Latest / The BioHub - by Avetix / Episode 134 - Amit Etkin - Founder & CEO Alto Neuroscience

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Episode 134 - Amit Etkin - Founder & CEO Alto Neuroscience · The BioHub - by Avetix

0:06
So welcome back to another episode of the Bio Hub.
0:09
Today I'm joined by Doctor Amit Atkin, founder and CEO of Alto
0:13
Neuroscience. Amit's journey is fascinating,
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from running a large lab as a tenured professor at Stanford to
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leaving academia to build Alto. Under his leadership, Alto has
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grown into a publicly traded company with four drugs in mid
0:27
to late stage clinical trials, all focused on bringing real
0:30
innovation to mental health, an area that's seen little change
0:33
in decades. This episode is all about
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timing, courage and building a culture of viewers that can
0:40
deliver. It's an absolute pleasure to
0:42
have you and me. It's.
0:43
A great pleasure to be here with you.
0:45
Look forward to this conversation.
0:47
Yeah, good. So I mean, let's just start with
0:50
your story, like I said, from large lab at Stanford to finding
0:55
Alto, what was the the turning point that made you leave
1:00
academia and start a company? Yeah.
1:03
So I think to understand that give you a little bit of
1:05
background just on on myself and kind of my academic journey.
1:09
So I'm an MDPHD psychiatrist and neuroscientist.
1:12
I basically trained and came up in a system where my expectation
1:19
was that I'd be an academic forever.
1:21
I did my MDPHD with Eric Kandel in a Columbia when was there
1:26
when he got the Nobel Prize. That was sort of the model for
1:30
me of being an academic for a long, long time as I moved to
1:36
Stanford, did my residency, went on faculty, you know, that that
1:40
was sort of the expectation. I never even thought about
1:42
frankly starting a company. But what we were doing was
1:45
trying to understand using various tools in neuroscience,
1:50
so ways to measure activity in the brain, ways to kind of
1:53
analyze that data and and interesting and new ways
1:57
understanding the role of biology of individual people.
2:01
We started to approach questions that over time, I came to
2:05
realize more and more put me kind of against the limitations
2:11
of, of the sort of structure of academia and simultaneously
2:15
opened up the opportunities of getting outside of it.
2:18
And you know, you're always sort of taught to follow the science
2:21
and, and it's I think critical, especially coming from academia
2:25
to understand where, where you are and following the science
2:29
versus other kinds of of roles or motivations.
2:33
But essentially, as we progress, we were able to to do things
2:37
that we would consider to be the basic building blocks of
2:40
precision psychiatry. That is the ability to
2:43
understand the biology of the person, to predict something
2:47
that is distinct for that person.
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So, for example, in depression, you give an SSRI, can you
2:52
predict response? And more importantly, it's not
2:55
just analyzing the data to do that within a data set.
2:58
Can you predict response that generalizes to other data sets
3:01
so that you're really now finding something that's true?
3:04
We did that for medication, for psychotherapy, for brain
3:07
stimulation treatment, and got to the point where, you know,
3:12
the realization was that this is solid, this is reproducible, and
3:19
that therefore we had kind of a choice.
3:21
We could keep doing this and write more papers and get more
3:26
grants and all of that was going very well.
3:29
I was a tenured full professor, as you mentioned, at a large
3:32
lab, over 30 people. I had an NIH Directors Pioneer
3:35
award, which is really the epitome of kind of, you know,
3:40
achievement, if you will, in academia.
3:44
But that felt like it would be kind of doing the same thing
3:46
over and over. And, and, and rarely do you get
3:50
enough sort of funding and, or speed to be able to do it at
3:55
such a scale to such an impact that you can actually get into
3:58
the real world. More often than not, you're just
4:01
writing papers about what that future implication is.
4:04
Or you could take the next step, which is now that we've built up
4:08
a foundation, how do we actually develop new treatments and
4:12
deploy those treatments in the real world?
4:15
Something I've called the the last paragraph effect, where in
4:18
the paper the last paragraph is kind of what you want to be
4:20
doing because it's that future looking thing.
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Now we had a chance to do it and, and I decided to take the
4:25
leap. And as I try to explain to
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others who are looking at similar decisions, despite a lot
4:32
of talking to people and feeling like I had figured things out,
4:36
the thing that protected me the most in that process was my pure
4:39
naivete about what starting a company and doing this involves.
4:45
And it's been extraordinarily rewarding.
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I, I should say, to start that from that perspective of like,
4:53
let's follow the signs. We think we can build a business
4:56
around this. And then let's learn as we go
5:00
that I, I don't look back in any way regret any of that.
5:03
You know, giving up a tenured position obviously is it's a big
5:06
deal from a personal perspective, but there's just
5:09
been so much excitement, so much fulfillment that has come from
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taking this route that has taught me that, you know, the
5:18
safety of academia or the perceived safety of academia
5:23
holds you back and holds the science in fact, back.
5:28
I love that. What What was then the
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inspiration behind Alto, and what was the problem you were
5:36
most passionate about solving? So when we started, we actually
5:42
had three different pillars to Alto and this, you know, is
5:47
coming from an academic lens. So you, you take your data and
5:50
you kind of extrapolate into the real world and you take, you
5:55
know, sort of an idea of I mean, I'm, I'm a clinician too, right?
5:58
So I understand how clinics work and how physicians and, and
6:01
patients work, but you sort of have an idea of how things might
6:05
play out in the real world, even economically in, in rolling out
6:08
your ideas. And so our three pillars were as
6:10
follows. 1 is optimized care with existing treatments.
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We had have biomarkers that are predictive of, as I mentioned,
6:22
medication, you know, the psychotherapy even as you think
6:28
of that as sort of a talk therapy, it's a biological
6:31
intervention, just the same brain stimulation treatments
6:37
that are given to more resistant population.
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So it seems like you can stage treatment in a useful way,
6:43
improve clinical outcomes, save money for for healthcare
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systems. That was one pillar.
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The other pillar, because surely pharma would want to use these
6:51
kinds of tools to improve their trials, was to be a partner to
6:56
pharma, same concepts, but now around development of new drugs.
7:00
And then the third pillar, the one that actually at the time
7:03
seemed hardest was to develop our own drugs.
7:07
We learned very quickly. This is again where naivete and,
7:12
and and then openness to just new experiences and learning
7:15
from feedback was extremely instrumental and very motivating
7:21
for me. We learned very quickly that
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that first pillar is actually going to be the hardest that the
7:27
taking even what seems like it makes perfect logical clinical
7:31
sense and in embedding it in the way at least within the US and
7:36
certainly I think rest of the world, but our lens was
7:38
primarily US. The way the healthcare system
7:41
actually works in practice was going to be really, really hard.
7:45
I mean an uphill struggle no matter what the clinical outcome
7:50
improvement would be. There's just the realities on
7:53
the ground. We'd started to work with
7:55
pharma, in fact, had helped reposition a drug with one of
7:59
the big pharmas into another indication based on data.
8:03
But we were seeing that we were, we were basically perceived as
8:05
more of a vendor than a partner. And that didn't feel like it was
8:10
the best use of our our time and effort.
8:12
And simultaneously, we realized that actually the thing that
8:16
felt the furthest away was actually the closest and, and,
8:19
and allowed you to control much more precisely how you develop
8:24
drugs and what kind of of information you bring into it.
8:28
And so that let us early on to really focus down on that One
8:34
Direction and to understand that the opportunity here is massive
8:40
to develop the first really personalized approach in a field
8:46
that has never had it for decades.
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And with that, do a few really important things bring
8:51
completely new mechanisms in a systematic way into the clinic
8:55
and to patients, increase the odds of success of drugs and
9:00
therefore increase their effectiveness in the real world.
9:03
When you when you so you know who it works for and who not and
9:07
to be to kind of create a systematic approach that doesn't
9:12
just apply to our own pipeline, but it can apply broadly to the
9:16
field. And so when you think of like
9:17
what that would mean, if we can solve this, that changes how a
9:21
clinician and a patient thinks about their care, right?
9:24
We think about running a test, which we don't really do except
9:28
to rule out things, rule out hypothyroidism and depression or
9:32
whatever the case may be. So run a test that tells you
9:35
something about your disease in an objective way that has
9:38
tremendous implications for even areas like stigma and
9:42
involvement in care and so forth.
9:44
Guide treatment based on some knowledge of the patient.
9:48
That's more than just random chance that's got to make people
9:51
feel good about the process, right?
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And reset the bar for what acceptable slash our target
10:01
ought to be for a new treatment. All of that seems like
10:06
completely obvious. Why wouldn't you do that if you
10:08
were saying oncology, where this has already been happening for
10:11
20 years or 15 years really? But in psychiatry, it's a
10:17
completely foreign concept. So that's what we really wanted
10:21
to solve is, is not just bring a drug to market, not just even
10:26
bring a pipeline of drugs to market, but a wholesale change
10:29
in the way we approach psychiatry, a wholesale change
10:34
in the way we implement insights from neuroscience into the
10:38
clinic. The kind of thing that I felt
10:41
like my whole training had been for.
10:45
But to your first question, you just don't really know when it
10:48
comes to you and says this is the time to do it.
10:51
This is the time to take the leap and push the signs to the
10:55
Max because it's not always the right time.
10:58
It just got to become the right time over the course of the
11:02
evolution of our work and the timing within the field.
11:05
I think for being open to this kind of work I.
11:09
Think that's interesting as well because even when you founded
11:12
Alto, you still have these free scenarios.
11:16
We could go in these three different directions.
11:18
It's not like you had the real clear plan that you now have
11:23
obviously gone gone through, but it took a while to get to that
11:26
actual clear plan, much of what was interesting.
11:29
Yeah. And, and you know, that's in a
11:31
way, I think the Hallmark probably of a lot of, of
11:37
businesses, you know, if you look at kind of the, the origin
11:41
story is, is the learning that happens as you get in there when
11:44
you're trying to do something new.
11:46
So if if you're taking like a pretty established thing,
11:49
whatever it is, or you're developing 1 drug where you're
11:52
not going to really move around all that much because that's all
11:55
you have, right? If you have a, if your thing is
11:58
a chemistry platform, like a way to discover things, well, that's
12:02
a different perspective, right? But when, when our thing is
12:06
trying to learn how to do precision psychiatry and all of
12:10
those pillars were precision psychiatry pillars, right?
12:14
You have to understand, you know, the, the sort of plan
12:18
coming in, in contact with the reality, right?
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As I think Mike Tyson says, you know, everybody has a plan until
12:24
they're punched in the face. You know, you, you, you have to
12:29
be able to adapt because it's, it's all new.
12:33
Nobody's thought of this answer before, which means as we're
12:38
formulating our answers, we're testing them against input from
12:41
other people, input from our own work internally.
12:45
And that's always going to be the case.
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So even as we progress in our in doing our science with our
12:53
singular focus, we're always learning, you know, where are
12:56
the opportunities that didn't exist a year or two ago?
13:00
But because of fill in the blank, the latest being, you
13:03
know, AI actually holding a lot of of promise that allows us to
13:08
think about things differently. And and not just think about
13:10
what we're doing now differently, but think about
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what we could be doing once we hit commercial, you know, stage
13:16
with one of our drugs differently.
13:19
You know, one of the so we have, you know, five kind of core
13:23
values that we have defined early on in our existence and it
13:30
kind of re examined, reaffirmed and try to live by every day.
13:35
But one of them is rethink everything.
13:37
The idea that, you know, you may have a really well thought
13:41
through plan, idea, whatever, but you're getting feedback all
13:46
the time. And you should be open to the
13:49
possibility that you're wrong about something and or that
13:52
you're right, but need to optimize and and kind of pursue
13:55
something in a different way. And we try to live that, which
13:58
means that at any point in time, you have to be open to learning,
14:02
You have to be open to that kind of feedback and, and in a kind
14:08
of humble way, respond and understand.
14:12
Like, look, our, our mission and our vision is broad.
14:16
And it's this high impact change how psychiatry operates, how we
14:21
get there has to work in concert with the, the realities on the
14:26
ground. And the more you embrace that,
14:28
frankly, the more fun it, it, it, it becomes to figure out how
14:33
you're going to adapt and, and you know, what are you getting?
14:36
And we've gotten that in spades all along.
14:38
And that, you know, for me personally is honestly like the
14:42
joy of learning. That's the fun part of being at
14:46
the bleeding edge and and probably what drove me to even
14:51
do this in the first place. Yeah, I love your approach to
14:55
culture as well. Like you talk about these five
14:57
values, but it's it's more than just, you know, writing them on
15:02
a wall. I mean, you talk about like
15:04
living and breathing and, you know, dying these values that
15:09
first of all, just talk us through some of them and then
15:12
secondly, that that how do you really distill those behaviors
15:16
across, across a business like Alto?
15:19
So I mentioned rethink everything that is probably one
15:22
of our most critical values closely coupled with that is let
15:27
data do the talking. You know the that I think a lot
15:31
of people and you see this certainly in academia where
15:36
ideas kind of live a long life, sometimes in absence or or
15:42
against data that you know, might lead them to change.
15:48
You know, never let data get in the way of a good idea.
15:52
For us, it's quite opposite. We, we are constantly seeking
15:56
the kind of go no go data at any level in running the
16:01
organization or in doing our science that would help us
16:06
understand whether we're on the right path or not.
16:08
And so that takes the form, for example, of always make sure
16:13
that you can replicate something on the science side of, of, of
16:16
kind of the, the operation. Always make sure you can
16:19
replicate something. So you're not just driven by an
16:23
optimism bias, you know, because you're doing or confirmation
16:27
bias because you're, you're doing something, you kind of
16:29
imbue it with positive value. So always put those gates in
16:33
front of you simultaneously. Another value is operate with
16:37
urgency, which is, you know, it sort of brings those things
16:41
together. You don't want to sit there and
16:42
just kind of navel gaze all the time and analyze data.
16:46
You're really trying to achieve something and not from like a,
16:49
we have a, you know, guidance for a particular timeline with a
16:53
study. You know, the operate with
16:55
urgency is much bigger than that.
16:57
We're trying to bring things to market for patients that have
17:01
impact and we want to do it now. And So what is the most direct
17:06
and effective way to do that related to that is is invested
17:10
in impact where you always have to remember how what you're
17:14
doing has an effect on that goal.
17:16
And we never want to find ourselves doing something
17:19
because it's doable or because it's there.
17:21
It has to be the right thing and ultimately the right thing that
17:24
lines up with our mission and finally in this together, which
17:29
says that, you know, we we will live and die as a team.
17:32
There's no individual here. You know, there's no I in team
17:37
as people like to to sort of catch phrase it, but really
17:41
living that not, I have to say, is one of the biggest things
17:44
that difference between academia and industry is that in
17:49
academia, you'd say, well, there's no I in team, but
17:51
there's a me in team. There's always a sense of
17:55
somebody who is the first author and they're getting the credit
17:58
and, and the other folks may be contributing to that story.
18:01
They may feel like they haven't gotten the the sufficient credit
18:05
because they're not first or Co first or whatever.
18:07
So there's always sort of this positioning thing even amongst a
18:11
group that gets together, gets along together, You know, very
18:13
well in industry that's not the case.
18:16
We only have One Direction, right?
18:20
We are doing a trial, we are submitting results to the FDA,
18:24
we're submitting for approval, we're marketing a drug, whatever
18:27
the case may be at different stages.
18:29
There is one organization and one activity.
18:32
And and that's been incredible actually.
18:34
And seeing how people really, truly collaborate and really
18:39
like proactively think about each other, it's been awesome.
18:43
And so, you know, to your question of like, how do we
18:46
reinforce this? We're constantly using that
18:49
vocabulary, right? We, we actually just internally
18:52
people have gotten to the practice of referencing these
18:56
values as they talk about any one thing, they're like, you
18:59
know, in some conversation and say, like, well, hold on, let's
19:02
rethink everything. And, you know, it's a way to
19:05
step back and, and question and and so forth.
19:07
We also twice a year get the whole company together for an in
19:10
person event and we give awards for each of these values, people
19:16
who are these values nominated by their peers.
19:20
So you you're recognized for living that value by your peers,
19:24
which on both sides reinforces that you know that value and
19:29
that behavior. You know, we all try to live it
19:32
as as leaders, but I think it's it's the whole group living it
19:37
and understanding that it does resonate with the way we
19:39
operate. That makes it quite real and
19:42
quite a kind of an accelerating force that helps people organize
19:49
themselves around on what matters for the organization.
19:55
I mean, any kind of culture talk I always really enjoy, but I
19:59
feel like the thought that you've got into yours and the
20:02
cohesion with Alto and the industry and and it just works
20:07
so well. I I want to change the subject
20:11
to something a little bit different as well.
20:13
You. The last time we spoke, we we
20:15
talked about being cash frugal in the market.
20:19
I mean, now more relevant than ever, but you've always had this
20:22
mindset. I mean, I suppose just like how
20:28
have you instilled that mindset across the whole company and why
20:32
is it so important? So in founding Alto, we
20:37
explicitly took a portfolio theory approach to what we're
20:41
doing. So understanding number one,
20:44
that we're doing something that's different.
20:46
There's going to be and there just have to be opportunities to
20:49
learn. You can't get it right, you
20:51
know, get everything right the first time.
20:54
So encountering those opportunities, adapting and
20:58
ultimately hitting your target, that has to have a particular
21:03
perspective around how you diversify risk.
21:06
And with that comes how you spend money against those
21:09
opportunities. So, you know, you could have an
21:13
infinite number of of drugs you develop or you can have a focus
21:16
number, but you probably shouldn't only have one because
21:20
that decreases your your opportunity to learn and adapt.
21:24
But in selecting your your pipeline, thinking about how you
21:27
diversify across those so that they're uncorrelated as much as
21:31
possible so that you can actually embed learnings without
21:35
the whole thing depending on one, you know, you know, leg of
21:40
the stool, so to speak. Yeah.
21:42
And when you, when you kind of anchor it that way and when you
21:46
look at then how we did our capital formation to be able to
21:49
fund all of it. And we have 4 drugs right now in
21:52
the clinic, all of which are going to read out in the next
21:55
two or so years and cash into 28 beyond that.
21:59
In other words, that allows us to take a really full swing
22:03
across everything and make sure that we hit on, you know, hit
22:06
the ball on at least one, if not more.
22:09
And we're really designed to be an org that should at least lead
22:14
to 1 commercial drug with the opportunity for multiple in the
22:19
near term. That lens then washes across
22:23
everything, right? So you have fixed resources, you
22:27
have multiple goals, you understand how they interplay
22:29
with each other and timelines and so forth.
22:32
So how do you value anyone asset or another?
22:36
How do you put resources to one thing or another?
22:39
How do you define what you do based on the available resources
22:44
and the right way to do things? So, for example, we decided
22:49
early on that a focus of ours was going to be to have an
22:54
internal operations team and an internal engineering team.
22:58
The operations team to run the trial, the engineering team to
23:00
build the tools to be able to do things like EEG in a patient's
23:05
home and kind of real time quality control to set up our
23:09
own cognitive battery for assessing, you know, things like
23:12
memory, attention and and so forth that are our biomarkers.
23:17
And, and that has paid off in many different ways.
23:21
But it's very deliberately a resource question and a is this
23:26
the right thing to do question in probably we understand the
23:30
resource part easily in the beginning, probably did not
23:35
appreciate just how much it was the right thing to do until we
23:39
got into things and really started learning as you know, as
23:42
events happen, you know, it's the kind of thing that if you
23:47
make the right decision, that does reinforce itself.
23:51
I think people feel like, OK, because we have that lens, we
23:54
now can do X and that leads to kind of new opportunities from
23:58
there. But that perspective then of
24:02
we're trying to do multiple things, we're owning as much of
24:05
it as we can, builds a understand what you do and how
24:10
much you spend and, or, and, or what the risk is associated with
24:15
that spend at all levels of the organization.
24:18
And so it becomes actually quite easy to be frugal because your
24:23
orientation is not just saving money, it's trying to do the
24:26
right thing for the programs. And like I said, it just opens
24:30
up opportunities, right, that we would not have had.
24:34
And that itself is reinforcing. So I don't think there's anybody
24:37
who in the, in the whole organization that their first
24:42
response is just spend money and, and like throw money at a
24:45
problem. Like we're, we're always
24:47
thinking, again, going back to our values, how do we actually
24:50
derive value out of this experience?
24:53
How do we hit our target better? How do we make sure our trials
24:57
have the best chance of working? And how do we bring drugs to
25:00
patients most effectively and and most quickly?
25:04
I mean, it seems seems logical now, but you're right.
25:07
When when you were first starting Alto, you were in a
25:11
much stronger market. You were a first time founder
25:14
and CEO. So yeah, these things aren't
25:17
obvious about those points. They seem obvious in hindsight.
25:21
But like, yeah, I, I mean, I think your cash management from
25:26
an outsider's perspective looks pretty incredible when you look
25:30
at it now. I mean, we, we did, you know, we
25:34
did this, the alcohol before this.
25:37
And you talked about how you had four clinical trials and you
25:40
would hit all four data points the next two years and you'd
25:44
still have cash runway for another year.
25:46
I was like, OK, yeah, that's a very strong position in today's
25:51
market. Absolutely.
25:53
Right. So.
25:54
So that's the other thing that again, I didn't really
25:56
understand early on, but but came to understand is just the
26:01
reality is of like 2020-2021, you know, bled into the the pain
26:06
of 2022 and beyond in, in terms of the market is, you know, how
26:11
much your decisions ramify over years.
26:15
And you know, there are, I think within biotech, there's been a
26:23
lot of history of a lot of kind of profligate spending coming
26:27
from an academic lens. We've never had that luxury like
26:31
that. Just no academic really has
26:32
that. So you're inherently thinking
26:34
about how you husband resources in the right way and and it's
26:40
paid off, right. So, so we are very careful with
26:43
how we did the capital formation.
26:45
We still have more than half of the total cash we've raised over
26:49
time and able to get through all these programs.
26:53
That feels incredibly reinforcing for that decision
26:56
that leads to I think a lot of good decisions going forward.
26:59
I mean, we just brought it in new asset in Alto two O 7 this
27:04
past June. That has probably the highest
27:07
probability of success given all the data behind it across the
27:11
the the pipeline and may very well then be our first drug to
27:16
to market clinically couldn't do that if we didn't have that
27:19
perspective. So again, taking a lens brings
27:23
opportunities that you'd never anticipated and putting yourself
27:28
in in that position, you know, it's the sort of a hard to
27:35
capture thing because you have to be able to see the future.
27:37
Could we have seen the future in 20/20/21 for what it is now in
27:41
terms of the market? Probably some people could have,
27:43
but people certainly weren't acting that way.
27:46
So it didn't. Probably most people didn't.
27:50
But you have to be nimble and adapt to the realities and that
27:53
includes your own results. That includes externalities.
27:57
You just always have to be ready to be.
27:59
Nimble. Yeah, definitely.
28:02
And yeah, looking ahead even further, what's the the big
28:06
picture long term vision for Alto and for precision
28:11
psychiatry as a whole? So let's look at what it is
28:15
right now, right? And, and I think there's two
28:18
buckets to look at. One is depression, one is
28:21
schizophrenia. So in depression, we have a lot
28:24
of treatments. They don't work all that well
28:27
and they're pretty much the same.
28:28
So you know, SSRI, SSRI, they're a little different.
28:32
There's some other drugs kind of in the mix, but for the most
28:34
part, you're looking at drugs that are tweaks on things
28:39
discovered basically through serendipity decades ago,
28:43
fundamentally nothing new. And a lot of people don't
28:47
respond to them. So what we need there are two
28:49
things, new mechanisms and more effective drugs.
28:53
That's where I see a sea change happening in psychiatry on the
28:57
depression side is getting rid of like the constant cycle
29:01
between drugs that take a while until you figure it out.
29:04
And they most of the time don't really work to drugs that are
29:08
more effective the first time. Just more effective in general
29:13
and more effective in a different way.
29:14
You know, Alto two O 7, which I just mentioned is a a dopamine
29:18
agonist together with an anti medic agent.
29:21
That mechanism, the dopamine agonist Prampxil has shown
29:25
really outsize effectiveness signals in other trials,
29:29
including a large well conducted trials and treatment resistant
29:33
depression. Bringing that kind of drug to
29:35
the market with that kind of size outcome difference from
29:39
placebo, that could be a game changer for these patients and
29:43
raises the bar in turn for everything else.
29:46
That's what we're going for in depression.
29:49
On the other hand, in schizophrenia, in schizophrenia,
29:52
we have a lot of antipsychotic drugs.
29:54
They're reasonably effective against psychosis.
29:57
They carry a lot of side effects, but they're reasonably
30:00
effective. What they don't touch is the
30:03
cognitive impairment associated with the disease.
30:05
And, and that's actually the basis of the disease.
30:08
Most people would, even many people would perhaps argue that
30:12
that schizophrenia has a cognitive disease more than it
30:14
is a disease around psychosis. Cognitive impairment starts
30:19
early. It's there throughout the course
30:21
of illness. It is, you know, the biggest
30:24
determiner by far of disability and disability is massive for
30:28
these patients. Can't hold down jobs, can't hold
30:30
down relationships and so forth. And obviously our cost to the
30:34
the medical system and the Social Security system and so
30:36
forth. But right now there's no
30:38
treatments for that whatsoever. And so the potential for using a
30:43
biomarker to both find the right patients for an intervention,
30:48
but also understand even how an intervention works, a biomarker
30:50
is an outcome, as we're doing right now in the study of a drug
30:53
called Alta 1O1PD4 inhibitor in schizophrenia, holds the promise
31:00
of opening up a a treatment area that has never had anything.
31:05
And if you think like fundamentally, what does that
31:07
mean for these patients who are ill their whole lives and
31:12
struggle with this, that could be a complete game changer on
31:16
its own. It may not be the most initially
31:19
effective treatment. That's usually the case right,
31:21
where the first thing in then gets built upon.
31:25
But that's exactly what we're in a position to to do is get that
31:29
first in Rd. then figure out how you build and adapt from there
31:32
and keep building it onward, much like has been happening in
31:36
in depression. But that first step becomes the
31:39
big one. That is the game changers
31:42
introducing not just a first in class, but a first in category
31:46
treatment that has me excited as well.
31:48
Fast forward those two pictures, say 5 to 10 years from now, the
31:55
expectation from everybody involved, I mean everybody,
31:59
patients, doctors, clinicians, companies, funders fundamentally
32:04
changes. Right now you have areas which
32:07
finally have treatments for the first time.
32:09
You have areas with much more effective treatments where the
32:12
goal is really around personalization and and
32:17
refinement of an algorithm for treatment.
32:21
That sounds like a very different psychiatry.
32:23
And I think just a lot of your listeners probably in their
32:26
day-to-day lives, themselves or family members or friends, have
32:30
struggled with mental health challenges of various sorts.
32:34
They know what a system right now is like that fundamentally
32:38
needs to change. And that's where I see all of
32:41
this going. You know, it's a big swing.
32:44
And, you know, we'll see how things play out.
32:47
Obviously, science has a word or two, you know, between where we
32:53
set things up and how they end up.
32:55
And it could be better, it could be worse.
32:56
That's the act of discovery in science, but we're aiming for a
33:01
big swing. I think what you guys are doing
33:04
is, is awesome and I think the way you're doing it, it's been
33:08
like it, it, it's pretty inspirational.
33:13
I just want to move on to my, my final question.
33:16
I mean, and it's the same question we ask every guest.
33:19
So if if you could have dinner with any scientist or innovator,
33:23
past or present, who would it be and why?
33:26
So I'd say Thomas Edison actually for some of the same
33:31
reasons that we've already been discussing, which is, you know,
33:36
the the saying that people have sort of attribute to him.
33:39
And I don't know what he actually said is something like,
33:41
I know 1000 ways to not build a light bulb or 10,000 ways or
33:46
whatever that number ended up being.
33:48
Or what exactly he said doesn't even really matter.
33:51
It's the idea of, of experimentation, of, of kind of
33:57
learning and adapting and growing and, and understanding
34:00
that a, a set back is actually just a stepping stone to that
34:05
next discovery. And importantly, that when
34:09
you're going for something really big, I mean, a light bulb
34:13
is literally the, the image we have of a new idea, right?
34:17
In the cartoonish sense that the road to get there is going to be
34:22
hard. It's going to be certainly non
34:24
linear, it's going to be unpredictable.
34:27
But as long as you're bringing into it the right mindset and
34:32
that you're flexible and, and you're able to take an
34:35
information and you find the right people around you to build
34:39
that and, and nurture it, then you'll find success because
34:43
you're ultimately trying to do this for the right reason and in
34:46
the right direction. I love that, I love what you're
34:49
doing and me definitely will keep an eye on Alto and it's
34:54
been a pleasure having you on. It's been a pleasure here
34:58
discussing this with you and we all hope for the best here.
35:02
There's a lot that needs to change in our field.
35:04
Yeah, and please everyone like and subscribe to the Bio Hub and
35:08
thank you so much again, Amy. My pleasure.