Okay, we're going to get started with our next session. I'm Andrew Tai, Senior Biotech Analyst at Jefferies, and it's my pleasure to have Amit Etkin by my side. Welcome.
Thank you.
I forgot to mention, from Alto Neuroscience. Maybe give us, spend two minutes, three minutes talking about the Alto story, what you're trying to achieve, and what milestones we can expect over the next 6 to 12 months. You've got a lot of them coming.
Great. Thank you. Basically, this is a precision psychiatry approach. What that means is we try to understand the biology of the brain of individual patients and use that to guide both who best responds to a treatment and how to best develop a treatment, knowing the effects of a treatment on the brain. That really comes in the background of our field being one of tremendous need, really high prevalence, but very little innovation. There is a tremendous opportunity. When you define patients purely clinically, there is a lot of room for improvement, as we have seen in other areas. All of our programs have embedded biomarkers in one form or another. In some cases, it is early stage. Understanding what the biomarker does that indexes what the drug might benefit, right?
The drugs don't come with labels like, "Use this for depression, use this for schizophrenia," or "Use this in a particular population." We've got to figure it out. Alto 203 is an example of that that's coming as an early stage study this month. Alto 101, which comes in the back half of 2025, also a little bit later stage, but using biomarkers as outcomes. Importantly, using biomarkers to define populations. Who's going to get benefit of the drug differentially over a placebo? Who is just going to get a placebo level effect? We have now three phase II-Bs doing some aspect of patient selection coming up in 2026 and now in 2027 with a new asset we've added in.
Okay. How have you chosen these unique biomarkers? How do we know these are the perfect biomarkers for these drugs to maximize efficacy in these sets of patients?
We start with, and really even how we created our pipeline, be it on the biomarker or on the drug side, is understanding what you can even measure and manipulate. In other words, there may be things where you manipulate some system, but if you can't measure the effect, you're never going to get precision, right? Or you can measure a system that you simply just don't have leverage points on. It's about choosing that right mix. For us, increasingly, that's focused in a couple of areas where we've developed some really, really deep expertise for years now, going back to my work at Stanford and then as Alto, which is around cognition and around dopamine, both obviously tremendously important across psychiatric disorders. We've been using behavioral measures of, say, learning and memory to understand plasticity in the brain.
We've been using EEG to quantify dopaminergic signals that we can start pressure testing as we go. Do we have the right signal? Does it replicate? Does it yield the kind of effect you want? Does it allow you to translate to animals? In all cases, that has proved out with signals that we keep working on and refining. In the course, we look at signals and discard them if they're not robust and reproducible. There is this dance, if you will, between understanding the mechanism of a drug, how to measure that effect in a human, how to find the right humans, how to do this, by the way, at scale, because we know that these patients aren't coming in with these tests already.
We have to position something that can be used in the clinic by, you know, I'm a psychiatrist, so I can speak about, you know, clinicians in sort of frank ways. People are not all that sophisticated with brain tests. We have to be able to deliver that interpretable outcome to them easily. Across our programs, we've sought those proof points. We've required prospective replication. When possible, we anchor against large data sets of patients that tell us that that's an important signal or that's an important population to target.
What is the latest, greatest on the FDA stance around this approach? You're pioneering a new space or an area within psychiatry. Could it be arguably a double-edged sword because maybe there's technically more risk? I'm curious if you've spoken to the FDA lately.
Yeah. So, and I'm going to get in trouble for saying this, but I actually don't think it's that hard. Because in a fundamental way, what FDA is looking for is clear definition, right? So who are your patients? What is your exclusion, inclusion, exclusion? And can you defend that? When you just have a diagnosis, of course, that's one way to do it, right? But if you have a diagnosis and a biomarker, still inclusion, exclusion, we haven't touched the outcome measures. Those are the holy, the kind of sacrosanct element that they are much more resistant in changing. Cognition is understood in depression. Cognition is understood in schizophrenia. EEG, or well, there's already a diagnostic out there actually for ADHD with EEG. Those things are stepping stones that are understood. If you can be concrete, why are you using this marker?
How have you shown that it replicates? What kind of signal and benefit does it produce? Ideally, how does it link into the mechanism of action of the drug and/or disease? There is already a roadmap for that. They have an enrichment guideline that has been out since 2019. We have interacted with them directly in the Alto 100 program, for example. That is exactly the conclusion that came out of it: if it makes sense and you can be concrete and precise about it, they are going to be supportive of it.
Got it. So you do have multiple catalysts reading out, but hot off the press is you did acquire an asset just earlier this week, what you named Alto 207. So maybe describe this compound and what intrigued you about this compound.
Yeah. This is a fixed-dose combination of pramipexole, which is a D3-preferring D2/D3 agonist approved for Parkinson's, and ondansetron, which is a drug that's a 5-HT3 antagonist approved for the treatment of nausea. The combination achieves two things. It achieves the robust efficacy in terms of antidepressant effect that's been seen in trials of pramipexole when used alone, titrated slowly, because its major limitation is side effects that lead to dropout like nausea and vomiting. That is exactly where the ondansetron comes in to really cut out that major limitation, allowing you to go higher and faster with a drug whose mechanism we understand deeply. The motivation and enthusiasm for this comes from two places. One is, as I mentioned, we've understood a lot about dopamine biomarkers.
Iteratively, we can now see biomarkers that increase, biomarkers that decrease, all in kind of a bidirectional manner in humans and in animals. It gives you a lot of purchase on this question of how do you manipulate that system most directly. That brought us to pramipexole as a direct activation of dopamine, really motivated by the biomarker approach, which also told us which population to go after, in this case, treatment-resistant depression. On top of that, there has been data, including data presented really for the first time broadly, publicly on the investor call we had this week, that shows clear antidepressant effects of pramipexole in that slow titration when used alone in TRD patients. That was a large study out of the U.K.
You put those two things together, something we understand the mechanism of, that has compelling efficacy to where we can solve the main limitation, think of like a Coruna approach, and now a faster time to market by virtue of both being in a registration-like phase II-B study to start with and using a 505(b)(2) regulatory pathway. Think here about the precedent of AUVELITY with Axsome. That is a pretty winning combination and something we are really quite excited about. Fits beautifully into our strategy and, frankly, gets us something that can work for patients pretty near term. That itself is exciting alone.
Why would the old sponsor Chase give it away for less than $2 million?
Chase is a small biotech company led by two really illustrious drug developers who have been at this for a very, very, very long time, and career-wise, a very long time. It really just became time in their career and lives to pass this along, hopefully, we think, to the right group developing this. For us, it became the right time to acquire this compound, given all the biology and the clinical data that I just outlined. It is really understanding, hey, look, this has a high probability of success. How you structure the deal can be the right way to make sure that it is a high likelihood of these milestones being seen, but it has to also be developed by a group like ours that is well-positioned to do that in as fast a manner as possible.
Can you remind us what the combination has shown in depression on MADRS scores so far?
This is actually really, really cool what they did in combining these drugs. They were able to get to a dose, first of all, the dosing is five times faster than the label. They're also able to get to a higher dose than what has been done in any other study before. If you look across studies, there is a clear dose-response curve. You want to go higher. Also, because of the advantage to tolerability, they were able to do all of that within just eight days of dosing. That's a lot to achieve. With that comes the MADRS changes. You see at eight weeks, a Cohen's D of 1.1 over an eight-point difference in MADRS scores between drug and placebo. This is a study of 32 people.
Obviously, a small study needs to be replicated and brought to a large-scale multicenter trial, but a really good place to start. Also exciting, at two weeks, you see an effect size of almost 0.6, pointing to exactly the rationale for the drug, which is if you can get more on board faster, you can get rapid effects in a population where that's a rarity. That kind of effect size, people do not usually see ever at the end of the trial, let alone at two weeks. That came with an effect on CGI and sort of global measures that are themselves very—that measure is generally not very sensitive to drug effects in terms of differentiating from placebo in acute trials, and yet it showed the same 1.0 effect size. The drug then was, on top of it, as you would hope, well tolerated.
That's pretty compelling for something that could enter the clinic in really just a handful of years.
Or a marketplace in a handful of years, you meant.
Yeah.
What is the next steps for this program? When do you start the phase II-B and yeah, where do we go from there?
Phase II-B starts first half of next year to read out in 2027, which I should emphasize is well within our cash runway, which remains into 2028, even after this acquisition and obviously executing on the program. Between now and then, it's mainly regulatory interactions to make sure we're positioning the study as best as possible to be registration supportive, and obviously CMC and other type work to get us just ready to launch a clinical trial.
The D3-preferring component, that's my understanding is it's a generic approved for Parkinson's. Has it been prescribed off-label for depression and has a combination been prescribed off-label? If so, how much?
Yeah. So the drug has been prescribed by itself off-label. To our knowledge, that combination has never been prescribed off-label. Whenever anybody's used pramipexole alone, they tend to just follow the FDA label, which means really, really slow titration. That's what that U.K. study did, which used this very slow titration in a structured study, reaching a dose still that was far lower than what the Alto 207 combination has reached. That showed a massive, massive effect in treatment-resistant depression. That's in a structured study, right, done by experts who run a TRD clinic and have focused on this mechanism for a while. When you look at actual clinical care, we actually looked at the NIH's All of Us database, which is 100,000 MDD patients. Two thousand of them had gotten the drug.
The vast majority never even reached the very bottom end of efficacy, which we think is maybe around 1 milligram. And virtually none got to the dose that you'd need for TRD, that was what was done in the prior Chase study. That's the reality, right, is that people dose very gingerly because of the side effects, because it's a very hands-on dosing approach because of these side effects. They also tend to make those jumps very slowly. Average time between dose increments in that dataset, 273 days. You're never going to get to anything that's meaningful at that rate, right, for the vast majority of patients. That's exactly what the rationale is then for this combination approach.
The question would be, how does this exactly fit with your overall strategy of leveraging biomarkers to generate better efficacy in certain patients? Have you found a biomarker for this compound, or does it still need to be proven out?
We actually are already well on our way to a biomarker. We used EEG to get a biomarker of dopamine stimulation, which actually already pointed out that within a TRD population, as opposed to less resistant MDD, there is an enrichment already for a hypodopaminergic state. There you are already starting by picking your clinical population as an enrichment strategy. That will give you a broad label because of just how FDA defines clinical populations. The way we see the progression here is that the label itself will be broad TRD, but have a complementary biomarker. It will not be required for use, but we will find you the people who respond better or tolerate the drug better. That will be an enrichment biomarker approach.
That's already something that we've, in the context of the Alto 100 program, discussed with FDA, the concepts of complementary and complementary biomarkers kind of in a broad class where they're helpful but not required. And biomarkers that would be more like companion diagnostics actually required for the label. We think we have a pretty good sense for where that will be. The approach we'll take is obviously pre-specification of all these biomarkers in the phase II-B, so we can already use that information, and then validation as part of the phase III. That would be a pretty exciting label, right, is you have the broad approval with a large effect size, but there are some people who respond even better, and that becomes a good case for payers.
If you're looking at as a, do I give them a generic antipsychotic, or do I try at that point a branded medication that can help push the pharmacoeconomic argument further in favor of the drug?
I see. Earlier you mentioned you can leverage the 505(b)(2) pathway. Can you remind us what exactly can be bypassed? Is it abuse liability studies? What exactly is the strategy here?
505(b)(2), as opposed to 505(b)(1), which is the typical path taken by NCEs, 505(b)(2) says there's already all this information out there that FDA has on safety and efficacy, and I'm going to use that information as the base. It's exactly what Axsome did with AUVELITY. In this case, we're really leveraging the safety information on pramipexole and ondansetron. Ondansetron, I should note, is only approved for a three-day use around chemotherapy. That's where it was developed. There's safety data out there in much more chronic use, but not part of the label. That allows our label to expand use in the combination, which is itself protective. We can leverage all of the data that's out there and jump right to late-stage trials, filling in a few holes around basically tox and safety of the combination together because each of the parts is known alone.
That streamlines the path to registration. We've seen Axsome use it incredibly successfully in supporting AUVELITY, which has been a very successful commercial drug for them. We think it also supports a commercial argument. Payers certainly have not differentiated which pathway a drug goes through if it shows a clear effect.
Understood. So that will start, again, phase II-B in mid-2026. What do you need to do in the meantime before you start the study?
CMC and regulatory interactions, and off we go.
Okay. In the meantime, you do have other readouts coming up, including one any day now. I think that's Alto 203, your H3 inverse agonist compound. Maybe briefly describe the study structure. Ultimately, what do you want to see? I believe there's two phases to the study.
Yeah. This is a much, much earlier phase study from what we've just discussed. This is the first study in a patient population of this H3 inverse agonist. The goal is really a pharmacodynamic question. What does the drug do on a variety of levels? An H3 inverse agonist is a regulator of multiple other neurotransmitters. That includes dopamine, so that's interesting for cognition and subjective response. Acetylcholine, norepinephrine, and histamine. Acetylcholine and norepinephrine are relevant from a cognitive perspective, and histamine is relevant from a wakefulness perspective. We're looking at all of those features. We're looking at single doses of the drug, subjective response, cognitive benefit. We're looking at EEG changes. We're looking at wearables as an index of arousal and wakefulness. Basically, the goal is tell the story of what the drug does.
In as much as the drug does anything, how does that drive its development? That kind of joint picture of what the drug does, we can also compare to what the drug did already in a previous study in healthy individuals where, again, single doses were used. That allows us to really get a pretty good solid sense of the biology here. From there, we develop it clinically. In terms of the stages, as you mentioned, the primary outcome here is a crossover in the single dose. Everybody's their own control. You get two doses of drug as well as placebo. That's what's powered. For safety reasons, because we ultimately want to dose this drug chronically and it's the first time in patients, we have a four-week dosing, but that's a parallel group design. In other words, now you're splitting that whole group into three.
That is really to describe safety and PK when done over four weeks and not really powered itself for any kind of clinical endpoints for sure. It can give you some information that may be helpful in developing the drug further.
In the single dose component, like you said, there's three arms, 25, 75 microgram versus placebo. Single doses, but each person is his or her control. Ultimately, is the VAS score going to be the same for every patient because they're taking different things? What exactly do you expect to see on the VAS score changes?
Yeah. The VAS, the VAS or visual analog scale that Andrew's referring to as a measure of acute subjective effects, we've seen benefits of the drug in a phase one in terms of causing benefits on alertness and mood with single doses in healthy people. That is part of what we'll be looking for here. Also with that single dose, we have measures of cognition. Does the drug improve cognition, as you would hope an H3 inverse agonist might? Does it change wakefulness and arousal? Again, you'd hope that's what an H3 inverse agonist does. It showed benefits on both cognition and on polysomnogram measures of sleep and arousal in a prior phase one study. All of those are outcomes. Really what we're hoping for is some clear signal that helps you guide how to develop it.
Obviously, statistical significance is an important cut point there, but also is that signal similar to what we've seen in healthy individuals? Does that signal help tell you what kind of clinical populations to develop it in, be it in depression where it is now or anywhere else, right? This is just the first stab at what this drug does.
Right. When you press release the data, will you also share what the biomarker is for this compound, or does that come next?
Inasmuch as we discover one, we'll be able to share it. For us, anytime we have something like a stratification marker, it has to be robust. There has to be really good evidence for it, ideally replicated. That's the bar we'll be holding ourselves to.
Ultimately, should you find this biomarker due to robust data, the efficacy measure in later safe studies would be MADRS or SHAPS? Which measure do you think?
If it's depression, certainly MADRS is the accepted outcome. SHAPS would be, I think, unlikely to be accepted by regulators, but could be the cognitive part of it, right? Cognition in depression has been understood by FDA to be an important target. Could be an entirely different disorder based on the spectrum of effects. That's why I want to emphasize it's an early-stage pharmacodynamic study. Success here is really seeing what the drug does in a clear and convincing way. Ideally, even finding a biomarker to stratify. That puts us light years ahead. I mean, just even to double down on that point, right? If we can use a single dose study first time in a patient population to get a biomarker that can put you into late-stage trials in that population or elsewhere, that would be pretty awesome, right?
That's not something we've ever been able to do in the field. We keep trying to push ourselves in that line, really pushing ourselves to understand what is the right almost surrogate biomarker endpoint to use for a trial. We'll talk, I think, in a second about 101, which is a great example there. What is that bit of biology that you can test acutely even for the drug's effect that tells you how to develop it and tells you which patient, as you dose them in later-stage clinical trials, is actually deriving the biological benefit that turns into a clinical benefit? Those kinds of things are like no does in other parts of medicine. They're completely novel in psychiatry. That has us really excited too.
Great. And so then shifting gears to 101, which has a second-half readout in CIAS cognitively impaired schizophrenia patients. Here, I believe you actually have a biomarker chosen. Is it the theta EEG biomarker? Or can you remind me the biomarker?
Yeah. Let me just give a little bit of quick background on CIAS. The heart of schizophrenia is not the positive symptoms, delusions, hallucinations, and so forth. It's actually the cognitive impairment. Starts early and is most predictive of clinical course over their life. That cognitive impairment has no treatments right now. The importance of this area cannot be understated. The tricky part, though, is understanding what you actually need to move in the brain to be able to impact that. That's actually how we built the program and gets to your biomarker question, which is, what is the right way to measure the neurophysiology of cognitive impairment or CIAS, cognitive impairment associated with schizophrenia?
What is the right neurophysiology to measure that you can take into an animal, that you can take into an early-stage human trial, that you can take into an early clinical trial, that you can follow in the course of a long-term treatment clinical trial? We found what that is. That is something that we call the theta response or low-frequency response in your brain when you hear a sensory or auditory tone. It is reduced in schizophrenia, and it correlates with poor cognition, but in particular with something called processing speed, which is how fast you are able to process information. We show that the drug has a dose-dependent effect on those measures, processing speed and theta. We have found the right biomarker, we think, as a surrogate marker in schizophrenia. We have shown that the drug affects it. In fact, cognition and EEG are correlated with each other.
That's a great setup for a proof of concept trial where you're trying to move both that EEG signal and hopefully start to detect the change in cognition in a population often that's very hard to move, right? The primary outcome of this trial is EEG. Because we've already aligned EEG and processing speed, we're selecting patients based on processing speed as a proxy for the EEG. It becomes even easier. To your earlier question about kind of FDA perspectives, it's almost definitional for cognitive impairment in schizophrenia. People in the past have actually not selected their patients with schizophrenia to actually have a cognitive impairment in CIAS trials, which seems a little odd, but that's what people do. Here, we are specifying its cognitive impairment as measured by processing speed abnormalities.
We have a patient selection approach, takes out about half of CIAS patients, finds those that are more directly affected. We have an outcome that's tied to the pathophysiology here that we can even see move in animals. That becomes, I hope, a very logical, very rational way to develop a drug in this space where you can follow the biomarker, you know why you're selecting patients, and allows us, after this trial of success, to move right into a late-stage phase II-B trial.
In this study, it's a crossover study. You're looking, like you just said, EEG as the primary outcome measure, processing speed. When I think about traditional CIAS efficacy measures, the matrix battery, you're not evaluating that in the study, or are you?
We are, actually. Part of the challenge in schizophrenia is when you try to bite the whole thing off, you can't often. That may be for a range of reasons. The drugs in the past may not have worked well. There's no stratification. There's a lot of different things. If you go after the whole matrix battery, there's so many components to it that you actually don't really know where your signal is. That's why we're focusing first on processing speed, areas like memory, which are also sensitive to the effects of the drug, but also measuring broadly. What we're trying to understand is this is only 10 days of treatment. How does 10 days of treatment on the most sensitive surrogate outcomes, if you will, ultimately translate into the big kahuna in terms of changing cognition writ large?
Processing speed already correlates 0.8 or 0.9 with cognition as a whole as measured by the entire battery. You try to find the right tip of the spear, but we'll be starting the foundation for what larger clinical trials will be by measuring broadly. We'll see 10 days of dosing, just to preface this, right? In patients with schizophrenia who've been cognitively impaired since their teens, we have to set the bar correctly for this. Success is really moving the EEG signal and maybe promising effects on cognition. If we see cognition move significantly, I'll be over the moon.
Yep. And so we did not have the chance to talk about your other program, which has also data, was it mid-2026? But cash-wise, you reaffirm that you have enough cash into 2028 to see up to five catalysts maybe to read out?
Yeah. So five phase II trials, of which three are phase II-Bs. We have two of those coming up in 2026, one that Andrew alluded to, as well as one in bipolar with Alto 100. And that's a lot of opportunity within current runway and gives us a lot of legroom forward.
Okay. That is all the time we had, but thank you for this nice update on Alto. And thanks, everyone, for listening.
Yeah, my pleasure. Thank you.
Thank you.