My name is Dan Delfico. I'm one of the associates on the healthcare banking team here. This morning, it's my pleasure to introduce you to the Absci team. With us today, we have Sean McClain, founder and CEO. Just a reminder that at the end of the presentation, we'll have a Q&A session, and we'll be passing around the mic. So, with that, I'm gonna turn it over to Sean.
Great. Thank you, Dan and J.P. Morgan, for hosting us here. As Dan said, I'm Sean McClain, the founder and CEO of Absci. We're a data-first generative AI drug creation company. You may recall from last J.P. Morgan, we announced some really exciting breakthroughs in generative AI for biologic design. Being able to design an antibody from scratch with a generative AI model, being able to feed in a target of interest, and then being able to have a binder to that target. Now, that was a big technical feat from an AI perspective, and we've took it one step further this last year. We were able to show not only could we generate a binder to a target of interest, but we could actually create a differentiated drug asset with generative AI, being able to create a potential best-in-class.
This is for TL1A, which I'll be talking about in the presentation moving forward. What this is enabling is being able to generate better biologics faster for patients. The momentum that this has created has been really phenomenal over the last couple of months, and we're taking this momentum into 2024. Some of the recent momentum and success that we've had are new partnerships with pharma. We recently signed, roughly over, you know, roughly $1 billion in overall value with two very important partners of ours, AstraZeneca, as well as Almirall. These are focused in oncology and dermatology. We additionally achieved our 10 active program guidance for 2023, and we've built out a world-class internal asset portfolio, built by a world-class team of drug hunters and AI scientists.
And in that pipeline is our TL1A asset, where, again, we used generative AI to design a potential best-in-class asset. Now, how does this platform work? It works through our Integrated Drug Creation platform. It's data to train, AI to create, and wet lab to validate. As I said at the beginning of this presentation, we're a data-first generative AI drug creation company. We start with the data. If you look at GPT-4, it was trained on the whole internet. We do not have that much data within biology, and particularly with drug discovery. So we've built scalable wet lab technologies at Absci to train these generative AI models. Now, what are these assays? These assays are built on our E. coli screening platform. What we've been able to do is actually scale the production of antibody drug candidates that we want to test and screen.
So, we can go from producing, you know, traditionally in the industry, thousands or tens of thousands to millions. And this production then goes through a screening assay, where we're able to interrogate every single E. coli that's producing a different drug candidate and look at the binding affinity, what epitope is it binding to, the drug functionality, and the manufacturability. And this data, along with publicly available data, is fed into our proprietary AI models to ultimately then use the AI to create. And as I said at the beginning of this presentation, we, we came out last year with a breakthrough AI model, a de novo design model, where you can design an antibody from scratch. How does this work? You take a, a structure of a target, whether it's a known structure or something you created in AlphaFold.
You feed that into the model, and you're able to specify the epitope that you want the antibody to bind to, and you're able to then create, have the AI create the CDRs or the regions of the antibody that bind to the target from scratch. Now, these models are not 100% accurate. We need wet lab technologies then to be able to validate the accuracy of these antibodies that are generated with our AI models. We're constantly always improving. The models are never gonna be where we want them to be. We're always gonna be adding new attributes. So having this iterative cycle where you can generate data to train and then also using that same technology to validate is super important. And we're able to validate over 3 million unique AI-generated antibody designs in a single week.
And that whole cycle time, from training to validation, all occurs within a six-week time period. Now, this is what's allowed us to have the success that we've had on these breakthrough de novo models, is being able to rapidly iterate. We're able to figure out what data sets do we need, what is the architecture of our models. And this is, again, what's allowing us to continue to increase the accuracy of our models, as well as being able to add in new attributes. And additionally, we need these AI models to actually generate differentiated assets. And again, outside of the binding affinity, we're looking at the functionality and going into in vitro and in vivo work. And again, this is all done at our facilities using that core technology I just described on the previous slides.
Now, this Integrated Drug Creation platform and this AI, these AI models, are being applied throughout the drug discovery process, from novel target discovery using our reverse immunology platform, to de novo design of antibodies, to then the AI lead optimization of these antibodies. And what I'm gonna be talking about in this case study is the last two, using this de novo model, along with the AI lead optimization model, to create a potential best-in-class asset with TL1A. Now, what value are these models actually providing to our own portfolio and to our pharma partners? They're creating a differentiated, they're creating biologics that are differentiated, that are faster to market and at lower costs. We're able to use these to access new biology. You know, partnerships like with AstraZeneca and Almirall, you know, want to use these for not.
for targets that have been hard to drug, like GPCRs and ion channels. That's enabling first-in-class assets to be discovered with these models. We're able to increase overall probability of success by being able to hone in on all the attributes you want the first time. So being able to use these in a multiparametric way, honing in on the potency, the affinity, functionality, manufacturability, the first go-around. This is enabling and building in best-in-class features and enabling you to have best-in-class assets. Additionally, we're able to reduce the amount of time it takes to get into the clinic. As I said at the beginning of this presentation, we have applied this technology and actually shown that this is possible, not only to create a best-in-class asset, but to actually reduce the time.
To generate the TL1A asset that we've generated with this model, to get the in vivo efficacy, we did it in roughly 14 months, and we'll be in the clinic or have an IND in roughly 24 months. So we're going from essentially 5.5 years to get a drug into the clinic in roughly now 24 months or 2 years. And that's costing us $14 million-$16 million, versus the $30 million-$50 million normally it takes to get an asset into the clinic. And the last aspect that I'll talk about, which I actually find quite interesting, is on the IP side. So since we're able now to search a much larger search space with our models, we can now actually start to differentiate on an IP level.
But in order to actually get claims issued, you have to enable it. So it goes back to actually the wet lab validation. So, we're able to have the models generate a lot of sequence diversity that you can't see, and then we're able to enable those patent claims by actually going into the wet lab and validating those. We can validate over 3 million unique AI-generated designs in that week time period I had just talked about, and that's enhancing IP protection in a way that we haven't seen before. So it's creating differentiated assets that have IP protection that, again, hasn't really been seen without the use of generative AI. Now, the platform has been validated by industry-leading partnerships. The last two that we've recently announced over the last couple of months are AstraZeneca and Almirall, focusing on oncology and dermatology.
These are roughly $900 million in overall value, and that's in addition to the royalties. So we're able to share in the upside with our partners if the drug is successful. Now, on the right-hand side here is an illustration of how our partnerships are structured. So we have an upfront payment, and then we additionally are paid for the research funding, and that's in addition to the upfront. That covers our costs. And then if a partner likes the asset that we've developed for them, they can in-license it. That's a milestone payment, and we continue those milestone payments throughout the clinical development process. And then ultimately, once the drug is approved, we're able to share in the upside with royalties. Now, AZ and Almirall are not the only partnerships and collaborations we have.
We have over 16 industry-leading active programs that are currently being worked on at Absci, along with our three internal pipeline programs. I like to see. One thing I really love about this industry is it's a team sport. In order to get these better drugs to patients, you know, faster, we all have our own domain expertise's, and partnering with leading institutes like large pharma companies like Merck and AZ, you they bring that disease, you know, biology expertise, the late-stage clinical development, the manufacturing. You know, we're bringing our AI models, and you're able to then ultimately, again, with these partnerships, get better drugs to patients faster.
We're not only partnering on the drug discovery and development side, but we're also partnering on the data and compute side, which you can see on the left. We have a leading partnership with NVIDIA that we signed a couple of years ago. Additionally, we have data partnerships. Not only are we generating data in our own wet lab, but we have data partnerships to feed into our reverse immunology platform to discover new novel targets. Now let's dive into the internal pipeline that we recently disclosed at our R&D Day back in October. We have three internal programs. Two of them are potential best-in-class, the other is a first-in-class. The first one is our TL1A asset, which I'll be diving into further.
The second is a dermatology program, ABS-201, and the last is an immuno-oncology first-in-class target, which came from our reverse immunology platform. I won't spend too much time on this slide. This talks about the clinically validated TL1A asset, and it's a large market opportunity, in particular for inflammatory bowel disease, but there's also some really other exciting indications that are out there outside of IBD. So in order to validate that generative AI can actually create better biologics faster, we applied this to the TL1A asset.
This is the first asset that we've applied the de novo AI model towards, where we were able to take the TL1A structure, feed that into the model, and then specify the epitope we wanted the antibody to bind to, and the model was able to generate CDR sequences that were unique, that bound to that epitope of interest. Again, this is the first time we've implemented this technology, and all of this work that I'm going to be showing you was done in 14 months. In vivo validation of this antibody using this technology in 14 months. This is really a breakthrough in terms of how quickly you could generate a potential best-in-class asset. So we're not just looking to show that we can design a binder.
We want to design this to have differentiated properties, and the properties that we looked at to really be differentiated were, you know, looking at, you know, can we engineer this to have higher potency, have longer dosing intervals, so extended half-life, do sub-Q injections versus IV, and also have favorable developability? And then additionally, I think this is a great use case from an IP standpoint, how could we use the AI to get outside of the existing IP from the competitors, but then carve out new IP landscape for our own, which we have successfully done with this program? Now, let's dive into the data here. So we started out with the model generating all these unique antibody sequences.
We went in the lab, we tested them, we narrowed it down to 3 advanced leads: ABS-101A, B, and C. And if we look at the affinity and how it compares to the two innovator molecules, which are designated as MK-7240, as well as RVT-3101, the Merck and the Roivant competitor molecules, you can see on the left-hand side that we are able to generate antibodies that have a superior or equivalent affinity to the competitor molecules. Now, how does this then, you know, translate into potency? It has favorable potency when compared to the innovator molecule.
We did actually show that we could have superior potency when compared to the innovator molecules, and this potency assay was an apoptosis inhibition assay in TF1 cells. And again, this is showing that we could generate an antibody from scratch that had higher potency than the two innovator molecules that are currently in the clinic. Now, one of the other aspects that we wanted to engineer was increasing the overall half-life, so we could have improved dosing intervals. So being able to potentially go from once a month to once quarterly. So we did Fc engineering, and on the left-hand side here, we have the in vitro assays, where we showed that we could increase the FcRn recycling, and indeed, that is the case.
We then wanted to show in vivo that this is truly the case, that you could have increased half-life, and you have preliminary data on the right-hand side that shows that ABS-101B does have improved half-life compared to the Roivant competitor molecule. And the rest of the in vivo PK studies here will be done in the next couple of weeks. But again, this shows that we are able to engineer this molecule to have better half-life, leading to hopefully improved dosing intervals. Now, the last piece of data I'm gonna share on this asset is the epitope mapping. So as I'd mentioned at the beginning of this presentation, we can use the AI to condition on the epitope.
One of the interesting things about, you know, conditioning on the epitope is you can change biology, and you can differentiate on the IP front. So what's interesting is that the Roivant molecule is known to have high ADA. It doesn't have a great immunogenicity profile, but the Merck molecule actually has very favorable ADA response, as well, you know, leading to better immunogenicity or lower immunogenicity. We believe that this is actually due to the epitope. So what we did was we conditioned our model on the Merck epitope. And you can see some of these actually are in slightly different, or there's a little bit of an epitope drift there. And we wanted to do this, one, from a biology standpoint, but then two, also from an IP standpoint.
And so we're able to achieve, you know, a superior potency than both these molecules, but hit a different epitope, which we believe will lead to actually lower immunogenicity in the clinic. So again, being able to use generative AI to differentiate on biology, but also differentiate from an IP perspective. Now, what does this look like all together in one package? How can we, you know, use this to create a best-in-class profile or an optimal therapeutic profile? Well, if we look at where we want to be with this asset and the preclinical data that we have does support us being able to achieve these attributes, we will have a potential best-in-class here.
So, we do believe we will have lower immunogenicity due to targeting the same epitope as the Merck molecule, higher bioavailability, being able to do sub-Q auto injection, and going from once quarterly or once monthly dosing to once quarterly. And again, we believe that this will create a best-in-class TL1A asset, all designed with generative AI. Now, going on to the timelines. So it took us 14 months to generate a differentiated profile that was in vivo validated. And it's gonna take us, roughly another, you know, 9 months-12 months to get our IND enabling studies done and be in the clinic, or have an IND in roughly 24 months.
And so we're gonna be initiating our IND enabling studies in February with the goal to have an IND in Q1, and then enter into our phase I shortly thereafter. Again, I just wanna highlight this. We're using the de novo platform to be able to create a differentiated asset and be able to do it in a very, very rapid timeframe. This is really the future of how drug discovery is gonna be impacted by generative AI. Now, it's not just AI that's, that is helping move things forward. It's a mix between the human consciousness as well as our artificial intelligence. And the team that we have here is extraordinary pioneers in the field. I mean, we have world-class drug hunters, like Andreas Busch and Christian Stegmann, along with the rest of the team that has been built out.
We have world-class AI scientists at Absci, world-class synthetic biologists, all coming together to ultimately see this vision through and accomplish what we've accomplished to date. We additionally have a state-of-the-art 77,000 sq ft campus, where all of the data is being generated and all this validation work is being done. Because, again, the success that we've had is not an AI-first approach, it's a data-first approach. It's that integration that allows us to do what we do. We've recently, over the past two years, built out an extraordinary executive team and board. One of the recent board members we brought on, which we announced yesterday, is Sir Menelas Pangalos. He is the Executive Vice President of Research at AstraZeneca.
You know, it's just a testament to bring on somebody of Mene's caliber to really validate what we're doing here. And it's really an honor to work not only with Mene, but my executive team as well as the board. Now, in summary, going back to the beginning, last year, we showed that we could use a de novo model to create an antibody binder. But now we've taken it one step further, where we're actually showing preclinical data where we can differentiate on the biology, creating a potential best-in-class, being able to create biologics faster than we have previously at lower costs. And this is all happening because of our Integrated Drug Creation platform, our data to train AI to create and wet lab to validate.
Being able to do that in a rapid 6-week time period, being able to validate over 3 million unique AI-generated designs in a week's time period. And the platform has been recently validated by industry-leading partnerships like AstraZeneca and Almirall. And we've shown, actually shown, that generative AI can create a potential best-in-class asset with TL1A. Now, this momentum that we've had coming into this year, or the momentum we had, you know, at the end of last year, is gonna continue into this coming year. We have exciting catalysts coming up in 2024. Not only are we gonna continue to sign breakthrough partnerships like the ones we've signed with AZ and Almirall, but we're also gonna have exciting data readouts. We're gonna be entering in our IND enabling studies in February for TL1A.
We have some exciting in vivo validation studies that will be completed for ABS-301. That's our first-in-class IO target coming from our reverse immunology platform, and we're gonna have a development candidate selection for ABS-201, which is our potential best-in-class dermatology target. And then we'll have the IND submission for ABS-101 in Q1 2025. It's a really exciting time to be in the field of generative AI and biology, and we have now taken what was once a vision and really put concrete data behind it to show that this actually can make a difference in making better biologics for patients. Thank you.
Thank you, Sean. We'll open it up to Q&A now, and we'll have a mic coming around. Yeah, front row. We'll also have Zach Jonasson, CFO, and Andreas Busch, Chief Innovation Officer, coming up for the Q&A portion.
Is this working? Yeah. Great presentation. Thank you, Sean. Is there a reason to. Like, how many paying pharma collaborations do you think you could do? Like, is there a reason to, like, is there a max amount that where above that it becomes a focus issue, or is it better to have five or 10? Or like, what does great look like on that pharma pipeline?
Yeah, absolutely. We really want to be able to use these pharma partnerships to go after disease areas that we don't have expertise in. So it allows us to create this differentiated portfolio of assets that we couldn't do ourselves. You know, like, I think oncology is a, you know, perfect example. Like, we wouldn't plan at this point in time to take something into late-stage clinical development in oncology because we don't have that expertise. But you know who has that expertise is AstraZeneca. And so we see this as helping, you know, create differentiation for our portfolio, and then using that upfront cash then to focus in on the assets that we find really exciting for our own internal pipeline, to continue to develop those.
And so we see this as a kind of we're gonna continue with these partnerships, but they're gonna be very focused. And I think we're gonna see them be more focused and much deeper in terms of how integrated we want to get with, you know, some of these large pharmas, 'cause they do bring expertise that we don't have, and I think we can have that synergy. And so we do see it continuing to grow, but I don't think you're gonna see, you know, it expand, you know, I guess depth-wise in number of companies, but you could see it expand depth-wise on the number of programs per, you know, large pharma partner.
I'll just comment too, Charlie. One of the things that's been exciting over the last year is looking at efficiency gains in that cycle that Sean described, where we generate data, we train the models, we validate. So we're seeing efficiency gains, and we're seeing the models improve, so that gets to more and more capacity as we look into the future.
Thank you for the nice presentation. You talked about immunogenicity is related to the epitopes. I'm curious about the reason of the statement.
Yeah, absolutely. So, one of the reasons we believe that it is that the epitope because if you look at, like, the T cell activation assays, the profile for all three actually looks pretty favorable. And so we think it's more of a complex issue, like with, you know, stimulating the, you know, a B cell response versus more of a sequence liability. So we think it's more, you know, a structure-based immunogenicity of where it's actually binding than, you know, a sequence liability. That's at least our hypothesis at the beginning.
Now, we'll have to see how that translates into the clinic, but that's kind of the best scientific hypothesis we can come up with at the current moment.
I see. Thank you.
Maybe to bring this back to the internal pipeline and some of the partnerships, how does the growth of the internal pipeline change the business strategy, if at all? And from there, have you thought about maybe potentially partnering some of the internal assets?
Yeah, I can take it at a high level and have Zach dive a bit deeper. So one of the things that we see is really exciting about this platform is, again, not only creating differentiated assets faster, but it's actually enabling a new business model, where we can actually take assets further and get them to inflection points that are very meaningful in a much shorter and at a cheaper cost. And so we do plan on continuing to invest heavily in our own internal pipeline, just like we have with ABS-101, and take it anywhere from a drug candidate phase to a phase II, and then sell it off at that point in time.
I think, like, what you're seeing is that pharma's reaching earlier and earlier into these partnerships. If you look at, like, I think, the Harpoon Merck acquisition, I mean, that was a phase I oncology asset they bought for $600 million. And if you can, you know, in a very rapid manner and in a very cost-effective way, you know, get to a phase I with an oncology asset, let's say, for the 301, that could be, you know, these economics start to make a lot of sense.
So instead of partnering at the target phase, where you're waiting for these milestone payments out in the future, you can actually bring these payments forward, bring cash in, and kind of repeat that cycle. Again, we don't plan on taking anything late stage, but I do think it enables a new business model that is able to kind of bring in these payments a lot sooner with, you know, higher upside because we're taking them to differentiated value inflection points.
Maybe one other aspect to double-click on is the wet lab AI integration. Can you talk about how that works in practice?
Yeah, I mean, we touched on it quite a bit today. But again, it's this six-week cycle time where we're generating the data in-house to train our models, but then using that same technology to validate, and we can validate over three million unique AI-generated antibody designs in that time period. And again, I think that's what has allowed us to increase the accuracy of the model, as well as being able to add on new features from a multiparametric modeling perspective. I don't see us stopping that anytime soon because we're always continually improving these models and adding new attributes, and you're always gonna need that AI wet lab integration.
Maybe I add a quite important and very often overlooked cultural aspect. AI scientists and wet lab biologists do have a different cultural background, and they speak a different language. I think it was a very big effort to really get them together as a team, teaching them to speak the same language, expressing what the needs are on both sides. We clearly overcame that gap over the last year to a degree that, you know, from not talking to each other to really celebrating experiments together, and I think this is one thing which I would consider a true differentiator for us in our approach.
And then maybe one more from my end. Could you just touch on how or what differentiates Absci from the other AI drug development startups out there?
Zach, I feel like, since you were on the VC side, looking at these, I think.
Yeah, Sean mentioned before I joined Absci, I had built a venture fund and was investing in this space, and so, my perspective is there's a number of things. One of them is what Sean really articulated throughout the presentation: data. We're data-first. There's no way, in my view, to build a successful drug discovery effort unless you create your own data. Those functional data sets don't exist in the public. You have to build data sets that are tailored to your models, and that you can leverage with AI, and that's something that we do at Absci at scale. The other thing I would point out is we have this integration that Andreas just talked about with the wet lab and the AI teams. That's a big effort that we've undertaken, and it's really important.
I've seen lots of companies where it's sort of like there's an AI team, and they do some wet lab. The two don't talk. Somehow that's supposed to work. Very unlikely. And the third thing I would highlight is, you know, we've taken our time to come to developing our own pipeline, and that's because we wanted to have truly exceptional talent in place who has experience in drug discovery. So when I look across the industry, I, I really don't see other AI companies with the kind of discovery capability we have with Andreas and the team he's recruited that used to work with him at Bayer and Shire. So we're very well set up to develop our own assets into, you know, further clinical development or preclinical development where they're suitable for larger partnering deals.
Yeah. One thing I also want to mention about our own development program. Again, we see this as a differentiated portfolio of assets. We're never gonna be a biotech company that takes 80% of our working capital and invests it into one asset. And so even if we do take something into a phase II, it's gonna be a phase II that, you know, that is affordable, we can move quickly on, and doesn't, you know, concentrate our capital in one particular asset. Again, we wanna be able to use this for creating that differentiated portfolio that is diversified, and isn't a concentrated bet on one asset.
All right. If no further questions, thank you, Sean and the Absci team, and thank you all for joining us.