Avacta Group Plc (AVCT) Earnings Call Transcript & Summary

Welcome to Science Day 2026. We're happy to have you here. I'd like to start by introducing the agenda. There will be 6 separate talks by the scientists. You will only be bored with me for the first few minutes. I'm actually going to ask our scientists to join me up here on stage. And I'm going to have them introduce themselves. I'm going to have David start and we're going to pass the microphone all the way down to introduce the team.

I am David, I think I've met 1 or 2 of you before maybe in the science event about 18 months ago. So it's nice to see some of you again. And I head up the biology at Avacta.

I am Curtis [indiscernible] I'm here at about 3 years. I originally working in the preclinical scene [indiscernible].

I am Victoria, I have about 15 years experience in research [indiscernible] for 4 years.

Good morning, everyone. I'm [indiscernible] chemistry. I did a medicinal chemistry prior to joining the company about 2 years ago and was on the chemistry discovery side [indiscernible].

Hi, everyone. I'm Francis Wilson, I'm the Chief Scientific Officer at Avacta. I've been med chemist for over 30 years, the last [indiscernible]

Good morning, everyone. I'm Ruairidh Edwards. I am the Associate Director of Clinical Science and Translational Medicine at Avacta. I have worked at Avacta for about 4 years now, focusing mainly on the clinical science and translation medicine.

Good morning, everyone. My name is Omer Butt. I'm Head of Regulatory Affairs at Avacta. I've been working in regulatory affairs for the last 20 years, starting my career with the U.S. FDA. I've been in Avacta in a consultant role to permanent one for the last 2 years with one goal in mind of getting this product to the clinic and further. So looking forward to this.

Excellent. So I'll have everyone take their seats. And Francis is going to stay up here with me. I'm going to do just a bit of an introduction, sort of an overview of what the agenda is going to be and what you're going to see. What pre|CISION is? So we're going to start with the evolution of pre|CISION. Pre|CISION is designed to widen the therapeutic index of a given therapy for cancer. For the most part, we know that our most effective therapies in oncology, I gave many of them myself in the clinic, are the most toxic drugs that humans have ever invented. We accept that toxicity because where we don't kill the patient, we see generally that we kill the tumor. And so the idea behind pre|CISION is to widen that therapeutic window. The therapeutic window is the difference where we see toxicity and where we see efficacy. You've seen this slide many times before. There's a couple of things hidden in here. So this is FAP. We show it as a crystal structure. It's an enzyme. Enzymes have kinetics. When we get into the patients, we also will see different kinds of kinetics. I want to explain to you in this first section, what you're going to see in terms of chemistry and how to think about it. As you listen to each of the presentations, I want you to judge, are we in the biochemistry are we in the pharmacokinetics in the patients. So the pre|CISION peptide, which is depicted here, is bound to a given drug. The peptide importantly is 2 amino acids, two, an antibody, which forms the basis of an antibody drug conjugate is 1,300 amino acids. They're that different. And yet, we think that we have found ways to make this behave and found ways to tame this pre|CISION platform. So the peptide is removed by FAP and the released -- the payload or the drug, the active part is released into the tumor stroma interface. It's released extracellularly. It is a pure bystander effect, which is a term that we have borrowed from the ADC space, bystander effect, meaning that the drug gets out of the cell and can now move into a target low or even a target negative in our instance, tumor cell. Tumor cells in general do not express FAP. FAP is only on the stroma. You'll see this as we move into the biology. Our biologists have now studied everywhere from low FAP to high FAP to try and predict what are we going to see in the clinic. Everything that we do from chemistry all the way to biology is to predict what will we see when we get to the clinic. We aren't using FAP as a target. We're using it as an enzyme. So very different. We're leveraging the enzymatic effects. Enzymes are important with ADCs, and we'll talk about that as well. They use a different enzyme called cathepsin. But an enzyme is any number of proteins that essentially create a chemical reaction. Chemical reactions have kinetics. We've learned how to tame it. Our chemistry team and biology team together, the biochemists have learned how to tame this enzymatic reaction, which is what has led us now to the second chapter of pre|CISION. In order to do that, we have to understand the kinetics, the kinetics of that enzymatic reaction. Kinetics, meaning what is the speed, how can we control it? That's exactly what this amazing scientific team has done. They have figured out how to control the kinetics of an enzymatic reaction when it happens in an animal and in a patient. So we're going to talk about kinetics of the chemical reaction. We're also going to talk about the kinetics of the drug and how it is metabolized in a patient. The kinetics of -- here, this is the biochemistry. So we think about this in terms of -- this is actually from a chemistry textbook. It's perfect that we're in this room because we've learned how to tame the chemistry of pre|CISION. Now you can have an enzyme and a substrate, and that creates a chemical reaction. We can't change FAP. FAP exists in every patient. We can measure it. We can know how much FAP is there, but we can't change it. Nothing that we can do to make FAP work better. It works as it works in the patient. What we can change in this chemical reaction, and you're going to see how we've learned how to change it, is the substrate. We can change the shape of the substrate. We can change the concentration of the substrate. The shape, Francis is going to tell you how we've changed the shape of that substrate. The concentration when we get to the clinical talk, which is 4 talks in, we're going to show you how we change the concentration of it. That's just dose in the clinic. So the pre|CISION is FAP and it cleaves -- the pre|CISION is the substrate in this chemical reaction. What happens next in the reaction is that we form an enzyme substrate complex. We then create because of that chemical reaction, an enzyme and a product. We now understand exactly how this chemical reaction works. We've done this now. How many of these have we made?

We have done 500 [indiscernible].

500 of these that we've made, 150, we've detailed kinetics according to this one. Now the kinetics are really understanding the biochemical reaction. We're going to talk to you about Michaelis–-Menten kinetics, really understanding that the reaction velocity, how fast does this reaction occur? It occurs very fast with AVA6000. We learned that in the clinic. We understand what the preclinical was. And now we understand to a deep level how AVA6000 behaved in the clinic. It's what has now allowed us to control this. Biochemistry, this is a lot of these physics. Michaelis–-Menten kinetics, it is how does an enzyme substrate complex become enzyme product. And how does it go back and forth? And this team understands how to tame this. We're going to tell you how we did that. We're going to talk to you about a different kind of kinetics because we can understand the chemical reaction but that chemical reaction now occurs inside a patient in a tumor. And it releases its product both into the tumor as well as into the bloodstream. Understanding how that released product, which is actually the payload, the released payload, how it releases in the tumor and then in the bloodstream is the pharmacokinetics. Our translational medicine team has done an amazing job with understanding the difference between our kinetics and that of probably one of the most successful drug classes in oncology right now, which is an ADC. Understanding the kinetics of the reaction all the way through to the kinetics in the patient, which we model in the animals, is how we've learned how to tame pre|CISION and how to make it do exactly what we want it to do. I mentioned ADCs. I was at BioEurope just a little over a month ago now on a panel, and we talked a lot about ADCs. It's probably the hottest area right now in oncology. And here's the little trick in the middle there going, yes, but what about the peptide drug conjugates. Let me tell you about the difference between these. So I mentioned a monoclonal antibody is 1,300 amino acids. It's a very large macro molecule. It has to be manufactured in cells, can't be manufactured as a small molecule. The pre|CISION peptide, two amino acids. And that's what's amazing here is that we actually contain this and we understand the kinetics of this better then we believe it's understood about the ADCs. Now an ADC, this slide is actually in response to a question that I got from 5 different investors in the last week, which is why are we happy about the 3x going full speed ahead to -- why are we happy about it? ADCs are the original sustained release mechanism. How do they do it? It's called a salvage receptor. It's the neonatal receptor, but the immune system has figured out how to protect antibodies from being degraded. There's a place where they go, it's called the lysosome and the lysosome inside each cell degrades proteins. Proteins turn over very quickly and cells know how to get rid of the ones that they don't want. They'll get rid of antibodies very easily, except that the immune system has figured out how to save the antibodies. That's why this drug can last for weeks in a patient. And so it's brilliant, attach the payload, it releases slowly from -- but our peptide doesn't have that. We don't have the protection mechanism of an ADC, and that's why we had to design and we had to invent the sustained release mechanism. So traditional ADCs versus pre|CISION, what are the advantages? Or what are we trying to solve here? So I mentioned that an ADC has an enzyme. We have FAP. FAP is tumor-specific. It is only expressed in the tumor. And ADC uses a general protease cleavable. The protease that it uses is called a cathepsin. Cathepsins are in lung, they're in liver. And that's why we see some of these toxicities. We have a guest coming in that's going to talk to you a little bit about the toxicities of ADCs and what we're trying to do here. We think by having this tumor-specific release that we will eliminate some of those toxicities. AVA6000, very important for us, even though it releases quickly, and we see peripheral exposure of doxorubicin, we don't have pneumonitis. We have almost no liver toxicity, no cardiac toxicity. Why is that? We're not getting the nonspecific release. And FDA asked us to look for that. Very slow uptake kinetics. So Ruairidh and Curtis are going to talk to you a little bit about the difference between an ADC and a PDC. Why is this an advantage? Well, the payload release is seen within minutes. And that's how we get a Cmax. That's how we get a very nice maximal concentration in the tumor. The bystander effect. So when ADC relies on this to kill those tumor cells that have low expression. How has Enhertu done to -- well, it's a very effective bystander effect. We work entirely on the bystander effect. And then finally, there's a very nice market opportunity. So FAP is expressed in 90% of solid tumors. Pretty much every solid tumor indication is going to be susceptible to pre|CISION. Bringing me to the Tempus collaboration. I'm just going to highlight it here. We're going to talk about it in a couple of different because it just keeps coming back. But 90% of solid tumors, not relevant for any hematologic malignancies, leukemias, lymphomas, not relevant. But every solid tumor should be susceptible to our drug. I'm going to move into the generations now. I'm going to talk a little bit about Generation 1 because that's where we are in the clinic. We're with AVA6000. Generation 1, and you can see here, if you let your eyes wander to the back, you can see the active site of FAP but in there is the actual chemical structure of AVA6000. It's a direct link between the pre|CISION peptide to amino acids and the drug. It's a very efficient substrate. It all breaks down very quickly. We knew that when we walked into the clinic. What's important, though, is that then we got to measure how it works in patients. And we got to measure down to levels of where is the drug going? We did the biopsies. How quickly does it release? Where does it release? Where is free doxorubicin? We studied all of that. As we were studying that, exatecan came into the picture. So what are the 4 things that we found with AVA6000? Four things. The first, faridoxorubicin eliminated the cardiac toxicity. We wanted a drug that if there was a toxicity that we could eliminate, check box, we would say pre|CISION works. And that was the cardiac toxicity. We're going to present all the data that we showed to FDA. We're going to present that to you by the end of the first half of this year. We dramatically reduced the hematologic and the GI, everything else is dramatically reduced by pre|CISION. We do get leakage out. It's a very efficient substrate, and we knew that, that was going to be one of the aspects of this one. Third, we concentrate the drug way better than we ever expected. 100:1 is our median. And then finally, we do see evidence, preliminary evidence of activity in soft tissue sarcoma as well as in salivary gland cancer. Salivary gland cancer bars down below are those tumors that are shrinking. The one patient that is growing, okay, you could argue there's a second one. But those patients, the tumor is growing. The vast majority of the patients see tumor shrinkage with this drug. Now I mentioned kinetics. We understand the biochemistry of how AVA6000 works. We then moved it into the clinic. Here's substrate concentration. Remember, substrate is the precision medicine. As we increase the dose, we increase the amount of released drug in the tumor, very logical, right? What didn't work out was if we increase the amount of FAP that we get better release, we don't. So even at low levels of FAP, even at 1 plus, this was a little bit surprising to us. Even at 1 plus levels of FAP, we get nice cleavage. So the FAP, you almost don't worry about it anymore. What we worry about, though, is the concentration of the substrate because in an individual patient, remember, we can't modulate. We can't change the amount of FAP. We can understand it, we can know it, and that's the middle here. Then we convert this to a ratio because we want to compare it to conventional doxorubicin. Conventional doxorubicin is not targeted. It doesn't know the difference between tissues and tumor. And so its ratio is 1:1. But with pre|CISION, with releasing it right in the tumor microenvironment, we see a median change here of 100:1. Now I mentioned we were going to get into some intense pharmacokinetics. So we understand the kinetics of the chemical reaction, take it all the way now into the patients. These are the data that are going to help the chemists to design the new. So what did AVA6000 teach us? The first is tumor concentration, way better than we expected it to. So in the next version, we got to maintain that. The second, AVA6000 has a very short plasma half-life. As it stays in the bloodstream, AVA6000 disappeared very quickly for 2 reasons. The first is it all cleaves very quickly, and that's the main route of metabolism, meaning how does the drug break down. It breaks down into peptide and released payload. But if it stays in the bloodstream, it also disappears very quickly. We did not see 24 hours later, very much detectable AVA6000. We needed to change that. If we wanted to get a sustained release mechanism, we needed to fix that. The third thing, release doxorubicin in the plasma, very blunted Cmax. That's exactly what we expected. But as the AUC increase, that's where we're saturating the system. Remember back to Michaelis-Menten kinetics. As we give higher doses, we're going to saturate that enzyme. Now we actually want to do that, and we want to hold the drug right in the tumor. And Francis is now going to take it over from here.

Okay. So what we're showing you here is how we've gone from the Generation 1 to Generation 2. So the key difference here is that exatecan doesn't fit in FAP if you attach it directly to the pre|CISION peptide. We had to introduce this linker group. And this linker group is really one of the keys to this because it gives us an opportunity. We move the exatecan away from the active site. And as we introduce this linker, that allows us to build in additional interactions to the FAP. And it's that those interactions that allow us to control how tightly it binds and how quickly it releases. We've got a bunch of different chemistries around that linker that allows us to do that in different ways. And again, that's really important because that gives us opportunities as we move on to thinking about other payloads. Again, we can essentially dial in the kind of kinetics that we need. At the other end of the molecule, what we call the CAP group, that's the other part, again, where we're able to because we understand how these molecules bind to FAP. We understand how to retain them there longer or how to make them be released quicker. So essentially, just to kind of summarize, we've got 4 pieces to this molecule. We have the CAP group at one end. We have a precision peptide that's the same across all of them. That's the bit that gives us the real specificity. That's the bit that FAP cleaves really, really exquisitely, but the bit we're able to control is the bit either side. And that's the bit that really then tells us how quickly it's going to release or how long it's going to stay in the tumor microenvironment. Okay. So what do we have to do? If you recall, we needed to keep -- we need to have lots of the drug in the tumor. So we need it to be cleaved and cleaved well, but we don't want it to be cleaved too quickly. The difference between exatecan and doxorubicin, exatecan's ability to stay in the body is much less the body gets rid of it much faster. So we can't rely on the tumor holding on to the exatecan in the way that it does with doxorubicin. With doxorubicin, you really -- it's a perfect molecule for simple precision because it's cleaved, it goes into the tumor and it stays there a long while. Here, we've had to adapt things. We need to keep the payload around longer in order to then to be cleaved and released. So we slow the rate of cleavage.

So if we go all the way into the patients, the half-life of doxorubicin in a given patient is 36 hours, meaning it hangs out for a number of days. It doesn't disappear from the bloodstream. But exatecan, I still remember our first meeting where we talked about this, its half-life is only 9 hours. So 25%, you're going to reduce the half-life by 75%. When exatecan was developed in the clinic, it was dosed daily, never going to work. It's never going to be commercially viable for a patient to keep coming back to the clinic every day. And so that's where we needed to develop this one.

If we had the same release mechanism as 6000, it is no good. It goes too quickly. So you need to be able to keep the parent molecule in the tumor and then also slow down how quickly it's released. So you have a kind of a depot of the compound there and you get the sustained release mechanism. That's what gives us the sustained payload delivery. And then the rest of it then carries on. We're in the same kind of place. We get this high Cmax in the tumor and we get that release payload. And that's what we then -- we've taken it to the clinic, and we'll soon be getting the results. I'm going to talk very quickly now about the Generation 3 when this slide will come back a little bit later. But essentially, what we've done with the next generation is to build on this understanding of what we can do with the linker because not only can we use the linker space to adjust how the compound or the drug interacts with FAP, we can actually link on 2 separate payloads and release those essentially simultaneously. So one FAP cleavage releases 2 separate payloads. Just as we can before though, we can still -- we're still in control of the kinetics and the affinity for this. So again, dependent on the properties of the payloads, we're going to be able to adjust how quickly they're released. You've got 2 payloads, you've got a kind of a bigger component of the molecule now that is payload compared to the peptide. So things like solubility and sort of normal drug-like properties of the molecule. Again, those are things we're going to need to be able to adjust. But we've got -- that's again, where we can adjust the capping group and the linker to do that. So you'll hear a little bit more about this later on in the presentation, we'll show you some of the really exciting data we've just started to generate.

Excellent. And I think with that, we are moving on to our next talk, Tom and Curtis.

Thank you to Chris and Francis for the excellent start to today. Curtis and I will talk through some of the preclinical findings with our second generation of the pre|CISION program, so AVA6103. The differences between Generation 1 and Generation 2 have been kind of gone over very briefly. But to summarize again, this is AVA6000, our Generation 1 PDC, consists of a capping group, the pre|CISION peptide and doxorubicin the payload itself. And this is a very efficient substrate for FAP. So after that FAP binding event, it's cleaved very quickly, resulting in release of doxorubicin in a very short time frame. To go from generation 1 to generation 2, we had to make a couple of changes. So we made changes to the capping group of the molecule to introduce additional interactions with FAP, increase the potency of the binding with FAP. And we also introduced this linker element. So these 2 changes combined resulted in something that's much less efficiently turned over by FAP. So slower turnover, sustained release of the payload rather than just kind of spiking it in essentially. How does this actually play out? So it comes -- it results in a change in the half-life. So AVA6000, our Generation 1 PDCs, with this very rapid cleavage, we get a very short half-life of AVA6000, and we're essentially just spiking doxorubicin into the tumor microenvironment. AVA6103 with a much longer half-life, the parent PDC persists for a much longer time period. It's got a half-life of hours as opposed to just minutes for 6000. And we get this sustained prolonged time period over which exatecan is released directly into the tumor microenvironment. So we get release over this period of hours rather than just minutes. What is unique about the pre|CISION peptide as well is it's highly specific. So Chris kind of introduced enzymes just now, but FAP is actually a member of a subfamily of enzymes called the DPP proteases. And there are several others which are very closely structurally related, including PREP, the most closely structurally related one. And what the pre|CISION peptide allows us to do is released payloads with a great degree of specificity. So in this experiment here, we incubated AVA6103 with a panel of DPP enzymes and measured the amount of exatecan that was produced. So I want to draw your attention to the blue bar here and the gray one just to the left of it. The gray is in the no enzyme condition and blue is in the presence of FAP. So after just a 30-minute incubation in this experiment, you can see we're releasing a good deal of exatecan compared to when there's no enzyme present at all. So this tells us that 6103 is inert when there's no FAP present. There's no release of the payload without that additional FAP. And you can see in all of the other enzymes that we tested, there was no release of exatecan either. So the pre|CISION peptide allows us to be highly specific for FAP over all of these other enzymes and allow for very targeted delivery of our payload of choice. These 2 factors combined, so the sustained release and the specificity of release to give us optimized delivery of exatecan, which is what 6103 is designed to do. So as I just mentioned, 6103 is completely inert in the absence of FAP. It's only when we add that FAP, we get payload release. This reduces or eliminates rather any systemic release of exatecan. It's only in the tumor microenvironment. And this has allowed us to dose 6103 at a much higher dose than conventional exatecan, and it's much better tolerated. This kind of sets us up really well for the ongoing clinical trial. And we've seen some really exciting data that Curtis will go through in a second, where thanks to this sustained release and specific release, we see both parent and tumor persisting in -- parent and payload persisting in the tumor microenvironment for a period of 5 days, whereas exatecan is eliminated completely within just hours. So I'll hand over to Curtis now to go over that data.

Thank you, Tom. So I'm going to talk you through some of the preclinical PK and efficacy data we generated with 6103 and why we're really excited by these results, and we're really excited to see what comes out of the clinic once we eventually get those samples analyzed. So here, I'm going to walk you through some of the PK data that we've generated in the HEC-FAP mouse model. And this is a mouse that has been implanted with cells that express FAP. Once those cells grow into a tumor, we administer a single dose of AVA6103, and we monitor the concentrations of either AVA6103 or the release payload for a period of 5 days. And basically, this enables us to see how well AVA6103 functions in a tumor setting, but it also really nicely demonstrates the sustained release mechanism that we speak about a lot. So what you're looking at here is the concentration of 6103 or exatecan and then here over time through a 5-day period. So the first thing that we noticed is that the tumor and plasma PK, we demonstrate a really favorable ratio when comparing the payload concentrations in the tumor and the plasma. And I'll tell you what this is. So in blue here, what you're seeing is the intact AVA6103. And what you can see here is that it increases in concentration and is sustained for a 5-day period. So we're seeing concentrations of AVA6103 all the way up until the final 5-day time point. And it's basically adding as kind of a payload reservoir where you're slowly drip feeding essentially 6103 into the tumor, so it can be cleared by FAP and exatecan release. In the red line here, this is the released exatecan that we find in the tumor. So what you can see is that we're achieving really high concentrations within minutes of dosing. So we've got massive concentrations of released exatecan in that tumor at really high doses that sustained all the way out again for 5 days. And what's important to note is that this concentration that we achieve is well above the toxic level that is needed for cancer cells to be killed, but remains far below that toxic level that is seen in humans. And the turquoise line here, what you're seeing drop down, this is the exatecan levels in the plasma. And this is the really exciting bit because that massive drop that we see, we're seeing concentrations of exatecan in the plasma that are gone within hours. So within the first 2 hours, the concentration drops rapidly beyond 4 hours, there's absolutely nothing in the plasma. And this really exemplifies just how targeted AVA6103 is, and it's doing exactly what the chemistry team designed it to do. We can achieve this really high initial concentration, but also get this prolonged exposure over time, which is one of the advantages that 6103 has over many ADCs out there. So if we take a bit of a further look into the tumor plasma ratio of 6103, and we add in some 6,000 preclinical and clinical data for context, we really get to see just how well 6103 is performing. So what we have here on the left-hand side, this is doxorubicin concentration in the HEC-FAP mouse model at 1 hour. So this is 1 hour post AVA6000 dosing, and we're looking at the concentrations of doxorubicin in the tumor and the plasma. And what we found in this preclinical setting is that we're achieving around a 4:1 tumor to plasma ratio. So it's about 4x higher in the tumor than it was in the plasma. Now if we translate this over to the clinical data that we've seen, Chris touched on this, already at 24 hours following 1 dose of 6000, we are now seeing a 100-fold higher concentrations of doxorubicin in the tumor than in the plasma. So we can actually see that here, we were underestimating just how much warhead we would be seeing in the patients when looking at this mouse data. Now in this right-hand plot, this is AVA6103 in a preclinical setting. So we have exatecan concentrations in the tumor and the plasma at 2 hours after 1 dose of 6103. And what we're seeing here is already a 99-fold increase in the exatecan concentrations in the tumor over the plasma. And this is why we're really excited because if we translate what we know from here where we looked in the preclinical setting at a fourfold increase to the clinical setting where we're seeing a 100-fold increase, we really feel that this can be an underestimation of what we're actually going to see in the clinic because in a preclinical setting, if we're already seeing a 99-fold increase in tumor concentrations, we're expecting to see far higher when we start looking at those clinical samples. And this PK data really helps us understand the behavior of 6103 and how much warhead is released over time because now that we know how much drug there is in the tumor, we can then figure out how this translates to efficacy. And if the concentrations we see in the tumor here will actually result in tumor regressions. So here, I'm going to show you some of the efficacy data that we've generated with AVA6103. So this is performed in a number of PDX models, which is essentially just patient tissue implanted into a mouse. And what you can see on this left axis the tumor volume on the right-hand side is just over time. And we can see how these tumors respond if they're either treated with vehicle in gray, exatecan in red or AVA6103 in blue. And in these 2 gastric models, what you're looking at here essentially is in this blue line with AVA6103 dosing, not only are we achieving significant tumor growth inhibition compared to exatecan in red, but we're actually seeing complete regressions in these animals. And that's what you're looking at here on this figure. So this is the best change in tumor volume at any time point. And what you can see here is that we're getting complete tumor reduction. So a complete eradication of the tumor in these animals. And even those that aren't complete eradication, we're seeing reductions in tumor volume of up to 95% and above. And I mentioned we did this in a number of models. So we've done this in colorectal cancer as well, one of the indications that we're looking at in the FOCUS-1 trial in the clinic right now. And again, we are seeing significant increase in tumor growth inhibition compared to exatecan, but we're also seeing really robust responses where we're getting nearly 90% tumor reductions in these animals. And one thing that I should have mentioned on the previous slide, what you're looking at here in these brown dots. So these are pieces of tissue from each of these mouse models, and we stain them for FAP. So it essentially tells us how much FAP there is in these models. And what's really important to note is that regardless of how strong that FAP expression is, we are still seeing robust durable responses in these animals. Just like Chris mentioned, with the 6000 trial, FAP expression almost doesn't matter with this mechanism. Two further models we did, again, pancreatic cancer, really nice tumor growth inhibition, really strong responses. And again, it doesn't really matter how strong that FAP expression is, whether it's high or mid in these tumors. And finally, we did this in a small cell lung cancer model. So again, one of the indications that we're looking at in the clinic. And this was our best result that we had out of all the efficacy studies we've done where we saw 100% of the animals saw complete responses, complete eradication of the tumor. And this is where we really feel that there are advantages over traditional ADCs that really require high expression of the target, whereas we see really robust responses even in those animals in models that have [indiscernible] FAP expression.

I'm going to invite Ruairidh and Curtis to stay up here.

Thank you, Chris. So me and Ruairidh are going to give you a walk through some of the generation 2 payload delivery and some of the in-depth analysis Ruairidh has done with looking at comparing 6103 to some of the traditional ADCs out there, some of those blockbuster drugs like Enhertu and where 6103 really has its advantages over these drugs. So this slide here, what you're looking at here is comparing the mechanisms of action between pre|CISION PDCs and ADCs that are currently in the clinic and how each of these use what we call bystander effect in a slightly different manner. So the bystander effect in cancer therapy is essentially when a nontargeted cell is either damaged or dies from a site to a toxic compound that is released from a neighboring cell that was the initial target. So each of these has slightly different use of the bystander effect. Firstly, we'll talk through the pre|CISION bystander effect, which is what we refer to as a simple bystander effect, which is where upon administering AVA6103, for example, it will remain inert until it reaches that tumor, at which point it will be cleaved by the FAP in these CAFs here that surround the tumor and that will release the payload exatecan. And because this occurs extracellularly outside the cell, it can then enter both FAP-positive fibroblasts and FAP-negative cancer cells. So it's a simple bystander effect, very quick, very efficient. With ADCs, it's a much more complex thing. Upon administering ADCs, once they travel through the blood and they reach the tumor, they then have to get past this really tough exterior of stromal fibroblast or stroma. And in cancers such as pancreatic cancer or colorectal cancer, there's a really high tissue pressure. So it's tough for these ADCs to get through. But once it breaks through that barrier, it then has to be internalized into the cancer cells before it can be cleaved. So with 6103, it can be cleaved outside and it can act straight away. Whereas with ADCs, it requires internalization first, only then can it be cleaved and release the warhead. But then once it's released into the cancer cells, it then has to diffuse back out of that cell to target the non-targeted cells. And that's why we call it a complex bystander effect. And so Ruairidh is going to talk through some of the things that make this simple bystander effect a lot more exciting, I guess, the 6103.

There we go. So Curtis gave a great overview there of the differences in mechanism of action between ADCs and our AVA6103 compound and the precision mechanism in general. What we've managed to do now is we've actually managed to leverage data that's published in the literature to compare our AVA6103 compound to clinically validated ADCs that have been approved in the U.S. and throughout the globe in the last few years. And those 2 ADCs are Enhertu and Datroway. Both of these ADCs are actually -- they contain the same payload, deruxtecan, very similar to exatecan, however, slightly less potent. And the data that's out there in the literature really looks at mouse model data. So something that Curtis has already touched on before. And what we've been able to do is compare the data that they've published with our own data from our HEC-FAP model. And from this, we've come up with 3 key take-home messages that we think is very encouraging to see from our AV6103 compound when compared to these ADCs. The first one is rapid tumor penetration. So when we compare the time it takes for us to see our maximum exatecan concentration in the tumor to the same for the deruxtecan released by the ADCs, we see that our maximum time to concentration is about minutes. It's really quickly after administration of our AV6103 drug. When we compare that to the ADCs, it's about 24 hours a day or longer. So there's this delay and that delay from a mechanistic perspective is something that Curtis has already touched on. There's a lot of processes that ADC has to go through in order for the drug to be effectively released. Now the second advantage and the second interesting part, and this is the thing that excites me the most is the maximum concentration observed within the tumor. When we compare our AVA6103 drug to the ADCs Enhertu and Datroway, we see up to 40x higher maximum concentration of exatecan in the tumor compared to the equivalent of the deruxtecan released by these ADCs. And so that's an enormous amount of drug. That's a lot difference. That's a really large quantity to be delivering to the tumor. And so that really gives us an encouraging information in terms of how much we can get to the tumor as well as potential for cell killing, tumor cell killing, et cetera, which we need in order for us to see tumor shrinkage in patients. And so finally, I want to touch on this tumor selectivity index, which is another advantage, I believe. And this is how effective the drug is being able to target the tumor over other tissues. And so we've created this index to try and understand that as a metric. And all it is really is taking the overall exposure of the payload in the tumor versus the overall exposure systemically in the blood. And when we look at this as a ratio, we can understand when we look at the selectivity, how much more selective AVA6103 for the tumor versus Enhertu or Datroway. And what we find is it's nearly 3x higher. It's nearly 3x greater. It's more selective for the tumor than these ADCs. And I'm not going to stand here and tell you these ADCs aren't effective because they are. They're very effective. They're blockbuster drugs. And that's something that really excites us because if we're seeing selectivity far greater, that really is encouraging us to -- and exciting us for the data that we hopefully received soon from the clinic. So fortunately, Curtis has done a bit of the hard work for me. He's explained the red line there. So this is basically some of the pharmacokinetics that Chris touched on earlier. And so what we're looking at here in this graph is the exatecan released by AVA6103 and the deruxtecan released by Enhertu in this case. And the blue lines, deruxtecan, the red lines are exatecan and the dotted line is plasma and the solid line is tumor. So the first point of note, like Curtis mentioned, is the high Cmax. So the very high concentration that you see in the tumor at very early time points. And this is another encouraging factor that I won't go into any more detail because I've already told you about it. But the second thing is the overall exposure. So if you look at how long exatecan is hanging around in the tumor form, it's very comparable to what Enhertu is doing. The exposure of the drug over time is very, very sustained. It's the sustained release mechanism that Francis spoke about can be observed. The final thing is the difference in plasma exposure. We can barely see the amount of drug that's released in the plasma because it's so short-lived. It just disappears within minutes of dosing. Whereas when you look at this deruxtecan released by Enhertu, it hangs around for 7 days. And okay, it's low concentrations, but that's enough exposure and continual exposure for there to be potential toxicities. And that's ultimately something that we want to avoid for patients. And so to see this difference is very encouraging. And as I said, the selectivity index that we created really highlights that selectivity. We're sitting at selectivity of 126 versus Enhertu of 47. So we are far more selective for the tumor than the plasma. I'll pass over to Curtis, who can talk a bit about the efficacy data that we have from these models.

Sure. Thank you. Yes. So what you're looking at here is some efficacy data from the same paper in the same model that Ruairidh showed. So this N87 model. What we've got here is a GMT model, and these have both been treated with Enhertu. And what this slide is trying to get across is that Enhertu does work really well, but it requires a very high expression of this target. So you can see, as I mentioned, those little brown stains there, that is showing the target expression. Just like we did with FAP, we look at how strong the target is. So with really strong HER2 expression, you are seeing nice tumor regressions and complete responses. But as soon as you drop that down slightly, you're no longer seeing the same results. You're no longer getting far more tumor growth inhibition and you're no longer getting complete responses. But why we're so excited is because with 6103, if you look at this FAP expression here, it's far, far lower. And even in a low FAP setting, as I mentioned, we still see really strong tumor growth inhibition, really good tumor regressions. And Ruairidh is going to talk to you a bit more about some of the other compounds that we've done comparative analysis with.

Thanks. So I did mentioned does comparison against other ADCs. So that really is an ADC that was -- I think it was approved last year, 2025. And it's another blockbuster drug by Daiichi that is basically has a similar mechanism to Enhertu. However, it targets a tumor target called TROP2. Still requires the same mechanism that Curtis mentioned before. And so there's again some data there that we managed to leverage through AI to basically try and understand the differences in concentrations in the tumor and plasma. This one is quite striking to me. The difference between the tumor and plasma concentration of exatecan released by AVA6103 and deruxtecan released by Datroway is nearly a lot different. We're achieving a Cmax about 40x greater than that of Datroway. Datroway is a very effective drug. It's known to be quite an effective way of killing tumors, and it's shown to be very effective for patients. And so for us to see this window, this difference in concentration and exposure, we see it over a prolonged period of time, again, really excites us about what data we can expect from the clinic in the coming months. And as I mentioned, so 40x greater in terms of maximum concentration, but we're twice as much drug exposed to the tumor over time. And again, I won't go through the plasma in any more detail because I've already kind of mentioned, but the difference again is quite striking. We don't see this release that kind of prolongs in the plasma with the ADCs. We see it short-lived for AVA6103. We don't have a large exposure of that drug in the plasma. And finally, this slide just kind of summarizes everything together and so we particularly the plasma here. All we're looking at is the tumor concentrations for Enhertu, deruxtecan released by Enhertu in the blue here. And in the darker blue, we've got the deruxtecan released by Datroway and our AVA6103 compound and the exatecan released in the orange. And you can really see the differences here. This is something that makes us quite excited about what we can expect to see in the clinic. And as we gather some PK data from both the plasma and also the tumor biopsies, we hope that we can bring in some really exciting information from the clinic in the coming months. So with that, I'll stop, and I'll hand it back over to Chris, who's got some really exciting stuff to tell you about AVA6103 in the clinic.

Thank you. So as Ruairidh mentioned, and as we talked about very early on, the one thing that we can alter in the clinic, we can't change the FAP levels, but we can alter the dose. We can alter the substrate concentration. And that's exactly what is going on right now. Dave Liebowitz, our Chief Medical Officer, was supposed to be up here. His shoulder fell apart last week, and he had to put it back together. So he can't really carry a suitcase. So Dave is still in Santa Fe, and in exchange for him not being here, he brought a guest. We have arrived at the clinical point of the presentation. And so without further ado, I would like to turn it over to our Chief Medical Officer, Dave Liebowitz. Dave?

Thanks, Chris. Hi, I'm Dave Liebowitz, and I'm the Chief Medical Officer at Avacta. And we're very excited to talk to you today about AVA6103, which is our FAP-enabled exatecan molecule. Maybe, Chris, you could show the first slide.

Absolutely. Let's bring up the slides.

Okay. So I'm going to just give a brief introduction, and then we're going to be joined by one of our esteemed investigators on the study, and I'll be letting him introduce himself. His name is Dr. Alex Spira. So the pre|CISION platform is unique in that rather different than ADCs, which target a specific protein on individual cancers and so have limited scope of what they can be used to approach. Our platform, the pre|CISION platform is a peptide drug conjugate, which is specifically cleaved by a protein called fibroblast activation protein. And what's interesting about FAP is that it is expressed in the tumor microenvironment on cancer-associated fibroblast. And so if you look at the left-hand side of this slide here, what you can see is the expression of either weak or strong FAP in a variety of solid tumors. And so this platform with pre|CISION we can approach multiple targets rather than being limited by tumor-associated antigens. What you can see here is that about 90% or more of solid tumors express FAP in the tumor microenvironment. And what we've learned from one of our earlier programs is that we don't need very strong expression of FAP to give some indication of activity in the clinic. And so even 1 plus by immunohistochemistry has demonstrated the ability to have some clinical activity. And so if you look at that in the teal and the darker blue, you can see that the vast majority have either strong or weak expression of FAP. So we're very excited by this. It allows us to concentrate our payload in the tumor microenvironment and would spare the systemic circulation of high amounts of drug. And so this allows us to use more toxic payloads and can be administered systemically. So that's why we're excited, and I'll turn it over now to Dr. Spira to introduce himself.

Hi, everyone. I'm Dr. Alex Spira. I'm a medical oncologist focusing on lung cancer, but also really focusing on Phase I drug development at Virginia Cancer Specialists and NEXT Oncology. And thanks, Dave and Christina, for having me today.

You're welcome, and thank you for joining us. So Alex, I gave a brief overview of why we're excited as a company about this. But what excites you as a medical oncologist that treats patients?

So I think a couple of things. So one is this is novel, right? I mean we need to get past the typical antibody drug conjugate that we've seen so many of in minimal variations. But that's really the secondary thing. For most of our -- for most of these antibody drug conjugates, a lot of things have been looked at, but what really limits us is the side effects. And while everybody makes -- thinks they make a perfect antibody, perfect linker, perfect payload, the reality is that some of that payload is distributed in the system that leads to side effects. And there's 2 things that happen with that, right? One is you increase side effects for patients, but you also limit the amount of drug that you can give because of this as well. So it's not only the side effects, you reach the MTD at lower doses. And that's important because we've seen certainly a lot of these side effects from the payload, cytopenias, GI side effects, et cetera. So here, you basically have a drug that's activated only at the tumor site because the drug is activated only when it hits the tumors by breaking down of this peptide moiety by the FAP. And that's super important for lots of reasons. You also have to think about it differently. It's not actually going to the tumor, but it's going to tumor microenvironment. One cancer I really want to point this out about is pancreatic cancer. We know that the pancreatic cancer is a very high-density stroma. So a lot of fibroblasts there. If you look at a lot of these imaging and scan reports, you may see improvements in liver metastases, but you don't get the primary shrunk. And one of my esteemed colleagues has taught me this time and time again. So if you can really get it to the fibroblast, you can really deliver high concentrations of drugs to this area and cause tumor shrinkage of these areas, not only of tumor cells, but where you can't actually deliver the drug because of where all these fibroblasts, which is really a structure, right? It's like cement or straw around it. And it's not only developing the drug, but it's getting the drug to the tumor site. A lot has been done over the last decade trying to minimize the side effects of both antibodies and ADC technology. There have been numerous masking agents that get broken down in tumors, but this is such a unique entity and moiety that it really offers promise to really all those tumor types because you're really thinking about it not on a tumor type basis, you're thinking about the tumor microenvironment, which is completely different about how we think about things, right? We're looking where these fibroblasts are rather than the antigen expressed on the tumor cell.

Yes. I think that's a really nice summary of why we're excited also. Let me ask you another question. I'll go into it shortly. But as an investigator, you're well aware of the tumor types that we're studying in our trial. How do you view those in terms of unmet need and how the standard of care therapies perform in these tumor types?

I think it's very important, and I'm going to talk about a couple in particular. So talk about pancreatic ductal carcinoma, gastroesophageal junction cancer as well as other tumor types such as colorectal cancer. So we've made a lot of headway over the last few years. And certainly, the last 24 months have been dominated by RAS drugs. But the reality is that those RAS drugs will have a limited benefit for pancreatic cancer. And certainly, for other cancers like GE junction and colorectal cancer, we've really been devoid of new drug development. Right now, I spend 90% of my time seeing trial patients. And I see 2 to 3 colorectal cancer patients a day almost looking for something. And I tell them, we really haven't made much headway beyond FOLFOX and FOLFIRI in a decade. We've had some other approved drugs with literally single-digit percentage response rates. So there's a huge unmet need for all these tumor types where we've really made some headway in other tumors. Pancreatic cancer patients are definitely going to need more therapies because where do you go after RAS and GI tumors are a huge area of unmet need. And there's other tumor types, obviously, as well. But those are the 2 major ones that stick out to me.

Yes. Thanks, Alex. And I think you brought up a good point there when you mentioned FOLFIRI. We have topoisomerase inhibitor as part of that drug combination, but it's much less active than exatecan, which is what we're studying in 6103. In terms of thinking about that, how do you think patients may respond to 6103 who have already seen FOLFIRI?

It's a great question. And I will tell you that's always a question if you give people the topoisomerase 1 inhibitors. But by and large, in other tumor types, we haven't really seen irinotecan affect ADC efficacy in other tumor types. So I'm fairly safe and comfortable in saying it's an okay thing to do. And almost all clinical trials do allow it because there has been evidence of efficacy where these drugs work. I do realize, most -- we've been talking a lot about ADCs, but we don't have a lot of TOPO1 ADCs approved yet. So it's still under development. But I think from what we know, I feel very comfortable in saying, hey, this is certainly an okay thing to do. And I'm not concerned really at all about efficacy right now.

Great. I'm going to go ahead and present a little bit more about patient selection, unmet need and how we go ahead and do that. And Alex, if you want to stay, you're welcome to add anything that you think is important. So as I showed you on the previous slide before we started speaking with Alex, the vast majority of solid tumors express a reasonable amount of FAP in the tumor microenvironment. And so how do we go ahead and select the types of tumors that we want to study in our trial. It's largely -- Alex touched on it a little bit based on unmet need. And we picked some types of tumors like colorectal, pancreatic, gastroesophageal junction and a few others, which I'll speak to in a moment because of the high unmet medical need. And so with all of those options in terms of selecting different types of tumors that patients may have and study those, how do we make that decision? So once we have a group of tumors that really do have an unmet medical need, we want to really interrogate those in, in vivo models. And so one of the most powerful models that we use is one where we have patient-derived xenografts. So these are tumors from real patients, and we look at how well the drug works in these types of cancers. I'm showing here colorectal cancer, but we have very similar data for the cancers under study in this trial. And so what you can see, if you look at the left-hand side, you can see a high FAP expressing colorectal patient-derived xenograft in mice that are bearing these tumors. And the vehicle control, as you can see, really doesn't slow down the tumor growth, which is measured by tumor volume. Exatecan alone does work to some extent, pretty well. But when you look at AVA6103, you'll notice 2 things. First of all, we deliver a lot more of that because of the safety of this based on the mechanisms that Alex and I described earlier. And you can see that we get really sustained inhibition of tumor growth long after the last dose of drug is given. If you look at the right or the middle panel, which is labeled colorectal #2, you can see that there's a little bit or quite a bit less FAP expression. And you can see that in that tumor as well, we get very good control with AVA6103 compared to exatecan alone. The right-hand side shows basically some waterfall plots. And you can see that we get really very deep responses with AVA6103. So again, this is colorectal cancer, which is one of the diseases under study. And as I said, we had very similar data with multiple other tumor types. Okay. So here's something that's really interesting. This is a relatively new analysis that we did. We looked at co-expression of FAP and Schlafen 11, and it predicts indications with the highest likelihood of response to AVA6103. Schlafen 11 predicts sensitivity of TOPO1 inhibitor mechanism. And so with exatecan, this is particularly relevant. What you can see is that the X-axis has FAP expression and the Y-axis is Schlafen 11 expression. And what you can see on the far right in each panel is that there's a lot of tumors that have high expression of both. And so we really believe that this is a tumor target that is very amenable to treatment with TOPO1 inhibitors. Also, the middle of each graph has more weak expression of FAP and still high expression of Schlafen 11. You'll remember on one of the previous slides, I mentioned that we don't need extremely high expression to have activity. We can have modest activity. And so we would predict that tumors that fall in the middle of these graphs would also have potential to respond well to exatecan. You can see -- sorry, Chris, can we go back one second. You can see that the 3 panels represent 3 of the diseases under study, hormone receptor positive breast cancer, colorectal cancer and pancreatic cancer. Okay. So I'm going to briefly touch on our trial design and then also how we execute the study. What you can see is that this study is divided into 2 arms. There's arm 1 where patients are dosed every 3 weeks. And then there's arm 2 where patients are dosed every 2 weeks. And you can see that colorectal cancer is in both arms. But otherwise, patients are either going to be in arm 1 or arm 2, depending on the type of cancer that they have. Part of the reason for this is that the standards of care for these tumor types fall into these dosing intervals. And so later down the road in development, if we do want to combine this after we demonstrate that AVA6103 has activity. If we want to combine this with standard of care therapies, we've already studied it in the right context. This design is called a BOIN design, and it's basically an adaptive design that allows you to study the safety of a drug and move up to dose levels where there is potential for efficacy quicker than standard designs. And so this really acts to allow you to treat more patients more quickly at doses that have potential efficacy. We are studying up to 6 dose levels in this trial, although we're hoping that we see something more in the middle range of our dose levels. You can see that after we determine a maximum tolerated dose or a recommended dose for expansion, as you like to call it, that we'll open up to 6 cohorts in our Phase IIb portion of the study -- I'm sorry, Phase Ib portion of the study. I think the one other thing that's important is that as we move up in the later phase of this study, we are going to have a lot of centers open. Well, in the early parts of the study where there's less patients that are going to be treated at each dose level, we can make headway with -- as we open sites and don't have to wait until we have the maximum number of sites open.

That's great. So there you go, Dave. I think this is your last slide.

Yes, this is the last slide. And so as I already mentioned, the approach to dose escalation, and I'll also show you how we stagger this is designed to minimize exposure of patients to suboptimal levels and also reduce the risk of toxicity. And that's one of the advantages of this adaptive design. Some of the key design elements also in the interest of safety is that there is a 24-hour stagger that we instituted between the initial study dose -- I'm sorry, the initial subject dosed in the study and any subsequent patient that would be dosed. This way, there was some untoward effect that we were not anticipating, we wouldn't expose more than one patient to that until we saw that it was safe over the first day. In addition, as we move up in dose level, we also can -- we also do wait 24 hours to dose the second patient. One interesting thing about our study is that the 2-week dosing interval has a higher dose intensity than the 3-week dosing interval. And so if we clear a dose level that our safety data monitoring committee determines has a good safety profile, we can move the Q 3-week dosing regimen up to that dose level, even if we hadn't fully enrolled at that dose level in the Q 3-week arm. So this is a little bit about how the study is designed. It's really designed to move very quickly through the earlier dose levels. We also have the ability as we get up to perhaps a maximum tolerated dose to do what we call a backfill at a lower dose. And this is to really expand a dose level below what we've determined to be a maximum tolerated dose to see what the efficacy is at a dose that may be a little bit better tolerated. So I hope this was informative for everybody, and thanks for your attention.

Thanks, Dave, and thanks, Dr. Spira. I appreciate both of you joining us, and we're going to head back into the room. Any final comments from either of you. We're really excited that this is now rolling in the clinic.

Very excited. I mean, a, it's novel compared to whatever else we've done, and it really focuses on some of the issues that really need to be overcome both on drug delivery and toxicity. So it's really exciting. And it's also looking at a patient population that a lot of other antibody drug conjugates have ignored. So really excited. Thanks for having me today, and thanks for letting me be part of your program, Dave and Christina.

Thank you both very much. Appreciate it very much, and we'll turn it back over into the room. Thank you both. We're go into Generation 3 and I'm going to invite David and Victoria up. And then finally, we're going to go -- so we started with enzyme substrate and Michaelis-Menten kinetics, then we are going to go all the way to IND approval and invite Omer to the stage to talk about regulatory.

Okay. Thanks, Chris. David and I will cover -- we'll share some exciting data on our dual payload technology, which is the Generation 3 pre|CISION. So Francis has already introduced the schematic of the dual payloads. But just as a reminder, essentially, we have 2 different payloads incorporated into a single molecule, and it's released through a single FAP cleavage event, which releases the 2 different payloads. For the remainder of the talk, we've incorporated for payload 1, a topoisomerase inhibitor. And for payload 2, we've incorporated an ATR inhibitor, which is an inhibitor of DNA damage response. So on this slide, we tested whether the dual payload compounds can induce tumor cell killing in a FAP-dependent manner and whether they can induce killing to a higher extent than a single payload. Here, we used an MDA-MB-231 triple-negative breast cancer cell model, which has partial resistance to topoisomerase inhibitors. Here, the cells were treated with the dual payloads with or without recombinant FAP or we treated with the controls, TOPO1 isomerase inhibitor, ATR inhibitor alone or the combination of TOPO1 inhibitor and ATR inhibitor. And we measured the percentage of tumor cell death, which is shown on the Y-axis, and we tested 3 different compounds. And what we see here is that addition of FAP greatly increases the activity of the dual payloads to induce tumor cell killing. So we are seeing with no FAP in the dark blue, virtually very minimal killing, whereas in the light blue with FAP, the dual payloads are inducing maximum 100% tumor cell killing. And what's important is that the dual payloads and the presence of FAP are inducing more tumor cell killing compared to a TOPO1 isomerase inhibitor alone. And this is reproducible across the 3 different dual payload compounds that we've tested. So here, the takeaway message from this slide is, firstly, the activity of the dual payloads is FAP dependent, and this is exactly the mechanism that we've designed. And secondly, that these dual payload compounds are able to elicit greater tumor cell death compared to a single payload, which is brilliant. So next, we asked the question, could the dual payloads reverse resistance in an engineered model. As you heard from Dave earlier, Schlafen 11 is one of the genes in tumor that is predictive for response to topoisomerase inhibitors. So here, we knocked out Schlafen 11 gene to create a topoisomerase inhibitor resistant model so that we can study the effect of adding in inhibition of the DNA damage response through ATR inhibitor. We treated Schlafen 11 expressing wild-type cells or Schlafen 11 knockout cells with our dual payload compound in the presence of FAP, which is shown in blue or with the single topoisomerase inhibitor shown in pink or the combination of topoisomerase inhibitor and ATR inhibitor combination. And again, we measure the percentage of tumor cell death on the Y-axis. What we saw as expected is that the Schlafen 11 wild-type cells were more sensitive to TOPO1 isomerase inhibitor shown in the solid pink line, whereas the knockout cells were more resistant to TOPO1 inhibitors. This is shown in the pink dash line. Importantly, the dual payload compounds reverse TOPO1 inhibitor resistance in the Schlafen 11 knockout cells to a similar extent as the combination of TOPO1 inhibitor and ATR inhibitor. We also looked at the markers of pathway regulation to confirm that both payloads from the dual payload compounds are active. First of all, we looked at the topoisomerase 1 enzyme, and we saw that the dual payloads in the presence of FAP reduced levels of TOPO1 to a similar extent as treated with TOPO1 inhibitor alone and this is compared to the vehicle. This shows us that this is -- the reason why we see a reduction is because TOPO1 inhibitors target the TOPO1 for proteasomal degradation. And this tells us because we're seeing this reduction is telling us that the dual payloads are releasing the TOPO1 inhibitor payload 1 and that it's active. Secondly, we looked at phospho-CHK1. This is a marker downstream of ATR activation. And what we see here is that the dual payload in the presence of FAP induces lower levels of phospho-CHK1 compared to a TOPO1 inhibitor. This tells us that the dual payload is also releasing the ATR inhibitor and that it's active. Thirdly, we looked at gamma-H2AX, which is a marker of DNA damage. And we see that with the dual payload compound in the presence of that this band here, we see that there's much higher levels of gamma-H2AX induced by the dual payload compound compared to a TOPO1 inhibitor alone. And this shows us that the dual payload is inducing a lot more DNA damage compared to a single TOPO1 isomerase inhibitor in the knockout cells. So the key messages on the slide are that the dual payload compounds are releasing both the payloads, the TOPO1 inhibitor and the ATR inhibitor, both of those payloads are active and also that we're seeing tumor cell killing in a resistant model with our dual payload compounds. Next, we tested whether the dual payloads are also active in a more physiological model. So this is a model, a 3D co-culture model of FAP-negative tumor cells and FAP-positive fibroblasts. And we see that the dual payload compound in the tumor model cultures where there is no FAP-positive fibroblasts has very minimal activity. It doesn't induce a lot of tumor cell killing whereas in the co-cultures where FAP positive fibroblasts are present, this is now the light blue line, we see an increase in tumor cell killing to almost 100%. And in some of cases, some of these compounds are inducing higher levels of tumor cell killing compared to a single topoisomerase inhibitor. So this model demonstrates that we are able to release both of the payloads and induce tumor cell killing and that is dependent on the presence of FAP-positive fibroblasts. So next, I'll pass on to David, who will go over the in vivo data that we have for this program.

Thanks, Victoria. I think all the data that Victoria has just presented really highlights the cell biology mechanisms that really underpin the whole pre|CISION technology, but also highlight some of the great data we see with these dual payload compounds as well. So I think this has been really -- this is really important. And we've got more -- we had a poster meeting in the U.S. last month. There's more of this kind of data, this mechanistic data on that poster as well. So go and have a look on the website, you'll be able to see that. It's really quite nice. There's really some very good science that the team have done. In addition to the in vitro work, just in test tubes, we've also been doing some -- we put these compounds into in vivo mouse models as well. I don't really need to do an introduction, I think, to these pharmacokinetic data sets that we've been talking about earlier in the Science Day. What you can see we've got 2 different compounds here. What we're looking at is released payload in each case. And because these are dual payloads, of course, we've got 2 payloads to look at. We've got an ATR inhibitor in the blue or the topoisomerase 1 inhibitor in the red. And in the solid lines, again, we've got the payload that's in the tumor. In the dotted line, we've got the payload that's in the plasma. And again, very similar to what we've seen with some AVA6103. We've got about a 100-fold ratio in terms of the payloads that are in the tumors versus the payload that's in the plasma. This is a much more simple analysis than we've just been talking about only at 3 different time points here. But nevertheless, this does highlight the same principle that we're getting this really nice tumor localization of our payloads, and it's much, much lower levels being released in the plasma. We can do the same kind of tumor selectivity index analysis as well, again, that our models have been able to do. And we see some very nice data here as well, some nice numbers, again, similar to what we see with AVA6103, showing again, we've got this real tumor localization of both payloads compared to what's released in the plasma. Something else is very important to analyze when you're doing some of these in vivo models is to check to look at these pharmacodynamic biomarkers as well to make sure that your release payloads are actually hitting the targets in the tumor. This is again some of the work we've done. Again, this is the same model, as I've just shown on the previous slide. And this is a representation of the Western blot. So Victoria showed the Western blot raw data. This is a quantification of a similar kind of western blot analysis. And what you can see again, you can see you got the vehicle treated mice compared to mice that have been taken at 4 hours, 24 hours or 48 hours, harvest the tumor and look to see what's happening to the specific biomarkers in those mice place time points. And you can see here again, the topoisomerase 1 enzyme itself, this is rapidly degraded upon treatment with the dual payload compound showing that we have got target engagement in the tumor as well. So our dual payloads are indeed hitting the tumor and hitting the right targets within the tumor. And again, topoisomerase itself is as soon as it gets stuck on the DNA, then the cell wants to rapidly degrade it, so it can get all its repair mechanisms hopefully going in to fix the problem that our topoisomerase inhibitors cause. We can see very clearly extended up to 48 hours. We see this very nice reduction in the topoisomerase once this persistent targeting of this biomarker. We also looked at some DNA damage markers as well, gamma-H2AX, Victoria showed on the previous slides as well. And again, we can see we get the increase in DNA damage continuing up to maybe beyond 48 hours. And also cleaved PARP as well. So this is a marker for apoptosis, which is a programmed cell death. And again, we can see this increase over time. We only went to 48 hours. And hopefully, this will persist beyond that time as well, showing that we are getting the right pharmacodynamic effects, the impacts on the specific proteins that we'd hope to see having release of the payloads specifically in the tumor. And finally, this is all very recent data as well. We've got some efficacy data as well, again, in tumor-bearing mice. You'll be familiar with some of these models that have been shown in some of the previous slides. This is the HEC-FAP model that we touched on before, the cell-line derived xenograft. Again, so the mice inoculated with tumors. And then in this case, we've given our drug or the topoisomerase inhibitor or ATR inhibitor at 3 doses on a weekly basis. You can see here tumor volume, the vehicle-treated mice and in this case, in the HEC-FAP model, the ATR inhibitor treated mice, so the tumors just grow very rapidly. But in our 2 compounds that we've used here in the blue lines, the tumor growth just completely flat lines. We are seeing this complete response. And this is some weeks after cessation of drug administration as well. So this has done very nice. We just still -- the model is still ongoing. We still have no measurable tumor in those mice at all, whereas the mice that have been treated with the topoisomerase 1 inhibitor alone, just the tumor is just starting to grow. So this is looking quite attractive. The other model we've done, again, you'll be familiar with this gastric patient-derived xenograft model. Again, it's the 3 doses on a weekly basis. The vehicle, the tumors grow quite rapidly. ATR inhibitor in this time is doing something in terms of tumor growth inhibition. The topoisomerase 1 is doing something, not great. But again, you can see for our compound in the light blue here, our dual payload compound is giving us the best tumor growth inhibition. But one thing we did for this model as well, we also tested side by side. It's the first time we've done it. We tested side-by-side an ADC as well. This is Enhertu. And before we did that, we just checked that HER2, the antigen to which Enhertu binds is expressed in this PDX and it is expressed in this PDX. So it's a valid experiment to do. And if you look at the dark blue, the Enhertu is giving some clear tumor growth inhibition, but it's kind of not doing as well in this model as our dual payload compound. So I think these are some really exciting data. The program started not that long ago, but I think the chemistry has done a fantastic job of making compounds. And I think the biology team have done some fantastic work as well, investigating the mechanisms and demonstrating activity of these molecules. So yes, it's all full steam ahead. I think I'll pass it over to Omer.

Thank you. It's going to be very difficult to follow up that exemplary science, but I have no science here. But just -- my goal here is just to give you an idea of where we're headed, why I am here and what our next path is to get this into the clinic and eventually to commercialization? So why regulatory? Why are we talking about it on Science Day and why it's important? It's important because it's the key driver for program value, right? So there's this amazing science and there is a need in patients. And where regulatory is, it's somewhere in the middle. You have to bridge that gap to bring that science to the patients. I mean a lot of people from academia, they publish a lot. There's a lot of work. But here, the goal is what we do has to be brought from the lab to the bedside and eventually available to everyone. So that's what regulatory is going to do. What it needs is a pathway. And what is this pathway? Some of you may already know this, some of you it might be new, but we've seen a lot of preclinical data here, a lot of work that's done in the lab, in vitro, in vivo. And then we've seen some data that is in Phase I, which is actually in patients. But it's a little pathway, and you see it grows a little, which means it adds more value after Phase I, there's a larger trial of Phase II, there's Phase III and eventually a commercial approval, which is where we're all headed. What it really does this development path is it really is adding confidence. And you'll see in the next couple of slides why it adds confidence. But as the data builds for any science, the more data we have, the more confidence we have. With it, investment as uncertainty goes down, investment goes up, and it's constantly evolving. So how is that done? I put up regulatory authorities. We're working with FDA right now. That's where the 6103 trial is, but it works in a very similar fashion with MHRA or EMA anywhere in the globe, but the goal is that they're gatekeepers. There's a lot of science. Science is good, it's perfect. But what the authorities are doing, they're overseeing to make sure the most important thing to them is public safety. We want to make sure that your patients are safe and you're doing it in an appropriate manner. What it's going to do is it's going to actually align from a risk standpoint. We want to lower our risk. The lower the risk gets, the easier it gets for us to get to approval. And how do they actually do that is by using the 3 pillars, right? The 3 pillars of regulatory are safety, efficacy and quality. The most important, which never goes away is safety. We go from preclinical to a clinical model in patients, it's safety. From safety, we go to trying to assess the efficacy of a product you still have to look at safety. And at a commercial scale, right, your one pack of paracetamol should be exactly the same as another pack of paracetamol, which is quality of the material, but you're still looking at safety. The safety never goes away. So that is what your health authorities are going to do to actually help you move forward. Health authorities are here to help. I promise you that, no matter how we look at them, FDA, MHRA, everyone is there. They want the product out to the patients, but they just have to do it in an appropriate way where they look at these 3 pillars of time. So which is the biggest hurdle to start with preclinical. That is why a lot of this information today presented to you was preclinical. Yes, there was clinical. There was some patient information as we go throughout the year, we'll get more patient data, but a lot of preclinical data because it actually demonstrates the model of what's going on and how we want to transition it into patients, right? So I put a little bit of a diagram in there where there's a lot of lab work, animal work. We've demonstrated the mechanism, the biology. We've assessed it that it's safe in cells and mice, but now let's move it into the clinic and actually treat the patients, and that's the transition into the clinical setting, and that's basically your IND. An IND for those who don't know, is an investigational new drug. That's how they call it in the U.S. It's any drug that's in clinical testing. We call it CTAs or clinical trial applications in the U.K. and Europe, but they're pretty much exactly the same thing. But the goal is here to align whatever you've done in preclinical that it works the same way in the clinical setting. So the transition into human testing. Because I talked about the IND, I'm going to mention a few things and why it's important at Avacta and some of it that you've heard in the last couple of months. So one of the key IND milestones, which is a study may proceed, it is actually looking at the huge chunk of data that was in the preclinical setting, your animal reports, your in vivo data, assessing it by a health authority and saying, we find it appropriate. There is enough evidence in here that you can transition from a preclinical scientific idea into human. Go ahead and dose your humans, and we're willing to look at it. I'm going to throw a number at it. It goes up and down. But just for you to understand, 90% of your assets will not make it into an IND. The agency does not agree or they just fail before that. So it's a huge testament of moving from preclinical into a Phase I trial. Now it's a matter of growing it and getting to a place where we can have it for all populations. So it's the first major value inflection point where the regulatory strategy is so important that you can get into an IND, get an IND cleared and start your clinical trials. And for that, I'll show you this, this is the news you've probably heard and it's been announced for Avacta, for 6103, from discovery of the 6103 molecule going into clinic, it took us less than 12 months. That is a testament itself of the work that's been done, the science that's backing it up and then just the timeless effort that's been put in by the people, the scientists of the company to move forward, 24 to 36 months is an average time. That's very standard. If someone puts in -- starts -- a molecule is discovered and first patient in is 24 months in, everyone looks at an industry and goes, yes, that's pretty good. Good job. We did it in less than 12 months. It was very, very fast. A lot of hours were spent on it. All of them sitting here can tell you that. But our goal is speed without compromise. There's still a lot of science. There's still a lot of work that is done in the back. If you remember the 3 pillars, we're still working on the safety, the quality, the compliance and hopefully getting the efficacy that we want as we move forward. So where are we? We are in Phase I. We've seen a lot of Phase I data. It is the primary focus of a safety and tolerability study, optimizing dose. Dave Liebowitz went over different doses, escalation of doses and finding the optimal dose. We're going to do that. Evaluate appropriate PK and pharmacodynamics. And then it's the initial validation of the program. Your Phase I is working, you're getting data out of it. Let's move forward, expand it, go into a Phase II larger patient population. A lot of these studies is where the agencies will look at it, the FDA, the MHRA will look at it and say, yes, you're heading towards commercialization. It's the design. It's the design, how you're going to treat your patients. And then a Phase III trial, which is a much larger trial, which is often called pivotal trial, which gives you a full basis of approval. It's where it transitions from a company that's in clinical trial to a company that's actually a commercially viable and available drug. So what I just want to bring up on this is how we get to a place where we derisk a lot of this and we add more value to the program. And I put here FDA because FDA just gives us a lot more areas to interact with them. This is just how the agencies are set up. MHRA does too, but it's a little more organized in a different way. But there's a pre-IND meeting with FDA, which gives us early guidance on the development of programs. Then there's end-of-phase meetings with them and then there's pre-BLA meetings. But what I want to add is that what these meetings do is a lot of uncertainty that may be there in our minds in a program. We can have these discussions with the science people at the FDA. There's medics, there's clinical scientists, biostatisticians, everywhere, everyone there, and they reduce that uncertainty. And one of the drivers for us getting into clinic has set a monumental pace is our FDA interactions. We had excellent FDA interactions, asked just the right questions, got excellent feedback and then incorporated that feedback into our applications, the studies, the designs of the animal studies presented that data and they let us move forward accordingly. I'm going to leave you with something that is of more interest to you, but I'm not an investor person. I'm a regulatory person, but I do understand that regulatory really does tie in where how the value of a program goes up. And basically, I tie it with what we just talked about, the discovery space, the preclinical space, and then that's the biggest chunk where you've got 90% of programs don't move forward, but we've moved forward. And now we're going into the phase and running those trials appropriately. The FDA, the MHRA is always going to have oversight in it. We're always going to be interacting with them constantly, providing them updates, providing them data, and they provide us feedback as we go, quality of the product on the safety of the product, everything that's happening. And then eventually to get to a place where we are 100% aligned from them from a risk-benefit ratio and the full approval of the drug. I just leave you with this. Again, it's a staged derisking approach. I think Avacta has taken that approach that to start that derisking at the earliest places, a lot of companies will start derisking towards a later place. So you'll see a quick Phase I, a quick Phase II and then a very long Phase III or hurdles you hit in Phase II and Phase III. But our goal is to take those hurdles out as early as possible and then kind of move the program more in a nimble, swift manner to get to the place where we get to the health authority and they see the data that's there and say, okay, go ahead and move forward. There's need out there, and we'd like you to move forward with the program. That is all I have. I know I went quick, but it was an overview.

Fantastic. We're actually on time unlike last time. So if you would join me in thanking these amazing people. I said that the third expense is the team. It's the most important. This company doesn't run without all of these folks. I'm really happy that there were 3 Americans that participated today along with our colleagues from Britain as well, Scotland is part of Britain, but I'm getting there. I'm learning. But anyway, if you would join me in thanking them. Great job guys. Great job.