Speaker 1:

From the New York Stock Exchange at the corner of Wall and Broad Streets in New York City, welcome inside the ICE House, our podcast from Intercontinental Exchange, is your go-to for the latest on markets, leadership, vision, and business. For over 230 years, the NYSE has been the beating heart of global growth. Each week we bring you inspiring stories of innovators, job creators, and the movers and shakers of capitalism here at the NYSE and ICEs exchanges around the world. Now, let's go inside the ICE House. Here's your host, Lance Glinn.

Lance Glinn:

The future of mental health care is being radically reimagined. After decades of trial and error treatments, science is finally catching up to the complexity of the brain. Breakthroughs in neuroscience, precision medicine, and AI are unlocking the biological signatures of condition like depression, bipolar disorder, and schizophrenia, paving the way for faster, smarter, and more personalized care. No longer one size fits all. Treatments are beginning to target the brain's unique wiring offering hope, where there was once only guesswork. Alto Neuroscience, that's NYC ticker symbol ANRO is rewriting the rules of mental health treatment by putting the brain, your brain, at the center of care. Using cutting edge AI and brain biomarkers, they're matching patients to therapies with surgical precision, turning guesswork into guided science. Our guest today, Dr. Amit Etkin, founded the company in 2019 and serves as its president and CEO, leading Alto's innovation efforts to match patients with the right treatments so that they can get better faster. Amit, thanks so much for joining us inside the ICE house.

Amit Etkin:

It's my pleasure.

Lance Glinn:

So you founded Alto Neuroscience back in 2019 after more than a decade of leading cutting edge neuroscience research at Stanford. But just for those unfamiliar with what you do, walk through Alto Neuroscience's mission and the work that you're doing to match the right patient with the right treatment so they can get better faster.

Amit Etkin:

Starting with where we are right now in the clinic, I think is a good place to understand the rationale and how we develop, which is that when we make diagnoses, and I speak here as a psychiatrist as well, we're really just asking people about their symptoms, their subjective experience, and we're putting people in big categories like depression, 5% of the population any given year, and then we give them medications that are basically medications that have been identified decades ago through serendipity that have been tweaked. So kind of the same medication, but just choosing through random chance. And when you combine all those things, you get tremendous amount of imprecision and poor outcomes. And so what we try to do at Alto and what we did at Stanford, to build up to that, to form the scientific basis is two things at the same time.

One is identify signatures in people's brains. So what we call biomarkers, brain tests that tell you about what's actually going on with that person together with a new medication that works differently from what's out there that can get a better effect, both by being different and by being paired with a test that tells you who's the right person to get that medication. And on both fronts, really hoping to transform psychiatry from essentially a purely subjective science to one where you have objective and still the patient's experience is part of it. But we actually have tests, we actually have new treatments.

Lance Glinn:

So I want to speak further to that because you mentioned for decades it's just been a little bit of tweaking, but pretty much the same sort of a symptom checklist, trial and error sort of basis, trial and error prescribing. And even that's happened even as other fields have sort of embraced more personalized medicine, mental health as a whole has sort of lagged behind. So how is Alto really working to bring psychiatry into this age of precision medicine that you were just referring to?

Amit Etkin:

So in many ways, taking a page out of what other fields have done, what oncology has done very successfully is start to partition populations and use the understanding of the biology to drive treatments. In some ways what we're doing is a bit in reverse. We have a number of candidate treatments that we put together, really exciting new drug mechanisms. And then in our trials we leverage data that we collect in the trials as well as a lot of other data that we've built to understand, okay, if you have this drug, what is the right signature in the brain for that person? So using tool like machine learning in the trials to tell us who it is that best response and then purposefully develop for that population.

And so taking a lot of the innovations that we see around us, machine learning has come a really long way. The depth of data that we're collecting on people's brains that we're doing in a really scalable way, thousands and thousands of data sets ultimately can go into informing one of these questions. That's how we're moving the needle and frankly, as you said, we need it. We're a field that just has not moved forward in a very long time.

Lance Glinn:

And you mentioned machine learning and we're going to talk a little bit about that and AI a little later in our conversation, but I want to speak to some recent developments with Alto recently acquiring Alto 207, a fixed dose combination therapy for treatment-resistant depression. Just first, what was the strategic rationale behind this acquisition and why is it the right addition to Alto's pipeline at this stage of the company's growth?

Amit Etkin:

So we've done a lot of work like [inaudible 00:05:55] is referring to on the biology to understand dopamine. Dopamine is really important for a variety of things, depression, Parkinson's, a number of other conditions. And so it's about who has dopamine problems and how to fix those dopamine problems. We've made a lot of progress on both fronts and we started to understand especially the more resistant forms of depression as those that are the most efficient in dopaminergic function. And so then the question becomes how do you fix that? And so that's where a drug called Pramipexole, which directly stimulates certain types of dopamine receptors comes in, has a huge evidence base behind it saying actually there's quite a lot of potential efficacy here for people with more resistant depression, but it also comes with substantial side effects.

And so Chase Therapeutics, from whom we acquired this compound or this combination, Alto 207, figured out that the way to get the drug out there is to mitigate the side effects by pairing it with a different drug. In this case a drug called Ondansetron. And that's what Alto 207 is, is harnessing an understanding of the biology of dopamine in individual people, especially in treatment resistant depression, getting added directly with Pramipexole and then blocking the side effects that you get with Pramipexole. So you get higher doses, faster dosing, more rapid effects for a population that has very, very little available to it. And then the bonus here is that we're able to bring it directly into a late stage clinical trial, a phase 2B trial that will launch next year and has the potential to be part of a phase three registration package. So that means a time to market is fairly near term in kind of biotech timelines. That package of reasons becomes super compelling for a company like us.

Lance Glinn:

Yeah. You mentioned treatment resistant depression remains one of the most challenging areas of psychiatry, and you've said that this deal underscores the strength of Alto's platform and your broader vision for innovation in psychiatry. As founder, CEO and president of the company, what does the addition of Alto 207 just represent not just as a product, but just as a signal of Alto's growth?

Amit Etkin:

So it builds on a lot of knowledge that we've built, that we've gathered on the biology. So it just from that alone comes from a position of a lot of confidence in our platform, in our ability to understand biology, but also as we now push into later and later stage trials, it's a high probability of success program because of everything we know about Pramipexole, so it allows us to start thinking of what is the path towards getting something out on the clinic and being commercial stage, that's a game changer for a biotech company for sure. It also allows us to start thinking about where else do we find that same biology? How could you leverage this combination approach to improve outcomes in other conditions, bipolar disorder, bipolar depression for example, that often is treatment resistant becomes another obvious target and starting to build out from there. So it's really itself compelling, but I think builds on frankly a lot of years of work to understand how do you target which brain systems in which people.

Lance Glinn:

So I want to speak further on Alto 207 before we get to our conversation on AI. This is a recent publication in Lancet Psychiatry. It highlights compelling results from the PAX-D study at Oxford or conducted at Oxford. So having this data published brings a level of visibility and credibility to what you're doing. So what do the publication of these findings mean to you? Not only as a researcher and founder, but as someone working to change the treatment landscape in depression?

Amit Etkin:

So what was really striking about that study is how incredibly effective this medication Pramipexole was for highly resistant patients. So what they did is they looked at 150 patients with treatment resistant depression, and this was all done in the UK, so through their health system, people who had been ill for 10 years or so. So really resistant the kind of people who are exactly the type of people you want to be serving. And what they showed was not just that it worked, but that it was three times more effective than a typical antidepressant would be.

Lance Glinn:

That's incredible.

Amit Etkin:

It's a totally different ballgame.

Lance Glinn:

It's a game changer.

Amit Etkin:

Game changer, right. But that came with the side effects that we know, which is, you get nausea and vomiting type side effects as you dose more of this drug. That's where that our combination Alto 207 comes into solve that part and let you dose faster. But it's the backing to say this mechanism is validated, and when it works for those people in that study who could tolerate it, it was night and day as a difference. So that becomes exciting, right? Is to be able to roll something out that you think will actually have an impact.

Lance Glinn:

Yeah, it's incredible. And as it continues to go, I'm sure that impact will be shown more and more. AI, I want to get to that obviously is being used across biotechs with its applications and psychiatry still emerging, right? I think still emerging everywhere. We're just past that sort of ChatGPT moment of AI, and now we're getting into all these different use cases that it has these thousands and thousands and thousands of use cases. Just what are some of the most unique opportunities and challenges too of using AI to understand brain-based disorders and match patients to the right treatments?

Amit Etkin:

So I'll give you a very concrete example with Alto 300. So Alto 300 is a drug called Agomelatine, which is actually an antidepressant approved in Europe and Australia, not in the United States. So we're developing it for the US, the question was what is the right biomarker. Well, we didn't know exactly what that signal would be, but we knew through EEG or brainwave recordings that we could potentially find that signal. So we did the study, used machine learning to decode information from these brainwave recordings, identified and replicated that signal. So we're confident in what that signal is, but we didn't know what that signal meant.

In other words, we can find it using these tools to kind of take the needle out of the haystack, so to speak. And it's consistent, but does it have any relevance to the biology of the drug? As it turns out, because it's a measure on EEG that you can actually start looking at, even in animals, we were able to link that machine learning identified totally data-driven signal back to the mechanism of the drug itself. So we understand it relates to dopamine and certain serotonin receptors that the drug impacts. So that's where a combination of using AI or machine learning for discovery, but then driving hypothesis-driven basic work using that can lead to entirely new perspectives.

Lance Glinn:

Absolutely. So this next question, I love asking to people in either biotech or pharmaceuticals. We've had a host of answers over the course of the various people we've had here on the podcast in the industry. AI, there's such a hope and a belief that it can play an important part in overall R&D acceleration and drug development. One of the hopes involves its ability to speed up timelines, excuse me. I've heard answers from, hey, it could speed it up years to, hey, it could speed it up, but only a slight, slight amount. How do you see AI helping to potentially in the drug development cycle or help make earlier stage decisions with overall more confidence?

Amit Etkin:

So our focus is on late stage trials, and that's really where the cost and the timelines are most severe in biotech and the risk of taking a drug that you don't really know who you're treating with because you don't have a targeting approach, as is the traditional approach in psychiatry into late stage studies, and then you just don't know what's going to happen. That's where I think AI has the biggest impact is finding the right people, being able to run the right trials to be much more targeted and turn what is interesting biology from an early stage into a much more targeted, more effective, and ultimately more personalized clinical development effort. That is certainly in our space, that is an area that's completely untouched, but even in other areas of medicine, that's the innovation when it comes to what I think is one of the hardest parts, which is showing efficacy in people, that's often been relatively untouched by AI. A lot of people focus on earlier stages. I think there's a really ripe field to be had in late stage trials.

Lance Glinn:

And when it comes to just AI as a whole, right? There's always the issue of trust when it comes to it. And you see over the last few years, there's just a ton of information being thrown at the common person, someone who obviously understands how to dissect the information, can sort of choose, okay, this is obviously correct, this is just fluff. This is just hype. But not everyone can do so. So how are you, through Alto Neuroscience the way you guys are using AI, helping build that trust so that people know, okay, we can trust essentially Alto Neuroscience with the AI that they're using?

Amit Etkin:

So we try to keep it as explainable and simple as possible. So the sort of ChatGPT very complex models, that's not what's driving the patient selection and the biomarkers. We're using simpler forms where we can actually say we know exactly what signals are driving it, and increasingly we know why. And that why, even though as a clinician you may not really need it, that is if something works, it works, right? But having the why in the example of Alto 300 Agomelatine that I gave, that actually gives you a lot more of that trust in that signal. So yes, it works, but I have a mental model of why it works and then I can fit it into everything else I'm doing with a patient.

Lance Glinn:

It's that education, giving that why to the consumer.

Amit Etkin:

That's right.

Lance Glinn:

So hey, this is why it's happening.

Amit Etkin:

And if you think of the way psychiatry operates now where your doctor just gives you a medication and they don't really know if it's going to work, and they might give you sort of a rationale.

Lance Glinn:

That trial and error that we were talking.

Amit Etkin:

Trial and error, and who knows, right? The ability to say you have this and biology and therefore because of that reason, this drug will work for you, I think gives a model for the patient of the way medicine's supposed to operate in a field that just hasn't had it.

Lance Glinn:

So as someone who's both a neuroscientist and a company founder, how do you think about the really, again, the ethical guardrails that need to be in place when applying AI to brain health, especially as models become more predictive and more personalized? We talked about it, there's thousands of use cases for AI right now. There's going to be thousands more as weeks and months and years go by, but how do you think about those ethical guardrails to again, build on that trust?

Amit Etkin:

For us, it's in a way fairly straightforward in that we're developing medicines to help people. We're not trying to decode things going on in their brain and in some ways invade a private space. We think we should all maintain some private space in our heads. Being transparent is absolutely critical. Talking with the FDA and making sure that from a regulatory perspective, everything makes sense. And then engaging stakeholders, be they patients, clinicians in this process to make sure that we've got our mission aligned with their goals. And I think at the end of the day, that is what maintains trust. That's what maintains an ethical approach, and we just got to find a way for people to feel better, and that's a collaborative effort with a patient and the clinician.

Lance Glinn:

So, Amit, as we wrap up our conversation, what does success look like for you? Not just in terms of FDA approvals or trial data, but in terms of real world change that you hope Alto creates?

Amit Etkin:

So I would love just as a first step to create that first medicine for the very first time in our field, which is guided by actually understanding what's going on in the person. That to me would be a career high point. But then if we can get there, the next step is truly transforming where we are as a field, where we're not just waiting two months before somebody gets better and you know it or not, and you throw some other random thing at it, but we're faster, more purposeful, more effective, and we no longer think about psychiatry as this antiquated profession. I spent my training years in psychiatry feeling that even from other physicians, just as you walk around the wards, you feel it. I really want that to change. I want people to understand mental health is also brain health, and that we're going to finally have the tools to intervene.

Lance Glinn:

Well, we appreciate all you're doing for psychiatry, for mental health, and we thank you so much for joining us inside the ICE House.

Amit Etkin:

It's my pleasure to be here.

Speaker 1:

That's our conversation for this week. Remember to rate, review, and subscribe wherever you listen and follow us on X @ICEHousePodcast. From the New York Stock Exchange, we'll talk to you again next week inside the ICE House. Information contained in this podcast was obtained in part from publicly available sources and not independently verified. Neither ICE nor its affiliates make any representations or warranties, expressed or implied, as to the accuracy or completeness of the information, and do not sponsor, approve, or endorse any of the content herein. All of which is presented solely for informational and educational purposes. Nothing herein constitutes an offer to sell, a solicitation of an offer to buy any security, or a recommendation of any security or trading practice. Some portions of the preceding conversation may have been edited for the purpose of length or clarity.