Hi everybody, my name is Ash Verma. I'm a Small/Mid Cap Biotech and Specialty Pharma Analyst at UBS, and our next company is Alto. With us, we have Amit Etkin, who's the founder, president, and CEO, of Alto. I, hi Amit, can you hear me okay? Absolutely, pleasure to join you here. Excellent, so yeah, I mean, I would say that, you know, I'm a little bit new to this story, and this is something as we've talked to more, you know, experts and KOLs in the MDD space, we've been flagged about Alto and what you're trying to do is kind of unique, so would love to understand and talk about some of the different programs that you have, like 100 and 300, and dive into, like, what are the trial designs and the specific biomarker strategy that you're pursuing. So that's the game plan over the next 25-ish minutes or so. Maybe it'll be helpful if you can give us a little bit of a high-level background about the platform, about your company, so that if there are investors who are new to the story they can get a sort of like a handle on where you're coming from. Yeah, so let me start by defining the problem that we're after, and then I'll give you some of my own background, how that has led to Alto, and then what we were focused on. So we know in psychiatry this is a big problem that's not going away, right? It's not getting any better on its own. It's only gotten worse with the pandemic and, you know, just the way things have been, for years, frankly. Very little innovation in terms of new mechanisms. But the worst part of that yet has been that in the clinic every drug that is brought out, whether it's new or not, is used with trial and error, which means that most of the time it fails for patients and it takes a long time to figure out if you do get to the right medication, whether, whether there even is a medication for you. So that's ultimately the problem we're trying to address, and if you see where that problem came from, it came from how the drugs were developed in the first place, which is taking a diagnosis like depression and assuming that it's all the same thing, and then just throwing one drug after another at it and, and hoping one makes it. The problem with that is that there's tremendous biological heterogeneity in any disorder, depression or any other disorder. They were never built to be a situation where the diagnosis maps onto biology in a clean way. And so frankly, it's up to us to figure out what that right mapping should be to find the people for whom a drug is particularly well-suited and exclude what is often a majority of people who actually don't respond any different from placebo. So I'm an MD-PhD psychiatrist and neuroscientist. That's what I've been doing as a faculty member at Stanford for a decade prior to starting Alto. And it was all about finding the ways in which we can measure brain function directly or indirectly, scalably, informatively to really make this a compelling data-driven approach that would allow us to partition populations, be it in depression, PTSD, schizophrenia, addiction, doesn't even matter. There's partitions to be made in any one of these massive disorders. And then use that to, in the lab, understand how standard of care can be deployed better, and at Alto develop new treatments based on that kind of information. And so if you start there, the need to partition populations, and then us having tools to do that, an informative, mechanistically oriented tools, then you put those together with a drug pipeline that seeks to take advantage of the learnings from that kind of platform. And you can start to really make some interesting outcomes, and it becomes a real engine that you can do this across within an indication across different drugs and mechanisms, across indications for a given drug and mechanism, and, you know, look for opportunities, sometimes surprising opportunities to leverage the right biology now that we're actually measuring it and we're doing it at scale. So we can do this routinely in our clinical trials. We're doing it in patients' homes. We're doing it in clinical trial sites. We think a lot about how that R&D effort translates into the commercial effort. You can see that across our programs, which we'll go into, in a little bit more detail. Yeah, I guess maybe like, you know, kind of like a theoretical or philosophical question that I'll ask, on this approach overall. I mean, if you look at, you know, CNS or psychiatry market, like these are big patient populations. Some of these, like, biomarker or like, patient characteristic type, you know, like approaches, they work well in some sort of like niche, patient population where, you know, like the scale allows for you to build that type of infrastructure and, you know, economically, it's like a different economics when you talk about like those niche patient population because in terms of how you're looking at the price point and, the level of number of patients that you need to reach is kind of very, very concentrated, right? So, you're coming in from like a very different approach on how this has worked and the scale at which it has worked to what you're trying to do in CNS. So, how—what is the sort of your thought process on that? Like, if you were to try to use some of like these computerized neurocognitive batteries or EEG variables in CNS indication, like could that really work at scale or not? 100%. So that is the right question to be asking. And it's the one that we ask all the time whenever we engage in any sort of, you know, research effort. So at base, our biomarkers need to be ones that are scalable to the level not just of the size of studies or kind of early clinical populations, but ultimately we're talking for MDD alone in the United States, 20 million people. And that approach therefore needs to be informative on the one hand, but equally deployable. And so what that means if you look, for example, at the biomarker for ALTO- 100, which is a test of memory, performance-based objective test of memory, that's a web-based behavioral measure. It takes maybe 20 or 25 minutes to do in its final commercial form. What we're doing now is something close to what that final form would be in terms of how we implement it. The patient does it themselves. There's no tester required. So just imagine what would it look like if you attach that to a direct-to-consumer ad? Or if the doctor could have them do it there on any device in the waiting room and you get your answer right away. An EEG similarly is seen as something that's often a more specialty tool within neurology, but doesn't need to be. So we've actually already been recording EEG in our trials in patients' homes. And we've been working with EEGs collected by the patient on themselves in their home with software that we have developed to do that. And you can deploy a very low-cost EEG system. At the end of the day, to get the biomarkers out there, maybe you're just making it available for free for the patient. That's a way to get into the drug, and access and information about the drug. Maybe you have the clinician do it. In either case, the expertise for doing it and for reading it is already embedded in the way it's done and the algorithms used, to find those patients. Got it. Okay, so with that, maybe we can go over some of these, like, specific pipeline opportunities. I mean, I think, your leading program going after MDD or PTSD, just like, yeah, what sort of like, clinical pathway do you see? When do we expect to see some data that can start to de-risk this? And then we can kind of like, walk down the pipeline that way. That's good. So let me start with ALTO-100 and let me frame where that one comes from. So we've known for a long time that, presence of cognitive impairment, so that's an objective measurement of cognitive impairment, not just somebody saying that they have difficulty concentrating, that that, characterizes a group of people with depression, at least a third of that population, and some studies, over 60%, who tend to respond more poorly to standard of care treatment. Because they're more, they're more homogeneous biologically and can't be found using just symptom measures, it gives us the opportunity, if we understand what's going on in those people, to target their biology and their depression in a different way. And the theory that has long been proposed is that that phenotype of cognition and mood impairments coming together is associated with a deficit in neuroplasticity in the hippocampus, the ability of the brain to change and respond to changes in stimuli. And so our hypothesis was a simple one: find a drug that enhances hippocampal neuroplasticity and target it for these people who have an impairment in hippocampal plasticity. We looked around and ultimately identified ALTO-100 as that drug, brought it into a Phase 2a study. The goal of that Phase 2a study was to prospectively replicate a biomarker that we identify with a different part of our Phase 2a data. This is a study of over 200 people with depression or with PTSD, and we did it both in depression and in PTSD and found the same biomarker. In this case, it's a test of memory. Memory is a measure of hippocampal plasticity. And so we've therefore identified and prospectively replicated that those people with poor hippocampal plasticity, their depression responds better when they're given this drug that enhances hippocampal plasticity. And as a first-in-class agent, it—we also know it works through an important growth factor pathway called the BDNF pathway, but doesn't bind to targets of known CNS therapeutics. And we also know that those people don't—the people who have poor memory, this cognition biomarker, they're not going to respond any better to placebo because we've looked at that also in multiple data sets where cognition was measured along with response to placebo. So you wrap up then all that information with a drug that's actually very well tolerated, novel mechanism, targeted a group of people that makes sense mechanistically and who we can measure with a web-based behavioral test that's scalable. Prospective replication of the patient selection biomarker. That puts us now into a Phase 2b study that was started back in January of 2023. The 266-patient study, aiming to understand in those patients with this poor memory cognition phenotype what's the drug versus placebo response and efficacy outcome there. We also bring along a small group of people without the biomarker for various reasons. We don't expect much response in those people. Then we're on track now to read that out in the second half of this year. That's really the first major opportunity to see what what we'd call precision psychiatry has in store, actively selecting patients, in this case, at over 30 sites across the U.S., as well as work done in patients' homes through a decentralized clinical trial infrastructure we've set up. It looks a lot like what a phase three study would look like in sample size, in approach, and in the patients being enrolled. I mean, for this type of like biomarker-driven strategy, like do you think that there is a, like a limitation from how many sites are available in the US or outside the US that can actually execute on a study like this? Like did you run into that type of a hurdle? We have not. So we have, essentially, we're looking for good quality sites. Right? So we're looking for sites that know psychiatric patients, preferably that treat psychiatric patients, so that the people they bring in are going to be high-quality participants. But then we don't actually count on them knowing how to do any of the biomarkers because we teach them. And in teaching them, we've also been doing this for a long time. We've learned both how to teach them and how to ensure high-quality data collection through software that we develop and give to them for them to use during acquisition. And so right now we're getting very, very high QC pass rate for data collection. That has not been a barrier at all. And as I mentioned, we have data collection happening in people's homes in a decentralized clinical trial framework, which is frankly more like what clinical care is going to look like, ultimately down the line. So we've tested a lot of these things in our trials in the phase 2as that we brought into the phase 2Bs. We've tried to change as little as possible in how we do these trials. And then that should translate well into a phase three. Then we do all sorts of other things that are more what I'd consider best practices when running trials that mitigate placebo responses in the first place, 1:1 randomization, not assessing patients too frequently, using an external rater for baseline severity assessments. And we also give everybody an open label option after the double blind to decrease any anxiety about getting placebo in the first place. Yeah. Could you give me a sense? I don't know if you disclose this, but like, for the, let's say, the phase 2b study that you did, doing it this way where you're using a lot of these, you know, cutting-edge technology and some of these sophisticated tools, like what's the cost implication of that versus versus if you were just like doing a, plain vanilla, clinical trial? So it's a very interesting question because part of the plain vanilla trial is actually using a CRO, right? You, that is the most common way in which these trials are done. What we've actually done is build our own clinical operations team internally. So we have about half the company is a clinical operations and clinical development core. And we have an engineering team that develops the software and then implements it and trains sites. And a data science team that makes sense of the data that we're seeing in our trials, as well as a lot of archival data that we've built on these disorders and these biomarkers. What that actually means is that we're both closer to the data and better able to ensure high-quality clinical data, high-quality biomarker data, and actually do it at half the cost of the usual clinical trial because we don't involve a CRO. So in many ways, we get better outcome and save money, by finding a system that's more fit for purpose for doing these kinds of trials. And, you know, our sites, frankly, have really enjoyed being part of these trials because it's different. Because they get to learn things that they haven't been exposed to. And so all of that's gone extremely well. Yeah, I mean, I guess, from like, if you're standing up a clinical operations team from, you know, from ground up, like is this do you think that there is a need for such an organization to exist on an ongoing basis, just based on the depth of pipeline that you have? Or do you think that this is potentially something that you can offer as a service, if you are able to reach some sort of like validation point, at a given point of time? Yeah, so right now we have far more that we could be doing, just in terms of the number of pipeline assets, number of indications. You know, we're trying to make sure we stay focused and execute on the things that are in front of us. And so I think if you look over the near to intermediate term, we're going to stay with this model, with this internal, operations model. And equally through phase three, I think we're already set up to do that. If you actually think of the two phase 2Bs that we're running, one with 100, one with 300 together, that's like a phase three program. And we just move our staff. And importantly, their learnings that they've gotten by doing these trials from one study to the next, from one program or indication to the next. So it continues to pay off. I don't see that that needs to change. We haven't really thought about offering that as a service because right now we've got so many things on our plate to do. We have four phase two readouts in the next, you know, in this year and next year. That's a lot. I'm sure there'll be more on top of that as we see what the results look like and see what other opportunities present themselves. Yeah, got it. Okay, so now like just for ALTO-100, I mean, so you said that the phase 2b data coming in second half of this year, right? Like what would you consider like good outcome of that study? Yeah, so that's a really good question because I think one has to anchor on what's out there. So what's out there is trial and error, 0.3 effect size for the most part. We're studying both monotherapy and adjunctive treatment with ALTO-100. So think of monotherapies being the SSRIs, the SNRIs around 0.3. But well tolerated generally. Antipsychotics, similar effect size, but generally not well tolerated. ALTO-100 has been very well tolerated in both use cases. The same memory biomarker, stratified and enriched in both use cases. And so where we've ended up powering this study based on what we've learned from the phase 2A and based on these the standard of care that that's out there is at a Cohen's d of 0.4, which is above standard of care. But it's something where if you can achieve that, that's a really clinically significant result and certainly gives you room above, should you get there. But the important part is we've powered the study to allow us to detect a clinically significant result in a population that we know responds less well to standard of care treatment. Right. Okay. And so what about like 202, like that the second phase two that you will have readout right after? ALTO-300. That yeah, so ALTO-300 is a different setup. So ALTO-300 is actually a drug called agomelatine, which is an approved antidepressant in Europe and in Australia. It's an NCE in the United States. So we know its efficacy. It's also extremely well tolerated. It's the best tolerated of all antidepressants, all approved antidepressants. And so what we've done is developed that drug for adjunctive use only. The contrast again is an antipsychotic. So, you know, the effect size clinically for an antipsychotic, clinicians would heavily weight against the weight gain, the metabolic effects, the movement disorder, and akathisia that you can get with those drugs, none of which happen with ALTO-300. In this case, we didn't know what the biomarker would be, so we used a machine learning approach that we've previously used and validated extensively in different contexts. To apply to EEG data and tell us what is that signal for selecting patients. That signal, again, just like ALTO-100, was prospectively replicated. That allows us to take it into the phase 2b. We also saw that placebo outcome and an outcome with standard of care treatment was not predicted by that biomarker, so it was specific for the drug. That EEG, again, is being done at 30+ sites in the U.S. and in patients' homes, so similarly scalable. Has been going on since the summer of 2023. On track to readout in the first half of 2025. Conceptually, the same approach as ALTO-100. Selecting patients, enriching, that's the primary efficacy population. Bringing a small group of people without the biomarker in alongside. But the biomarker is independent. It's not correlated with the ALTO-100 biomarker. The prevalence of the ALTO-100 biomarker is about 35%-45% of the population. ALTO-300 is about 50%-55%. Together they cover about three-quarters of the depressed population. With a drug that should be better than a standard of care option for that patient. Yeah, yeah, okay, that's great. I think that those are pretty helpful to try to understand. I'll just, you know, take a minute here to pause and see if there are any questions from investors, and then we can come back to the line. For the Q&A session, if you have a question, please click on the raise hand button, which can be found at the bottom of your Zoom interface. If you have dialed in via telephone, please press star 9 to raise your hand and star 6 to mute and unmute. When you're selected to ask a question, I'll call on your name and ask you to unmute yourself. I'll stand by for any questions to come in. No questions at this time. Back to you, Ash. Yeah, great. Okay, so I mean, I was looking through the pipeline here. I think you have a few other early stage programs that you're pursuing. I mean, the 100 series and the 200 series that you've described, like the cAMP and the dopamine pathway. Yeah, what can we, you know, expect to learn from those pursuits? Right, so those are both entering proof of concept trials this year and then will readout next year. The goal for each of those, they're earlier stage, so we have not done the patient selection yet. Is first understand how to dose those kinds of drugs based on biomarker readouts. For example, with ALTO-101, which is a PDE4 inhibitor, where we're looking for a cognitive enhancement signal relevant to schizophrenia, as a cognitive impairment associated with schizophrenia or potentially other cognitive disorders. We reported in December in a large phase one healthy volunteer study. Using an extensive set of biomarkers that we saw a dose responsive increase in measures of cognitive processing and cognition itself. So that's a go signal that tells us the drug affects the brain. We can do dose selection and indication selection based on biomarkers. The alternative, by the way, if it didn't affect those biomarkers, is that drug would be shelved because you wouldn't know how to develop it with a precision approach. You'd be throwing spaghetti just like in the usual way. The other thing, though, that we did with that drug is we reformulated this as a patch. And the reason we did that is that at Cmax, it's well known that PDE4s, just like ours, have a class-wide dose limiting adverse event around nausea, lightheadedness, dizziness. And by formulating it as a patch, you get a nice steady PK that avoids Cmax, but how are you going to know which PK to hit? That information comes from that PD study. So we'll first report on the safety and the PK associated with the patch first half of this year, assuming that that's positive. We'll then transition into a proof of concept trial in patients with schizophrenia where there's stable positive symptoms and they have a notable cognitive impairment. Looking for improvements in cognitive processing as a proof of concept. In a very similar way, but with a phase one PD study that was done by the originator, ALTO-203 is an H3 histamine inverse agonist. The interest there is that the H3 system, which is a brain-specific histamine, has nothing to do with the gastric and kind of immune system histamine that we think about in other receptors. H3 regulates other neurotransmitters, and for us, the most interesting one is dopamine. That drug has been shown as a unique pharmacological property to drive dopamine release in the reward system in animals. Then in humans, it's been found to acutely increase positive subjective emotion, which is basically what it would look like. If you were to measure dopamine in the reward system of a human being. That told us again about the dose. That's the go signal for dose and indication selection. Now we'll enter first half of this year. A proof of concept trial in depression with anhedonia to readout first half of next year, and we'll say more about the design and the outcome there when the study launches. So that's 4 different phase two studies, different assets, all proved out with biomarkers in different ways. two of them in phase 2B, where these are large, you know, informative, well-powered studies. And that's just in the next, you know, let's call it 18-21 months over the next this year and next year. That's pretty exciting for all the work that's gone into setting up the promise of precision psychiatry. We're now going to see it bear fruit, hopefully. Right, right. No, that's great. Excellent. And yeah, I mean, I think as a recent IPO, anything that you can comment on, just like the finance runway or, you know, that'll be helpful to just call out for investors too. Yeah, so we wanted to thank all of the investors who came into the IPO and of course those who supported us for years beforehand. We're very happy with that outcome. We're now well funded into 2027 with the existing programs and of course cash well beyond that. The last readout comes in 2025 that's planned. So we'll say more about that in earnings and so forth. But as you can see, we try to also manage our cash very carefully and how we do our internal development programs and our internal operation team. So always thinking about cost efficiency and the value created by our efforts. Yeah, great. All right, that's great. Thank you. Thank you so much for taking part in our conference. And yeah, I would love to stay in touch and, you know, follow the updates of the story. Looking forward to it. Thank you for having me on. Yeah, all right, Amit. Thanks. Take care. Bye.