Good morning, everyone. On behalf of our team at Absci, I'm thrilled to welcome you all to Absci's 2024 R&D Day. We have an exciting program of presentations, speakers, and updates that we are pleased to be sharing with you all, and I would like to thank everyone, both here in the room, live in New York City, and on the webcast, for your time and attention today. Before we begin the presentations, please take a moment to review these disclaimers, as during the program today, we'll be making some forward-looking statements, which involve risks and uncertainties, and you may refer to our filings with the SEC to learn more. This morning, we issued a press release and posted the full slides that we will be presenting, which can be found now on Absci's website.
Looking at our agenda for today, we are pleased to have a lineup spanning Absci's leadership team across the board and are honored to welcome some guest speakers to present today as well. After the prepared presentation, we'll open the floor to Q&A and conclude with a reception for those here in attendance. For those here in attendance, refreshments will be available for the entire duration of the presentation in the room next door. And with that, thank you, everyone, again for your time and attention today. And I'm excited to be handing over to Sir Mene Pangalos, Absci's Board Director and Co-Chair of Absci's Scientific Advisory Board, via video recording to kick off our presentation.
Hi, everyone, and welcome to Absci's R&D Day. My name is Mene Pangalos. I'm previously the head of research and development of biopharmaceuticals at AstraZeneca. I was there for 14 years, and I'm incredibly excited now to be part of Absci's board and chair Absci's Scientific Advisory Board. During the course of the next few hours, you're going to get to hear about some really, really cool science and actually what engaged me to want to become part of their board. There is a lot of noise about AI-aided drug discovery and design, both in the small molecule and antibody space, and I spent a lot of time building teams and working in this space.
I think the Absci's AI-driven antibody design platform, for me, is probably one of the best platforms I've seen in this space, particularly because of the fact that I think it's one of the first companies that can truly do now de novo design of monoclonal antibodies, which is truly exciting. The reason de novo design is important is because I think it's going to enable Absci to be able to develop new therapeutics to targets that have previously been very, very difficult to drug with traditional screening methods. Again, I think during the course of the day today, you'll hear some examples of some targets, such as the HIV Caldera, that haven't been possible to drug with traditional monoclonal antibodies thus far.
Since the last time that I think you heard from Absci's R&D Day, you'll see a number of new partnerships that I think highlight the strength of the platform. There have been partnerships with companies such as AstraZeneca, Memorial Sloan Kettering, Almirall, and I think there are more partnerships to come. And finally, you're going to hear, I think, some really interesting presentations that start to highlight not just the really cool de novo design platform, but actually also some of the pipeline assets that are now within the portfolio, including our TL1A antibody, our prolactin receptor antibody, and a variety of other antibodies targeting immunology and immuno-oncology targets that I think will propel the company to great strength.
Over the next two to three hours, you're going to hear some really exciting science about what I believe is one of the best de novo antibody design platforms using artificial intelligence. You're going to hear about some really exciting pipeline assets that have the potential to be best in class and differentiated. And you're going to hear about how this platform can really identify antibodies and therapeutics to targets that have never been drugged before, which I think is unique and super exciting. And I hope you leave this meeting feeling confident that Absci has the tools and the capabilities and the know-how to turn its AI platform into something that can generate really cool, novel therapeutic antibodies that will create huge value.
Our thanks to Sir Mene. And now, please welcome Absci's founder and CEO, Sean McClain.
Thank you, Alex. Thank you all for coming today. It's great to see all of you. We have an exciting lineup today, and so I'll dive right into it. Our mission at Absci is to get better drugs to patients faster using generative AI design. We are a data-first generative AI drug discovery company. But we didn't start off as an AI company. We are a synthetic biology company that figured out how to scale protein-protein interactions, how antibodies interact with targets of interest. And we were able to scale that from thousands or tens of thousands to millions in a given week. And this was right around the time deep learning was taking off at Google with the transformer in 2018.
It was this idea if you could take this data with these transformers, you could really go from this paradigm in drug discovery of searching for a needle in the haystack to actually being able to create the needle. In our case, a biologic. Being able to develop a biologic with all of the attributes you want. Being able to have the epitope specificity. Being able to have the developability, the manufacturability. What this would enable is you to actually go after these challenging targets, these undruggable targets that still exist today, and being able to leverage generative AI to not be a "me too" or a "me better," but actually to be able to tackle challenging problems that still exist in biology that we haven't solved. Today, you're going to see how we've taken that vision and made it a reality.
We have exciting updates that we're going to be sharing on our platform, our partnerships, and our pipeline. On the platform side, we've made tremendous progress in being able to show we can use generative design to be able to develop antibodies from scratch to difficult targets. You're going to be hearing from Amaro today about how we've utilized this platform to ultimately drug HIV and potentially create a neutralizing antibody. You're going to hear about the successes that we've had on our AstraZeneca partnerships, as well as Almirall. Additionally, this year, we've announced exciting new co-development partnerships with Memorial Sloan Kettering and Twist. And finally, we're going to end the day talking about our advancing pipeline. We have ABS-101, which is our potential best-in-class TL1A asset. We're going to be unveiling ABS-201, a category-defining drug for androgenetic alopecia. It's against anti-prolactin receptor. We're really excited about this.
We see this as a huge opportunity, and last but not least, we're going to end the day talking about our oncology pipeline, ABS-301 and ABS-501. Now, we're wanting to utilize AI to tackle challenging problems, and we've developed AI tools to enable this. First, we have our de novo model that's allowing us to ultimately go after hard-to-drug targets, such as GPCRs and ion channels, and additionally, as I mentioned before, being able to target the Caldera region in HIV, a huge breakthrough.
Additionally, we're able to utilize our lead optimization models to be able to do multiparametric modeling, to be able to develop smart biologics that are conditional, being able to engineer molecules to have selectivity, bind to this, don't bind to that, be able to engineer in agonism versus antagonism, and again, being able to do multiparametric modeling, being able to develop affinity, developability, manufacturability, all at the same time. Now, there's a lot of AI drug discovery companies out there. And what makes the best companies successful in this space are four key ingredients. First is leading AI models. At Absci, we are leading the way in de novo design of antibodies. We have some of the best AI scientists in the space that are pushing this forward. Second, you need compute at scale.
We have partnerships with Oracle and NVIDIA that are allowing us to scale our compute as we increase the amount of data and increase the model complexities. Third, you need a data advantage. As I'd mentioned, we are a synbio company, figuring out how to scale protein-protein interactions. We're using this data for model training, as well as being able to use it for the validation of our models. And last but not least, you have to have drug discovery expertise. We have an amazing team here at Absci that has over 10 drugs approved under Andreas Busch's leadership. And this is what has allowed Absci to have a leadership position in generative AI antibody design. Now, let's talk a little bit about our AI platform. We have our de novo design models, as well as our lead optimization models.
With de novo design, we're able to take a target of interest, specify the epitope that we want the antibody to bind to, and have the model design the CDRs that can bind to that particular epitope of interest. We also have our lead optimization models that enable us to have tunable pharmacology. And this is all enabled from our Lab-in-the-Loop . We're able to do this in a six-week time period, being able to go from data to train, training those models, to then ultimately validating those models in the wet lab, allowing us to rapidly advance our model designs and architectures. And this is a key reason why we've been able to advance our models so rapidly, is this Lab-in-the-Loop .
We're leveraging AI throughout the whole drug discovery process, from target selection to the de novo design, generating the hits, to ultimately then optimizing those molecules with our AI lead optimization models, and this is really allowing us to get to drug candidates very rapidly. With TL1A, we are able to get there to get to a drug candidate in 14 months. We'll be in the clinic in roughly 24 months, and this is really showing how rapidly we can progress to drug candidates and ultimately get into the clinic. At the end of the day, AI is a tool, and what we're leveraging this tool for is to ultimately solve these challenging problems that still exist within biology and ultimately creating novel and differentiated therapeutics for patients.
And we're able to do this through our de novo design model, being able to have the ability to target a specific epitope of interest. And you're going to hear in ABS-501 how we've actually used that platform to enhance potency and MOA. We're able to ultimately address difficult targets. You're going to be hearing about that today as well, and being able to have enabling features such as multivalency and pH dependency. And because we're able to search a much larger search space with AI, and we have the wet lab to then go and screen those, we're able to actually have much broader IP claims than traditional technologies could provide. Now, I like to think of this industry as a team sport. We can't get these cutting-edge therapies to patients alone.
We have partnerships with leading pharmaceutical companies such as AstraZeneca, Merck, and Almirall, where they're bringing in their therapeutic expertise. They have late-stage clinical development, the commercialization. They can partner with us on helping us commercialize these drugs. We have co-development partnerships with Memorial Sloan Kettering and Twist, where they're bringing in exciting new targets for us to work on. And then additionally, we have compute partnerships with Oracle and NVIDIA to allow us to scale that compute, and data partnerships for our Reverse Immunology platform and target discovery with institutions like University of Oxford and Aster Insights. And again, this ecosystem is extremely important to Absci in order to see our mission through, which is to get better biologics to patients faster. And at the end of the day, this is all made possible from our team of Unlimiters.
We call ourselves Unlimiters because we're making the impossible possible every single day. No one thought you could actually design an antibody from scratch with an AI model, and here we are today, able to do that. We see this as transforming the industry, and this is because of the amazing people we have at Absci, and I also think having a multilingual team has enabled this success. Our AI scientists understand protein engineering. They understand drug discovery and development, and vice versa. This is so critical for ultimately AI to have an impact in biology and for tech and biotech to converge, and last but not least, we have an incredible drug hunting and discovery team that's led by Andreas Busch. He's gotten over 10 drugs approved under his leadership, and he has taken this company that didn't have a pipeline to having four exciting pipeline assets.
His team has just done an incredible job of ultimately delivering these exciting assets that we're going to be talking about today. So it's with great pleasure and honor to introduce my good friend, Professor Andreas Busch.
Thanks, Sean. He always says professor when he wants to make me angry. Yeah, it's definitely a privilege being here with all of you today. It certainly has been a privilege being in the job I am in over the last couple of years. And it's a great privilege having my team with me, which some of you may remember from last year or from 12 months ago. They are still the same people. They grew about three years older over the last 12 months with all they did accomplish. So let's now transition over the next hour or so from what Sean has told you where we are in theory to the true data. And really, what is our task today is that the old promise AI is going to contribute in the future is not a promise anymore.
I mean, those of you who are a bit older remember that, oh, AI is going to impact R&D in 20 years or in 30 years. We try certainly to convince you today AI is today. What you see today is the product, of course, of human brains, but applying AI to every step of the R&D process. You have already heard that we have moved our drug creation pipeline nicely forward, and I will get into the details later again. This will be mainly done by Christian, Christian Stegmann, who is my SVP of drug creation. He will show you that we are on track to deliver what we believe will be a best-in-class TL1A antibody, and he will show you the new data we have accumulated over the last year.
We will talk a lot and tell you a lot about a super exciting asset, which is our ABS-201. Sean has already mentioned this is a prolactin receptor antibody with a blockbuster potential and a target quality, which in my career I have not seen very often and have seen lots of targets, and we will also tell you about the progress we've made with our Reverse Immunology platform and our oncology approach, where we have, again, gathered lots of data on our first immuno-oncology asset, ABS-301, and we will talk about another candidate where we generated a lead. All that a bit later, but we will start with a platform which enabled us to get there. This platform is headed by Amaro, who you will see very, very soon. Amaro is a cognitive brain scientist. That already is pretty scary.
But once you work together with him, you get scared every day by his brain and by the way he approaches science and forwards what we can do with AI. What he could really achieve over the last year, I think, is breathtaking. And it's great to say we are the de novo platform, potentially the leading one in the world. That's one thing. But it's much better if you can show the data which prove that, especially obtained in partnerships. And Amaro will convince you today that we have de novo designed antibodies in our AstraZeneca partnership on a difficult oncology target where there hasn't been any binder known before.
He will talk about an even more challenging target, which is a transmembrane ion channel in our collaboration with Almirall, and talk about what already Sean has indicated about the de novo AI design of antibodies to a super difficult target, which is the Caldera region of HIV, which we have been engaged in and working on together with Caltech, funded by the Bill & Melinda Gates Foundation. All that is addressing the de novo capabilities. We then will also show you that we have made very significant progress in our capabilities in multidimensionally AI-guided optimization of binders. One thing you should know about the natural optimization of antibodies is you improve affinity, and you have a negative impact on immunogenicity. Then you go back and improve in immunogenicity. You have a big impact on pH-dependent binding.
Then you do this, and oh, gosh, you lost affinity again. This is something we believe will make a big difference using our AI optimization approach that we, at the same time, have the model optimizing in parallel for several important parameters, which make an antibody drug-like. With this, it's my pleasure and privilege to ask Amaro to come up on stage and tell you all about the progress of the AI platform. Thanks a lot.
All right. Thanks, everyone. I'm Amaro. Not generally scary, I promise. I'm the Chief AI Officer here at Absci. I'm leading our AI research and engineering teams, as well as our high-throughput screening teams. And I'm excited today to be talking to you a little bit about our platform and then also telling you about two really fresh and, I think, important case studies that demonstrate the promise of our platform and demonstrate really its potential to create drugs, which is what we care about. So at Absci, we're working on developing AI antibody design tools to really solve two categories of problems. The first is to address complex and previously hard-to-drug targets.
We do that by using structurally informed models that enable us to bind specific extracellular domains that might be small or hard-to-drug in general, or creating antibodies that can bind target-specific conformations, which is part of a case study that we'll talk about today. That's important because these can be hard to develop via immunization campaigns. The second is to create a set of tools that enable us to have precise control over an antibody's attributes and characteristics. That's really what we're referring to as smart biologics here. We're designing the antibody to have specific characteristics that are informed by our drug creation team in partnership with Christian's group, where we design antibodies that really meet a target product profile to create a differentiated therapeutic.
So that could be things like engineering selectivity to minimize off-target toxicity, engineering agonism versus antagonism, or enabling us to do multidimensional co-optimization, which Andreas was referring to, where we're able to improve attributes without sort of sacrificing or introducing liabilities. So we are developing two different antibody design platforms. The first is our de novo antibody design platform. And that is our platform for creating novel antibodies without reference to a known binder. So this platform enables us to create epitope-specific binders given a target structure and designed in a framework of interest. So given an antibody scaffold, we design the complementarity-determining regions, or the loops that bind to the antigen. The second platform is our AI lead optimization platform. And that's really a set of protein language models that we apply to a lead antibody to optimize its sequence.
That's what enables us to do co-optimization. It enables us to improve antibody attributes while maintaining developability and introducing precise engineering of molecule pharmacology, which we'll talk about later. In addition to developing these two platforms, that development is really fueled by a Lab-in-the-Loop . I mentioned I'm leading the AI research and engineering team, but also the high-throughput screening team. I think one of the things that really makes Absci's unique is having this kind of integrated wet lab, dry lab team, where we can have a data strategy and a lab that's really supporting our ability to develop our AI tools. The way this Lab-in-the-Loop works, we call it a six-week active learning cycle. We use our AI platform to design antibodies. Then we send those antibodies to our colleagues in the wet lab, who measure their properties.
Those measurements are really data points for us because we can check them against our model predictions. Once we have those data points, we can feed that back into the dry lab, either to create a new data set to train the next generation of models, but also to inform the scientists that are designing these experiments and design pipelines what worked, what didn't work, right? Because the AI pipeline itself is a collection of models and also configurations, and we learn how to better apply those tools, so getting into our de novo design platform, we call this Absci Design. And it really comprises two categories of AI models for de novo design. The first is Absci Gen, or our generative models.
This is a set of diffusion models and other structural prediction models and sequence prediction models, where we, given a known target or a structure of a target, we predict an antibody-antigen complex. So that's an antibody bound in a particular epitope of a target protein. And then we use what's called inverse folding models to predict the sequence of the CDR loops that produce that specific complex. So using that platform, we can generate many, many, many structures because this is a diffusion model we can sample from. And then we feed those structures into a set of models that we call Absci Bind. These are scoring and filtering models that we use to improve the efficiency of our overall design process. So we generate many structures, and then we prioritize those using Absci Bind for validation in the lab. So this is an overview of the entire workflow.
So if you think about this is how Absci Design, or the platform, kind of works. We start by defining design parameters, so these are things like, what is the target antigen? Where do you want to bind? What's the framework region? And other characteristics of the antibody that are important to have a successful complex. We then apply the models, given those constraints, to generate hundreds of thousands of variants and filter these to subset them to the ones that we feel and the models predict are most likely to be binders. And that's through Absci Gen and Absci Bind. And then once we have that set of binders, we order them as DNA, and we screen them in the lab. And we can screen millions of designs in a week against multiple antigens.
We have high-throughput screening assays, as well as confirmatory assays, which really produce very high-quality data and enable us to characterize our antibodies in terms of their developability and binding at a very precise level. We're really, I think, as AI scientists at Absci, privileged to be able to have this data to use it to design the best possible tools. OK, with that, I'll go through a case study. This is an ongoing partnership and ongoing work that we're doing with Caltech in collaboration with Professors Stephen Mayo and Pamela Bjorkman at Caltech, funded by the Bill and Melinda Gates Foundation. The case study that I'm talking about is a de novo design case study where we're aiming to build a novel antibody to target the highly conserved region of HIV gp120, known as the Caldera region.
This is a good problem to demonstrate de novo design because there isn't a natural or synthetic antibody against HIV that binds the Caldera region that exists today. The reason there isn't an antibody that exists today is because the immune system and classical techniques for discovering antibodies cannot generate one. There have been numerous attempts to do this, but they have failed. They've failed for really, I think, two reasons. The first is that the Caldera region itself is hard to access. This is a small region of this antigen, and it's protected by a glycan shield. The second is that this region is only transiently available for immune surveillance. It's only available in the open conformation.
So when your immune system is sort of looking at this particular antigen, it doesn't see the Caldera region very often, meaning it's hard to develop an antibody. So our design challenge is to create a universally neutralizing HIV antibody by binding this unique and conserved epitope within the Caldera region of HIV. Okay, so now that we have the design challenge, we start by determining what the model inputs are. We started with the crystal structure of the HIV gp120 trimer. You can see that on the right. And we chose a framework of 17B. This is another binder. It binds a different epitope of HIV. The reason we selected this is because it supports a long, heavy-chain CDR3 loop. And then we selected an epitope.
The epitope we selected is in this Caldera region, where we selected residues which are 100% conserved across all HIV strains, or clades A, B, and C. Once we've done that, we use our model to design CDRs, or complementarity-determining regions. These are the loops that determine what the antibody binds. In order to do that, we conditioned the model with a set of constraints to really introduce the type of conformation and type of binding that we wanted. We conditioned the model to generate very long, heavy-chain CDR3s, greater than 20 residues. The goal there is to be able to slip that heavy chain into the Caldera region and get past the glycan shield and bind. We also designed heavy-chain CDR2 and light-chain CDR3 to bind to the HIV surface. That's to balance the interaction between the heavy chain and the light chain.
We'll show a video of that in a minute. Okay, so once we have the constraints, we use our diffusion model to generate many different structures. So in this case, we generated over 10,000 structures. And then we filtered those using biophysical criteria and manual review, in this case, to select our four best structures. So we went from 10,000 total and prioritized just four. And you can see a comparison of the heavy-chain CDR3s here of our four best structures along with the heavy chain of 17B. And one of the things to note here is that our heavy chains are actually all diverse from each other. So despite the constraints of the model, where we're saying we'd like you to design in this particular way, the model produced diverse heavy chains that all produce Caldera binders in a different way.
And if you compare those chains to 17B, which is the framework we used, you can see that these are all quite distinct, where you have structures S3, S2, and S4. The heavy chain is pointing in a different direction. And then if you look at structure S1, we have some secondary structure in that loop, which is not present in the 17B loop. So this is a video of a molecular dynamics simulation that we actually ran on a de novo designed antibody in complex with the Caldera region. And you can see here that the heavy chain loop is indeed in that Caldera region. And the light chain here is balancing out and bound to the HIV trimer surface. So we apply molecular dynamics, in this case, to evaluate the de novo designed antibodies and really understand what are the complexes that our models are predicting.
OK, so now that we've generated designs, we need to screen them in the lab, so this is high-throughput screening data from our lab in yeast surface display, where you can see our library of antibodies. Each blue dot here is an antibody, and on the x-axis, we have binding to the antigen, and on the y-axis, we have expression, so if you look on the left for clade A, you can see that our library has binding against the open conformation on the right and no binding against the closed conformation. Remember, the Caldera region is only available in the open conformation, not the closed, and then we also see, similarly, for clade B, our library has binding against the open conformation and not the closed conformation, so taking that high-throughput screening, we selected five unique HCDR3s, which were frequently represented in that library.
And we confirmed their binding in SPR, which is the ground truth assay in the field for measuring the affinity of antibodies. Overall, we looked at five unique HCDR3s. These were paired with multiple light chains. And what we found was that our designs bound against clades A, B, and C against HIV in the open conformation. So the fact that we see this binding in this preliminary data, binding across clades A, B, and C, indicates to us that we are indeed binding the open conformation of the Caldera because it's conserved across these clades and only available when it's open. Cool. So just to summarize the first case study that I'm talking about, we used our de novo design model to create a novel and diverse antibody which binds multiple clades of HIV, indicating successful targeting of the Caldera epitope.
Our screening cascade, both high throughput and in SPR, enabled us to select variants that had differential binding to the open conformation, not the closed conformation, and for next steps, binders from this study are going to be selected for affinity maturation, and structures of our de novo binder and their epitope specificity are going to be experimentally confirmed and solved to confirm the fidelity of the design structure with what we experimentally observe, so this is just to highlight, this is a really exciting result for us. This is hot off the press. We got this about a month ago.
This is really, to us, demonstrating the promise of our de novo design platform in generating a binder that nature really can't produce and is informed by being able to use a structural understanding of the antigen and using AI to design an antibody that will bind in that particular structure. This is really what de novo design is supposed to do. And I think this is the strongest, at least preliminary, case study in the field demonstrating these results. Cool. In the second case study, I'll be talking about our AI optimization platform and applying it for pH sensitivity. The challenge of AI lead optimization here is to be able to create antibodies that exist in really a vast sequence space. And you need to be able to, in order to efficiently search that space, be able to optimize across sort of three different axes.
You need to be able to create antibodies that are developable, have function, and have high affinity, and in order to do that, it's really helpful to be able to use an AI model to be able to search that space very efficiently, so our solution is to create protein language models that enable us to search a space of around 10 to the 19 possible sequences, which is approximately a million times larger than traditional or classical techniques such as phage display, and that enables us to identify functional, developable antibodies in a single step, ultimately making it possible for us to create differentiated molecules faster, so pH sensitivity is something that we've developed capabilities to engineer in-house. The reason that pH sensitivity matters is that it enables you to precisely control the way the antibody will interact in the human body.
And two use cases that I'm highlighting here, the first is to improve tumor specificity while reducing off-target toxicity. So the tumor microenvironment is acidic. And if you can engineer an antibody to bind in an acidic environment and not a neutral environment, you can potentially have an antibody that will bind when it's near the tumor and not bind in the presence of healthy cells. That potentially reduces off-target toxicity. And the second use case is around extending half-life via antibody recycling. So if you think about neutral pH, that's the typical pH. And then if you go into the cell after internalization, you have an acidic environment. So if you have a binder that binds more tightly at neutral pH and less tightly at acidic pH, you'll have a dissociation where that antibody can then be recycled again, extending the half-life.
Given the importance of pH sensitivity, this is how we went about engineering it. We start with data generation. We create a library for training a model. Here in this case study, we sampled mutations from 60 different positions on the heavy chain. We mutated both the framework region as well as the CDRs with up to seven substitutions. We biased those substitutions for all ionizable residues. One thing to keep in mind is that traditional pH engineering techniques only do histidine scanning. They only look at one of these ionizable residues. We then screened that library and used the resulting data to train a model to be able to engineer antibodies with tuned pH dependency. On the bottom here, you can see some of that data. On the left, we have predicted antibodies. Each point here is an antibody predicted by our model.
We have the predicted pH, or sorry, the predicted binding at an acidic pH, or pH 5.8, versus a neutral pH of 7.4 on the x-axis. And you can see that there's many, many, many predicted antibodies. And what we did was we then selected predicted antibodies for a couple of different characteristics. We wanted to see binders that bound at acidic pH and not neutral pH. We wanted to see binders that bound at neutral pH and not acidic pH. We wanted to see binders that bound at both neutral and acidic without any differential. And then we validated those. So if you look on the right, we have data from that validation experiment in SPR. So the red points are model predictions. And the black point is the parental lead, or the lead that we optimized using our protein language models.
On the left, or in the center, we have variants that we designed to be tighter binders at an acidic pH and have less affinity at neutral. And on the right are binders that we designed to have tighter affinity at neutral pH and less affinity at acidic pH. And you can see in the middle panel, all of the red points, or most of the red points, are above the y equals x line, indicating tighter affinity at acidic pH. And on the right, they're below the y equals x line, indicating tighter affinity at neutral pH. And if you compare those red points to the parental, you can see that our engineering was successful in that the predictions all have differential pH in the right direction with respect to the parental. And then so that was in Fabs.
And then we reformatted those as monoclonal antibodies and reevaluated their binding profiles to understand how these would behave as therapeutics. On the figure on the left, you can see those categories that I mentioned before, those three categories. The first is binding at acidic pH, not at neutral. And you can see that we have up to 100x differential here, where you have that red point at the top. And this is in a single round where we have a 100x differential of a binder that binds at acidic pH and not neutral. For the ones that we selected to bind at both, we see no differential in binding between acidic and neutral. And that, again, is engineered, right, because the parental has a preference for neutral pH.
Then in the dark red, we have binders that we predicted to have binding at neutral pH and not at acidic. And we can, again, see that we've produced the differential. So one of the things that's really nice about this, yes, we were able to achieve this differential binding and tuned pharmacology. But we were also able to introduce these mutations without creating any liabilities for stability, aggregation, or polyreactivity. And we have data supporting that in the appendix. We also found that the model produced mutations using all six ionizing residues going beyond what's typically achieved in classical pH optimization techniques. And we made those mutations in the CDR regions as well as the framework regions. And mutating the framework while maintaining developability is something that's quite difficult. So this is really showing the promise of our AI model to be able to introduce those mutations precisely.
Again, the sequences here were proposed from a space of greater than 10 to the 13, which is difficult to achieve or impossible to achieve using classical techniques. OK, so to summarize our two case studies, I talked to you a little bit about de novo design. We demonstrated and shared some data where we used our de novo design model to create a molecule that binds multiple clades of HIV, suggesting successful targeting of the Caldera epitope, which is a difficult-to-drug epitope. This represents our second disclosed success for our de novo design platform this year, the other one being our 8-K in our AstraZeneca partnership. We believe that this case study and the success in the first round with AstraZeneca supports and provides evidence that Absci's de novo design platform can successfully address difficult-to-drug target epitopes.
And then we also talked about our AI optimization platform. And here, I showed that our model is able to generate novel variants with, on average, 10-20x pH sensitivity, but up to 100x differential compared to our parental molecule. And this is just in a single round. And we also were able to design these leads without any liabilities, indicating the ability to be able to successfully search that fitness landscape in a really high-dimensional space. And we believe that this case study really provides evidence that Absci's lead optimization platform enables molecules and enables us to create drugs with differentiated pharmacology. Cool. And with that, I think I'm handing it over to you, right, Christian? To Andreas.
To Andreas. Yeah. OK, sorry. Cool. Thanks.
Thanks a lot, Amaro. I hope really everybody got the picture, you know, how we this year could indeed translate theory into practice, how we could really apply our models to generate antibodies to extremely difficult targets. And at the end, this is not just done for fun, but this is done because we want to see eventually drugs for people which need them. With that, we're going to transition to our pipeline and discuss how our platform helped us building up our pipeline this year. The presentation on our pipeline will be done by Christian Stegmann in a minute. What I would like to point out, if it comes to the pipeline, is the simple aspect that the characteristic of this pipeline has changed, if you remember one year back. In this one year, we could accomplish a significant progression of all of our projects.
If you look at the left, you can see that basically we moved all of our assets from early stage to much later stages. For the first candidate Christian is going to talk about, you see that we are very close to being in man and have accumulated lots of data supporting a very competitive profile. We have nominated another drug candidate. So we have now two compounds in IND-enabling studies. We have forwarded our immuno-oncology approach on a target which comes from our Reverse Immunology platform. And finally, we have decided on a lead structure in another oncology program we're going to talk about. On top of that, there's this small gray bar. We have added three what we believe highly validated targets. Before the break, we're going to talk about one project. Then we're going to take a break together.
That one project is our TL1A antibody for IBD. And Christian Stegmann will present on that. Again, for those of you who were not here a year ago, Christian is heading our drug creation efforts since quite some time now, almost one and a half, two years. Christian has a strong background as a drug hunter. He has an educational background in Stanford, Max Planck Institute. He was at the Broad Institute. He worked for Bayer. And he was research and early development head at Vifor until I kidnapped him for Absci against his will. And I'm super happy having Christian here and leading our drug creation efforts. Christian.
Terrific. It's a privilege to be here on behalf of the drug creation and the innovation team at Absci and to share some updates on the pipeline. My name is Christian. I'm looking after drug creation. Drug creation essentially is in charge of taking AI-generated binders, creating drugs out of them, and then taking them all the way into clinical development. I'll start with our most advanced pipeline asset, ABS-101, which I think most of you are familiar with. I'd like to share some updates if the screen allows me. All right. ABS-101 is an AI-designed antibody. It's designed to achieve a competitive therapeutic profile with a potential for clinical differentiation. Recall that when we set out, we really wanted to hit a very ambitious target product profile. We could deliver. We could deliver high affinity and potency.
We could deliver binding to the monomer of TL1A as well as to the trimer of TL1A. We could deliver high bioavailability. We think we have a profile that allows for low immunogenicity in the clinic, and I'll share some new data on this. We could deliver favorable developability as well, and in the end, we think we have a profile that allows convenience for the patient based on half-life extension and subcutaneous dosing. This is data we have disclosed earlier, but just to recap, we have a clinical development candidate that has picomolar affinity to both the monomer of TL1A as well as to the trimer of TL1A. On this chart, you can appreciate that we benchmarked ourselves here against three first-generation clinical competitors. You can see a molecule from Sanofi. You can see a molecule from Roche and a molecule from Merck.
You can appreciate that the Sanofi and Roche molecules, they don't bind to the monomer in our hands. The Merck molecule, while it has some residual monomer binding, it is actually a very low affinity to both the trimer and the monomer. Compare that to ABS-101, which really has single-digit picomolar affinity to the trimer. We're almost reaching single-digit picomolar affinity to the monomer as well. This translates then also into cell-based function, as measured in an apoptosis inhibition assay in TF-1 cells, where we could show a low single-digit nanomolar IC50 in this apoptosis inhibition assay, really clearly outperforming the Merck molecule and roughly equivalent, if not superior, to the Roche molecule. I'd like to share some new data. They address the issue around immunogenicity. Recall that immunogenicity is an effect that you observe with a large number of antibody therapeutics.
Patients develop anti-drug antibodies. This limits the efficacy in some cases and clearly is a property that has been a burden for a large set of approved monoclonal antibodies. We set out using generative AI to work on this, and we would like to share some new data on this today with you, so recall that when we set out with this program, TL1A, we hypothesized that the epitope that the antibody binds to may induce what is known as immune complex-driven immunogenicity. In other words, the epitope that the antibody binds to may influence the uptake of the immune complex by, for example, macrophages, and this differential uptake kinetics can drive anti-drug antibody rates, formation rates, and hence potentially impact immunogenicity.
To this end, what we did is we've created immune complexes of TL1A with different antibodies: ABS-101, MK-7240, which is the Merck molecule, and RVT-3101, which is the Roche molecule. You can appreciate that when you perform an internalization experiment of these immune complexes in THP-1 cells, which is a monocyte cell line, that both visually on the left-hand side shown in the micrographs, but also with quantified fluorescence on the right-hand side, that you have a lower internalization of our molecule ABS-101 as well as the Merck molecule compared to the Roche molecule. This is a molecule that has been described to elicit antidrug antibodies to some degree in clinical trials. With this data, we are pretty confident that we have a molecule in hand that has the potential of significantly lower ADA rate in clinical development.
I'd like to recap as well our pharmacokinetic data in non-human primates. On the left-hand side, you can see data that we have disclosed earlier. We could demonstrate a two to three-fold extended half-life in non-human primates over clinical competitors. And this is supporting a dosing interval of every two months up to every three months. We could also show that we have achieved an enhanced biodistribution in non-human primates compared to these antibodies in clinical development based on modeling. And this could be an advantage because biodistribution may actually help us in overcoming the need of a loading dose, which is what clinical first-generation TL1A inhibitors display in clinical development. So we achieved as well high bioavailability in non-human primates up to 80%. And I'm also excited to share news around our GLP toxicology studies. So we've completed the in-life phase of the tox study.
There were no treatment-related adverse findings during the in-life phase. And that necropsy histopath is pending. And I think we disclosed earlier as well that we have been really successful in making the excellent developability profile of this antibody a drug formulation. So we've achieved a high drug, high concentration formulation up to 200 milligrams per milliliter. And we've also performed, for example, a tox study in a subcutaneous setting, which is conducive for clinical development. So with this, I'd like to summarize where we are with ABS-101. We've compared ourselves to the three first-generation TL1A molecules that are out there. And we've shown here on this slide seven attributes in which we think we differentiate. We talked about high affinity and potency. And trimer TL1A binding, clearly an attribute where we are outperforming, as well as monomer binding.
Recall that monomer binding may indeed, based on literature, be a potential differentiator and may potentially address non-responders in a late-stage clinical setting. I talked about the potential for low immunogenicity. I talked about bioavailability as well, the ability to actually deliver a subcutaneous injection. And then finally, convenience for patients, which is in our case dosing every two months to up to once quarterly. So where are we in terms of our timeline? Recall that we have nominated the development in the first quarter of this year. Feels like a long time ago, but it was just in the first quarter this year. We have initiated IND- enabling studies in February. Meanwhile, we've completed the GMP manufacturing in a Sub-Q formulation at high concentrations, where we've completed PK and tox in- life. We could also see a low ADA rate formation in non-human primates.
As I mentioned, no treatment-related adverse findings. In the first half of next year, we'll initiate a double-blind placebo-controlled trial. We expect to have a phase I target engagement data readout in the second half of next year. This interim readout, we think, is going to be a key de-risking event because what we'll hopefully be able to show is target engagement in a sustained half-life extended setting. Demonstrating that over a sustained period of time, we can demonstrate targeted engagement and essentially de-risk our target product profile significantly. All right. I think with this, we are ready for a break. I'll invite you for coffee and breakfast. I think it's in the adjacent room. Anything to add, Alex?
10 minutes.
10 minutes. All right. 10 minutes. Short break. Thank you.
Thank you, everyone, and welcome back. And now, to begin the rest of our presentation, I'd like to welcome Andreas Busch back to the stage.
Thank you. I hope you all could refresh a little bit because fun is over. Now we're getting into even more exciting data. We're going to talk now about our newest drug development candidate, which we have announced earlier this month. It is an antibody against the prolactin receptor, which may have caught you by surprise. Also, the indication we're going to address. We are in the midst of starting, of course, the IND- enabling activities and hope, again, to be in patient in record time.
I said you might have been surprised about this target and the indication, and I want to let you know why we are so incredibly excited about this particular drug candidate. First of all, there's a huge clinical unmet need coming together with a ginormous commercial potential. There's a significant unmet clinical need for androgenetic alopecia. There is a large market in the U.S. alone. You have 80 million patients, with probably 5 million being added as a side effect consequence of taking GLPs every year. It's a ginormous market, and talking about the unmet medical need of patients will be Anthony Rossi today, which is a privilege having you with us, Anthony, and describing the market will be Mike Jafar.
The fact that the patients are so highly motivated to get this type of treatment is also reflected by Amaro working his butt off to optimize our lead this year and being committed to be the first patient in our phase I study. What increases further our scientific rationale is, of course, the aspect we work, what I would consider in my career, on the highest scientifically validated target, and this for both, for efficacy as well as for side effect. Both is, of course, very important in this indication. When it comes to efficacy, we have data in a naturally occurring non-human primate model showing that an antibody against the prolactin receptor indeed causes a reversal of androgenetic alopecia and hair growth, which is pretty striking when you start a project. This is what you want to see at the end of your preclinical characterization.
And on the safety side, we have nothing less than a human knockout, a human knockout, a woman which was perfectly healthy. She also could become pregnant. She bore two healthy children. The only consequence was that she had problems with lactation. And that tells you how safe prolactin antagonism really is. You will see all sorts of supportive pharmacological profile data on prolactin receptor antibodies from Christian right after Anthony and Mike have told you about patient needs and the market. And finally, what is a dream target for somebody from Pharma R&D like me is if you have a chance of having a super straightforward clinical development path with an early proof of concept. As you can imagine, and Anthony will go into that, and Mike also, the measurements of effects are straightforward when it comes to hair growth. They're sophisticated but straightforward and super reliable.
The chance of, of course, enrolling patients in phase I already is super high. We have indeed what I would consider ideal targets in our hands: efficacy, great side effect to expected close to nil or nil, clear development path. This all comes together with a huge unmet medical need and with a great commercial opportunity. With this, I want to introduce Anthony Rossi. Dr. Rossi is a dermatologist. He is an attending dermatologist at the Memorial Sloan Kettering Cancer Center. You can think about what he's doing as a dermatologist in a cancer center. He is also a professor of dermatology at the Weill Cornell Medical College. He has an extraordinary background in hair research, I shall call it, with contributions to many clinical studies. He is very excited about the innovation we bring forward here.
Besides all those qualifications I just named to you about Dr. Rossi, there is, of course, one I should not miss, which is Anthony is the son of a barber and a hairdresser. And who else should lead our clinical studies in the future than Anthony? Anthony, welcome on stage.
Thanks for that warm introduction. That's amazing. Yeah, actually, their hair salon was right around the corner here on 57. So I grew up right around here, which is amazing. So I'll talk about the unmet clinical need for alopecia and why we care so much about hair. And you should too if you already don't care about hair loss, which many people do. And so I do work at Memorial, which is not far away from here. It's a cancer center. And I straddle both worlds of being a dermatologist and dermatologic surgeon who deals with skin cancers on the whole body. But I also deal a lot with cutaneous oncology, aesthetic dermatology, and most specifically hair loss within that realm. And it may sound not familiar, but we do see a lot of hair loss, actually, especially in our patient population.
I've been fortunate enough to work on multiple clinical trials and just finished up a clinical trial treating hair loss in breast cancer patients undergoing chemotherapy as well as endocrine therapy. That just should tell you that even in that population where they're undergoing life-saving treatments, they still care about hair loss. It's a big problem. That's in one population, but we'll be talking about the whole population of hair loss and what many people are familiar with, which is androgenetic alopecia or male and female pattern hair loss. Dermatology is an amazing community. There's really amazing research coming out of it. I'm fortunate enough to be on the editorial board of our largest journal, the American Academy of Dermatology. I sit on some of our boards, including the American Society for Dermatologic Surgery.
Fun fact, I grew up, born, raised, and lived my whole life in New York City, not far from here. But I do like to get out from time to time. So just some idea to set the stage on why we care about androgenetic alopecia and alopecia in general. It's really because the patients that we are seeing are coming in asking for this day in and day out. And again, even at a place like Memorial, where people are thinking about surviving from their cancer, they still care about their hair loss. And we see patients cut across all demographics. So that's all genders, young and old, right? So this is not just a disease of an older generation. All races, ethnicities, and really, it spans across the whole population. Most of the time, these people are coming in self-diagnosed. So they've looked this up on the internet.
They notice that their hair is thinning. They notice a parent that's thinning or has lost all their hair. And they've tried all these various over-the-counter medications, whether it's topical or even direct-to-consumer oral medications. So they come in a bit frustrated and overwhelmed to begin with. And they're really looking for FDA-approved therapeutics that are really safe and efficacious. Because, like you know, in the hair world, there can be some snake oil that's sold online. And so they really want to circumnavigate that. In particular, female patients represent an unrepresented population because for the longest time, pattern baldness was always associated with male pattern baldness. And that really has changed because we know that so many females are affected by androgenetic alopecia, and they're really speaking up about it in chat forums, mom groups.
Really, females are becoming more and more aware that they too lose hair at an early age. And like I said, hair loss has a huge significant psychosocial impact in our community. It really affects quality of life, dating, mating, job. And even for our cancer patients, the loss of hair really signifies an illness that they need to move on from. So there's many different types of alopecia that we encounter. But of course, the two main common ones are male and female androgenetic alopecia or pattern baldness, as we call it. This is the vast majority of all alopecia patients and what people really recognize as hair loss. And it's estimated about 80-90 million Americans are living with this condition. And while we think of it as a male-dominated disease, it affects about over 50% of men by the age of 50.
But even in women, about 40% of women notice this hair loss, hair thinning, and pattern baldness by the age of 50. So it's very much almost equal. However, it looks different. And you can see on the left and the right, in the male, you have this classic bitemporal, basically thinning. The hairline is receding back. It just keeps going back and back and back. You get this crown vertex scalp thinning. And really, it's a pretty predictable pattern of hair loss. In females, it's a bit more insidious. So they keep the frontal hairline. So in fact, they don't really recognize it because they might have a lot of hair. They might have high hair density. But they start to shed and thin out over time. But keeping that frontal hairline sort of preserves the look of the hairline, which is really important. But their part widens.
It starts to become thinner, more sparse, less dense. They notice it when they start to pull their hair back in a ponytail. So they may actually present later unless they're really hyperaware because they know their mom or their dad has had hair loss. That old adage that you only inherit hair loss from the male parent is not true. We know it's from both parents. What's really interesting now in the environment with the GLP-1 agonists and the rise in prescriptions of this is that we're seeing hair shedding and hair loss because of this. There's new reports that are coming out that show that about 5% of patients who've been treated with tirzepatide, the triple G, experienced an adverse event of hair loss. Patients are coming in with less hair and also much thinner. But yeah, they're really concerned about their hair.
So when we actually see these patients, you do give them all the treatment options, but they're actually not that many, right? And they've heard about many of them before. So if about 50 million men are affected in the United States, they've known that there is topical minoxidil. And it is FDA approved. We know this as Rogaine. It really requires continuous use. And the thing about topical minoxidil is that it's only going to help prevent further hair loss. It really is not made or intended to regrow hair. And so they know that they need to start it and use it. But compliance is a big issue, right? Using something every day that's greasy and it's formulated and you have to put it on your scalp before you style your hair is an issue. And I often see people drop off.
What's also interesting is that the efficacy really varies individually because you need an enzymatic conversion of minoxidil to the active form in the scalp for it to work. And not everyone expresses that same enzymatic activity. So it's sometimes hard to predict who will and who won't respond. There are some side effects, though it's topical. So they're not life-threatening. But there's a temporary hair shedding phase that goes on when you start topical minoxidil. And it actually freaks patients out to the point where they usually stop using it because they see increased shedding. And they become aware of this. And they think that it's getting worse at a more rapid clip. There's also changes in hair texture. And every once in a while, you can get systemic absorption that'll give you palpitations or hypotension because minoxidil was originally designed as a blood pressure medication.
So switching gears to the oral class of medications for male pattern hair loss, we have finasteride, which people, again, know pretty well. It was FDA approved. But again, this requires a lifetime of use and daily compliance. So it's a daily, once-a-day pill. There is an issue, again, with compliance alone because we know that you need to take it pretty regularly to see the growth. What my male patients are really concerned about and what they've heard about already and they read about before they even come to see me is the sexual side effects. So because this blocks conversion of testosterone to DHT, you can get some hormonal effects from the blockade, including loss of erection, loss of libido. And there has even been described this permanent finasteride syndrome, which is linked to a permanent sexual decrease.
And so when they hear this, it's automatically, for some people, a no-go. And they don't even want to think about going on it. It also cannot be handled by pregnant patients because it's teratogenic or it can deform the fetus. And there are good reports that it does slow the rate of hair loss and can regrow hair. But again, this is a lifelong drug. There are so many other options on the market for devices, including low-level laser light therapy. You might have seen ads for red caps that you wear. Again, the efficacy is very low. And it's mixed. And again, it falls into that category of like, does this actually work or is this a sham device? We do have a surgical intervention, which are hair transplants, which actually work very well because hair is an immune-privileged site.
And if you take hair from the back of the scalp where it's growing and usually is not subjected to patterned hair loss and you transplant it into an area that is losing hair, it will maintain its immune privilege and continue to grow, which is amazing. However, your hair will still recede around those transplants. And that's a devastating look. So you really shouldn't or can't transplant someone who is not actively on medication to suppress their hair loss that's ongoing. It would be unethical to do that because they'll continue to lose hair. So when we switch to the female androgenetic alopecia, which again represents a majority of what we're seeing, there's even less treatment options. And this is very frustrating for female patients because hair has a high psychosocial impact in this population.
And we think about 30 million women in the United States alone are affected by this. So of course, we do have topical minoxidil that they can use. And it has been FDA approved for use in women. But again, the same issues arise: compliance, efficacy because of the enzymatic conversion, and then the same side effects of this temporary hair shedding that really worries patients. finasteride is not approved for women because of the teratogenicity of it. And also the mechanism of action, which is the testosterone blockade, we don't really think it works in women for the mechanism of action for androgenetic alopecia. But we sometimes do try it in postmenopausal women where their hormonal balance shifts postmenopause. Again, the same things about low-level laser therapy as well as hair transplants exist for females with limited efficacy.
And females are not as sort of prone to go get hair transplants as the male community is. So there really is a lack of innovation in the androgenetic alopecia space for the past 25 years. The last FDA-approved drug was in 1997. So we have a huge disease burden with a lack of innovation. And both patients and clinicians are really searching for the better treatment options for hair regrowth and not just hair loss cessation, right? So we want to regrow hair. That's the Holy Grail. We don't want to just slow hair loss. We want it to be safe and with minimal side effects and really have a duration of effect, right? Because all the drugs now, once you stop them, you lose hair and go back to baseline, which is really unfortunate. And of course, you want to overcome that compliance issue by a convenient administration.
We'd like it to be FDA approved. What does that actually look like? This gets into a little teaser of the hair cycle, which I geek out over and I love, but not everyone feels the same. We have three main phases of hair growth. The anagen, which is the growing phase, which can last like two to six years. In some individuals, it actually lasts longer. If you ever see people with frequently long hair, they have a long anagen duration. They should be studied. From anagen, you can go to catagen, which is a transition phase. This is where the prolactin receptor is involved. In catagen, you have this increased apoptosis or cellular death. You have this shrinking or miniaturization of the hair follicle.
It sort of just is in this transition phase for two to four weeks. That then switches over to telogen, which we call it the resting phase. The hair just sort of lies dormant in that phase. This can last anywhere from three to five months. A telogen effluvium arises when some sort of stressor or something comes out. You can then shed all that hair or you shed that hair to make way for new anagen growth. When you have androgenetic alopecia, your hairs are not going back into the anagen phase or staying in the anagen phase. They're either going into catagen and then basically into telogen. We see mineralization of the hair follicle, which is quite unfortunate. We need to get it back into the anagen phase. Along comes ABS-201.
Like you heard, it's a super novel mechanism because it doesn't exist in any of the pharmacologic options that we have right now. It's a new paradigm for targeting a different mechanism of action that's going to get you into the anagen phase. That's super important because that's when the hair is growing. It's in active growth. You have new hair. All of our therapies that we'd like to do want to get hair into that anagen growth phase. By blocking the prolactin receptor, you prevent this transition from anagen to the transition phase of catagen. You keep the hairs in anagen. They continue to grow. This causes significant hair regrowth and not just stopping hair loss, which is super important. I keep harping on that point. That's the real holy grail.
It has minimal side effects, which you'll hear about, and durable effect. So there's a longer duration of effect, which is very important. And there's convenient dosing, which overcomes this whole compliance issue that we talked about with daily dosing. So how do you get from bench to bedside? And I do span both worlds being at Memorial because I do work with oncology patients. And I've been on many oncological trials. And those are very much involved, which they should be. But the path to hair therapeutics is much more straightforward because, A, we can visibly see hair. It's on the skin, right? And it's easily measured. And the ease of recruitment by far is probably the easiest way to recruit to a trial.
My last hair trial occurred in a very, very short amount of time because so many people are eager to join into these trials because they've been trying so many different things for so many years, so you have an ease of patient recruitment. You have a high level of KOL interest, which I'll talk about next, so basically, many dermatologists who really love treating hair. You can conduct multi-center trials, so in big academic institutions and hospitals, but also in private practices that are centered around hair, these tools that we use to calculate hair loss and hair growth are very easily accessible, and it's really minimally or non-invasive, so what do we look for when we define the endpoints for hair growth? These are digital trichoscopic images, so it's basically a follicular dermatoscope, which is like a dermatologist's stethoscope or something like that.
We can really look at the hair magnified on the scalp and then, using AI analysis, digitally count all the different types of hair. So you'll see different colors. The green is where you want to be because those are the terminal hairs in the anagen phase that are growing. These yellow and red ones are those miniaturized hair follicles that you want to get rid of. So we look at measures like terminal hair growth, total hair count, and total hair density. So these are quantitative measurements that we can really show, hey, look, this really regrew that hair. And then there's many secondary endpoints that we look at. And for the FDA, quality of life has become a big measuring tool for secondary endpoints.
There are many validated measurement scales like the Hairdex, the Hair-Specific Skindex- 29, and the Men's and Women's Hair Growth Questionnaire that have been validated already in previous studies to show the quality of life effect that this intervention has on the patient itself. What's also really interesting and you will hear about is that we'd also look at hair color repigmentation. So believe it or not, this has actually shown to actually repigment hair, which is very interesting because premature graying is an issue for some. And while gray may be a hot color now, maybe not many people would want it later on. So I know a lot of people went gray during the pandemic. So it's something that we would look into as well. And then this is the network that we have already put together. We had an amazing advisory board of hair experts.
They're all smiling because dermatologists are very happy for the most part. We actually deal with happy patients, even though hair is a very frustrating hair. Patients cry. They get really emotional because it has such a high psychological burden. But treating them is very rewarding because when you can get hair regrowth, it's really pleasurable. We had a great KOL meeting. There's about over 500,000 alopecia patients treated each year by this group alone. It just shows you the magnitude and the burden of disease. All of these people are ready, willing, and able to step up and actually conduct these multi-center clinical trials. I'm very excited for a new novel mechanism in hair loss and hair regrowth. I hope you are too.
So I don't know if you're introducing Mike or I am, but oh, I get the pleasure introducing Michael Jafar. He's an amazing person and friend. We first met maybe over a decade ago when he was leading the team at Allergan in their new injectable, which they changed the game in aesthetic dermatology, basically. It was sort of a revolution. But not only is Mike the guru of medical aesthetics, he's also a dear friend. So take it away, Michael.
The doctors never like to introduce me. Pleasure to be here, Sean and the Absci team. Thank you. I don't think people really understand or appreciate the magnitude of this project.
Having spent my entire career in the medical aesthetic field, I get the opportunity to either sit on several boards or be involved in quite a bit of projects, which I turned down because the space has had a lot of me toos over the years. At Allergan, we always had a saying, this is pre-acquisition, that the last frontier to really tackle is hair. We were category captains in toxins and fillers and breast implants and body contouring and skin care. But hair was such a challenge. And the first product that was approved was in the 1980s. I think the last product FDA approval was 1992, 1993. So there wasn't a lot of innovation purposely driven and designed for hair. And that was a category that we sought after heavily. So I spent nearly 20 years at Allergan.
Toward the tail end of my career, I was leading strategy, M&A, and commercial assessment. We did about $9 billion in M&A just in the aesthetic category over a span of about two years. This is pre-AbbVie acquisition. Within all of that, it was in body contouring, fat, fillers, and toxins, and so on and so forth. The search and the process to go about getting into hair was very tough. I wish we knew about this program at the time because I don't think we'd all be here if Allergan had it to say. Beyond kind of my span at Allergan, I also advised BCG on M&A within the aesthetic market. I since stood up a family office in a kind of a seed to series A phase within the medical aesthetics community.
Needless to say, my subject expertise is a little less technical than Amaro's, but it's predominantly in the commercialization and the scalability of companies within this category. The exciting part about this, or at least this category, is, and I've spent some time in eye care. I used to manage our glaucoma business. And it was very typical. You had four million patients diagnosed with glaucoma, potentially another two to three million that are not diagnosed. And that was kind of the market. You had a few thousand glaucoma specialists and some ophthalmologists. And that's kind of what set the TAM for that industry. When it gets to this category, it is by far one of the most elastic categories you'll see within health care. You get the appreciation, the margins, and the valuation of health care, but you get the scalability of a consumer kind of platform.
When I started my career at Allergan in the Botox space, the category was built around your typical 45-year-old female. That was the market, and if you look at all the reports now, you will see as many millennials, Gen Zs as you would, 45, 55, and so on and so forth, so that elasticity doesn't really happen in dry eye and oncology and so on and so forth. It does happen here within this ecosystem. Beyond that, the market was solely focused on female. 93%, 94% of the community was driven by a female audience. A woman was the patient. Now, about 15%-20% of that market is driven by male and growing. When you get to hair, it's 60% driven by male, so the appetite to get into this category, to really expand it beyond our current focal point, I think, is a key tailwind here.
The willingness to pay for this, this is a cash industry. There's hardly any friction between a product approval and its scalability. We always used to have a saying when we'd model lifetime values and NPVs that within six to eight months, you could project within a 90% accuracy what your true peak sales would look like because the adoption of these products within this community moved very fast. When I would launch a product in eye care, I'd wait two to three years to really get a gauge within what the payer community is going to really adopt and how it'll be adopted and if there'll be step edits and so on and so forth. And then there's a patent cliffs on the end of it. So the complexity of a launch in a traditional pharma is clearly not here. And I think that creates a lot of excitement.
As Dr. Rossi said, you also have a community that is hungry for technology that is willing to adopt and move products through the system rather quickly. And then lastly, vanity can work for or against you. In the case of the category that we're in, people care about their appearance, not just physically, but emotionally. And I think that drive and what kind of the social space has done in our lifetime has driven the investments and the willingness to pay for a category that has an impact on your appearance. Those are tremendous tailwinds, I think, for a program like 201. To give you context of the value of the market and how it's currently broken down, on the left-hand side, you'll see the stacked bar graph. The driver of this space is injectables. Injectables demand tremendous margins because they work 99% of the time.
A Botox, a filler in our category, has the best of two worlds. It gives the physicians the delivery on the promise, and it gives the patient the immediacy and the satisfaction. Outside of injectables, it starts to kind of degrade in terms of level of consistency and efficacy. So to have a project like this that can deliver on its outcome, obviously pending the finalization of the data, I think will drive and demand tremendous premium. Market's projected to get to about $10 billion. These are OEM manufacturer dollars. This is not retail value. The retail value of this is nearly $20-$30 billion. It's just to give you context of the size of this market. Injectables, skin care, energy-based device, and then the breast augmentation all fall within this category.
In terms of the providers on this, yet another kind of nuance within health care that you don't really see. Having spent seven, eight years in eye care, you have ophthalmologists and you have optometrists. That's about all that cares for the eye, right? In this category, over the last, call it 20 years, you've had dermatologists and plastic surgeons really drive this category. But if you were to follow Botox's inception, it was really founded and discovered by an ophthalmologist. So beyond dermatologists and plastic surgeons, you have ophthalmologists, you have primary care physicians, you have nurses, nurse practitioners, RNs, med spas. The provider base continues to grow. Nearly 700 to 800 new providers enter this category. And the driver there is because of the margins, the cash, but also the consumer demand. There's about 12,000 Starbucks in America.
There's over 40,000 providers injecting Botox on the aesthetic side in America. The consumer demand relative to the provider base is always unmatched, and hence the interest to get into this category. Beyond the customer base, meaning the providers here, on the consumer side, the elasticity has also been proven. There's the lipstick effect that we all know in terms of economic downturn. What alcohol and lipstick are the two things that are resilient to the economic downturn? And so is predominantly aesthetics. When you index the growth of aesthetics to the S&P in any given time in our lives, you'll see the growth will always outpace the S&P market. If anything, when the market rebounds, this category comes screaming. And we saw this in COVID. We saw it in the 2008, 2009 downturn. So that resiliency is driven by what McKinsey published a few years ago.
They surveyed about 2,000 consumers, and they asked, "Where's your willingness to spend in all categories?" Appearance was number one. Health care was number two. Fitness, nutrition fell way down. This is the backdrop to what we're dealing with. And again, the excitement that I have here behind this project from a commercialization standpoint is it would be considered a category captain and a leader because there hasn't been anything this profound. I'll close on the slide because this is the part that could be as conservative as it shows here, but could be wildly off in a very positive way. From a consumer standpoint, there are about 60 million consumers in America today. This is not global, right?
U.S. numbers that are interested in getting a Botox filler, so on and so forth, at the right price point, at the right time in their life over the next, call it, two years. That's kind of the potential addressable market. About 7 million patients are being treated annually about two or three times a year. That drives an OEM market size of about $4 billion. As I said, heavily focused on a female market and then really focused on a couple of indications, glabellar, so on and so forth. When it comes to alopecia, and again, U.S. market, about 80 million males or females are concerned about this category. If you were to assume the same conversion rate, which I think is a very conservative estimate, you'll have about 9-10 million patients being treated in a given year.
If you were to assume a very conservative cost for this type of treatment, you would, on the low end, assume this thing to be about $7 billion in terms of market, on the midpoint at $14 billion. The growth beyond that, I think, is really going to be dependent on the final label, but also the commercial strategy, which I think this is the exciting part to be a part of it. Thank you for having me. Appreciate the opportunity, Sean and the team, and congrats on this.
Now that you've heard about the unmet medical need in androgenetic alopecia as well as the market, it's my pleasure to take you back to the science. I'll talk about how we created ABS-201 and how we intend to develop it. Let's start off with a little bit of prolactin receptor biology.
I think after hearing Dr. Rossi and after hearing me, perhaps there's a case to be made that prolactin could be renamed into Pilostatin. Pilos, Latin for hair, and statin for inhibitor. Keep that in mind because that's actually exactly what happens when you take a human hair follicle and you treat it in vitro with 400 nanograms per milliliter of prolactin or pilocetin, if you will. You can immediately observe there is inhibition of hair shaft elongation. Treating ex vivo a hair follicle with prolactin results in dramatic changes in histology that is apparent on this slide. You can see that the dermal papilla shrinks. You see a diminishment of the hair matrix volume. And interestingly, you see a cessation of pigmentation. Compare these two histology slides. The pigmentation is getting lost.
So this could be a very interesting upside for our mechanism because potentially it could restore pigmentation as well. Andreas's already alluded to safety. So now that we established efficacy, let's talk about safety. The strongest type of safety you can think about when you address a novel mechanism is human genetics. In the absence of treatments, in the absence of molecules that work, that have such a mode of action, the strongest evidence we can think of is human genetic evidence. And I'll point to a paper referenced here on the slide, and we'll also have a backup slide that you can download on our website that shows some details on this. There is, and Andreas's alluded to this, there's a case report in the New England Journal of Medicine about a 35-year-old woman who presented with postpartum agalactia. So she was unable to breastfeed. Otherwise, she appeared healthy.
Hormone levels, blood serum parameters were completely normal, and she had two healthy kids. The only reason she presented at hospital, she was unable to breastfeed. And it turns out she is a compound heterozygous loss of function carrier of the prolactin receptor gene. And that's remarkable because think about a lifelong loss of function of the prolactin receptor. All that it does is apparently it gives you difficulties with breastfeeding. So this is probably the best type of safety data you can imagine for a drug target, which is why Andreas and many others at Absci, including myself, of course, are very excited about this target. Let's talk a little bit more about the hair growth cycle. And thanks to Dr. Rossi for introducing us to this. So the catagen phase is indeed what is critical for the prolactin receptor mode of action.
So the prolactin receptor drives the hair follicle into the catagen phase. And our hypothesis is that treating with ABS-201 allows us to push the hair follicle out of the catagen phase back into the anagen phase. And keep in mind, the anagen phase is a long-lasting phase. So it typically lasts in adults anywhere between two and six years. Therefore, we expect that this treatment will have a long-lasting effect after treatment cessation. We also believe, and I mentioned this earlier, based on histology, there is an upside in terms of restoring hair pigmentation, which is unheard of with existing treatments. So in summary, we think this is really a very safe and efficacious mechanism to potentially treat androgenetic alopecia. There's additional evidence, and this is data disclosed by a competitor. So what they did is they looked at a certain monkey strain.
These are the so-called stump-tailed macaques. What's particular about the stump-tailed macaques is that they develop spontaneous alopecia after puberty, interestingly, both in males and females. These monkeys, they lose their hair spontaneously. This is perhaps the best model you can think of in terms of studying androgenetic alopecia. What they did is they treated for 28 weeks at 40 milligrams per kilogram subcutaneously, so at very high doses for 28 weeks. You can visually appreciate that there's significant hair regrowth, really different from existing treatments, hair regrowth occurring in a spontaneous alopecia model. As I mentioned earlier, the anagen phase lasts long, and it does last long in monkeys too. After treatment cessation, you can see that the hair growth remains several years. As I also mentioned earlier, this seems to be efficacious in both males and females.
So really, again, another compelling piece of evidence that prolactin receptor is a promising mechanism for androgenetic alopecia. So what we did at Absci, and I fast forward here because the team has done a lot of extensive in vitro and in vivo PK characterization, which I'm not going to present. You can have a look at the backup slides to go into those details. But I want to show one piece of data that we have generated and we find very exciting. We did a hair regrowth model in mice. So these are healthy Black 6 mice that have been shaved at day zero. And we observe hair regrowth at 25 days. And you can see we have four arms. We have an untreated arm. We have an isotype control. We have a minoxidil arm, which is standard of care.
In this case, minoxidil 5% has been applied topically once daily. And then we have, of course, our treatment arm, ABS-201, which is our clinical candidate at 30 grams per kilogram once weekly. And just visually, you can appreciate you have patchy hair regrowth in the untreated arm. You have patchy regrowth in the isotype arm. You do see some efficacy in minoxidil, but clearly there are some non-responders. And you have actually a very compelling regrowth effect with ABS-201. Another piece I wanted to share with you in terms of data is developability. Recall that this is an aesthetic indication, and manufacturability and dosing convenience is super important for this project. So we set out our AI design engine to really come up with development candidates that have excellent manufacturability. So we looked here at four different ways to assess developability. We looked at solubility.
We looked at diffusion interaction. We looked at acidic stress forced degradation, and obviously freeze-thaw susceptibility as well. And you can see in all these categories, our molecule, ABS-201A, which is our development candidate, is clearly outperforming a competitor molecule. And I also mentioned we have a second molecule, ABS-201B, that is nearly as good. And I think it could provide optionality, potentially looking into additional indications as well. So to summarize, ABS-201 is our potential best-in-class prolactin receptor antibody designed using generative AI. We have delivered high affinity and potency, and I'll refer to the backup slide here. We've delivered a promising candidate in just over a year. I showed you excellent manufacturability and developability profile, enabling a high concentration formulation and excellent stability. We anticipate, just as for ABS-101, we anticipate low immunogenicity. And we have also engineered extended half-life to deliver longer dosing intervals.
So with this, I conclude on ABS-201. And oh, I have one more slide. This is the plan, what's next. This is our timeline. As mentioned, we delivered a development candidate in the fourth quarter this year. We plan to initiate, or we have initiated, IND- enabling studies in December, so right now. And we plan to initiate in the first half of next year, first in human clinical development. And again, here, coming back to Dr. Rossi's comments, the opportunity for a clinical trial in this space is really remarkable because in such an indication, it is very straightforward to come to a rapid proof of concept because there are excellent ways you can design endpoints. And there is a patient population that's out there waiting for treatments, and recruiting will be very straightforward.
So with this, I conclude on ABS-201, and I switch gears to talk about ABS-301. ABS-301 is our first-in-class clinical asset in a very exciting immuno-oncology space. Before I talk about ABS-301, allow me to talk a little bit about Absci's Reverse Immunology approach. Reverse Immunology at Absci is a platform that is focused on a very interesting structure. We focus on tertiary lymphoid structures. TLSs are centers of immune activity, and they develop in chronically inflamed tissues. Those tissues can be, for example, in cancer patients adjacent to a tumor, or they can be in autoimmune disease patients. It's been shown that antibodies that are occurring in these TLSs are specialized for local antigens. These antibodies play a significant role in the progression of chronic disease and cancer. This really sets them apart for antibodies that are in peripheral blood.
And that's why Reverse Immunology at Absci is unique because we focus on TLSs. Another exciting piece of evidence is that TLSs are associated with longer progression-free survival and better response to immune checkpoint inhibitors. So to summarize, TLS-derived antibodies have been shown to have a lot of promise, and they are actually also associated with apoptosis in cancer cells in patients. This is what we focused on for discovering ABS-301. The way we do this is we collect samples through partnerships from patients that express markers in TLS, and we look at immunoglobulin reads from RNA-seq data of these biopsies. Then we assemble the immunoglobulin chains, computationally reconstruct the antibodies. We actually express them in the lab, and then we run a target identification campaign using high-throughput proteomics, also called deorphining.
Once we've deorphined the target, we validate that target using, for example, surface plasmon resonance or biolayer interferometry. And then we end up with a fully human antibody and its cognate novel antigen target identified. And this novel target is usually a first-in-class target. And that is exactly the case here for ABS-301. So when I talk about ABS-301, I have to ask for your patience because we will not disclose the target of ABS-301 today for strategic reasons. But what I do want to share is that ABS-301 addresses an immunosuppressive cytokine. And we think this is super interesting because we could show in primary human cells that delivering ABS-301 can rescue pro-inflammatory signaling through inhibition of this immunosuppressive cytokine. What does that mean for cancer treatment? Well, if you think about cancers, cancers are very elaborate in terms of evading immune response.
And this particular cytokine is suggested to maintain an immunosuppressive environment through inhibition of signaling. So our hypothesis is that if you treat with ABS-301, you can restore this signaling by blocking this immunosuppressive cytokine. And then that allows you to promote immune-mediated tumor cell killing. With this, I'd like to introduce another highly renowned external speaker that we have here, Dr. Luis Diaz. Dr. Diaz is a member of our scientific advisory board. He is head of the division of solid tumor oncology at Memorial Sloan Kettering Cancer Center. His lab is focused on developing novel tools and approaches for treatment of cancer. He has numerous scientific contributions. I just want to highlight one here. Dr. Diaz was principal investigator of a study of pembrolizumab that secured the first FDA approval of a tumor indication based on a biomarker rather than on a tumor location.
He's a member of the National Academy of Medicine. He holds an MD from University of Michigan and had his residency and fellowship at Johns Hopkins University. Welcome, Dr. Diaz.
Thanks to the Absci team for inviting me to speak. I'm really excited to speak. When we talk about cancer, and especially cancer development of drugs, what's most exciting to us at Memorial Sloan Kettering, which is right down the street, where we see about 50,000 new patients, are new targets. We have multitudes of studies with existing targets. Really, new targets is really what interests us. Because if you think about the 50,000 patients we see each year, about two-thirds of those are no longer with us at two to three years, the existing targets, while exciting in the realms of immunotherapy, targeted therapy, even novel chemotherapy, cellular therapy with CAR-Ts or TILs, have not met the bar for so many patients. New targets are incredibly exciting to us.
When Absci approached me about this, what was really exciting is not only identifying new targets, but how they identify them using the TLS approach. I think that's quite exciting. About this target in particular, one thing I want to point out is that its uniqueness. You can oftentimes find targets that are either molecularly defined. This uniqueness pattern is quite fascinating to me. It is focused on squamous cell tumors. Squamous cell tumors are a subset of tumors that impact a variety of different tumors throughout the body. If you can think about it, one of the most common ones is lung cancer. Squamous cells of the lung cancer, esophagus, head and neck, very common tumors of the skin, of the GYN tract. What we see here is high levels of expression throughout these tumors that are almost exclusive.
So if you look at the hematopoietic tumors, you don't see the high level of expression. If you look at other epithelial tumors, like the colon cancer, breast cancer, and others like that, you don't see high levels of expression. So exclusive to the squamous compartment. The other thing that's interesting is that it is not modulated by currently existing treatments, whether that's immunotherapy, chemotherapy, or targeted therapy on these squamous tumors. So I'll point your attention to the bar graph on the right. And there you see that there is not a change in the level of this target pre-treatment versus post-treatment. And that's quite important as we go forward. Because oftentimes, we make the assumption that the target is still there post-treatment, and you have to confirm that with the post-treatment biopsy.
We already know that there's stability in this target, and that it will be there after first-line, second-line, maybe even third-line therapy. The other thing is that it points to the biology that you just heard, that this may be an immunosuppressive target, and when the levels are high, what is one of the best markers of immunosuppression in the local tumor environment? Lack of the effector cell, which is a CD8 cell. And what you see here on this graph, where you see CD8 cells, is when the levels are high, CD8 cells are low, and we know that with checkpoint inhibition, we know that with T cell engagers, that if you have a low level of CD8 infiltration, they don't work as well. There's something suppressing that immune environment. Could this be one of the agents doing that? Very possibly low.
As the levels decrease, the infiltration level of CD8s increase. And this is from pre-existing data. This isn't with intervention with the molecule, but it suggests that this has a big role in suppressing the immune environment. Now, you don't see any sort of regulation or modulation of T regulatory cells, which is also interesting, which says that there's no increase in T cells, T regulatory T cells that might be immunosuppressive themselves. And then in terms of potential impact, when we talk about squamous cell, it may not sound as sexy as EGFR mutation or KRAS mutation, but this is an entire class in and of itself, much like mismatch repair deficiency was a class in and of itself. This is a subset of virtually every tumor type that has a squamous counterpart.
If you think of lung cancer, you see that 30% of lung cancer has a squamous component. What's challenging about squamous tumors is, despite the fact that oftentimes they do respond to chemotherapy and they do respond to immunotherapy, the majority of patients don't achieve a cure. Despite the fact that we have a response and maybe a prolongation and early survival, they don't achieve a cure, especially with diseases like lung cancer and esophageal cancer. I think here we really have an opportunity to not only discover a new target, potentially suppress it, see if it modulates immune response, and then eventually modulate the tumor and hopefully control it and potentially even cure it either alone or in combination with existing therapies. I'm going to pass it back to Christian here, but thank you very much.
Thank you, Dr. Diaz. So with this, I'll move to our last disclosure of our internal pipeline today. It's ABS-501. And what's special about ABS-501 is that to some degree, we've already talked about it. Last year, we told you that we had run a case study on HER2. And we've asked our zero-shot AI de novo engine whether, without ever seeing trastuzumab, it can design HER2 binders. And it did. The answer is yes. Using this AI model, we were able to come up with a number of sub-nanomolar binders to HER2. We've had hits in the added distance of up to 12 amino acids in the heavy chain CDR3 regions. And we selected 50 hits with less than 10 nanomolar affinity, expressed those as monoclonal antibodies, and we determined binding affinity.
11 of those were, in the meantime, further characterized, and three of them are shown here on this slide, which we also then took forward to in vivo characterization. So if you look at the variants that are AI-designed on this slide, they have an added distance between two and seven amino acids. They have also an affinity, just like trastuzumab, of less than 10 nanomolar. But what's really interesting is if you look at the epitope mapping view, you can see that these antibodies really display molecular interactions on the HER2 antigen that are clearly distinct from trastuzumab. So that led us to ask the question, does that make a biological difference? Are these potentially interesting novel HER2 binders we should explore further? So what we did next is we looked at antibody-dependent cytotoxicity, and we essentially tested these molecules in vitro.
It was interesting to observe in this particular ADCC model, the EC50s were actually quite a bit lower than trastuzumab, really outperforming the existing HER2 binder and approved drug. Keep in mind, the model, again, has never seen trastuzumab, and it has not been optimized. These are really taken directly out of the AI model into a cell-based assay. When we had these results in hand, we asked, what can we do next? Obviously, the next step for a drug creator is to test those models in vivo. We've reached out to a renowned expert in the field, to Dr. Dennis Slamon at UCLA. We asked him, would you be interested in exploring novel HER2 binders in an oncology setting in vivo? He agreed. He helped us to run in vivo studies in his lab.
We delivered the binders to him, and we tested these HER2 leads in several xenograft tumor models. We tested them in EFM-192A, which is a HER2-positive trastuzumab-sensitive model. And you can appreciate our designs, our leads are roughly equally as efficacious in terms of tumor regression as trastuzumab. Then in the middle panel, we tested another model, which is called MDA-MB-361, again, a HER2-positive trastuzumab-sensitive model. And also here, with some variability, we are clearly inducing tumor growth inhibition. But the real surprise was for us when we tested these AI designs in a trastuzumab-resistant model. And this is the JIMT-1 model shown here on the right-hand side. Again, this is a HER2-amplified model that is trastuzumab-resistant. We could see a statistically significant tumor growth inhibition compared to isotype control and clearly also superior to trastuzumab.
So surprisingly, the JIMT-1 tumors are resistant to Trastuzumab, but clearly sensitive to variants three and four. And if you map this back to the epitope view, it is quite remarkable that the changes in binding the HER2 interface could potentially drive this efficacy we see here. So we are now at a stage where we explore options. What could be potential uses for such a molecule? And we've conducted a significant effort talking to experts and positioning these leads. And it was really interesting to hear from a number of key opinion leaders that there is actually a significant unmet need left in the breast cancer space. And HER2, which many of you have heard of, is an amazing molecule with excellent efficacy. But it's interesting that leading oncologists are actually only moderately satisfied because it's actually quite a toxic drug.
So it has some incidents of interstitial lung disease. And there seems to be really interest in a space that is alternative or post-HER2. So we are currently exploring multiple paths to possible therapeutic development using these leads. This could be, for example, a modality switch. It could be a combination therapy. When we talk about modality switch, obviously, one idea is to develop an antibody-drug conjugate or a multispecific. So we are exploring all these opportunities. And a key role in this is obviously our collaborator, Dr. Dennis Slamon here. And it was a really exciting moment for us when he sat down with Andreas and talked through these developments. And I'm happy to share a little video here. He can't be here with us today, but he had a sit-down with Andreas, and I'm happy to share that recording here. Oh, yeah, one more slide on Dr.
Dennis Slamon. He is, as you may know, Chief of the Division of Hematology at UCLA. His research was instrumental in really discovering HER2 as an oncogene. As you may know, it's an oncogene that is amplified in a significant share of breast cancer patients. He's been instrumental in the development of trastuzumab, and he's very renowned in the field. Without further ado, I'll switch over to the recording we have with him.
Dennis, really great that you could take some time to talk to us for our R&D day. I mean, for those of us today who don't know you that well, maybe you can tell us a little bit about your HER2 background and the fact that most of them who know you consider you the grandfather or father or grand, whatever, of the HER2 field.
OK, yes. I'm Dennis Slamon. I'm the Chief of the Hematology/Oncology Division at UCLA and Director of Clinical Translational Research at the Jonsson Comprehensive Cancer Center. I've been involved in looking at new targets and target identification for development of biologic therapies against targets for the last, I would say, 30 years. Our first real foray into this area was when we identified the HER2 alteration that occurs in about 20% of human breast cancer and realized that this alteration was associated with a more aggressive subtype of breast cancer and that it resulted in an expression of a protein that might be amenable to targeting therapeutically.
And then we worked collaboratively with industry, with Genentech at the time, to develop antibody therapeutics, which initially was trastuzumab that became the drug Herceptin for this disease that has ended up treating now more than three million women globally and has been a real gratifying and, to some degree, a real success story. But there's room for improvement, and I think we continue to do this kind of research here at UCLA.
This is great. And what you accomplished with trastuzumab is, of course, breathtaking. The reason we got in contact was that we actually designed, with our AI-guided platform, antibodies against HER2. And we discussed with you, is there a good reason to test those antibodies in your models with your background knowledge and expertise? And that's why we got you one compound, which we call ABS-501, not really expecting a much different profile than trastuzumab. But maybe you can tell us a bit about your experience and what you found out with this compound.
We have tested the new Absci anti-HER2 antibody. It was developed using your technology. We tested that against the known therapeutics, in particular, starting with the mainstay trastuzumab, and have been very impressed with the performance in terms of head-to-head with the antibody that was developed the old traditional way with using mice and hybridoma technology. What we've seen with the Absci antibody is, obviously, the characteristics of the antibody remain the same in terms of PK aggregation, all things that you've already worked out at Absci yourselves. But what has been and the KD being very, very good.
But what has been impressive is to see its performance in models that we have developed that are resistant to trastuzumab, both de novo resistant as well as acquired resistant models, in the sense that in a head-to-head comparison, the antibody, the Absci antibody, has performed quite well and superior to what trastuzumab gives us. So that's been really enlightening and exciting to be involved in doing that work with you.
On the potential path for this compound to development, of course, we have to ask the question in a space which we consider pretty crowded, like the HER2 space, with trastuzumab, with ADCs coming in. How do you see still room left for improved therapies?
There's no question that not all patients who have HER2-positive cancer respond to either the naked antibody trastuzumab or even ADCs like T-DM1 or Enhertu. There's plenty of room, I think, still for improvement, but as you pointed out, it is a crowded space, so I think that the idea moving forward would be what can occupy that space for those patients who have de novo resistance or acquired resistance to the established therapeutics that are there.
I think both alone, the antibody Absci developed either alone or in combination, much like what we've done with trastuzumab, with other small molecule inhibitors like tucatinib, where the data show that the combinations are superior to either single agent alone, and maybe will circumvent some of the safety issues that have been seen with some of the recent ADCs like Enhertu , which is very effective but has a very significant tolerability profile that is challenging, especially if you want to move it into early breast cancer.
Looking at our approach, looking at our antibody platform where we try to generate antibodies based on structures, not the traditional way, but really AI-guided, what do you feel about taking this path, taking this approach now with this particular experience? Was there any surprise to you? And what do you think about us engaging continuously for the future?
So I wouldn't say there was a surprise. I would say there was learning for me in terms of working with Absci to find out if this approach would really work. It's an intriguing approach when you hear about it, but it's different when you actually see it in practice. And so actually taking an antibody that was generated through AI and comparing it head-to-head against antibodies that have been generated traditional ways that are in the clinic and have a proven track record and seeing that the performance is certainly at least as good, but perhaps superior in the models we've looked at for the HER2 story, I think that bodes well for other targets using this technology for other targets going forward. So I think it's something that's here to stay, and I think it will grow.
I look forward to the possibility of collaborating with Absci in terms of looking at some of the new targets using this technology for developing the therapeutics.
Thanks, Dennis. I mean, I can tell you only from our side, it has been absolutely great and has been a great benefit getting to know you, working together with you.
We feel exactly the same way. I think the future is going to be pretty exciting for this collaboration.
So, I feel a little slighted that I didn't get an introduction from Andreas. But maybe sure. I'm Zach Jonasson. I'm the CFO and the CBO. And I'm going to talk a little bit about our business strategy. First, just a reminder of what we've seen today. So, I think we've seen today some really striking advancements in our AI platform capabilities and also how we're applying them to design differentiated and novel assets. And those assets have epitope specificity. They have optimized epitope interfaces, enhanced potency in MOA. And we're now addressing, as you saw today, very difficult targets that are not addressable with traditional means. And we're now in a space where we can engineer differentiated pharmacology as well. So, if we zoom out, we're using this platform to generate and build a portfolio of differentiated assets.
Each of these are sort of segmented into different partnering strategies. So just to walk through that for a second, we've talked today about our four internal drug programs that are wholly owned. We also have started generating or putting in place collaborations that are co-development structures. And those allow us to develop assets with partners like Memorial Sloan Kettering. And I'll talk a little bit more about that in a subsequent slide. We have a couple of legacy programs that are partnered around the SynBio platform. And then we have eight drug creation partnered programs with large pharma and biotech companies. So if we break these apart just to zero in on the different partnering strategies associated with each set of assets or asset creation, the first is in the drug creation partnership category.
This is where we work with large pharma or biotech partners in a structure where they provide upfront payments, R&D payments, selection fees, commercial and clinical milestone payments, and royalties. And in these collaborations, the partner typically brings us their target, and we deploy our AI platform to design a differentiated asset against that target. Examples of those partners include Merck, AstraZeneca, and Almirall. And across those three partners, we have a total deal value that approximates $1.5 billion. That's calculated by adding up all the upfront payments and all the eligible payments associated with milestones and selection fees, but not including royalties. In the co-development category, which is a creative partnering structure that we started this year, these are partnerships where we're jointly contributing IP. So we work with a partner like Memorial Sloan Kettering, where they typically would bring a target where they've done validation.
These are typically novel targets. We then deploy our AI platform to design novel and differentiated antibodies against those targets, and we do this in a 50/50 cost share and 50/50 upside sharing arrangement. Now, importantly, these programs or these partnerships allow us a great deal of flexibility and optionality. They have opt-out provisions, so we have the opportunity or the option, but not the obligation, to continue to support these through development past the drug candidate phase, and examples of these partners include Memorial Sloan Kettering, as I mentioned, Twist Bioscience, and PrecisionLife. And then in the third category are our internally developed assets, which are wholly owned, and here, the strategy is to take these to a proof point where then we would transact on these, so either through an asset sale or an out-licensing structure that would provide upfront payments, clinical, commercial milestones, and royalties.
In this category, we have several ongoing discussions that cover several of the programs that were discussed today. I'm going to step back for a second. Today, you've heard from several KOLs that we're working with and collaborating with, including Dennis Slamon just recently. You've heard a little bit about some of our progress and our partnerships. We thought it would be important for you to hear from one of our partners. I'm going to play a brief clip with Andreas again, who's also our Chief Interviewer , speaking with Karl Ziegelbauer. Andreas doesn't like my German pronunciation always. What you're going to hear is Karl speaking about what it's like to work with Absci and how we're applying our AI platform to their very difficult and challenging targets that are important to them for dermatology indications.
Hey, good morning, Karl. It's great seeing you.
Good morning. Great seeing you as well, Andy.
First of all, let me just quickly introduce you. You have taken over R&D at Almirall just a couple of years ago and have achieved a remarkable transformation of the R&D efforts and the portfolio at Almirall. And congratulations to that. Maybe you can just tell us a little bit about what you're searching for in a partnership for your research efforts.
Yeah, thank you. Happy to do so. So here at Almirall, we are focused on discovery and developing novel solutions for patients suffering from skin diseases. So our focus is medical dermatology. Using what we call an end-to-end approach, we have a track record of bringing successful compounds to patients based on our discovery, development, and commercial capabilities, mainly across Europe, but also in the U.S. and working with partners around the world. What we are seeking for our partners in drug discovery is scientific and technical excellence, which is the most important thing.
Basically, also partners that have established a technology or an approach that allows a novel way to do drug discovery and delivers novel ideas at competitive cost, and at the same time having a team of scientific experts that can advance the science and the development of novel therapeutics and be able to solve problems that come along the way in a collaborative manner. So, to your second question, why have we selected Absci? I mean, our goal is to work with the best partners and the best experts around the globe. Again, integrate this into our end-to-end approach for drug discovery and development. What we find in particular interesting with Absci is the strong combination of an applicable machine learning model combined with excellent wet-based capabilities to basically test those predictions, which allows very rapid and efficient learning cycle to discover and optimize totally new antibodies.
And that we find in particular exciting, as well as that Absci has the scientific expertise that is required to work on those targets in terms of understanding the target biology. We believe that Absci can help us to find function-blocking antibodies against targets that are in particular difficult to approach, such as ion channels or other targets where other approaches have failed.
Thanks, Karl. Certainly appreciate your confidence in what we're doing. Would you want to mention what your experience is with the collaboration and with the progress in the collaboration?
Let's maybe first start with the collaboration. I mean, this collaboration is mentioned into what I said earlier, our end-to-end approach for drug discovery and development in bringing novel solutions to patients suffering from skin diseases. With our collaboration with Absci, we are focusing on the discovery part, which I think Absci can do very well and is best equipped to do. You mentioned already that we work on a difficult-to-drug target. Yeah. And the key is really to find antibodies that are very selective, very potent, but also function-blocking, which is, I think, unique and very, very challenging combination. Now, while Absci is focusing on the early stages up to pre-clinical candidate identification, Almirall will take over after that and will do the pre-clinical development and the clinical development, and eventually also the commercialization.
With that, we have very complementary capability that we bring together to solve, let's say, a long-standing challenge in drug discovery. In terms of where we are with the collaborations, I think we are well on track and according to plan. We are very excited about the energy and the expertise the Absci team is bringing to the table and to our collaboration. We have established, I think, a very good communication amongst the joint team that allows us to address the scientific challenges that come up with creative ideas that can be very rapidly put into practice, can be tested, and then the next step can be taken. When I look at our joint team, the level of scientific excellence is absolutely amazing. We are very optimistic in terms of taking the next step for this program and looking forward to that.
Overall, we are very happy with this collaboration. The way Absci approached us, their capabilities, their scientific excellence, and looking forward jointly progressing our program, hopefully soon into clinical development and maybe all the way to the market to be able to help patients that suffer from skin disease.
I'm very pleased at where we are. I thank you for your trust. And I hope that we can, of course, repay the trust you have in us. And we're very confident that we can deliver an important asset on this target. Thank you so much, Karl.
Thank you. Happy to do so.
Now that you've heard a little bit from one of our partners, I want to transition to going back and looking at our monetization strategy for our internal pipeline. This also applies to our co-development pipeline that's just starting to emerge. As a reminder, Absci is not looking to take any of these programs into late-stage clinical development. Rather, we're looking to assess partnering and out-licensing opportunities as we achieve key proof points that can generate value. And so we're looking to monetize potentially at the phase of a DC package, an IND, a phase I readout, potentially in some cases a phase II readout. And that's how we look at monetization of this platform. As I mentioned, we have a number of co-development programs that are starting. Those are long-term partnerships where we're just starting to identify which targets we're going to work on with those partners.
And there we have the optionality to participate in pushing those into further development, but again, looking at monetization points along the development path. So when we look at this portfolio, I think one of the things that's really exciting is we believe it generates a tremendous amount of value, but it also spins out inflection points and monetization options as we move forward in time. So just to highlight a few of those on the right-hand side here. In the second half of next year, we're looking forward to sharing an interim data readout from our phase I trial of ABS-101. In the first half of next year, we're looking to share information around a DC package or a drug candidate package for our ABS-301 program.
And then looking at our partnerships, we're looking forward to next year to providing more milestones and progress points that we can share with you for our existing partnerships with companies like Almirall and AZ and Merck. But we're also looking to sign, and we expect to sign, additional new partnerships with large pharma and biotechs. And then thirdly, as I mentioned, we're just starting to initiate some of our co-development program work. So we look forward to updating you about the progress on those programs through 2025. And then finally, I'm going to end on just some of the high-level business metrics. So currently today, you heard about our four named internally wholly owned programs. We have a total of 23 partnered programs when we include the co-development partnerships, the drug creation partnerships, and the three legacy programs around SynBio platform.
As of the end of September 2024, we had $127.1 million in cash, cash equivalents, and short-term investments, which based on our current plans is sufficient to fund us into the first half of 2027. With that, I'm going to hand it back to Sean McClain.
So first off, all of the amazing innovation you saw today and the breakthroughs would not have happened without our team here at Absci and our board of directors, scientific advisory board, our advisors, the KOLs that have helped us craft the messages on our pipeline. All of this would not be possible without them. So I just want to take a moment to recognize all of them and the great innovation that they continue to provide at Absci.
As Mene talked about at the beginning, there's a lot of hype within AI drug discovery, a lot of noise, and I hope today you've seen how we are cutting through that noise with execution, successful execution, and being able to show how we can deliver breakthrough therapies to patients using our de novo AI platform. For example, being able to target the Caldera region of HIV, where no other technology has been able to provide an antibody, and now potentially having a breakthrough vaccine here is really incredible. This is why we do what we do at Absci every day and why we're wanting to leverage AI, not to create me- too or me- better drugs, but to really be able to solve challenging, complex biology problems that still exist today.
And you've seen that through our successful partnerships with AstraZeneca, being able to target a challenging membrane-bound target, along with Almirall working on an ion channel. And then our collaboration with Caltech and Bill and Melinda Gates Foundation working on challenging HIV programs. And then additionally, how we are leveraging our AI platform to create a truly differentiated portfolio of wholly owned assets, which you've seen today, that range from IBD to dermatology to oncology. It's a very, very bright future for Absci. We have a lot of exciting catalysts that are coming up that Zach just mentioned. And to highlight a few of those, we have ABS-101 entering the clinic early next year with a phase I interim readout the second half. We have ABS-301 that is we're planning on having in vivo validation and efficacy studies on that the second half of this coming year.
And then additionally, with our new exciting alopecia drug, we plan to start our IND- enabling studies or have started our IND- enabling studies for that, with the plan to be in the clinic in the first part of 2026. There's a lot of exciting catalysts in the near term, and we're just getting started. We're continuing to innovate, and we truly here at Absci are innovators where we're taking what is seemingly impossible and making it a reality every day. Thank you all. With that, we'll now open it up for Q&A. Alex, do you want to?
Okay. Is that good?
Yeah, I think that's good, yeah.
You can sit here, please.
Yeah.
It's nice for you.
All right, we do have time for Q&A now. So I'll just ask that everyone in the room please speak in the microphone and for the benefit of those in the webcast, announce your name and affiliation.
Thanks for the presentation this morning, George Farmer from Scotiabank. I was wondering if you could talk about the path that you have in mind for the anti-Caldera program. Don't see that in your pipeline slide, but it looks pretty intriguing, and then I have some follow-ups.
Yeah, absolutely. So we are developing that with Caltech. And we do plan to look to potentially partner that asset, but we do own a part of that asset and do plan to ultimately partner that. We haven't included it in the pipeline yet because it hasn't gone to lead stage, but we are very excited about progressing that forward.
Okay, great. And then on ABS-101, two questions here. So the lower ADA rate that you see with this antibody, was that just serendipitous or was that kind of part of the design that the platform enables?
It's absolutely by design, so again, there are two parts to immunogenicity. One is essentially sequence-based, and here we benefit from leveraging our AI drug creation platform in terms of having high humanness that elicits low ADA rates. In this particular case, there's a second level of immunogenicity, which is, as I mentioned, the antigen-antibody immune complex that seems to drive immunogenicity, and this is the particular piece I presented some data in here today, which we, again, by design, chose an epitope. We chose an epitope that allowed us to deliver candidates with potentially lower immunogenicity.
Okay, great. And then finally, when we see the first human data second half of next year, yeah, are there any biomarkers that we can use as a readout for target engagement to give us some sense of how this antibody compares to others in development?
Yeah, absolutely. So there is published data from the former Pfizer molecule. Now, I think it's the Roivant-Roche molecule that showed effects on soluble TL1A in plasma in their phase I study. So these are published data that obviously we'll benchmark against. In our particular case, obviously, we have a long-lived antibody, and we will be able to hopefully demonstrate sustained elevation of soluble TL1A in plasma.
Gil Blum, Needham & Company, thanks for the presentation today. A couple of maybe broader, higher-level questions. So it's a very diverse clinical development platform, talking immuno, oncology, dermaesthetics. Is there a challenge of developing expertise across so many spaces, especially the ones that are moving forward and clinically?
Yeah, absolutely. It's a great question. And that's why we rely on partnerships. So all of these assets that we're developing, we're ultimately going to partner them. And I think if you look at some of our IO and oncology pipeline, that's an area where we think partnering that sooner rather than later makes a lot more sense because those are very complex trials. You need the domain expertise in that therapeutic area. And so these are assets that I think we will partner sooner rather than later versus something like our TL1A or TL1A, we may take a bit farther along before partnering it. And so that's how we're planning on gaining that expertise and what we're planning on doing with some of those assets.
Maybe a little addition to that. There is a beauty about the platform, which can address in a very agnostic way any target, no matter what the indication is. So I mean, that I look at certainly as an advantage of the platform. When it comes, however, to build expertise, which addresses a very important aspect, that expertise is, of course, usually needed the most at the very beginning of the R&D process when it comes to deciding on targets, when it comes to really understanding a bit the disease biology. And that's where we have a focus, which is on targets coming from our Reverse Immunology platform. The first-in-class targets we take forward, of course, are based on our knowledge on Reverse Immunology and the TLS. You have heard Luis Diaz talking on it and Christian talking on it.
So this specific piece of the internal pipeline is the piece where we take forward targets based on expertise in Reverse Immunology.
A closely related question. How would you quantify the barrier to entry for someone who wants to do what Absci already does? I mean, there's quite a few private comps in this space that are, shall we say, expensive. How would you define your advantage?
Yeah. I mean, I think at the end of the day, it's this Lab-in-the-Loop that we've built and this data advantage that we have where we can rapidly go from data in our wet lab to training our models to then going and validating those. And that's done in six weeks. And that's how we're able to rapidly iterate on the model designs and the architectures. In a lot of cases in biology, it takes a long time to get a readout on a model, but in this case, you're able to rapidly iterate on those model designs. And I think that's allowed us to help recruit some of the best AI talent in the space to help us develop our own proprietary AI model.
So yes, we do have our own proprietary models that we are using, but we also have a data advantage for both the training and validation aspect. And I think that that's really given us a huge advantage in where we're at and why we've made the innovation so rapidly.
Gil, if I could add to that too. So before joining Absci about a year and a half ago, I had almost 20 years on the private side and the last five years of that investing in this space. And so I would echo what Sean said. There's just three things that really stand out to me. It's the data platform, which is very distinctive here where we do that lab-in-the-loop. And we have the wet lab facility to do that. And we spend a lot of time making sure we make that very efficient and make sure it provides the data that's usable. The second component is the team. So it's the AI team led by Amaro, who I think is amazing. I like to make him blush too. We've complemented that and integrated that with Andreas' team.
So we have true drug discovery expertise that's unparalleled if you look across the private space for other companies that are trying to use AI. And the third thing, which I'm really proud of today, is the results. I think they speak for themselves. I haven't seen any other private or public company using AI that can design against targets like the ones we talked about today, Caldera, what we're doing in ion channels, what we're doing with AZ. I think the results should speak for themselves.
Kripa from Truist Securities. Thank you so much for all the presentations. It's really amazing how you guys can focus with so many exciting projects. So a follow-up to maybe Gil's question is, especially your internal pipeline, you have four different assets now and different indications. And I know you talked about you'll decide at what stage to potentially partner them. I just wanted to understand the strategy. How do you go about that? How do you know whether you're getting the right value at each stage? That's one question, and then I have a follow-up question.
Yeah, I guess the.
Do you want to take it, Andreas?
I'll do the first part and then hand it to you. The short answer is it's asset by asset, right? There are some indications and assets where we think there are really big inflection points from getting through phase I, maybe some where we think we can actually afford and execute a phase II where we think that could substantially increase value. And there's some, in some cases, particularly in the oncology setting, where we might look to partner at an earlier phase, even DC or IND, where we think having the partner is really, really important to get to those later sort of key data readouts. So we really do look at it and assess it on a program-by-program basis.
You said it all. I mean, first of all, there is a market for different types of assets and different indications. That is, of course, something we have to pay respect for. But from an R&D point of view, I always want to partner at a stage where I know it is best for the asset. And clearly, it is better for, for example, for an IO asset to partner rather early on with an organization with a broad I-O background, supporting already a DC moving forward and getting better characterized into phase I versus, for example, a prolactin receptor antibody where we have, of course, both from the investments needed as well as from the obvious clear path forward with low competition into clinical development, a complete different option space when we decide to generate what type of value in the value chain.
So it really is, like Zach said, an asset-by-asset decision for which, however, we have an idea of what we want to do for each asset.
So following up on the tier one asset, that's a really cool asset. Just was wondering how you guys, where did you start? Did you start with the market? Did you start with the target? There's only one other company that seems to be developing it. So that also gives you scarcity value, right? And then another question around that, which is probably more manufacturing related, is especially since you're going to be targeting maybe not 80 million, but even if you think one-tenth of that population, how do you optimize manufacturing for such a massive population? Thank you.
Yeah. Well, Andreas, I think you should address TL1A since it is your favorite target.
I address the first piece because that is the piece which can be addressed best by somebody who had the benefit of working for a big pharma company and the frustration of working for a big pharma company at the same time. This target is actually, and we did not talk about that today, also an obvious target in endometriosis with a very compelling preclinical profile and preclinical scientific rationale. And that's where the target originally was discovered and profiled for. And that, of course, now tells you how also big pharma very often works. They search in their therapeutic area, focus in certain therapeutic areas for targets, and then for the right or wrong reason do not necessarily address those targets where their potential is potentially higher than in the originally found therapeutic area focus.
Whereas I continue to believe that this approach will be very valuable in endometriosis, I do think that a serendipitous finding in endometriosis experiments, where you open up the abdomen of mice for which you have to shave them to implant endometrial cells to do your endometriosis experiments, we saw that those mice just regrew their fur like hell. Not only that the endometriosis was suppressed very effectively by a prolactin receptor antibody, but they regrew their fur. And then looking into scientific rationale, you could very quickly identify, hey, that makes sense. So this finding makes sense. But now, of course, you have to take the next step and take this serendipitous finding forward in building a new case, which is very difficult in an environment where you have this therapeutic area focus where there's very little tolerance to go outside of a therapeutic area focus strategy.
I can tell you that there are a number of assets around in the pharma world which are similar to this particular project we described today in that context, not from the magnitude, not from the quality of the target, but there are tons of assets around which may apply their value to an indication different than the indication of discovery. And this is clearly what we benefited from here. So the finding was first to answer your question.
Hi, Morgan Gryga on behalf of Vikram Purohit at Morgan Stanley. So another question on ABS-201. What sort of dosing interval are you aiming for? And do you believe that this would be a chronic therapy or something that the patients use as they feel they require?
Yeah, great question. So we will explore a dosing. Obviously, we'll hope for an interval as long as possible. We think based on the data we have that every two weeks is probably the worst case. It's probably going to be once monthly, but really pending ongoing experiments in the PKPD work that's ongoing. In terms of the overall treatment cadence, based on the non-human primate study and stump-tailed macaque I showed earlier, I think it's reasonable to assume that it will be a six-month overall treatment regimen, and we will expect to see efficacy for the entire anagen phase. So that could be anywhere between three and six years. So it's not going to be a chronic disease-modifying treatment, but at least for one complete anagen phase, anywhere between three and six years or two to six years, we'll see continued hair growth.
Every six months, treatment for six months, and then treatment effect for up to six years.
Okay. That's very helpful. And just one more question also on 201. I know it's early, but what are your thoughts on the ability to combine with some of the approved agents in the real-world setting? And what could that look like?
Yeah, that's an interesting idea. Well, first of all, I think ABS-201 as a monotherapy is going to be very efficacious. So we think there's not going to be a lot of unmet need left after we treat with ABS-201. It sounds funny, but this is what the preclinical data tell us. It's a very compelling profile. However, if you think about it from a scientific perspective, there's actually evidence that prolactin locally in keratinocytes and epithelial cells actually stimulates, via DHEA, the metabolism of DHEA into 5-dihydrotestosterone. And 5-DHT is a very strong driver of androgenetic alopecia. So just from a mechanistic standpoint, there's additional evidence that suggests that this is going to be very efficacious in AGA. What I just said, though, could potentially mean that there might be synergy with finasteride. But clearly, that would only be applicable in men.
You will likely continue to have all the finasteride side effects, which you may or may not want to have. Our primary approach clearly will be monotherapy, and combination with finasteride is potentially interesting, but it remains to be seen whether there's actually a need for it.
Yeah, and Dr. Rossi, I don't know if you have anything else to add to that.
No, yeah. I mean, in the clinical setting, we do use multimodality treatments often with what we have today. So hopefully, there won't be a need for that. But yes, we often do combined medications for our patients depending on how well they're doing or sort of how aggressive they want to approach their hair loss. Some people really want to throw many things at it. Other people would rather do a stepwise approach, which the latter is usually how we approach things.
This is RK from H.C. Wainwright. A couple of quick questions. The first one probably for Amaro. Thanks for giving us those examples on the de novo antibody drug discovery. One of the things I was thinking of was how or when do you bring in factors such as polyspecificity and polyreactivity into your identification process? Because these are the things which generally are factors that lead to potential off-target toxicity that eventually leads to failure of biologics in the clinic.
So the question was, when do we incorporate polyreactivity into our design process?
Yes.
Yeah. So typically, we incorporate measures of polyreactivity during our lead optimization process. So we'll evaluate the candidates that we're taking forward across a range of developability attributes, one of them being polyreactivity, and can optimize for that attribute to improve specificity as needed. I would say in the current drug programs, we haven't had a problem managing polyreactivity, but it is something we're developing. We have assays for and can optimize out. So it would be the lead optimization piece of our platform.
Thank you. And the second question is on ABS-201. How different is ABS-201 from HMI-115, which is currently in phase II for alopecia? And also, there are antibodies against the prolactin receptor, which are being tested in different cancers, but none of them have shown efficacy there. So the target that you are looking at, is that different from those that are being tested in the cancer field? Thank you.
Yeah, great question. So let me answer these two questions one by one. So first off, in terms of differentiation from HMI-115, we know the HMI-115 molecule. We've characterized it. It has liabilities, particularly when it comes to its CMC and manufacturability properties. It also does not have an extended plasma half-life. And if you look at the dosing of that molecule in their clinical development program, I think it's pretty obvious that this is a not very attractive administration scale for patients. So you're potentially looking at multiple administrations sub-Q in a large volume. So it's, I would say, not ideal for an aesthetic indication. So that's one. And then regarding your other question regarding antibodies that have previously been developed in other indications, such as cancer, those are very different antibodies. Those have very different mode of action. So first off, our antibody is effector silent.
So there's no antibody-dependent cytotoxicity that you expect from such an antibody because that's what you don't want. Second, our antibody is a non-competitive antagonist to the prolactin ligand, which some of the other antibodies are not. And then finally, the reason this was initially thought as an interesting cancer target is that some types of cancer overexpress the prolactin receptor. And the intention of these antibodies was actually to use that as a homing device, for example, for an antibody-drug conjugate. So really very different pharmacology, not comparable to what we have in hand, a non-competitive anti-prolactin receptor antibody without effector function. Does that answer your question?
All right, and we have time for one more question, and then we'll invite everyone in the room here to please join us to continue the conversation with our leadership team in the next turnover, but the final question before we formally conclude.
Hi, this is Rhea on behalf of Julian Harrison from BTIG. On ABS-101 and ABS-201, we were just wondering if there was a clear reason why you elected FcRn recycling half-life extension technology. Is there a key point of differentiation versus other half-life extending technologies that maybe we aren't appreciating?
Yeah, great point. Absolutely. So half-life extension technology is clearly one of our key differentiators. For both ABS-101 and ABS-201, we think it's going to be key to deliver an improved dosing regimen for patients, so patient convenience, but also compliance. So a major issue in terms of long-term medication is patient compliance. And if you talk about a subcutaneous dosing, the less, the better. So that's our thinking.
Alex, maybe one more question.
Thanks for squeezing in. Yigal Nochomovitz from Citi. So with the alopecia drug, I'm just curious, to what extent do you understand the ability to target the efficacy? Because you saw the serendipitous data in the animal model, but as far as an aesthetic, in terms of where you want the hair to grow, what do you know about that level of detail? Because we saw, I don't know, I remember reading data from minoxidil 20 years ago. If you touch your forehead, you get hair growth on your forehead. You don't want that. So just talk about a little bit of that and have a platform question too.
Yeah, great point. And it comes back to the hair growth cycle. So there is data that clearly indicates that there is local hyperprolactinemia in the hair follicles that pattern baldness basically causes. So in other words, we don't expect ectopic hair growth from this mechanism. And that's because the places where you have ectopic hair growth, that's not where the hyperprolactinemia causes the catagen phase. So obviously, pending clinical development, which is where we will see this panning out based on preclinical data. And I think the best data to that end are the stump-tailed macaques. They regrow the hair where they lost it after puberty and not anywhere else.
Okay. And this is a broader question. If it's too much to answer now, we can do it offline. But I'm just curious, in your design discovery iteration cycle, once you do all the machine learning AI work, you get your candidate, you put it into preclinical. At that point, is there another iterative cycle where you get the preclinical data, you see it, and then you feed that back into the AI, and then you do it again? And the same thing could be said for once you get initial phase I data, you see it, you feed that back into the AI, and then it makes you better for your next candidate?
Yeah. I mean, so I think maybe one way to answer the question is if we had access to functional data, could we use that to refine our AI models? I think the answer to that is definitely yes. And we would love to scale up the ability to collect that kind of functional data. For preclinical models or clinical data, we haven't run any kind of iterative loop with our AI in that data. We could, of course, if we identified liabilities in those phases, address those through engineering, but it's not something that we've had to do to date. Is that fair? Anything to add to that?
Yeah. I guess the fundamental challenge for AI training is the amount of data you can collect. And the amount of data we collect in the early stage is several magnitudes higher than what we collect in the preclinical phase.
So we typically don't take tens or hundreds of molecules into GLP tox. We typically take one or two into GLP tox. So the amount of training data you generate later in the process is much more limited. But it's a good point, though. I mean, in principle, you can think about bringing back more scarce data if it's highly valuable and highly validated back into the design process, I think. And I think we are exploring this.
Great.
That will conclude. Sorry, Sean.
Oh, I was just going to say right before we wrap up here, I also wanted to thank our guest speakers that came in person, Dr. Diaz, Dr. Rossi, and Mike Jafar. So thank you all for coming today as well.
That does conclude the program. We would like to thank everyone here and on the webcast for your time and attention today and invite everyone who's here live in the room with us to join us next door for a reception with our team. Thank you, everyone.