Episode Transcript
[00:00:00] Speaker A: Having to wait two months until you know that a treatment is going to work, that is not good. We accept it because that's unfortunately the reality. But that's not good.
[00:00:13] Speaker B: Well, Amit, thanks so much for being on the show with us today for the audience. Amit is the founder and CEO of Alto Neuroscience. He's also an adjunct professor at Stanford. And we're really excited to have this conversation about his journey at Aalto, as well as some of the research he's leading with Precision Psychiatry. So, Amit, thank you for taking the time.
[00:00:30] Speaker A: Always my pleasure.
[00:00:32] Speaker B: A way I'd love to maybe just start here is just give us maybe a thumbnail sketch of your career and how you ended up in this situation here.
[00:00:38] Speaker A: So I'm an MD, PhD, psychiatrist and neuroscientist. I'd spent my whole career essentially being an academic, thinking that being an academic is actually the one and only calling there is in life. I did my MD, PhD at Columbia, worked there in the lab of Eric Kandel. I was there when he got the Nobel Prize, to absolutely no credit of my own, but it was a really awesome experience. He actually the psychiatrist and a neuroscientist himself. So that was a lot of my early neuroscientific, but also broader application and broader perspective education.
Came to Stanford to do my residency, went on faculty at Stanford, actually even as a resident.
And then from 2010 until I left to found Aalto in 2019, I led a lab at Stanford. And that lab essentially evolved over those 10 years to try to understand how we can cleave psychiatric disorders biologically in ways that are more useful. Initially, when the whole field was basically studying a disorder relative to healthy people, we started looking across disorders and seeing what might be shared or distinct across disorders because we define disorders by symptoms and nobody really looked across them. Then once we did that for a bit, then we looked within a disorder and looked across people and said, well, what differentiates people who have the same diagnosis over that time really evolved to understand what the opportunity is in psychiatry because, well, as a field, there's no shortage of opportunity because there's a huge need. But you have to know how to approach it. You have to know where the science is, how mature something is. And then only much, much later did I realize I actually wanted to start a company around it. That part was never really part of the consideration until maybe around 2017, 2018. I'd already gotten tenured then full professor, had a NIH director as pioneer Award. So all the academic plaudits as it Were, but, but it's only then, in the maturity of the science and seeing the opportunity in the broader world that I realize actually it's time to leave all that. Leave a tenured faculty position, which was not a decision to be taken lightly, especially at a place like Stanford. And take a risk and start a company.
[00:03:04] Speaker B: Yeah, and maybe take us into that. There must have been something really compelling in the way you were seeing the opportunity.
What was so powerful that you had to go do this?
[00:03:14] Speaker A: Yeah, so. So a couple of things. Just career wise, I achieved a lot of the stuff that I'd wanted to achieve and I was at that point being looked at for chair positions, and that didn't seem like the direction to me that I wanted to go. I wanted to keep pushing the science and, and see how far we can push it. But the, the most important part of that is what I call actually the last paragraph effect. So the last paragraph in a paper is often what you really want to be doing. Like the implications of this work are A, B, C, D. And that's the future direction. That's what you're really excited about. And I came to appreciate that Just the nature of how science is structured in academia, the nature of the funding, the nature of the kind of work you could do, limits you to just speculating about that last paragraph and what could be done going forward. Whereas doing this as a company, bringing the capital, the expertise and the much broader lens to solving the problem here, which is improving treatment in psychiatric disorders, that actually addresses the last paragraph that allows you to actually say, you know, those things I said we're going to do, that's what we're doing.
[00:04:25] Speaker B: And maybe take us into that thesis a little bit. What is in that last paragraph that you're exploring now.
[00:04:30] Speaker A: So let's start with actually where we are as a field, just to kind of level set. Right. So where we are as a field is all of our treatments we're discussing discovered initially through serendipity. Serendipity is not random chance. It's people paying attention, seeing things that are happening, taking advantage of them, and then iterating to improve on whatever tool they had. Right. But that means it's not really purposeful in the sense that you don't know what you're looking for and you can't develop new threads, new interventions, new diagnostics just through serendipity. As a consequence, all of the treatments that we have right now are given entirely through trial and error. And as a psychiatrist, I can attest to that. Frustration. Frustration for Patients frustration for clinicians simply not an effective way to go when most of the people in any disorder you give any treatment to will not respond to that treatment. That's not good. We need to actually sit as a field with that fact and be not like, accepting of that as a reality. It's not good. Right. Having to wait two months until you know that a treatment is going to work, that is not good. We accept it because that's unfortunately the reality. But that's not good. And so for us, the last paragraph was all around, how do you use biology that's robust and meaningful and specific enough at the individual place, patient level to actually decide what kind of treatment should that patient get based on their brain.
[00:06:02] Speaker B: Right.
[00:06:03] Speaker A: The brain profile. And most importantly, to use that information to develop entirely new treatments that aren't even remotely on the market. New mechanisms of action that allow you to treat new people in much more specific and more powerful ways. Thing is, it's not psychiatry that this is first being explored in this kind of approach. Precision medicine has been employed in oncology to tremendous success. It's just new to psychiatry. But that's what, to me was really exciting at the time, just seeing the science evolve and replicate and become really robust. Where we knew we could repeat it to now say, all right, we have an anchor. Let's now jump off from that point and say, how far can we reach? Where else can we go? And then try to set up a company that is balanced enough to work as a company, knowing the risks of drug development, knowing that we're doing something new in the field, but that in a reasonable amount of time we could find that success.
[00:07:07] Speaker B: I love that you're starting to paint a picture between applications in kind of maybe treatment planning and applications in discovery. Are there other kind of guiding frameworks you would use to help us better understand just the landscape of precision psychiatry in general, or even what is precision psychiatry? And then maybe let's dive into each of these.
[00:07:25] Speaker A: Yeah. So actually let's just define what that term even means. Right. Because a lot of people can use that term in different ways. So in the way I'm using precision psychiatry, it's much like precision medicine is used in other contexts. That is knowing something about a person.
In oncology, it's genetics, that's not really the case for psychiatry. But knowing something about a person that tells you about their biology, that lets you find a treatment that works better for them because of that biology.
Right now that doesn't exist in the clinic. This is a new concept as I Said it's not a new concept entirely. Right. It's precision medicine. Precision oncology, immunology, there's other places that leverage it highly. But in the clinic right now, it's a new concept. We don't do any tests in psychiatry short of like routine lab tests to rule out, you know, you have low thyroid when you're coming in as a depressed patient.
But the kinds of tools that we have available in research, if leveraged the right way, and if you think about the scalability of those tools from a research context to a clinical and commercial context, actually make that jump a lot easier than we think. So there's this constant sort of theme that I've seen in the literature in the past, which is that all of these kinds of applications are very much in the future, 20 years from now, maybe X, Y and Z. I actually don't think that that's the case. I think we underestimate what we can actually bring to practice now.
But if we don't think about it in how we do the R and D, it's not going to connect to the commercial and the clinical use. That bridge is there, should we want to cross it. In some ways, precision psychiatry is more of just going after this now that we have that scientific base and wanting to do it more than just there's been a fundamental new discovery and now it opens that door. It's almost a mindset in approach more than it is a completely unanticipated discovery.
[00:09:27] Speaker B: And you're starting to hint at maybe the, the dimensions that we should be looking at from a biological perspective. I'd love for you to just expand on, like what are the different biological markers or different biological analyses we should be doing in the context of psychiatry.
[00:09:40] Speaker A: So I'll tell you what we do. I'll generalize that a little bit. First to say there's almost nothing that's not good. There are things that just in practice may work better than others. So oncology uses genetics. You can learn a lot from genetics. It just doesn't really in practice explain a lot of the variability between people to be used at the individual patient level in psychiatry, like it can in oncology. But measures of the brain, especially measures that could be made simply scalably in somebody's home, those have a much bigger impact. And that's where we've anchored. So things like EEG or electroencephalography, brainwave recordings, how they kind of are generally known, that's a way to directly measure brain activity non invasively. And you can even do it In a home, you can even send somebody a piece of equipment. They can do it on themselves, guided by software. We're already doing all of those things. So that gets you direct measurement of brain activity, but also indirect measurement of brain activity in somebody's behavior. How they do a cognitive task, how they make decisions, how they learn from reward or from punishment, how they behave throughout the day is measured, for example, by a wearable, their sleep cycle, their activity pattern and so forth. All these things, you know, measure essentially index brain activity, but they do that a little bit more indirectly. So those are the suite of tools we use. Other tools, data we also collect, but I think are just a little bit tougher are things like blood measures. Blood measures are a little bit distant to the brain because the brain is in its own protected environment. CSF is, you know, is sort of its milieu and. And it's protected from access to the brain, to the blood. And so that creates a bit of a challenge in getting blood measures to really be useful. That said, who knows? There may be some interesting things there too. There's people. The things people are doing with video processing, audio processing, reading, using machine learning, and all the large language models that have been developed, text that people write or they say. There's a lot you could learn about psychiatric disorders and schizophrenia, that even how people talk is diagnostic in a way. So all of those tools, any of them could be the right tool. There's no, you know, natural you're in or you're out. It's simply taking that mindset of I need to be measuring these things, right? I need to understand how people differ. And I need to use that to systematically separate my population so that I know and replicate and make robust. The ability to separate from people will respond to a drug. People who don't, by the way, it could be an existing drug. That's not where we focus. But that's certainly a valid use of a precision psychiatry approach or a new drug. And if you really understand that process and you really understand the biology that you're measuring, you can even take that back to animals and you can drive discovery and you can understand from humans by measuring biology, how you go back and create entirely new drugs. So that's why I say it's sort of a mindset that there's almost no wrong answer. There's just, for us, the answers that we think are more practical and more scalable. Today, we're always looking for new technologies we could leverage in our work as we go.
[00:13:05] Speaker B: So it sounds like a large component of this is going from, let's call them maybe the traditionally subjective measures of psychiatry to more objective inputs that you can make decisions on. It sounds like you're making a big bet on brain measures. Could you maybe walk us through a tangible example of a brain measurement that might lead to a specific decision path you might take?
[00:13:29] Speaker A: I'll give you an example of a trial we have going on right now which illustrated actually a couple of different elements of this, which is even the discovery process itself. We're developing a drug called Alto 300, also known as Agomelatine. It's a drug that we're developing adjunctively for depression. So people who failed an antidepressant, you add that on top. It works completely differently from every other antidepressant out there. It also happens to be an antidepressant that's already approved in Europe and Australia. So we know it's an effective antidepressant. But like all antidepressants, it's only just slightly better than placebo. And the question is, who really benefits from that? And so we're developing it here for the United States, where it's not an approved drug using an EEG biomarker. So then you ask, well, in that EEG biomarker, what have we learned? Well, to start with, we didn't know what we were looking for. We knew that EEG has given us biomarkers for a lot of different treatments, be they standard of care or new mechanism. So we knew the EEG could deliver that to us, but we had to discover it out of the data. We leveraged a machine learning approach that we've been refining for years, since Stanford and doing a lot more since at Alto, to discover out of the data what is the signal that gives you that information.
We then discovered in a set of people, we had a completely different set of patients to show that that signal actually replicates and finds the responders to the drug.
We had placebo and we had standard of care data to show that that biomarker doesn't predict outcome for other things. It's really specific for that drug by nature of the machine learning model, where again, it's doing the discovery for us, able to simplify the signal. So it's actually signal that you get from a single electrode. So the ability to capture this, if you think of the scalability in the home diagnostic and kind of easy clinician diagnostic use, if you're calculating it from a single electrode, it's pretty exciting. It also is a measure that we've now been able to reverse Translate back to animals because it's a simple measure.
And so we're understanding a lot about the biology of the people who respond better or worse to the drug, to the point where we're even getting to molecular biology. By measuring eeg, we've discovered out of the data themselves something we would not have anticipated. But by showing it replicates, we know it's robust and specific and we've sorted out the path to scalability, again, that simple acquisition. So that takes a drug where, you know, it's an, it's an interesting different mechanism. But now if you can target it, especially for people who don't respond to standard of care treatments, becomes a really exciting new clinical opportunity. It's in a phase 2B trial. Now if that trial is positive, we move right to a phase three. We're talking about near term changes in clinical practice, potentially assuming that this continues to progress through all doing precision psychiatry, all discovery through machine learning, but all biologically informed in ways that one couldn't have anticipated if you were just taking a clinical lens at it. That's the objectivity that you mentioned and the fact that there's signal in the data that's robust, that's mineable and meaningful.
[00:16:47] Speaker B: I'm just too curious. When you start to peel apart the layers and you go, okay, for this specific subsegment, how much better is the kind of response rate you mentioned on average, a little bit better than placebo. But when you really start to kind of dial this in for the folks who this could be a good fit for, how much better does this look?
[00:17:03] Speaker A: Yeah, so broad strokes across the different programs that we've seen. If you go from an all comer population, that is everybody, let's say with depression in an unselected way, to a targeted population who by the way is still a sizable portion, 30, 40, 50% of the overall population, you're doubling the, the impact of the drug relative to placebo. The, the difference between drug and placebo, that magnitude doubles. That's a really big difference. And it's a really big difference actually for two really important reasons. Two separate reasons. One is the drug development reason. You're much more likely to be able to find a successful outcome if you have a big effect to be working with. And then that allows you to reduce the number of patients, to go faster, to make the whole drug development process more effective. But also clinically, it tells you sort of the same coin from two sides tells you two really important things. One is this is a drug that's likely to work for you if you're positive for that biomarker, really important. Right. But equally important is if you're negative for that biomarker, maybe this is not a drug you should waste your time with because you're not likely to respond either. Pieces of information really useful for a clinician and actually very valued by a patient as well. For, for obvious reasons. Right. People don't want to go through the frustration if they're not going to respond. And they certainly want to prioritize the things that they are going to respond.
[00:18:24] Speaker B: And both you and Jordan have been talking about how the kind of the current paradigm of depression treatment is trial and error where you try something and 50% of the time it doesn't work. And how frustrating is that to kind of wander your way through as a patient?
[00:18:37] Speaker A: Hugely. In fact, a lot of people just simply drop out of treatment.
I mean, think about it, right? If you're in the patient seat and you're coming to your doctor and maybe you have other medical problems and your other doctors do tests and they tell you given this profile, maybe it's a metabolic disorder and we're talking about do you get treated with a statin or some other drug for some other aspects of your metabolism, or you take a GLP1 or any of these things to fine tune what's driving your metabolic disorder, let alone cancer, which is a whole other world. But even I think most people would have, certainly most people probably my age would have an understanding of managing lipids and cardiovascular risk and so forth. There's a lot of data that comes into that. Now you go to your psychiatrist and you say you're depressed and, and pretty much any set of symptoms will work the same because the diagnosis doesn't really depend on the specific set of symptoms. And then they'll just pick something essentially based on what they think the side effects might be and maybe what your relative, maybe your parent or maybe you responded to in the past like a very loose information.
That's a big difference in how people even see their clinicians, the faith that somebody would have. So now imagine you failed a couple of treatments, right?
What's your sense in that psychiatrist? Obviously they're really well intentioned, they want you to get better, but they don't have the tools. Some people will just drop out.
They'll start to compensate by doing things like drinking or using drugs more and they'll try to self medicate all of these things. I think speed speak to the limitations that we have in our clinical practice that lead people to either Never get diagnosed, not go through a full treatment course, not keep trying drug after drug until they respond. I mean, that's just not good medicine. Yeah.
[00:20:44] Speaker B: And certainly a trust eroding moment to kind of feel like your physician is almost poking around in the dark with you without a concrete plan. I'd love to go under the hood a little bit on the discovery process. As you mentioned, Alto 300 approved in Europe and Australia.
How did you even end up in a situation where you started saying, let's go test this EEG thing in the US and see if this is a viable path?
[00:21:08] Speaker A: So we started in even a broader sense, trying to understand which drugs we should even bring into our pipeline from the same perspective, whether it's Alto 300, 203, 100, 101. We have a number of different drugs with different mechanisms, starting with the perspective of what can we measure from a biomarker lens. So think of this from an engineering view, right? So there's this idea in engineering that you can't manipulate that which you cannot measure, which is sort of a classic dictum, Right. So if you can't measure a biomarker that's relevant for that brain system that the drug manipulates, we understand a lot about that. From an animal pre clinical perspective, what does a drug actually do to the brain? Which parts of the brain which functions? If you can't measure something relevant to that, you have a hard time developing it. We basically started with a circuit brain circuit perspective of taking apart which are the key circuits that we think are most important to attack across a range of different diseases, and then mapping onto them biomarkers to measure them and onto that potential drugs. So Alto 300, for example, has two effects. It's actually a drug with sort of dual action. It stimulates melatonin receptors, which we know about circadian rhythms, we can measure that and it blocks a serotonin receptor called 5ht2c, which leads to an increase in dopamine and norepinephrine, noradrenaline in the brain. And we know how to measure that. And actually EEG is a great measure of both of those. So we knew that EEG could be one of the tools, wearables, could be another tool to tell you about circadian rhythms. So that fit that profile of note, there's things that aren't in that profile, things like social neuroscience, interacting between people that might be important for autism. I'm just not convinced we have great biomarkers for that yet. That's a field that is certainly evolving. That's not an area we prioritize that gives you a sense for how we pick the drugs, the biomarkers, and the approaches. But then you have to take that next step and say, how do I design a study that tells me, as a go, no go, did my biomarker pick up a signal? And so that's where we designed the study, where we're training a machine learning model, having separate data that we're replicating it on. And if that had failed, the drug would not go on. It has to be something where we can leverage insights through the biomarkers that allow us to de risk the development. Otherwise, why spend time and money on that program when we have several other programs, all of them in phase two right now in the clinic that you'd find, frankly, less risky and therefore spend the time and money on?
[00:23:48] Speaker B: So I'm imagining you have. And it's probably not a table, but conceptually a table of potential candidates with moas that, you know, mapped to biomarkers that could be predictive or could tell us something about those moas. But this is still a very long list. How do you whittle it down?
[00:24:02] Speaker A: So that's where we actually did a lot of work while we were in stealth, figuring out for each particular molecule, is this the right molecule and do we know why? Do we have evidence that says, rather than just generally it works on this mechanism, but specifically this molecule, does it do something that we're looking for? Let me give you another example, Alto 203, which we're also developing in depression, aiming at patients with anhedonia. Why are we doing that? Alto 203 is a drug called an inverse agonist. So it blocks the activity and reduces the kind of basal tone of a histamine receptor called H3 has nothing to do with the role of histamine, for example, an allergy, totally different receptor present only in the brain. What H3 does regulates a number of other neurotransmitters. And for us, the neurotransmitter of interest was dopamine.
So if you block H3, you should lead to an increase in dopamine. But where we really wanted to see dopamine increasing is in the reward system. Low dopamine in the reward system has been linked to anhedonia. Lack of motivation and pleasure to see in depression, you see in schizophrenia.
And so the question at hand, if you had that mechanism conceptually, is do all H3S function the same, or do you need to find the right drug that has the right profile? And actually, it was the latter. So we went through a bunch of different drugs, different candidates, working with different pharma companies, looking at their materials to figure out which one we wanted to acquire. This one actually had data in humans that after showing in animals that it drives dopamine release in the reward system in humans, giving a single dose of this drug made people feel better subjectively, acutely with a single dose, which is a really unique profile. That's actually what we're testing now in patients with depression is whether a single dose makes them feel better.
When you put all that together, you can see that we understood the biology, we know how to measure it, various biomarkers related to the reward system, electrophysiologically or with behavioral tests. We specifically found the drug that had the profile that we were looking for and that we knew that based on an early human study that if they hadn't done, we would have done and been able to show that, yeah, it looks kind of like what you'd expect from an animal study showing dopamine release. So that we've done it writ large. Right. It's not just the mechanism, it's not just the brain system. It's then honing down. Is, is this the right molecule? Which attributes does it have as a drug that allow us to develop it in a really purposeful way and then put the, put the gates in front of ourselves that we have to actively pass through. Not just kind of go blindly to some large phase 2 study before you find out that there's efficacy, but actually go through, show that a biomarker works, show that a biomarker changes in order to de risk before you get to the final big efficacy test.
[00:27:10] Speaker B: Now, when you, when you lay it out like this, it seems also logical and rational and not easy.
[00:27:16] Speaker A: A lot of work, A lot of work, but logical and rational for sure.
[00:27:19] Speaker B: Maybe. Talk to me about what makes this particularly challenging.
[00:27:22] Speaker A: So the core issue that we started with, right, is that people have not done a precision psychiatry approach. So let's think about this from even very practical terms. If I'm doing an EEG study in my lab at Stanford, I have my staff in the lab. It's one location, one set of equipment, pretty straightforward, even there, like there's ways to improve that, but pretty straightforward. Now, I want not just another lab or two that's an academic site, but I want a bunch of clinics and research sites at the scale of dozens to acquire the right data at the level of quality that I would be happy with. To let me make a judgment on a biomarker Moreover, I want it to happen in near real time, at scale, blindly, so that nobody who's acquiring the data knows the biomarker status. And I can do this all on the back end so we can run a really rigorous study. Just doing that is years of learning in terms of how to collect the data, how to write software that helps people who've never touched EEG until you walked in their door and wanted to do the study with them, collect really, really high quality data consistently. And so we spent a while just figuring out literally the logistics of that, the procedural aspects, the writing of software that does real time quality control and gives feedback to the sites and that informs us whenever something is going on so that we know how to troubleshoot and then gives them access to us to help whenever is needed. And now we're doing that in over 100 sites across the country in our trials. That process is part of what makes this hard.
And then that process though, if you get it right, not only allows you to do the right trials, but gives you that immediate jumping off point to clinical care. So, for example, we've already in our studies acquired data in patients homes. We've already started working with systems that are lower electrode count, so what's called a smaller montage, just fewer electrodes that a person can put on themselves, guided by software that essentially will do real time quality control and make sure they come out of that session with a quality control pass recording. Now you can see how scaling the R and D and then reaching towards the edge and thinking about what clinical care looks like ultimately makes it a very planful process. But where you need a ton of expertise, a huge amount of just trial and error and experience doing this to actually make it happen. Once you have that now you can look at different drugs, different disorders, it becomes actually quite a bit more scalable.
[00:30:14] Speaker B: Are you seeing others start to piggyback off of the infrastructure you're starting to build? I think a little bit about J and J building the infrastructure around these ketamine clinics. Are you seeing others starting to piggyback off of some of the work you're doing to make it easier to collect biomarkers up front?
[00:30:27] Speaker A: In practical terms, they can't piggyback off of our infrastructure because it's proprietary, but we are certainly seeing a lot of interest in following that path. We're seeing a lot of other companies, whether they're actually doing precision psychiatry or not, message around it, and that's encouraging. So, you know, I'll tell you an anecdote that I think Puts it into perspective. We came out of stealth in the fall of 2021 and at that time we founded the company in 2019. So we spent two years in stealth building in our pipeline and these tools and so forth. At the time we debated do we even use the term precision psychiatry? Because nobody was using it. It wasn't even like a concept that was in the kind of miasma. But we decided, look, this is what we're doing. Let's own and define this term. Now it's become a hot term. But in that, you know, it's like two, three years essentially since that really, since we came out and, and hopefully had a hand in showing that this is a really important path forward. To me that's really nice validation. I also want to stay ahead of everybody else who wants to come into the space. Right. That's part of the, the challenge and the opportunity here is leading a concept and approach while still having the advantage as a first mover and as a definer of the field. But that to me is positive, right? That there is an understanding that we have to do different and that this is the approach to doing that. And it's scalable not just in terms of how to scale it to the people, but scalable across mechanisms and disorders, that this is where we should evolve as a field.
[00:32:08] Speaker B: It certainly does feel like, at least in the zeitgeist, precision psychiatry is starting to be mentioned more and more. What do you think is special with this point in time that is garnering the interest?
[00:32:20] Speaker A: Maybe two things. One is just the depth of frustration in the clinic that we're really not moving anything forward. I think that frustration started in the mid-2000s where we had studies like STARD come out which were large practical trials sponsored by nimh, before which there was just a lot of drug company sponsored trials which were in kind of idealized non real world patients. And when you actually did this in a real world population, you saw just how bad things are in a way that every clinician already knew. But it made things plain as day. And that sort of started the slide of, of confidence, of optimism for a while. And pharma had gotten out of psychiatry because a bunch of drugs had failed because they weren't taking a precision approach. They didn't know how to develop them more purposefully. And the only solution was, well, look, oncology looks great. Let's go there. Let's just get out of psychiatry. That brought us into the 2010s. There's been a bit of a revival in psychiatry.
Drugs like carxt, Cobenfi, now that were just approved as a new mechanism, but they're few and far between. And that's a drug that's been around since the 1980s in various stages of development.
So we're at this point where clinicians are frustrated and realizing there just has to be a different path forward.
Developers, I think, are looking for new opportunities because there's actually no shortage of new mechanisms, new molecular entities for new targets that are coming into humans. And we're seeing that if we don't do development in a different way, they're going to fall off the same cliff that everything else has fallen off. Because structurally, you just can't go after these broad populations and get anything like a consistent approach, a consistent response.
So those things coming together, together maybe with things like machine learning and AI, I mean, not talking about even the large language model chatbots, but the idea of mining data, maybe some around computational power, but I don't really think that that's a big factor here. It's kind of the confluence of opportunity with new molecules, together with frustration that the status quo has to change, and.
[00:34:39] Speaker B: The way that we do these trials and the way that we approach development.
[00:34:42] Speaker A: Has to change, and the way we treat patients at the end has to change.
[00:34:45] Speaker B: Now, Ray Sanchez, I was just speaking with him about his experience over at Cereval, and he was saying that if he had one wish in the way that we do development, it would be for more objective outcome measures. We've spoken a little bit about predictive measures. How do you think about outcome measures? And does that fit into the framework of precision psychiatry, or is that something entirely different?
[00:35:03] Speaker A: Yeah, 100%. So let me actually illustrate that with one of our programs, and this is one in schizophrenia, looking at cognitive impairment in schizophrenia, which, by the way, is an objective measure to begin with. Right. Cognition is something you can actually measure as opposed to asking people how they feel. But this is an area with no. No approved treatments at all. Very few treatments, even in trials right now, everything that's been tried has failed for a variety of different reasons that, you know, may or may not be relevant to how we're developing our drug. So I'll tell you how. How we're approaching it from exactly the question you asked. So if we think about the drug's action, well, you think of ultimately you're changing cognition. That takes a while, and you're changing people's functioning. Because cognition is the thing that most impairs and most leads to disability in really patients with any kind of chronic severe Mental illness, But schizophrenia first and foremost, if the brain is your immediate target, you should be able to measure, using tools like eeg, the function of particular circuits that tell you is your drug on the right path? And that's the first thing we did. We gave different doses of the drug, single doses. And this is a drug that enhances neuroplasticity, works downstream of the synapse, which is really where treatments have in the past focused for cognitive impairment and schizophrenia. And we gave placebo. So we looked at people basically taking one dose, being their own control. Sometimes they take placebo, sometimes they take the drug.
Do measures in the brain with EEG change, and we saw that not only did they change across numerous measures, but we understood the dosing of the drug based on that information.
We then did a study of patients with schizophrenia and asked of all of those different EEG changes which are actually the most relevant in a reproducible, consistent way for their actual cognitive impairment and their dysfunction in life.
That told us that a particular, less well studied measure with the eeg, one that our drug had moved quite well, was actually the thing that in patients is most clinically meaningful. Now we put that together in a proof of concept trial in patients with schizophrenia where our target is that EEG measure that we've shown as a dose response effect by the drug that we've shown is most relevant to patients clinical state as the immediate target of the drug. And then from there, looking at cognition over time, that task itself is actually a passive task where people are listening to tones and it's a simple brain response to an auditory stimulus, which we know is perturbed at multiple levels. In schizophrenia, a lot of their cognitive deficits boil down to a lot of sensory deficits as well.
But that easy to acquire, passive, quantitative, objective, dose related, clinically relevant measure, that's not going to drive approval of the drug, but it's going to allow us to know how to develop it. And that takes a lot of subjectivity certainly out of it.
[00:38:22] Speaker B: It must be difficult to be blazing the path on new endpoints as well as new ways to kind of do these input measures or diagnostic measures. Like how? What does it take to be identifying and approving, proposing new endpoints like this?
[00:38:38] Speaker A: So here we're not thinking of it just from a technical perspective as an endpoint that FDA would have to be to approve. It's an endpoint that helps us develop the drug and know how to get us in a position where cognition and clinical outcomes are changed. But if you don't go through It. I think it becomes very high risk to know whether you're ever going to affect cognition and their clinical just, well, being at the other end. But I think, you know, to take your question a bit more broadly, right, there's a knowing that you, based on your best knowledge and best intentions, are on the right path and not letting naysayers certainly affect that while still listening to sources of skepticism, including our own, but knowing that you need to do things differently and that you've reasoned through why you're doing things and that you are on the right path. And yes, it's hard, but there is a reason we're on that path and then finding people around you. We've been able to hire phenomenal people at Aalto. I think our science, just from a pure work that we are doing perspective, has been much better at Aalto than what we could ever have done in the lab at Stanford, because it's a very focused effort with a lot of bright minds all around it, specifically for that purpose, organized as a team where everybody's really working together. It's different from an academic lens where people are kind of working on their own projects and going in different directions.
So if we feel that we're on the right path for the right reason and we have the right people and we're constantly checking ourselves, it's actually really exciting. I mean, it doesn't. It feels hard on the one hand, but it feels incredibly fun, incredibly rewarding because we are literally discovering things out of our data, out of other data, out of our kind of understanding, and changing our approach and mindset every single day. So you feel like you're at the frontier and yes, that's hard. Yeah, but it's also really rewarding. It's also really, really exciting. That's why I got into science in the first place, is to figure something out that nobody else had figured out and then go the next step and the step after that. So that's a. You know, you become an addict in a sense, to.
To knowledge, to insights, to breaking through the frontier. And it's been fun.
[00:41:05] Speaker B: A lot of the conversations we've had with CEOs, founders, this theme has come up that if it wasn't hard, it wouldn't be worth doing. And if it wasn't hard, it wouldn't be an opportunity in the first place. So over the course of this conversation, you've taken me through this path of precision psychiatry as applied to the clinic and in practice, treatment planning, outcome prediction, and then ultimately outcome measures. I'm curious, you made the switch into industry and, and being a founder here because you wanted to affect some change, because you believed it would be faster to do so this way. How are you anticipating the landscape moving if everything goes well in the next five, ten years?
[00:41:40] Speaker A: So, look, I'd love to see in the near term the first precision therapeutic approved. I want us to be the first to cross that line. That is a historical moment. If you just sort of think of the weight of history on, you know, our shoulders and others who might want to do similar things, the opportunity to show that this approach works for all the downstream effects. Right. But that opportunity to take a drug through approval to market, that is a career and life goal for sure. Right. And I want to see that happen. I want to see it in the near term.
I think the broader landscape becomes much more open for people thinking and doing things differently.
And I feel like my role, in fact, has been in part to help that happen. So I spend a lot of time with early stage founders focusing on essentially neuroscience applications in psychiatry, trying to bring them some perspective and experience. We've now taken the company all the way through going public. So there's a whole arc of just company creation, helping them understand how the research that they're doing in the lab could actually benefit people. And how looking at it not as an academic exercise, but as a company building in world influencing and shaping exercise could be really beneficial to all around.
I think, I hope that if we have these proofs of concepts, drugs that work because they are targeted, we unlock imagination.
So we want to be the leader there. And that's why we have a lot of drugs in the clinic at different directions, because we want to capture as much of that as we can and really affect the change as fast as we can across multiple different fronts. But that unlock from an imagination perspective for the field, for young, a postdoc or a faculty member, grad student, even thinking about how they can take something that they found and really take it to scale. That to me, is the most exciting element of all of this. And we've seen that in other areas. Right. The analogy that I think of most readily is things like electric vehicles. The technology is really not that complicated, but it's the wanting to put it together and putting it together in a nice package that companies like Tesla have done a phenomenal job doing. And they were all alone. And then they showed the path for everybody else to start doing it. And now it's unlocked a lot of really interesting opportunities. Vehicles of different sizes and scales and different application and the world thinks about things differently. Not all of that was one company doing everything. And it shouldn't be one company probably doing everything. So that is what I'd love to see happen. Let's call it in the next five years maybe. I think we could probably see that happen 10 years at most. If we're successful, there'll be a wave of new innovation and really excitement around an area of really prevalent diseases that have very, very few options for them.
[00:44:46] Speaker B: And there's certainly something historic about setting precedents.
[00:44:48] Speaker A: I mean, that's why we do this, right? Is when you're at that frontier and you actually accomplish something, it is meaningful.
[00:44:57] Speaker B: I mean, I'd love to hear, maybe just as a closing thought, if you had a magic wand, you could change anything about how psychiatry is practiced or developed today. What would you change?
[00:45:05] Speaker A: I think more openness, honestly, to collecting data for data sake.
It's one of the things that we have most limiting for us as a field is just literally getting information. There's so many clinical trials that don't collect any biology. There are so many patients who come through so many clinics where no information is collected. If we were a bit more collaborative, cooperative, if we saw the opportunity as a field to even just do some data collection, I think we would accelerate progress tremendously. It's very frustrating to have to start from scratch to collect data.
And I saw this at Stanford where when we were bringing patients into our studies, they weren't Stanford patients, they were coming into the clinic. We'd had to recruit them from elsewhere. There just wasn't the infrastructure and the orientation of mind to collect that data. And in oncology, you go to a university because you want to have access to the research. If you're not responding to the first line drugs, right? In psychiatry, that's not the logic. I'd love to see that changed and that could have huge accelerating effects all around.
[00:46:15] Speaker B: Well, Amit, thank you so much for taking the time today. I really appreciated the conversation.
[00:46:19] Speaker A: It's been a pleasure.
