Episode Transcript
[00:00:05] Speaker A: Welcome to the Few and Far between podcast. I'm your host, Chris o'.
[00:00:08] Speaker B: Brien.
[00:00:09] Speaker A: Ralph Waldo Emerson once said, it's not the destination, it's the journey. But what if it's both? Today's guest ventured beyond academia into the wild and woolly world of biotech. And his destination has resulted in FDA Fast track designation. Amit Etkin is the founder and CEO at Alto Neuroscience and professor of psychiatry at Stanford University.
By following the science, Amit has reintroduced biomarkers into the field of precision psychiatry, targeting cognitive deficits and schizophrenia for the first time. On today's episode, we'll talk about the problem of placebos in psychiatry, biotech culture, and what it's like white knuckling your way through the CEO experience for the very first time. Plus, we'll play our new lightning round, fast questions and bold answers from our podcast guests.
Also, don't forget to check the LinkedIn comments for Amit's favorite book, insights on mentorship and more. Okay, let's start the podcast.
Amit Etkin, welcome to Few and Far Between.
[00:01:07] Speaker B: Glad to be here. Looking forward to the discussion.
[00:01:10] Speaker A: So I think I saw some news today from you guys. You have a pretty exciting announcement. Will you tell us what it was?
[00:01:18] Speaker B: Yeah. So today we announced that we got fast Track designation for Alto 101 in schizophrenia, and in particular for treating the cognitive deficits in schizophrenia where there are no treatments. And it is really the perimeter of the disease. So super exciting.
[00:01:34] Speaker A: So what was that conversation like as you sort of worked your way through the process with the agency?
I gather from folks who I've talked to before that if you're coming into a space with no alternatives, they're sort of very eager to listen carefully. And hopefully, if they like your data, fast track is a more easy, easily achievable thing. Is that right? How does that all go?
[00:01:56] Speaker B: Yeah, you know, to be perfectly frank, our interactions with the agency have been very good consistently. And I found that they're very much willing to listen, very kind of driven by what you're presenting to them and the arguments and the data. And, you know, they. Everybody comes in with some sort of bias, of course, in life. And that, you know, FDA is not any different. Still people there, but they've actually been very open to the importance of taking a biomarker kind of precision approach, the importance of using that to address areas of tremendous unmet need.
And when you put all that together, there's a lot of opportunity. And in schizophrenia, we have a lot of antipsychotics that treats the positive Symptoms of delusions, hallucinations and so forth.
But it does nothing for cognition and cognitive impairment is. It starts early, it's there throughout life, it drives disability.
So the argument for the need is very clear. And then the data that we've presented thus far on Alto 101, including in humans, suggests that it's on the right path. So a lot of work obviously to be done. This is just one step on the regulatory side, but it's the kind of thing that I think will get people excited about what the potential for a really high impact area could look like.
[00:03:15] Speaker A: Yeah, it's a tremendous shot in the arm, I'm sure, to your team and to you. So congratulations. We'll come back a little bit later maybe to talk about how you build that credibility with the agency. Because I wouldn't say everyone has that description of their experience in interacting with the FDA or other regulators. And so, you know, thinking that through a little bit might be useful, but let's, let's level set with people. So, you know, when we were prepping, one of the things that I found, one of the many things that are going to be fun to talk about today, you left a significant tenured position at Stanford to go into the wild and woolly world of biotech. Will you talk a little bit about what that decision was like and kind of what your motivations were?
[00:03:59] Speaker B: Yeah, so I'm an MD, PhD, psychiatrist and neuroscientist. I've basically spent my entire career in one form of academia or another, either training as a student, student as a resident in, in psychiatry, and then faculty and, and basically the whole time didn't think about being in a company or starting a company. I'd advise some companies, but, you know, I was very much put along that path of, of academia early on. I did my MD, PhD with Eric Handel, gotten a Nobel Prize and spent his entire, in a very rich academic career.
He's in his 90s now, building that academic base. And so, you know, everything had proceeded really well.
I was a full professor, you know, proper tenure at a place like Stanford, that's phenomenal. Incredible. Had a big lab, an NIH Director's Pioneer Award. So, you know, achieving a lot on all fronts.
But it's really the understanding of where the science was, what my hopes were for, where the science would be. And then in a way, what you're taught as a scientist is to follow the science, right? To kind of let your insights direct you as opposed to like where you think you'll get funding or politics or whatever.
And that's really. That was a big driver, is feeling like the science is at a point in maturity where simply to follow the science means to do something different. Now there's a lot of personal risk and kind of thought about what that's like that we can talk about, but just from a, like, maturation of the science, deciding like, this is really the next step and then understanding. And that was its own separate journey, personally, what it would mean to leave a tenured position at Stanford. And this is not at a time where funding was like a challenge. This is the heady days of zero interest rates and not knowing we're going to go into a pandemic, not knowing we're going to go into a bear market and inflation and interest rates.
That was, frankly, have not looked back with any kind of regret. But that was sort of the moment of maturation as a scientist and leader, I think, where I became open to doing something different.
[00:06:24] Speaker A: Fascinating. Okay, there's lots to unpack there. You're right in the center of the target of the kind of stuff that we like talking about on this podcast.
So let's talk first about the.
You said, you know, following the science, it was time to sort of build a company.
[00:06:40] Speaker B: Will you.
[00:06:40] Speaker A: Will you unpack that a little bit?
[00:06:42] Speaker B: What.
[00:06:42] Speaker A: What was hard to still do in the lab?
[00:06:44] Speaker B: Yeah. So in a typical academic context, you're recruiting relatively small samples of patients.
You're doing things that often are not terribly reproducible because of that. It relates directly to the speed and the structure and the nature of the funding and all these sorts of things. But one of the core assumptions for a precision medicine approach in psychiatry, regardless of how it's applied, that we felt like we had met, was that things became predictable and reproducible. And the moment you. So let me take a step back and say, well, what is precision medicine in psychiatry? What does it even mean? Well, we understand that people are different. We understand our clinical criteria aren't great.
They're kind of artifacts of the past. They're checklists of symptoms. You can have any number of symptoms and meet the disorder. A lot of clinical heterogeneity, a ton of biological heterogeneity.
And there's a lot of differences in how people respond to treatment. For example, framed in the context of depression, where we have a bunch of treatments. And of course, there's other disorders like we just talked about with schizophrenia, where there are no treatments for a certain element. So taking depression as a canonical example, while people differ in their response, it doesn't mean that you necessarily can design a precision medicine approach around it. Because if it's just noise, then you can't predict it and you can't build anything on top of that. Turns out it's not just noise, which is good.
We are able to start seeing signals, for example, for antidepressant prediction, for psychotherapy prediction. So psychotherapy talk intervention is just as biological as, as medication is, fastens biological circuits and we can see it replicate, we can see it generalize. And that really told me that there's a there there for our ability to find these differences and start anchoring on objective biology.
That's really what the turning point in the science was because the implications are twofold there. One is if you stay in academia, you're limited to the kinds of things you can study in people in academia, which is treatments that exist now. We'd started to play with tms, with brain stimulation treatment, non invasive brain stimulation treatment, try to personalize it. I think that field is going to have a much, much harder time personalizing because intervention is not as well understood. So that was an attempt to do some treatment development. But for the most part you're studying medication, psychotherapy, brain stimulation, whatever the case may be, that's already out there.
What you're not able to do is develop entirely new treatments based on that core science. The only place to do that is in a company.
Yeah. And so saying the core tenet here that differences in outcome are predictable and marrying that with frankly my own training as an MD, PhD doing molecular cellular neuroscience, understanding you know, biology at that kind of molecular mechanistic level, driving an understanding of what kind of brain circuits and molecular pathways and individual drugs might be the most amenable to this approach that really came together then to be that pivot point in the science that has now really started to pay off at Aalto.
[00:10:07] Speaker A: Yeah, I guess so. Okay, that makes good sense. So you're seeing predictability and then you say, okay, now we need to do this a sort of scaled up environment under good clinical practice and in a way that will advance ultimately towards a treatment for patients, which makes a lot of sense, but here's a hypothetical for you. So the way it could have gone is you could have found a likely postdoc or PhD student in your lab.
You could have said we should start a company together. I'll stay here, I'll be chairman or I'll be senior senior advisor or something and I'll help with raising money and of course I'll be involved with design and all of this Stuff, but I'm going to keep my comfy chair here at the lab and, you know, good luck with this young person.
And you didn't do that. So talk a little. I think that's then about the personal decision, Right.
[00:10:59] Speaker B: Yeah. So I did initially, actually.
[00:11:02] Speaker A: Oh, really?
[00:11:02] Speaker B: Going down that path. Yeah. And. And looked for CEOs who were going to be sort of the right people, seasoned people, and actually I had one person that I was in kind of advancing discussions on who was a phenomenal individual, went on to do some really exciting things as well.
But. But where I ultimately got to is that the curiosity about entrepreneurship, about the kind of understanding that we'll need to zig and zag, which we definitely had to do early on as we formed our concepts, was something that. That I wanted to be part of in a very active way that I felt like maybe the company would be better if I were the one who was there doing it.
And then, you know, frankly, a big part of what I've always loved is just kind of exposing myself to new challenges, learning new things and, and running a company.
But actually being in that seat rather than sort of an advisor felt like the epitome of that. You know, I'd heard of people who, you know, who had this direct experience of starting a company, but being at kind of arm's length, and often it didn't go the direction that they'd wanted to go for various.
[00:12:19] Speaker A: I agree.
[00:12:20] Speaker B: Yeah. Yeah.
It also felt like, you know, look, I'm in Silicon Valley.
How could you, you know, not have some sort of entrepreneurial experience in life?
[00:12:30] Speaker A: How can I look people in the eye in the faculty dining if I don't do this?
[00:12:34] Speaker B: For crying out, you don't get your stripes right.
[00:12:38] Speaker A: I actually do wonder if that's a factor. You know, if you compare folks at other, you know, sort of comparably rigorous institutions, maybe there is a little bit. It's less of an obvious path.
[00:12:49] Speaker B: Yeah. Look, literally one of my neighbors a number of years ago left one big tech job to start a company in their garage, actually, just really in their garage without a couple of papers.
[00:13:01] Speaker A: It's a little too on the nose, you know?
[00:13:03] Speaker B: Yeah. Didn't work out for them. They went to a different big tech company. But. But it's that vibe here that you find all around you. I mean, there's a lot of people working big tech here, obviously, just, you know, in. In their roles, but a lot of people who started companies, some who've been successful, some not, and there's that frontier mentality. Of like, let's strike out, let's, you know, on some path, let's see what we can discover and what we can create. And if it doesn't work, it's okay to learn something. And that I internalized in my own frame as well because you know, at the time where I was making this decision, I was starting to be looked at for chair positions. Wasn't like super excited to be a chair because you're basically chasing around a bunch of faculty who don't necessarily want to do what you want to do, cat herdering, you know, exercise.
But I felt like I'd be a much better chair if that were my next step if I had this experience and failed because I tried.
[00:14:07] Speaker A: So, two quick thoughts on that in response. First, I think, I do think Stanford is an unusual place in the degree to which it endorses that. And one of the things that's amazing about sort of the US model is, I think embodied in that last statement you, you made, is if I do this and fail, it'll make me a better academic chair or senior leader going forward. And you know, I think I still think that is, if not unique, very, very unusually an American thing to, you know, the American entrepreneurial thing. Hey, I failed at this thing. That means I've got a lot of really good hard won experience as opposed to, I bear that mark, you know, as a negative, for shame or something.
[00:14:47] Speaker B: Yeah, yeah, it's a very west coast, like if you didn't fail, you didn't try. Right. Is the mantra certainly around here.
[00:14:57] Speaker A: Fantastic. Okay, so now let's talk a little bit about what that transition was actually like. So you know, you say, all right, I'm doing this, I'm going to start a company and then what, what happens? What does the initial transition look like and what are some of maybe the learnings or surprises along that journey?
[00:15:15] Speaker B: So, you know, we were pretty well funded at the beginning.
You know, I put together an $8 million see from folks who are really just around the lab and kind of friends of friends, you know, within that kind of world.
I had actually moved my Pioneer award over to the company and we'd gotten an SBIR grant. So we'd start out with $8 million additionally in federal funding. So that was pretty good.
There was a lot of learnings that had to happen very, very quickly in terms of like literally how do you do payroll?
[00:15:48] Speaker A: Yeah, right, right, right.
[00:15:49] Speaker B: Like what, you know, you need to find an office and all of these things. So, you know, a co founder who was, who'd Done, you know, started a company and done a lot of work on kind of the, on that sort of strategic and business side, who was extraordinarily helpful. I learned a ton from him and you know, he both did a lot of those things, but also taught me, you know, how to think about all of those things.
You don't really have that experience until you've been through it. So it was necessary to kind of just white knuckle it. In a sense, whatever you come against, you have to just figure it out.
[00:16:28] Speaker A: I think white knuckling is sort of part of the CEO experience in general that folks who haven't done it before might assume is not the case. But I think anybody who's honest admits there's a fair bit of that. Do you think, were you surprised or it sounds like not by the fact that there were gonna be a bunch of prosaic tasks that were gonna be really important to recruiting and retaining your team and growing the business.
Did you know right away I'm gonna need somebody who's got some experience with that. You describe this person as a co founder, so I'm guessing maybe the answer is yes.
[00:16:58] Speaker B: Right. So I knew that there's a lot of stuff I didn't know and that's probably the first key thing is being open to that learning.
[00:17:08] Speaker A: Yes.
[00:17:09] Speaker B: You know, this is also November, so I took leave November 1, 2019. We also didn't know we were about to walk into a pandemic. Yes, right. Which, you know, changed everybody's lives in various ways. But that itself, having that, that kind of core group of people who I could, I could think with, I can kind of draw on their experience in other contexts also helped us navigate this period of tremendous uncertainty.
And we were also refining our ideas for what the company would focus on at that time. So you absolutely need that core now. Once you've had that experience, you can reflect back and say, well, ABC is kind of now I know what I would do. I have a playbook. But there's always going to be some new thing you come across that's not in your playbook, at least not until you've had decades and decades of this kind of experience.
[00:17:58] Speaker A: I think that's right. Yeah. That makes a lot of sense. So that's the practical stuff. And maybe again Stanford's well worn path from academia into company formation. It makes sense to me that that would slightly be in the drinking water to kind of figure that stuff out. What about lessons that you hard won, lessons as a successful academic that you maybe had to Change or transition to in a corporate environment.
[00:18:21] Speaker B: So you know, it's an interesting question because it's one of the more surprising things is that as an academic you're ideally focused on trying to understand what's really like what's true. Right. That's a. Yeah, it's a truth, truth exercise.
You're probably doing science I think at a higher level with more emphasis of finding out what's true in a company.
And I say that because, you know, there's been a lot of knock around academics and reproducibility and yes publisher or parish and all these biases and so forth. And there's always something that'll come out of a research study and you know, there's not been all that much criticism or focus I suppose in the criticism around. Is this what you intended to find or is this something interesting that you found that may be true, may not be and is more hypothesis generating in a company? It absolutely needs to be true because you are basing the next thing you do on it.
[00:19:27] Speaker A: Yes.
[00:19:28] Speaker B: So the science became considerably more rigorous and focused and we could train our people to have the mentality that like we don't get excited about anything until it replicates. And that's like a full stop statement for us on everything we do always. And we've tried actually over the years to educate the field on this because that's not necessarily the way that the kind of biotech investors have necessarily thought about things. You think about things in terms of single readouts and of course whether significant or not, but there is a discovery process that has to happen. And when you do that discovery process in people, because you have to find biomarkers in people, that requirement for replication which uses terms like holdout data set that are more common to computer science than life science, that has been actually a constant education that we've had to do in the market. So people understand how we are trying to apply the most rigorous data analytic approaches in context where it's sort of like one readout at a time.
[00:20:33] Speaker A: Yeah, fascinating.
So that brings me something I want to cover and I would love you to weigh in on. So we know that some people leave academia and come to biotech or pharma and describe it as coming to the dark side, which always makes me grit my teeth because of the.
There are a lot easier ways to make money than joining or starting a biotech company as we know.
So I think the proportion of mission driven people and people who are trying to cure a disease or develop a meaningful treatment, improve the life of Some other human beings is incredibly high in this space.
But I certainly think a lot of people would assume, oh, well, the standard of science must be higher in academia because you're not subject to the demands of filthy lucre.
So will you weigh in on that a little?
[00:21:23] Speaker B: Yeah. So like I said, I think that the demands on the science are much, much higher.
You just got to find things that are true.
And the point about going to the dark side. Right.
Obviously salaries are generally higher in industry writ large than academia, but when you think about what's involved. Exactly. As you said, we're talking about an industry here where when you finally get a drug into phase one, you still have a 90% chance of, of failure, completely insane to do this. Which means you have to be committed to the science. You have to be willing for years and willing to risk a lot in the, in the kind of pursuit and hope of a big scientific breakthrough that therefore has big effects on, on humans.
So, so that raises the level of rigor. People don't. In academia, people kind of stop often with the sort of like, I have a paper, therefore I have a thing. Yes. Kind of mentality. Whereas I think on the industry side where you're, you're, you just want to make sure that it's like really, really solid.
That's the entry point to then testing whether you really do have a thing. And that involves often a lot more work, much greater financial investment to do that work. And on top of it, you have to worry about is that being done to the level that a regulator would accept, which is not something that academics really ever think about in terms of even how you conduct a trial, let alone how you lay the groundwork for what kind of indication and how would it be proved out and who would it really be for and how would you know if it worked and how do you know if it's better than what they have. And all these kinds of questions just.
[00:23:09] Speaker A: On that thing about, well, I have a paper, so I have a thing. I mean, I think that's a little bit about. That's the rule of that particular game.
And this is not my line, but somebody said the art of life, this is all paraphrased, but the art of life is not about winning the game, but it's about understanding the game you're playing and making sure you're playing the right one and then trying to succeed at it. So I think you're drawing a really stark differentiation there.
That makes a lot of sense.
[00:23:36] Speaker B: And look, I don't want to just be kind of ragging on academia, that's the place. No, of course discoveries are made and I loved my time in academia.
But the work for it to progress has to be able to mature and that means different levels of expectation. Like you're not doing open ended discovery in industry because nobody that makes no sense investing in that. Right, right, right. But you are advancing a piece of science to the point where you'd be fine with somebody, you know, your family member getting some drug, because you know, that it's shown effect, you know, effectiveness and, and safety. And that's just a whole other level of rigor that we just in our everyday lives count on and don't even think is, is like, well, you know, they kind of like got this paper in nature or something and that's like a good enough thing.
[00:24:31] Speaker A: Victory. Yes.
Yeah, that makes a lot of sense. So then when people are coming into the company, is that an explicit thing you have to retrain people on or do you think people just kind of pick that up by working with you guys?
How do you help people to make that transition? Because of course, many of the people you're working with, that is what their background is going to be and so they're going to come with a different maybe set of expectations.
[00:24:58] Speaker B: Right. So it's actually, it's an interesting question because partly, you know, we pick people who already have a proclivity to that or who understand.
Right. But one of the most fascinating things about the change from academia to industry, because a lot of the people, at least on the data science side that we're bringing in, are exactly in that vein that you mentioned, which people who've had the very significant academic background.
One of the things that changes like that, that I've always been shocked and pleasantly surprised by, is that in academia you're focused on your paper and your first authorship and it's kind of like a me first experience. Whereas an industry, you're really part of a team and we're going to live and die as one team and people make that transition seamlessly, quickly. Like there is a desire, I think amongst a lot of people and maybe we're just also picking the right people to be contributors to a mission.
And you are one kind of low ego contributor. There's no grandstanding, you know, you're not going to win any points by showing how smart you are and how dumb somebody else is.
Literally, people are looking for opportunities to work together and to help each other in a very positive but still scientifically critical environment. People made that shift like with there's no retraining. It's just they've naturally.
[00:26:27] Speaker A: I love that. I don't think it's hard to understand why that would be attractive to people, because, you know, we are. We're tribal creatures. And so being able to feel like you are part of something that is bigger than just you is a. It's a core human desire. So that part's not surprising. The thing that's interesting is how you did it.
[00:26:44] Speaker B: And we'll.
[00:26:45] Speaker A: We'll get there in a second. I want to talk about. About leadership and culture. But first, let's spend a couple minutes on precision psychiatry. I think, you know, many, many people may be familiar with biomarkers, primarily from oncology. So will you talk to us a little bit about, you know, kind of where this comes from and a little bit about the potential for the field.
[00:27:02] Speaker B: Yeah, so. So as I mentioned. Right. Where we have no. No biomarkers, no precision in the field whatsoever, we have.
Right. That are kind of grab.
[00:27:12] Speaker A: The good news is the field is wide open.
[00:27:14] Speaker B: Yeah, wide open. Right. And our concept, even of what a diagnosis looks like is rooted, like in the 60s and 70s and hasn't really evolved a whole lot since.
So. So we have a lot of heterogeneity with poor outcomes. We have trial and error use of treatments writ large in psychiatry and areas like schizophrenia with cognition that have no treatments whatsoever. Right. So that's the ground upon which we're building. So what is a biomarker? A biomarker is a test of some piece of objective biology that can be used in one of two important ways. One is to identify who should get a treatment. Let's call that biological sort of subtyping of disease. You have depression, type A, type B, type C, or whatever you want to call it, that are different biology, therefore need different treatments. And the other is the biology of what the drug does when it engages the brain. And both are really important. If you look at the world of oncology for sure, but increasingly also even areas like neurology, you know, INI as well.
If you come in without an idea of at least one of those two, how is my drug acting biologically to have a therapeutic effect? And then how am I selecting patients based on biology? You'll be kind of laughed out of the room. It just, it's. It's like as if you don't really know what you're doing. And yet in psychiatry, that's exactly what.
[00:28:40] Speaker A: That's just how we do it. Yeah, yeah, yeah, got it.
[00:28:43] Speaker B: Right. So, like, because logically Are you like, well, I'm not really sure what this thing does. I'm not totally sure how much to give, but let's just go for it.
[00:28:52] Speaker A: How about if we try it? Yeah, we'll try it in a kind.
[00:28:54] Speaker B: Of rigorous way, but yeah, and then, and then, you know, it's like, well, the results weren't totally clear. Let's just try more and bigger.
Right. It's just what happens. And so what we try to do is systematically de risk that process so early on. Like Alto 101 in schizophrenia is a great example. We're using biomarkers as outcomes. That's what's an outcome in the upcoming proof of concept trial we have in the coming months, where that outcome is an index of the biology that's most relevant for schizophrenia and cognition. We've done a lot of work to show what the right biomarker is. And when you move it over time, that'll lead to cognitive benefits, we think, and ultimately functional outcomes. So there you have your biological signal that you're impacting the right brain circuit, an idea of what dose you give, how long and so forth. And then on the choosing patient side, we do that across the board, including in that schizophrenia study, we're selecting the right kind of patient profile based on biology. And, and then we have a bunch of work in depression where we're in phase 2b trials, all with some sort of patient selection based on the biology that should enrich for the people who respond to the drug and eliminate the people who don't. Also, by the way, we have a biomarker even for placebo response. So we can now predict with EEG using machine learning approach that we've replicated twice already that predicts placebo response. So non specific response around all treatments. So you can now think of all of these pieces that start to give you an anchor on the biology. And what's the most fascinating about all of this is it's never associated with a clinical phenotype that you would pick up as a clinician. In other words, it's not more severe, it's not more anxious, more anhedonic or whatever. It's just different biology that presents as this level of symptoms. You know, you get drawn in as a clinician by somebody telling you about their specific symptoms. Sure, partly over time we've gotten conditioned to listen to particular symptoms as they come into our diagnostic criteria. But the symptoms are really often a measure of kind of individual idiosyncratic distress expression. They correlate much more highly with each other than they do with the objective constructs are supposed to index like sleep, appetite, cognition you could measure objectively. Never correlates with subjective symptoms. Yet appetite correlates really well with sleep, correlates with mood and so forth. It's really a construct of distress and idiosyncratic expression of distress. And it's going beyond that. That's where the precision psychiatry future really is.
[00:31:40] Speaker A: So that's fascinating. Okay, so two questions, I guess.
[00:31:46] Speaker B: Or.
[00:31:46] Speaker A: Maybe one, what is the response from the field to.
Sorry, let me break it into two. First, is this a generalizable trend that you would expect to see of precision psychiatry? Is this kind of what you think most drug treatment looks like in the future?
And then like what. How do people respond to you saying symptoms Shmimtums. Which is not technically what you said. But that's my summary.
[00:32:14] Speaker B: Yeah. Look, if you frame it as I framed it before of like knowing what you're doing.
Yeah. I can't help but imagine that this is the direction that things have to go. Yes. Now let's express it in a different way, which is if the first drug for, let's just say depression as an example comes out, that has a precision approach and that increases the effect of that drug.
What would you as a patient or as a clinician start to have as your expectation of the next drug? Right. So now you've raised the bar, which means that everything else has to be at that level or higher. And the way to get there is again through precision proved out by the first one. So. So I do think that that changes the landscape. Now, does that mean that every disease is immediately solved and we have great outcomes? Well, oncology suggests that's going to be a hard battle. And then people had.
Yeah.
[00:33:06] Speaker A: Although the progress is significant every year now, it is fascinating. But yeah, that makes a lot of.
[00:33:13] Speaker B: People don't get a precision treatment in ontology because it's still not.
[00:33:19] Speaker A: We just don't have enough.
[00:33:20] Speaker B: Yeah.
[00:33:20] Speaker A: Yes.
[00:33:21] Speaker B: Yeah.
So I think that is the direction of travel. And those first proof of concepts I think will go a long way. But let me give you kind of another vantage point to your other question, which is. Yeah, kind of like messaging around this. You know, when we came out of stealth in 2021, we were trying to decide like how do we express what we're trying to do and decided on precision psychiatry being that term, because that's the correct term. Right.
[00:33:50] Speaker A: Did you guys more or less coin that?
[00:33:51] Speaker B: We, we didn't coin it. I think other people in just had used it. But that idea and sort of been around, around, but people weren't really using it. So here and there, you know, a couple of academics might have used it, but really not from a company and certainly not an investor perspective. But we said, you know, it's the right term to use, so let's do it and let's educate people. In that time till now, there's been a dramatic transformation. So you now hear a lot companies trying to message around. Well, we're using EEG to find biomarkers. You know, we're doing precision psychiatry. It's now really picked up on the academic side. There's now funded centers through philanthropy around precision psychiatry. So whether we kind of rode a wave or started a wave, I don't know. But either way, it definitely does seem.
[00:34:41] Speaker A: Like there's a wave. Yeah, exactly. And it must be gratifying to feel like, you know, you guys were involved early, you were involved early in the development and popularization of this.
And yeah, like, like many, like many of these themes in medicine, when they, when they sort of cohere, they're a lot easier for people to wrap their, their, their heads around. And it does, what you're describing feels extremely logical in hindsight.
[00:35:11] Speaker B: Right?
[00:35:11] Speaker A: Like, like many, like many breakthroughs.
[00:35:13] Speaker B: Like many. Yeah, exactly. It's. The best ideas are the obvious ones. Yes.
Yeah.
[00:35:19] Speaker A: Okay. And then just double clicking one level deeper. This ability to predict placebo responders, that's a, that's also a really extraordinary thing you guys have done. Is that a, is that a company specific technology? Is it a methodology? Is it something that is used by others?
What, where does that go or what, where does it stand today?
[00:35:42] Speaker B: Yeah, so right now we're the only ones doing that in kind of a replicated, robust way. We have IP around it.
I think that's going to be an area that will continue to develop.
Placebo has always been a problem in psychiatry. Yeah, I think that that gets dealt with in a couple of ways. One is just simply having better drugs where you don't really worry about that placebo difference. And over time, by finding new targets, by having precision, we will have drugs that show greater and greater efficacy.
What has not worked in the past is playing with clinical trial designs, run ins and various other kinds of manipulations that fundamentally don't really move the needle on the effects of the drug. They might raise or lower your overall level of response, but really have an enriched for larger effect. So that we know which means we need a more powerful way to predict placebo and make sure that your prediction of Drug is not a prediction of placebo, which is something we do routinely whenever we find a biomarker. Because we have a lot of data sets on both placebo and on other standard of care treatments. We can make sure this is really talking from a depression frame that we can make sure our biomarkers for the drug are specific so it contributes even in that way too for our internal process.
But I think over time that refining of better drug response, lower placebo response, which is to say overall just less responsive person to anything, therefore needing a new mechanism that fits the biology. I now understand through my patient selection biomarker and that drug mechanism of action that is the direction of travel over time.
Got it.
[00:37:28] Speaker A: All right, now let's flip to leadership and culture. So you talked a little bit about people make the transition to being part of a team and they're gratified to do that. The gratified part makes sense to me.
Having built a lot of teams, I have also seen lots of things that can stand in the way. Even, even though most people want to work well with most people. That is, that is my, that's my conviction. After, after working with a whole bunch of different people across lots of different kinds of organizations, many have, many cultures are dysfunctional, there are blockers, there are silos, there are egos or all kinds of problems. So we talk a little bit about kind of how you thought about creating the kind of culture that you've obviously built at Aalto.
[00:38:13] Speaker B: Yeah. So to start with, I didn't really know what culture was. I thought that was sort of like a corporate term because yeah, in the lab we didn't have culture, we didn't have values that we codified. Like we were just people doing stuff.
But of course, everybody has culture. Whenever you have more than a handful of people, you have culture. It's just a matter of whether you put your finger on it and kind of go with it and understand what's driving that culture and what people value within that culture. And we did that very well actually within the lab, there was definitely a cohesive sense of who we are. So what we did at Aalto, not uncommon for a lot of companies, as we named our values, we have five core values.
And we re examined those values over time and continue to reinforce them. And that helps people understand who we are and how in our desire to work together towards a mission, what is it that we're trying to be? And so things like rethink everything is one of our really core values, which is open minded question. Not constantly in A navel gazing paralysis kind of way, but in a productive. I've learned this. Therefore should I think about things differently or how are we falling into a rut that we need to kind of get ourselves out of? Or whatever may be the case, Being open to new ideas and kind of a growth mindset and open to constructive criticism goes a really long way in making people feel like their ideas are valued and that we are always looking for innovation and just a different take on things.
So those kinds of things, I think reinforce positive traits in people. We actually give awards to our people based on our values. So twice a year we get the whole company together in person and then peers nominate other people for particular values that they think that person really in a context, you know, embodies. And so then we give out those awards and, and I think people feel they're like there's ownership of these values and they talk in those terms. So you'll actually hear those words used, you know, and something is happening. They're like, well, but you know, X, whatever it is, you know, think about that perspective from the value side of things. I think staying mission driven is also really critical. Like, you know, like you said, there's a lot easier ways to make money. There's a lot easier places to develop drugs than in psychiatry.
[00:41:03] Speaker A: Oh, good point. Yeah, yeah, true.
[00:41:05] Speaker B: If you want to do drug development, this is a very challenging area for all the reasons we discussed. The people who we have are in it for the right reason. They're not in it for a paycheck. They're in it because we are doing work in mental health, because we are pushing the envelope. And understanding that core shared mission and why it then reinforces the culture for everybody goes a really, really long way. Yeah.
[00:41:31] Speaker A: In some ways maybe that might be an advantage that when it is hard, the folks who are.
The folks who are just tourists aren't.
[00:41:44] Speaker B: Going to go there.
[00:41:44] Speaker A: They're going to go. They're going to go to probably do something else.
[00:41:46] Speaker B: Right.
[00:41:46] Speaker A: So I would imagine the kinds of people who think like this hard thing is exciting to me, that that's probably an innate self selecting that's helpful.
[00:41:55] Speaker B: That's right. Yeah.
[00:41:58] Speaker A: Let's talk about mentorship.
You didn't work in business before this? I think so. Right. We talked about your extensive education, of course, and then moving into academia. So were there mentors who were helpful to you or are helpful to you now?
And my experience of this personally is that I have mentors that I talk to about things. I have mentors whose advice I still remember and ruminate on.
And I have people I've never met, but who I listen to on podcasts or things like that that have also provided me with valuable guidance. So there's lots of sort of ways to go at this. But what kinds of mentors did you have when you were starting the company?
[00:42:43] Speaker B: So I think a lot of my mentorship, kind of the people I've learned the most from on the leadership side, sort of separate from the science side, not necessarily from academia. So somebody like Alan Schatzberg, who's one of our advisors at Aalto but was a chair at Stanford, Chair of Psychiatry, who I was in his group as a resident, who then hired me as a junior faculty member and who I've been close to ever since. Just watching how he has led the department. He led the department for about 20 years and balancing science and clinical and the sort of business side of an academic department and just how he was as a person with other people and created a very, very strong, positive, innovative community, really built Stanford to what it is today.
Seeing that has certainly had an impact.
I actually in many ways take mentorship from other people in my C suite and that's the case all the time. You know, whether.
[00:43:55] Speaker A: Same.
[00:43:55] Speaker B: Yeah, it's like you just, you, you know, one of the most fun things about, about that academia to industry shift is that you can hire senior people who are very experienced and you can really have like a high level, intense peer relationship with them where you're learning a ton and you're kind of creating and thinking about ideas together that you just can't do in academia. I mean, it's just not structured for that. And, and that's a unique gift, especially in an early stage company where you're just figuring out what you're trying to execute on.
But probably throughout the course of a company is critical. So it's about the people. It's also about the nature of the expected relationships between people.
[00:44:45] Speaker A: Yeah, I think that if you're hiring well, people should be better at something than you are.
And then you have board members and other external advisors and you can build this kitchen cabinet because being a CEO is a, it's a very lonely chair to sit in if you let it be. And you can reduce the amount that that's true by building this kitchen cabinet that's separate from an actual board or however one chooses to do it. And I agree sometimes some of the most insightful things come from it's.
It's not title depends from whom one learns.
That makes a lot of sense.
I'll ask, given your chosen field, how do you manage stress and how do you manage the many things that I'm sure are coming at you all the time?
I'm catching you on a particularly good day, I'm sure. So that's bad timing for this question.
Should have found you when something was really wrong.
[00:45:45] Speaker B: But.
[00:45:47] Speaker A: Talk a little bit about that if you would.
[00:45:49] Speaker B: So actually, I like the pace. I've always liked a faster pace. Even when I was in the lab.
I don't get terribly stressed about stuff like the pace and the intensity helps me focus and it helps me kind of like just kind of get the motivation.
So, you know, things are coming at you all the time, of course, you know, are they that different from other ways to live if you're leading an organization?
I don't know. I mean, ultimately, as a leader, a lot of things do come at you all the time, regardless of where you are.
What I have found is that I ended up being more optimistic, I guess I would describe it, than. Than I had thought. So, you know, as a scientist, you're always kind of thinking about, you're thinking critically and you're in a peer review and everything is sort of like in that blinded review frame of mind.
Once I've transitioned to now a bigger, I would say, opportunity set in industry and just sort of seeing the world for all of the many different facets of opportunity that exist around.
I just find myself always able to draw what, you know, what is most exciting, what I'm most motivated to do out, even when things are hard. Like, there's always just a reason to push hard and to innovate and to see the future for its upside, not just its downside, but really preferentially for its upside as a way to motivate doing those really hard things. There's really uncertain things that we have to do day to day.
So, you know, it's not like I'm sitting there, you know, I don't know, meditating or, you know, climbing, you know, rock walls or whatever as a stress reliever.
It's just a being there and being a, you know, a combatant in the arena, so to speak.
[00:47:56] Speaker A: A combatant in the arena, indeed. Yeah, I love that.
I think optimism is a muscle that you can develop and probably hard if you're a dyed in the wool pessimist to do. But dyed in the wool pessimists tend not to start biotech companies, in my experience.
Yes, exactly.
Okay, I love that. All right, we're going to flip quickly to some Quick questions in the lightning round.
And so you can say, you can answer or you can say pass if it's something you don't want to cover or don't think you have a great answer to. So first, is there an underrated paper in precision psychiatry or an underrated concept that you would like to shine some light on?
[00:48:37] Speaker B: Yeah, so one of the things actually that we've had to learn the hard way as a field is just how hard the science is. And that bar of replication that we talked about is.
So a guy named Martin Ahrens has released a data set that he's collected over years in a kind of real world clinic context where there's EEG and other kinds of data and clinical outcomes or diagnoses, something called TD brain.
And it's sort of, you know, it's out there. Some people who know about it are using it. He has a competition where he has a leaderboard where you put in a model that you think predicts outcome or whatever, and we'll test it on a blinded test set that you have no access to. And what's been most illustrative there is how poorly most of the models perform, which.
Right. It's like when you see a paper with 99% accuracy in predicting something, it almost certainly is not true. Okay. The reality is much, much lower. The gains are much harder fought. And that I think is certainly underappreciated.
It's not how a lot of publications have tried to position themselves, but it's much more reality. That's why we do what we do in terms of how we manage prospective replication as being required.
But my hope is that other people would start doing that too.
[00:50:04] Speaker A: Well, God, running a contest like that too is brilliant. It's a great way to get more value out of the data that he's collected and provide, provide a service to other researchers. Really, really, really cool.
I'm jumping around a little bit here. Is there a book that you would recommend for non scientists on brain and behavior?
Something for the, if you will, me of the audience, but also for other folks who might be listening?
[00:50:31] Speaker B: Yeah, so actually a book that I've been kind of reading on and off of late that I think is actually quite good on, on a number of different grounds is by Max Bennett. It's called A Brief History of Intelligence and it actually covers both brains and AI.
But looking at the evolution of brains from like, you know, early, you know, multicellular organisms to complex mammals like humans, and understanding evolution and brain structure and function, what it achieves in terms of behavior and then paralleling that with the evolution of AI systems and the concepts behind AI systems. A lot of what happens in AI now is like reinforcement learning, something that came out of work in neuroscience and in psychology in decades past.
So there's a lot of parallel there. And of course, he doesn't necessarily do this, but layering in the disease perspective the same brain systems that mediate a function, mediate dysfunction. Right. So you can kind of understand that. But. But I thought, actually it's a really well written, very accessible book that.
[00:51:38] Speaker A: Terrific.
[00:51:39] Speaker B: Really big concepts.
[00:51:40] Speaker A: Yeah, that's a great. That's a great one.
You know, you mentioned AI, and I realized that, unusually for this podcast, we have not talked about that.
What's your perspective, if you don't mind me asking?
Where do you. Where do you see this going? And I'll frame it slightly.
A lot of people who. Well, you're sitting in. You're sitting very close to the. The epicenter of AI development right now. A lot of people are saying next year agents can act independently, agents that can act at the highest level of human achievement, maybe beyond the highest level, pretty soon are coming. Do you see an end to this or do you think that the path. Does the future to you look like centaurs, AI enabled humans, super intelligence, whatever we would like to say about this? Amit.
[00:52:30] Speaker B: Yeah, there are corners of Silicon Valley that, you know, I probably kind of avoid, that are like sort of utopian techno dystopian. Yes, yes. But I'll say, like, look at Aalto. We do two different things. We do machine learning and AI machine learning, for example, to find biomarkers, AI is using large language models to understand our quantity, patients or their clinical presentations in different ways.
So there's certainly the machine learning. Like, we actually ought to be as simple as possible and not make it too complex so we can understand it and replicate it well, and regulators and clinicians can understand it. On the AI side, it's been awesome, I have to say, just seeing that development of tools and then again that mindset of, like, rethink everything. One of our core values is how do we use this new set of tools to bring in a new level of rigor into the clinical trials and visibility over what's happening across our sites and our patients.
So I think all of that is going to get better and better, even if it's just systematically embedding AI agents to do tasks more rigorously, more consistently, in a more visible way than you could as a human. That's great.
The insight that you get from interacting with AI has continued to improve the pressure testing of ideas.
I think it's all a very positive trend. Of course there are downsides. That's not what this discussion is about. But I think from a life science and biotech perspective, we have been light on kind of bringing in that kind of technology.
Just on the clinical development side, the trial running, the phenotypic characterization of patients, especially in psychiatry, as a really big untapped area of potential advances that are good across the board, across diseases, across different kinds of drug programs, diagnostic tools and so forth. So a lot of optimism. Obviously we're not trying to create super intelligence or any of this kind of couchword stuff.
Use tools in the most helpful and careful ways possible.
[00:54:52] Speaker A: Yeah, I think that's my experience from the clinical trials world as well, is that we're now at a place where maybe a year or so ago people were calling all the time to say, hey, I'm an AI expert, let me AI your business.
And those conversations were about as useful as they sound.
Now there are lots of interesting point solutions that people are developing.
A bunch of them still need to be validated, but they're all pointing in the right kinds of directions and they are designed typically to solve a hyper specific problem or repetitive task or to take something that has been batch, process, process previously and make it a continuous process because, you know, because we can scale it in a way that you can't with human beings. So I think we are already seeing some benefit from that and expect to see a bunch more.
But that is, but it is not, it is not. Hey, just give, give a large language model, a protocol and you don't need to run the trial or some craziness like that, which I do every now and then here from the less bioscience.
[00:56:00] Speaker B: Oriented.
[00:56:03] Speaker A: Corners of technology.
[00:56:06] Speaker B: Great.
[00:56:06] Speaker A: So. And then maybe one. In closing, this is a, this is in a way an odd question to ask you because you are a person who has had a lot of success, extraordinary amount of success in your career, but is there a lesson that you've learned from failure or struggle that you carry with you that you'd be comfortable sharing?
[00:56:24] Speaker B: Yeah, I mean, look, we had a trial ALTA 100 in depression this past fall that didn't hit its primary outcome. And when you drill down and actually I don't think is related to the drug or the biomarker, there's evidence that actually we're probably on the right track, but what we discovered was that there's a risk in the execution of trials that happens at point, some certain types of sites are part of everybody's trials right now that reflect a systemic risk in our field, especially for psychiatry. But I think it's probably true across the board.
[00:57:00] Speaker A: Certainly we see that kind of issue in other fields as well.
[00:57:03] Speaker B: Look, a professional patient profile writ large.
And that was, I mean, certainly if you're talking about a stock price move, that was very painful and unexpected seeing that outcome.
But those kinds of things are also instructive if you let them be. And so we have now tremendously increased our control and visibility over what's happening. You know, you could never be too careful, is kind of the conclusion, and figure out who the right partners are to work with. We've heard that since our outcome and other similar outcomes shortly after ours, for very similar reasons, that other companies have started to follow suit about what we've actually been tried to be public and loud and clear about, rather than just sort of sweeping an outcome, like really saying, here's what happened, it's painful, here's what we learned, and here's how we're going forward in a much more rigorous and hopefully generalizably better way.
[00:58:08] Speaker A: I want to just massively applaud that. It's, of course, tempting when things don't go right, even if you're positively oriented, to say, well, that was the past and we're moving on, and we certainly don't want to spend a lot of time talking about it. But trying to draw lessons from it and then being willing to share those lessons if you think they apply more broadly is a social good that extends beyond the company. And hopefully, and obviously we're seeing it even today with this announcement, the company's thriving, so that's really terrific.
[00:58:42] Speaker B: Yeah, I appreciate that.
[00:58:44] Speaker A: Amit Etkin, thank you so much for joining me today on Few and Far Between. This was lots of fun.
[00:58:49] Speaker B: It was a real pleasure. And looking forward to next conversations.
Likewise.
[00:58:56] Speaker A: Welcome, producer Adam.
[00:58:57] Speaker C: Hi, Chris.
I just. A great episode, and I think Meet Journey really captures a CEO's first steps into biotech, an area you're familiar with, of course.
What important aspects resonated with you in this episode?
[00:59:14] Speaker A: Gosh, the whole thing. I mean, I'm so impressed with people who are willing to take risk. And Amit stepped away from, you know, an incredibly secure perch as a tenured, you know, professor at Stanford to go and do this. And I was really struck by what drove him to do that. I was really moved by the, you know, the. Or really, really fascinated, I guess, by the point he was making about how hard it is to do this kind of work in a grant based system that doesn't really reward speed. It rewards kind of fully utilizing the grant. So I really, I really loved that and the idea of him leaping into the deep here.
[00:59:51] Speaker C: Yes, I really agree. And my next comment kind of goes with that. We talked about journeying into the dark side that is biotech, and we talked about that a couple of times on this podcast. But I thought that with Meat, he had some really good points on what you can and can't do in academia versus a biotech company.
[01:00:14] Speaker A: Yeah, as you know, I hate that expression that, that biotech is the dark side.
I think that's a, I think that's a failure to understand the limitations of what you can do in an academic research setting. And frankly, the kinds of people who are attracted to biotech, these are labors of passion and love. Nobody would commit this much of their life to study and then to try to bring a drug to market, understanding what the probabilities of success are. And this is a mathematically literate audience and therefore they understand the risks. So I think we can, we can dismiss that idea of it being the dark side. And yeah, I really liked this point that sometimes you just need more money than you can get from a grant and other times the discipline of the market can force you to move quickly, make decisions, avoid tangents, and really drive towards something that ultimately you can bring to the FDA and other other agencies and hopefully advance a drug.
[01:01:14] Speaker C: Yeah, it really is the impact though, on, on people other than yourself in some of these situations when you move into biotech and more of a private sector.
[01:01:26] Speaker A: Yeah, 100%. I mean, there is of course the personal thing of you're managing a team of people and you're responsible for funding for them so that they can, they can put food on their tables and, and feed their families and all that stuff. And then the bigger picture, as you said, is you're raising money from investors with the goal of having a big impact on something oftentimes really, really important, really, really scary that either is killing people or dramatically reducing the quality of their lives.
And yeah, I mean, I can't think of anything nobler actually to work on than that kind of task.
[01:02:01] Speaker C: Very much so.
So let's switch to biomarkers.
Amit has chosen to use biomarkers for outcomes rather than for identifying treatment opportunities, which is a very much a traditional format focus.
Is this a major breakthrough in psychiatry?
[01:02:21] Speaker A: I think it is. I mean, I really. It's shocking when you think about it. And he referenced this point that we diagnose based on kind of a preponderance of symptoms somebody has. You know, they check these things and therefore they have a diagnosis, but it's typically not a medical diagnosis. It's really observational.
And so the idea of having, first of all, focusing on actually treating the underlying condition, not just the symptoms, is really exciting. And then the idea that you can use a biomarker and have an actual, you know, an actual specific reference point that tells you that this person has this condition is incredibly exciting, I think.
[01:03:00] Speaker C: So I want to go back a little bit to the beginning. Can you help me and some of our listeners understand the difference between fast track designation and FDA approval? Does one lead to the other or are they more diverse?
[01:03:16] Speaker A: Yeah, they are different. I mean, they sort of result in it in the same outcome ultimately, or should do, which is approval for a drug that you're seeking. But sponsors can seek fast track designation at any point in the development process. And then they go through this exercise called rolling review. So the FDA can review sections of a drug application, an NDA or a biologics license application, a BLA as they get completed instead of waiting for the full package, which is how it works with trial traditional application. So you're getting much more frequent feedback from the agency, and more feedback, I find, is generally better. It means that you're likely to be aligned with, you know, and on the same path with.
With the agency going in the right direction. Yeah, yeah. So it just speeds up communication and review. It doesn't necessarily change the approval standards. The drug still has to meet the same safety and efficacy thresholds as any other. It's as I understand it. But it does enable you, I think, to get the process done quicker.
[01:04:18] Speaker C: Interesting. Okay, so lastly, Neath stated that whenever you have a handful of people, you have a culture. So what are your thoughts on people, employees driving culture in biotech, especially with so many remote employees out there right now?
[01:04:34] Speaker A: Yeah, I think first of all, I 100% agree with him. The fact that you haven't talked about your culture doesn't mean you don't have one. Every, every organization has one. And it often has a bunch of unspoken norms and expectations.
And then we as humans are pretty good at sort of figuring what those are, figuring out what those are, sometimes in ways that the leader might not want. So a much better way to do it is to be explicit about the culture you're trying to build and to make that a process. So I love the idea that not surprising, given who amit is as a person and given his areas of focus, that he's thinking about that or he thought about that quite early. We've seen that with some other biotech founders who talk about, you know, establishing values and norms right out of the gate. Other people don't, and they wait until later in the process. That's not necessarily a problem if you're small. If you are two or three people, then an implied culture can be. Or five people. An implied culture can, I think, be kind of okay. But I think you want to move to a defined culture as quickly as possible where we say things like, how do we give feedback to each other and how do we make decisions and what are we trying to accomplish? And stuff like that. So thinking about it and trying to be purposeful about the culture, I think is a really good idea.
[01:05:49] Speaker C: That's an excellent point. It really is.
Again, great episode.
[01:05:53] Speaker A: Oh, he's. He's terrific.
[01:05:55] Speaker C: And I encourage everybody who's watching listening to check out the comments and to leave comments about the episode as well.
[01:06:05] Speaker A: Yeah. And while we're saying that, Adam, let's ask listeners if there's somebody that you'd like us to talk to.
We're delighted to do that. We're looking for other CEOs like this, leaders like this in biotech who have something exciting to say about how their approach, how they're building their company, about their science or technology, and. Yeah, their approach to building a biotech, that's. That's. That's what we love. So if there's somebody out there that you know, we should be talking to, let us know.
[01:06:30] Speaker C: Exactly. All right.
Great episode again. Thanks, Chris.
[01:06:35] Speaker A: Thanks, Adam.
