling studies with the plan to enter the clinic early next year. We have ABS- 301, our novel IO target, which is going to have in vivo efficacy data in the first half of this year. In addition to our preclinical and clinical readouts, we also are guiding to one new large pharma partnership that will be announced this year, which has the opportunity to bring in substantial non-dilutive capital. You can see that this year, again, it's a catalyst-rich year with a lot of near-term value inflection points. With that, we'll open up for Q&A.
I'll kick things off, so you've advanced your AI platform over the last couple of years quite impressively. Do you see room for even further progress, and what does that look like?
Yeah, absolutely. As I mentioned, we see this very much how AGI is progressing. I think they have six different stages of AGI or different phases. We see de novo design in a very similar camp. We started off with one CDR, three CDRs, and now we're designing the whole antibody from scratch, going after challenging targets. And so we continue to see that progress each and every year. And I think that there is still a lot of room to continue to improve. One of the areas that we would love to get into is not only predicting the design, but starting to predict the functionality of an antibody. What epitopes should we be going after? And which of those are going to give us the functionality we're looking for? So I think those are kind of the next steps going forward in the future.
But yeah, we're going to continue to progress these models. And I think there's a lot of room left to continue to improve and grow.
I guess taking a step back, what are some of the key differentiating features and capabilities that your platform has that set it apart from your competitors and enables your drug creation pipeline?
I think Sean's going to let me take one, so I think you saw some of it here today. We are, in my mind, and I used to be an investor in this space, we're clearly the leader in de novo design, so we're now taking these models and we're designing against targets that you can't address in any other way. I think the HIV case study is a great example of that, but we've done other work with partners against ion channels and very difficult epitopes and transmembrane proteins that we can't share publicly where we've seen success, and we've seen success in a rapid amount of time. In one of those partnerships, we designed against a difficult target, de novo, in six months, we delivered leads.
So we're starting to see the ability to attack these targets that have fundamentally solid biology that can treat diseases where no one else has been able to drug them. And I think that opens up a whole new area of therapeutics that don't exist today. And then on the other side of that, with some of our lead optimization models you saw Sean talk about designing in pharmacology. And that's a direction that we've started taking early this past year. And you're seeing the results here. Now we're generating pH dependency in our molecules if that's something we want to engineer into a TPP. So that flexibility to design in pharmacology is also a key way to deliver differentiation and ultimately to deliver what patients need.
I think it's important.
I want to add--
Oh, sorry.
I think what you guys are doing regarding hair regeneration and pigmentation is phenomenal. I was just curious, how far away do you think you are from the human trials? I saw it was with the rats and the macaque, I think it was. But I was just kind of curious because it seemed absolutely impressive.
I did not understand. I'm sorry. Can you repeat that? I don't think we cut-.
Just how far away do you guys feel you are from human trials in terms of the hair regrowth and repigmentation? Because what you guys showed up there looked absolutely phenomenal compared to the minoxidil and what's on the current market today that's outdated.
Sure. I think Sean has tried to show you that now we have started with the pre-IND development activities. We assume at this point, given the normal timelines, that the first in man will happen in the beginning of next year. Having said that, of course, we certainly want to share the excitement again about the profile we expect based on non-human primate observations we've made on the efficacy side. But what is also very important, and I want to certainly spread this information to the audience, is that we have incredible information about the lack of side effects to be expected with this mechanism because we do have human knockout data described in a New England Journal of Medicine paper in which it was shown that a woman with a knockout of the prolactin receptor had a very, very healthy appearance, very healthy life. She even bore two children.
The only observation was that she could not lactate, which is the most obvious consequence, of course, of lack of prolactin signaling. So we are super excited to see what we expect to see very, very soon.
Hi. Thanks for the presentation. That was great. I was hoping you could maybe double-click a little bit and talk about the rubric you apply in developing assets and out-licensing them versus things you're going to sort of home-grow and develop however far through. Just what are you looking for in something you keep versus partnerships? How do you sort of think through that at a high level?
Yeah.
I think we look at our portfolio case by case. Partnerships, as Sean has indicated in his presentation, are always on our mind. The partnerships, they should fit to the individual asset. For example, I think TL1A is an antibody of which we're very proud of. It will be, in our assumption, the best in class, a TL1A antibody. However, it's going to be facing a very competitive environment in which one big player is very likely a very, very good owner and partner to take that forward. That is supposed to take place as soon as possible. In contrast to that, we do believe that 201 is one of those assets based on a very simple and fast and inexpensive, straightforward clinical development pathway. This is an asset which we can take much, much further before we ultimately consider partnering that.
And the last example I would like to mention is our 301 development candidate, which hopefully will be development candidate by the end of this quarter or next quarter. That's an IO compound. And of course, we do believe that in the IO space, it is important to gain the experience of the deep experience of somebody with deep IO experience, a big pharma partner, to join forces with us rather early on. So I think this is an asset which we would want to partner as soon as possible to get the maximum value and the fastest progression out of this asset. So asset for asset, a very different strategy.
Yeah. And I will say we are doubling down on 201. We see that as our flagship internal asset that we want to continue to develop ourselves. Obviously, there's a huge market potential. We have the potential to be first in the U.S. And we're going to continue to develop that. And I think some of these other assets, obviously, we'd partner sooner. But we see this as a fully owned asset of Absci moving forward.
I think we're out of time for today. Thank you so much for a great presentation and a great session. Thank you.