Absci is a public TechB io company focused on developing novel protein therapeutics by harnessing the power of generative AI. The company has established many partnerships with leading pharma and academic institutes, and also has developed a clinical pipeline with four best or first-in-class candidates. To discuss the company's development strategy in 2025 and beyond, I welcome Sean and Alex to this fireside chat. Good day, Sean and Alex. Thank you. Thank you for joining us, and glad to see you both and appreciate you accepting to talk to our audience this morning.
Yeah, it's great to be here. Thanks, RK and Marie.
Thank you. Sean, to just start off, you know, especially for some of the folks who may not be aware of Absci and what Absci stands for, can you give us a highlight on the business model itself and how you're differentiating yourselves against other TechB io companies in the field?
Yeah, absolutely. We founded the company 13 years ago, and we started off and still are a data-first generative AI drug creation company. We started out scaling protein-protein interactions, how antibodies interact with targets of interest, essentially looking at the functionality of antibodies. We were able to scale that data from, you know, traditionally being able to screen thousands to, you know, screening millions. This was right around the time when deep learning was taking off at Google. You know, they came out with their transformer in 2018. It was this idea of being able to take these generative models with this data and being able to go from this paradigm in drug discovery, this trial-and-error process where you're searching for a needle in the haystack, to actually now being able to create that needle, in our case, a biologic.
We are really focused on how do we use these generative models to solve the design of biologics. You know, why is this relevant? There are still hard and challenging targets to go after. You have, you know, ion channels, GPCRs, and there is a lot of exciting biology, actually validated biology, if you look at, you know, human genetic data or knockout data. Traditionally, it has, you know, been hard to drug these targets. If you can actually use design to specifically tailor an antibody to have all the attributes you want, you can unlock that biology and be able to create, you know, first-in-class, best-in-class assets. That is really where we are focused in on, using AI to go after the hardest and most challenging design problems that still exist in the industry.
Now, we are seeing a lot of success on, you know, the speed and the cost, being able to reduce, you know, the cost and the overall timelines. The value prop at the end of the day, though, is actually creating these differentiated assets that you couldn't create any other way. As you'd mentioned, RK, we have large pharma partnerships with AstraZeneca, with Merck, biotechs like Almirall. We also have, you know, tech partnerships with AMD and NVIDIA. You know, in addition to those partnerships, we're building out our own internal pipeline as well. We will actually have our first AI-generated antibody that hits the clinic this year, which we're really excited about. That's an anti-TL1A potential best-in-class antibody. Shortly after that, we will have our second antibody hitting the clinic, which is our anti-prolactin receptor antibody. We're really excited about this one.
This is two really exciting indications. The first is androgenetic alopecia, so common baldness amongst male and females, as well as endometriosis as well. We really see the ultimate value of AI being, you know, in the assets itself, and the AI is a tool to create these really exciting and differentiated assets.
Fantastic. I know about a year and a half ago when Alex initially told me that, you know, you'll be talking about a pipeline. I was like, wow, this is interesting. Here we are talking about first molecule going into the clinic soon. Obviously, you know, even though I have, you know, started covering the company for about two years now, I think, or maybe a little bit longer than that. The speed with which you have brought molecules to the clinic, you know, I'm sure, you know, there has been a lot of parallel processing that has happened, you know, before you could start unveiling the pipeline and start talking to us on the public domain. In general, you know, this is a question that we were trying to field, you know, during the panel session this morning.
In terms of the speed and also in terms of ability to parallel process not one program, but three programs, and to bring it and unveil all of them at one shot, which you did last year, what amount of resources had to be put into it? You know, if you compare that against a traditional biotech company, what's the skill differential that we are seeing having AI as not only a tool, but also a very helpful modality?
Yeah, absolutely. You hit the nail on the head on just how rapidly we were able to develop this pipeline. We were not considering building out the internal pipeline until Andreas Busch came on to the team, who was the head of R&D at Bayer, and then left Bayer to be the CSO at Absci. He came on at the end of 2023, beginning of 2024. It was then that we started building out the TL1A asset and the prolactin receptor antibody. In a very short amount of time, we were able to not only build assets there, but also, you know, two other oncology assets and definitely have a very early-stage pipeline, which we have not talked about. That was all in the course of two years that we were able to spin up a pipeline, create differentiated assets, and be in the clinic.
It was Andreas and his team that really, I think, helped us figure out what targets were the best to apply the technology towards. That engine was able then to rapidly spin up these antibody assets in a very rapid time frame and was able to show, I think, quite exciting differentiation. To put it in kind of dollars and cents and put it into some numbers, you know, with our TL1A ABS-101, we were able to get that in the clinic in roughly 24 months. Now, that's on average compared to five and a half years that it takes, you know, pharma or any, you know, other company to get assets into the clinic. We are able to reduce the time by half. Additionally, we were able to invest roughly $13 million-$15 million from start to finish.
That's getting the actual asset into the clinic for roughly $13 million-$15 million. That's compared to the $50 million-$100 million it normally takes pharma to get one asset into the clinic. What you're seeing is this massive increase in R&D productivity. You're now able to have more shots on goal. For every $100 million invested now, instead of developing one or two assets, you can develop five to ten assets. This really starts to change the paradigm of drug discovery, helping make it more efficient. What we continue to harp on is the efficiency is important, but being able to access that novel biology is even much more important and creates differentiation. It's been an exciting last couple of years developing this pipeline.
Again, it's not just one asset. You know, we've got, you know, four assets that we've publicly disclosed and then, you know, a bunch that are in early-stage development that have all, again, been developed over the past couple of years.
That's really great to hear, Sean, because as a prior pharma analyst, R&D efficiency was something which we used to harp on a lot with large-cap pharma and saying, you know, how much money that they need to spend to get one molecule into the clinic. Probably that metrics need to be brought, you know, in terms of looking at AI-based drug discovery companies as well, because that's something which some of the institutional investors are always looking at, like, you know, how much of my money is actually being spent to get a molecule into the clinic and get a shot on goal. That's excellent to hear. I'm going to pass it on to Lee for some discussion on your pipeline.
Yeah, let's talk about your 101 program first. We know there are several late-stage TL1A antibodies already in the pipeline. What we think is amazing is that Absci is able to show not only superiority in terms of convenience of use, but also potency as well as long half-life of your antibody. Maybe can you set up for us, what's the expectation of your interim phase one study, which is coming up very soon?
Yeah, absolutely. One of the, you know, key areas of focus for this readout is going to be the target engagement. If you look at the recent target engagement data that we shared at the JPMorgan Healthcare Conference this year in early January, we were able to show, I think, really nice target engagement compared to the first-generation TL1A competitors, Merck and Roivant. I think showed really nice superiority on that and being able to show that sustained target engagement. That profile that we shared is definitely, we want to see that profile translate into the clinic and hoping to see that in our phase one interim readout, which would be, I think, a big win. Additionally, being able to show that extended half-life translate from NHPs into humans. That is, you know, I think, again, really critical from a patient convenience standpoint.
You know, one of the, you know, recent stats that I heard on GLP-1s is after, you know, the first year of use, only, you know, 30%-35% of patients continue use. You know, what, you know, Lilly was saying on this was a lot of it comes down to patient convenience. No one wants to, you know, take a drug every week or every day. If you're able to, you know, have the convenience or the dosing be every quarter, that, you know, that kind of, you know, starts to change the paradigm on that. I think helps with patient convenience as well as adherence as well. I think that's going to be big. Also immunogenicity. We showed some exciting internalization data that we believe demonstrates that our molecule will likely have lower immunogenicity in the clinic.
This is a B-cell-driven immunogenicity. We are hoping to show some, at least some, potentially some early immunogenicity data in the phase one as well. That readout will be coming the second half of this year.
I see. How do you view the market opportunities for 101? We know it's currently designed being evaluated for IBD. There is also a potential expansion into MASH and eczema like Roche is pursuing. Curious what your thoughts are.
Yeah, we've definitely looked into other indications and definitely are exploring those. We haven't publicly disclosed any other areas outside of UC and Crohn's that we would plan to go into. It is definitely something that's on our radar, something we're thinking about. You know, another area of differentiation and something that we have big conviction in is the combo-based strategy within the IBD space. You have Johnson & Johnson that has, you know, firm conviction on this with their DUET trial that's going to be reading out. You have others in the space that are looking at combo-based therapies. You know, even AbbVie had come out with kind of their strategy on this.
We just came out in our earnings a few weeks ago talking about a novel first-in-class bispecific where on one arm we'll have the TL1A, and then on the other arm will be a target that is known, but there are currently no drugs that have made it to the clinic and shown efficacy here. There have been numerous reasons why these, you know, this pathway hasn't been successful, but we've figured out a way to, I think, get some really interesting biology out of this pathway and think that this will be a really exciting first-in-class bispecific and kind of, you know, really touches on, you know, our belief on the combo-based strategy and being able to really address the heterogeneity of the patient population that you're seeing. That is, you know, an exciting strategy for us moving forward.
I think, as you know, a lot of interest has kind of brewed from this from large pharma.
Right. Really looking forward to that. Maybe to expand a little bit on the bispecifics, what does this new undisclosed bispecific tell us about the potential of your generative AI modeling? We know that bispecific and multispecific is very high demand right now.
Yeah, absolutely. You know, from the bispecific approach, you know, we're using, you know, standard engineering approaches on that. In order to generate the arm, the novel arm, you know, there was engineering that had to be done on that. This molecule in particular, at higher concentrations, you saw agonism. We were, you know, able to show that we could, you know, completely remove that agonism and have it be an antagonist antibody. This is, you know, all being done, you know, through the work of our AI. Again, I think that that just, you know, shows how we can, you know, leverage AI platforms to overcome, you know, some of these challenges that have existed previously.
I see. Now let's move on to your 201, the androgenetic alopecia program, right? Tell us about your potential of 201 to address the unmet need. We know this is a field that hasn't seen much breakthrough for decades. What's your confidence in 201?
Yeah, absolutely. Hair is a very important topic for a lot of individuals. As you're seeing, there's med spas popping up everywhere. Everyone is trying to fix this hair problem that we have. You know, there's minoxidil, there's finasteride. With, you know, in some patient populations, it works great. In others, you don't see any hair regrowth. There's no durability. You constantly have to take these medications. The side effects on some of them aren't great. Patients are really looking for a durable product that can enable them to get hair regrowth and be able to, you know, continue to have that hair growth over a sustained period of time. That's exactly the TPP that we focused in on. We've seen that TPP play out in preclinical studies.
If you look at the mouse shaving study we did compared to minoxidil, we saw superior hair regrowth in our ABS-201 compared to minoxidil. In NHP studies, these, you know, stumptail macaques that naturally go bald, you know, an anti-prolactin receptor antibody was able to show, you know, complete hair regrowth. We think that there's a, you know, a very, very strong rationale for this mechanism. We're seeing in a naturally occurring model, you know, very strong efficacy. We're not only seeing strong efficacy, but, you know, also compared to what's out there with minoxidil, we're seeing strong effects. The other aspect that we're really excited about is not only are we seeing hair regrowth, but we're also seeing repigmentation as well, where you, you know, there's the potential to go from your gray hair to your naturally colored hair.
We have recently disclosed that we're expediting this program. We are on track to have an interim readout of a POC next year and plan to be in the clinic early 2026. Next year is going to be a very big year for ABS-201.
Perfect. Sean, you know, you have, as you stated, you have collaborations and partnerships, you know, from all aspects, right? Whether it's industry, academics, other institutes as well. Some of them are two-way relationships. Some of them are one-way relationships. How are you kind of utilizing these or, you know, capitalizing in terms of not only developing new molecules, but also trying to feed your models so that they become better going forward? You know, if you can kind of give us, I know you cannot get into specifics, but in general, how are you kind of managing these relationships to gain as well?
Yeah, absolutely. You know, every single one of these deals that we do are essentially underwriting the data that goes into our models. In all of the contracts that we've had, we get to keep the data that is generated in our wet lab. If we are generating the data through our proprietary technology, we get to utilize that data in our models. Now, if the partner gives us data, we obviously have a separate model that we use for that. That is a really exciting aspect, one, getting data that's being underwritten from our partners.
We also get to work on really challenging molecules with our partners as well, which allows us to really hone in our models to tackle some of these hard and challenging problems, which we see as a big benefit for us in terms of being able to rapidly advance our models over time. You know, additionally, the partnerships, and I mentioned this in our chat earlier this morning, is that, you know, I see this field as a team sport. There's domain expertise that we can leverage across the industry.
With these partnerships, we can, you know, go into indications that we do not have domain expertise in that really allow us to create a diversified portfolio and be able to ensure that these assets are likely to be approved because they are being partnered with a company that has the capital and resources to take them to the necessary stage, as well as has the disease biology expertise. These partnerships allow us to have that diversified portfolio.
Perfect. In closing, you know, what are the catalysts that we should be looking out for, you know, in 2025? Also, in terms of your runway, how are you situated in terms of funds to accomplish all the goals that you have set for yourselves?
Alex, I'll hand that one over to you.
Yes. I mean, look at the rest of the year. You know, there's a number of things that, you know, we're really excited about. I mean, in the next month or so, we're going to hit the milestone of becoming a clinical stage company with ABS-101 entering the clinic and looking to have an interim phase one readout in the second half of this year. Beyond that, we've also guided that we expect to have one or more new partnerships this year, including with a large pharma company. That's tracking very well. We can look for that this year. Going into next year, as Sean mentioned, we're accelerating the development of ABS-201 and look to have that in the clinic early 2026 and potentially have an interim efficacy readout sometime next year as well, which we think would be a huge validation point for that.
As for runway, you know, we ended last year with about $112 million cash on the balance sheet. You know, feel like we're in a very fortunate position to have that sort of strong balance sheet. Even after the year ended, brought in an additional $40 million, approximately $20 million was a strategic equity investment from AMD, the chip company. Another $20 million on top of that that we raised through our at-the-market ATM facility from two very high-quality mutual funds who had reverse inquiry into that. The $112 million at the end of the year, plus another $40 million after the year ended, we're looking at cash into the first half of 2027. To be clear, that does not include any potential upside from these pharma deals that we are thinking about and even a potential asset transaction.
For example, if we were to transact ABS-101, any cash involved with that would be upside to that assumption. We feel we're in a very strong position from that standpoint.
Perfect. Thank you very much. Thanks, Sean. Thanks, Alex, for joining us and talking to our audience this morning. Appreciate it.
Yeah, thanks, Martin. Yeah, thanks, RK. Thank you.