Acrivon Therapeutics, Inc. Common Stock 12th Annual Cantor Fitzgerald Global Healthcare Conference

Thank you so much, Lee, and thanks for having us here today. I'd be happy to. Everything at Acrivon is built around the AP3 platform. It stands for Acrivon Predictive Precision Proteomics, and that's what it is. It's AI-driven, and that's driving our entire pipeline that's very rapidly expanding. We have demonstrated and proven it in all the areas we set out to do. We use it for indication finding, so we identify sensitive indications before we actually enter the clinic. We've done that for all three assets so far. One of them is not in the clinic yet. We have two clinical stage assets in phase II. We have also shown that we could identify patient responders individually in a prospective manner. That's in our registrational intent trial with the ACR-368, the CHK1/2 inhibitor from Eli Lilly.

We have also published a lot on uncovering resistance mechanisms and so forth, which enable us also to rationally design drugs for superior single-agent activity, and that's what we did with our first-in-class dual WEE1/PKMYT1 inhibitor, which was designed to also activate PLK1, so we get a massive proapoptotic cell death. The platform itself is really able to uncover unbiased at high resolution at the protein level. We use phosphoproteomics, which is a way to measure activity states. We use it to look at what a drug does inside a cell. So we very quickly pass that cellular boundary, so it makes the path to the clinic very linear. There are very few surprises, and it can be applied at all stages of drug development. We've used it for rational drug design, as I said.

We can take early hits or leads, put them into cells, and we can see what they do at the signaling pathways and relate it to the disease-driving mechanisms. That's why it opens up for predicted biomarkers, massive amount of pharmacodynamic biomarkers, et cetera. What is also unique is that it's a massive amount of proprietary data we have generated for over 100 compounds now, DDR inhibitors, in-licensing assets we are assessing and comparing with competitor assets that are marketed or in clinical development. Also in the I&I area, we've done that. We did it for the Lilly asset we in-licensed. The platform really has a number of applications, and they are all proven, which is most important. So it's really, we have proven everything we set out to do with it.

The final thing I want to say is that it's, in a way, I would argue it's a first-in-class platform in the sense that we are not aware of anyone else taking such a systems approach where you can look at the whole signaling network inside a cell and just unbiased see what a drug does, what resistance mechanisms kick in, how it inhibits, and how potently the disease-driving mechanisms, and just unbiased match the mechanism of any drug with these disease-driving mechanisms. Our resolution is extremely high, close to 200,000 quantified map phosphorylation sites. We run quadruplicate samples, and we get these actionable data outputs. We have all kinds of interactomes the scientists can access, and we can make sense out of this. That's where the indication finding also comes in. Probably said more than I should, but that's really driving our platform and pipeline.

Sorry, our pipeline. We are in phase II-B registrational intent with our in-licensed CHK1/2 inhibitor from Eli Lilly. We are in phase II expansion phase with our first internally developed first-in-class dual WEE1/PKMYT1 inhibitor, and we are now aiming for an IND early next year with also a first-in-class CDK11 inhibitor, where we have identified aggressive AML as a very sensitive indication and proven that pre-clinically. I think that covers it without going into technical details.

Yeah. But it's really based on how the pathways are affected inside a cell.

So when we enter the clinic, we can see whether the drug, which was designed to do certain things at the pathways, actually do that at the first dose level, and that we also did with our first internal developed asset. We have a clinical PD AP3 assay as well to support our phase I trials. So lots of applications. Yeah.

Most healthcare biotech AI companies, they focus on how to, in a streamlined manner, it is oversimplified, obviously, what I am saying, but how to generate the best possible drug in the most efficient manner against a particular target. We take the opposite approach. We want to develop or match any drug with what it does inside the cell unbiased. If it is at the discovery phase, we can then optimize the drug based on the pathway effects. If it is an in-licensed asset, we can make sure we bring it to the patients that benefit from that drug. The AI is used uniquely in the sense that it is proprietary data that I had mentioned before. All our learning is on our own proprietary data, and everything is drug-centric. We obviously triage our AI capabilities and internal proprietary, massive amount of data. We are talking about terabytes of terabytes of terabytes.

We triage that against publicly available data as well. But the AI learning environment is actually based on our proprietary data, which is very, very unique, entirely, and not relying on external data. They do not lie. We compare, obviously, benchmark against clinical-stage WEE1 inhibitors and PKMYT1 inhibitors and CHK1/2 inhibitors when we in-license that, or INI1 inhibitors in the areas we are building up now. We just see directly which drug is best, has the most desirable pathway effects, and then we can both develop drugs based on that and also choose the ones for in-licensing that are best. The whole platform purpose is to overcome the limitations of genetics-based precision medicine. The reason we in-licensed the Lilly asset and had a chance for that, which was a flagship program, was they used state-of-the-art genetics genomics and concluded that they could not identify patient responders.

Genetics did not work. We proved that we could identify. We have had three prospective data cuts. Unfortunately, it slowed down enrollment, and that is what we paid a big price for. It requires a pre-treatment tumor biopsy, and that leads to how we got into serous endometrial cancer.