March 9, 2023 • 30 mins
Alice Zhang is the co-founder and CEO of Verge Genomics. Alice's problem is this: How do you use artificial intelligence to drive down the price of developing new drugs?
The company is using AI to find new disease mechanisms to target, and to speed up drug development. If using AI can help experimental drugs succeed even a little more often than they do now, it'll be a big win.
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Episode Transcript
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Speaker 1 (00:15):
Pushkin. Over the past few decades, it's become more and
more expensive to develop new drugs. It now costs over
a billion dollars on average to bring a new drug
to market in the United States, and of course drug
companies pass those high development costs onto us in the
(00:36):
form of higher drug prices. This has been going on
for so long that we have sort of gotten used
to it. But when you zoom out, it's strange because,
as I've said before on this show, and as I
will say again on this show, one of the main
things technology does is it makes things more efficient and
(00:57):
therefore cheaper. Over the past few centuries, we've seen technologies
make all kinds of things cheaper, everything from clothes to
food to TVs. So why hasn't new technology made drugs cheaper? Two.
I'm Jacob Goldstein and this is What's Your Problem, the
(01:17):
show where I talk to people who are trying to
make technological progress. My guest today is Alice Zang, co
founder and CEO of verge Genomics. Alice's problem is this,
how do you use artificial intelligence to drive down the
price of discovering and developing new drugs? Why is it
(01:38):
getting more expensive to develop drugs, despite the fact that
we have better technology to do it. Yeah. Absolutely. One
of the reasons is, you know, even though a lot
of the new technologies we've developed have made us better
at testing more drugs faster, but the fundamental problem is
that even if we can get a drug all the
way to clinical trials, which is the last step of
(02:00):
drug development, ninety percent of those drugs still fail. So
if you think about it, we're spending millions on each drug.
Of those drugs are failing at the last and most
expensive stage of drug development. And so really most of
that billion plus dollar figure you hear is due to
the cost of failure. Just to be clear, that figure
(02:22):
more than a billion dollars. It's you've got to include
the cost of all the drugs that don't work exactly,
the ones that do right exactly. So the ones that
do work have to pay for all the ones that fail.
That's the fundamental problem, exactly, And you're setting out to
fix that if you can. Absolutely, we think there's an
opportunity for AI to fundamentally shift really the failure rate,
(02:47):
and the most impactful time to do that really is
the failure in clinical trials. So can we predict before
we go in to these expensive clinical trials genes or
targets or drugs that are more likely to work in humans,
because even a ten percent decrease in that failure rate
could have massive I saw a number of up to
(03:11):
fifteen billion dollars annually in industry cost savings. You could
still be in a universe where most of the drugs
that go into clinical trials fail, but instead of ninety
percent of them failing, seventy percent of them fail, and
that would be a huge win. That would be a
huge efficiency gain. It would save a ton of money, absolutely,
And I think that's something that's underappreciated about AI and
(03:31):
really any technology, is that oftentimes people have this expectation
that this technology is going to absolutely transform a field overnight.
And I think what people don't appreciate is that most
of the time that doesn't happen. It's always step by
step incremental. But even a ten percent change would have
billions of dollars of cost savings and would be a
(03:52):
huge win for patients in the industry worldwide. I like
that frame, actually, I like that frame of maybe AI
can have drugs fail most of the time, but not
as much of the time as they fail. Now, like,
it seems very credible, It seems very plausible. Would you
put it that way? Yeah, it's all life is nothing
but a learning process, Yes, getting less bad at everything.
(04:16):
So I know you were studying to be a doctor
and a researcher not that long ago, a few years
ago before you started your company. Like, tell me how
you went from an mdphd program to starting the company. Well,
my PhD research was actually in using genomic analysis and
computational biology to analyze large scale data sets and find
(04:41):
new drugs that could improve drug development. And we found
that from our very first drug that was predicted from
our algorithms when we put it in mice after they've
been injured, help them walk and recover from that injury,
that nerve injury about four times faster than the leading standard.
And that was just the first drug that was predicted.
(05:03):
And I looked at this technology in this approach and
I thought, Wow, there's so much promise here. You know,
am I really going to just publish this and let
it sit on a bookshelf somewhere, or if I'm not
going to be the one to really develop this to patients,
you know who will, And when I looked out off
the field, I did not see a ton of biotech
(05:25):
or farmer companies that were truly computationally driven. Usually within
pharma companies they might bring in computational biologists to support.
There are scientists or their biologists, but there wasn't really
a genomics computationally driven company at that time. Now there
are many, but at the time there are very few.
And so I actually, you know, it wasn't a binary decision.
(05:47):
People always ask me, how did you make the courageous
decision to leap? It wasn't really like that. I think
what we did first is that we just took three
months three month leave of absence. We joined a program,
an incubator called a y combinator. We as you and
you and well me and my co founder Jason. And
(06:09):
the first question really was, you know, can we even
generate some data that validates that computational biology can predict
targets that work? And then when we saw some data,
the next question was can we even hire people that
want to come on? And the next question was can
we even raise money from people that will care? And
(06:31):
I think that is so such an important lesson because
I think people oftentimes get caught up in just the destination,
you know, is where I want to be? Is this
the career I want to have that they don't take
the first step, And really it's the first step that's
needed to actually get the data to even decide if
it's the appropriate track for you. And did you really
just keep thinking, well, this might not work, but let's
(06:53):
do the next thing. Were you in a place where
you could have gone back to the MD PhD program
for a while. Yeah. I took a leave of a
continuous leave of absence for probably over five years, probably
more than I should have, until the point where a
lot of my friends are like, are you really, are
you really gonna go back? And finally the medical school
(07:13):
is like, you're not really going to come back, let's
just terminate your leave of absence. But it was in
the first few years a really important safety net for
me that gave me the psychological safety to really take
a risk and really pursue a new idea that I
don't know if I would have otherwise. And I think
that's so important. I think for universities to provide is
(07:34):
that to recognize there can be more than one track
for people to do really excellent science and make an
impact more than just becoming a professor. And sometimes that
psychological safety is what's needed to help people find their
ultimate calling too. By the ways, so far, By the way,
(07:55):
what's a very brief definition of computational biology. It's really,
at the end of the day, in my view, just
the use of computers and data sets to understand and
biology better. By the way, what happened to that molecule
that you were testing in mice in grad school? That
(08:17):
seemed useful? I don't know. It's a good question. Actually,
I think the project was taken on by someone else,
but I'm not actually completely sure. So, Okay, you leave
grad school, you start a company you in fact now
(08:37):
have taken You have a bunch of molecules that you're
working on, and that seemed promising. But there's one that
is in clinical trials now right to treat als Luke
Gary's disease, a terrible disease that is very poorly treated.
And I thought that we could talk about the story
(08:57):
of that molecule of that drug as a way to
understand the way your company works. Can you just sort
of take me through the life of that drug? So far? Yeah, absolutely.
I'll start off just by talking about als and why
it's been so hard to discover the right therapy, and
then you know why how we did that differently. So,
(09:19):
as you might know, LS Luke Garrig's disease is a
really horrible disease. What happens is that these neurons called
motor neurons start dying, and most patients experience paralysis and
then death, usually within three to five years of diagnosis.
A very fast progressing disease, and there really aren't any
(09:40):
meaningfully effective treatments that really slow or stop the disease today.
So a very horrible disease with a horrible prognosis and
no available treatments, and why it's been so hard I
think to discover really effective treatments is really just the
complexity of the disease, and really any disease of the brain,
the brain is the most complex organ in the body.
(10:02):
So you end up having a lot of drugs brought
into clinical trials that worked in mice. I always like
to say we've cured LS or can There are many
diseases in mice a thousand times, but none of them
have really worked in humans. So what we did differently
was we started from day one by collecting data from
over a thousand ALS patients as well as controls, and specifically,
(10:24):
we collected samples of brain tissue as well as spinal
cords from these patients that actually passed away from ALS.
So you got samples from a thousand patients who had
died of ALS. How did you do that? So what
we've done over the last seven years is we've signed
partnerships with over twenty one different brain banks, hospitals, labs,
(10:49):
academic centers worldwide that collect these brain tissues. They're usually
donated from patients that have passed away from the disease
and whose families want to contribute to research. Could So
step one basically is get tissue samples from real patients.
And you said controls as well, right, So tissue samples
from healthy people as well, so that you can use
(11:10):
them as a basis of comparison. You have the samples, Now,
what's step two? So step two is that we put
an enormous amount of effort into quality controlling these, So
that's a big underappreciated step. They can be very noisy samples.
And then step three is that we sequence them, so
(11:31):
we profile, what is the expression of all twenty thousand
genes in the genome, and we also sometimes do DNA sequencing,
we look at genetic mutations. We also have a clinical
information about that patient, how long did they have the disease,
when did they die? And that makes for a very rich,
(11:52):
multidimensional data set, and that gives us essentially a global
snapshot of what happened in that patient. H okay, and
you and presumably the sequencing that you're doing on the
patient's tissue samples, you're doing the same sequencing on the controls,
the samples from healthy people. So now you have this
(12:12):
very large data set. What's the next step. So then
you have this snapshot of what happened, and the tricky
part is to figure out what caused it. I often
liken it to a plane has crashed, right, You're looking
through the rubble and you want to figure out how
the plane crashed and how that information can be used
to prevent further planes from crashing. So that's when our
(12:36):
software engineers and data scientists as well as machine learning
scientists come in and we have algorithms essentially to integrate
multiple data types all the way from the RNA, so
how the genes were expressed to genetic mutations to essentially
create a map of disease biology, and within the map
our networks of genes that are all interconnected that we
(12:58):
believe cause disease. And so I like to think about
it like when you're looking through a plane crash the rubble,
you want to find the black box, which I'll help
you figure out the cause of the disease. And by
having all the information, we essentially locate the black boxes
of disease, the targets that are really at the center
of those networks, and then we design drugs against those
(13:20):
targets that we believe can reverse disease. It seems like
differentiating between correlation and causality in this particular setting would
be really hard, right, Like to use the plane metaphor,
if you had a bunch of planes that crash and
a bunch that hadn't crashed, you might say, oh, like
the wings were off all the ones that crashed, and
that's why they crashed. But actually the wings came off
because they crashed, right, and it was something else that
(13:42):
caused the crash. I feel like that would be I mean,
an obvious problem. Yeah, that might be hard. To solve Absolutely,
you hit the nail on the head, and actually the
plane metaphor is a really great one here. For one
of the biggest challenges with looking at tissue from a
patient that already died is that you're getting the crash right.
You're not seeing video of before the crash. You're really
(14:03):
getting the crash. And the challenge is how do you
figure what caused the crash versus as well was just
the effect of the crash, like a burned wing, etc.
And one of the ways we do that is we
combine different data types. So we found that looking at
one type of data, for example, just RNA data is
(14:25):
in particularly helpful, but it's actually looking at where do
you get convergence signal that pulls through multiple types of
data to start revealing more compelling signal. So as an example,
we look at genetic data as well. So genetic data
is useful for looking at cause versus effect because it
contains information about genetic mutations that you were born with
(14:46):
as a baby that then lead to increased risk later
in life for a disease. And that's kind of nature's
human experiment for really cause and effect. And when we
layer that on that information on with the RNA data.
It actually gives us information about how the genetic drivers
are acting in these functional pathways, which is a big
(15:07):
issue actually with just looking at genetic data on its own.
So I wish I had a better I wish I
had a way to actually string that through to the
plane metaphor. But and there's a time for leaving metaphors behind.
Your company uses AI in drug discovery. I appreciate in
a certain way that you haven't said AI yet, but
(15:27):
also I don't want to not talk about it. I
mean in the sort of figuring out what's going on
in this step? Is that well, is that the first
instance in this process where you're using AI? Is it?
We're talking about that here? Yeah, I mean, I think
AI is a really broad term for any kind of
process where the computer is learning from something. So there
(15:48):
are all sorts of applications of AI in this entire process,
for example, how we're integrating the data sets together, how
we're inferring what are the central nodes or the key targets.
I would say the most classical use of A on
the way that most people think of it is then
(16:09):
once we have this network of say one hundred genes,
how do we actually find what the cause is? How
do we find what is the hub or the right
target to hit to turn off or on all hundred
of those genes. And that's where machine learning and AI
comes in handy. In a minute, Alice explains how this
(16:31):
actually works in the case of the ALS drug verges
working on. Now now back to the show. So okay,
Alice and her colleagues at Verge have collected all these
tissue samples from ALS patients. They've used the samples to
(16:52):
generate this huge data set that shows genetic variation and
changes in how genes are expressed, along with lots of
clinical data about the patients, and then they build these
basically these AI models to try to figure out where
in this complicated biological process that's happening in this disease,
where they should try to intervene with a drug, Basically
(17:16):
where they should try and target a drug. I think
of this oftentimes, like if you think of a map
of all the airports in the US, you want to
figure out how to go after the hubs like Chicago
or New York. You don't want to go an airport
in Kansas or I will wouldn't be very effective at
stopping airplane travel in the country. So there's a lot
(17:38):
of different pieces of information that we collect to then
infer what are the best genes that are not only
central within this network, but also there's independent evidence of
a disease causal effect or a relationship to disease. And
so you do all that in this instance, and what
(17:59):
do you figure out? So what the algorithms spit out
is essentially a ranked list of targets, all right, So
these are ranked list of targets that are predicted if
we could dry them, would restore that network back to
levels of healthy people and potentially slow or stop the disease.
And then what we do is we take those targets
(18:20):
and we start testing them in the lab, all right,
So we actually what is kind of cool about the
platform is we get all these targets from human brain
tissue and we also can test them in human brain
cells in the lab. So you get a list it's
basically genes to target. You either it says upregulate or
make this gene express more or make this gene express less.
Is that basically what the AI is out putting exactly, Like,
(18:44):
so how long in the instance of this ALS drug.
How long was the list? More or less, our initial
set of targets was twenty two high confidence targets, and
then we actually then generated another chut choosing updated data
of about thirty more targets as well. And what was
really striking when we tested these targets is that when
(19:07):
we tested them in the lab, we found that on
average over sixty percent of them, though more recently actually
around eighty percent of them actually validated in the lab,
so they actually protected ALS patient cells from dying, which
is very high. So we're really excited that we're actually
seeing very robust validation of the computational predictions, at least
(19:29):
in the lab. Okay, so you have this list, you're
testing it, something like half of them seem promising, you said,
sixty percent seem promising. What happens next? Okay, So what
happens next is that we so we test them in
these human brain cells. We understand the mechanism. One of
the really interesting findings from this ALS program and specific
(19:52):
is that when we looked at the network that we
found in these patient spinal cords, we found a new
cause of disease that was previously unknown so most of
the hypotheses in ALS, where many of them to date,
have really been focused around these protein aggregates, these clumps
of proteins that we can easily observe by ie that
you see in ALS patients. Right, A lot of them
(20:13):
are observational hypotheses. But what we found by looking at
a deeper cut of the data is actually, at baseline,
most of these patients actually had a baseline dysfunction in
their life csomal pathway, which I like to call the
garbage disposal pathway. It's what is critical to clear out
junk from the cell. And because patients were at baseline
(20:36):
vulnerable to these toxic insults, it wasn't so much the
protein clumps that were directly causing it. It was because
they're already vulnerable to these clumps of proteins that their
cells started dying. And is the idea that the gene
you're targeting is causing the cell's garbage disposal to not work, right,
(20:57):
Like you're trying to fix the garbage disposal by targeting
this particular gene. Yeah, it's a central regulator of that pathway.
And it was also a target that was ranked I
think it was ranked number one or number two on
the list. So just to be clear, how how do
you get from you know, so you have fifty or
so things to test, fifty or so targets, something like
(21:23):
thirty of them seem promising. How do you decide which
of those thirty to proceed with? Yeah, so that's a
great question. We get asked that a lot. I think
at that point it's a strategic decision. Right, you were
a startup, Right, we have to be able to develop
things quickly and capital efficiently. So we were lucky in
that sense that one of the top targets was also
(21:47):
a target that already had where the path to developing
a drug was relatively smooth, A lot was known about
that target. We could start doing chemistry and designing molecules
relatively easily, and the target itself had actually been tested
in the clinic for other diseases, not als, but things
(22:07):
like Crohn's disease and surrounds, so we did know there
was some safety data around hitting that target. We do
then for targets where we can't develop all of the targets, right,
we can only take focused bets for targets where there's
a bit more technical risk, Right, It might be a
bit more exotic. People don't really understand how it works.
(22:29):
There's not a lot of tools out there to really
develop drugs against it. That's where we might partner with
a pharma company to develop those targets. And we have
such a collaboration with Eli Lily where we developed our
als target, but actually Lily has the opportunity to essentially
take you targets number three through twenty two plus and
(22:53):
choose four of them to develop themselves. Oh interesting. So
in that way, you're essentially laying off the risk to
this giant pharma company that can afford to make more bets.
I'd say we're distributing the risk and we're allowing us
to really capitalize on the entire opportunity all of the targets,
because it's impossible for any small startup to do, you know,
(23:15):
thirty different programs. And it's actually in line with what
a lot of pharma companies are looking for. A lot
of pharma companies are looking for. What is that novel
target that no one else is working on that's kind
of unexpected, Where if we could really get a competitive
edge in here, this would be really meaningful for a
position within within drug development in the next ten years. Well,
(23:37):
and I mean it also seems compelling because even though
this seems like a more promising way to do drug development,
drug development is hard enough that anyone candidate drug is
probably not going to work, right. Yeah, An any biotechniqus
to be able to have a pipeline and the ability
to withstand I think some failures because I think it's
(24:01):
unrealistic to expect one hundred percent of what you try
will work. But that doesn't reflect on the technology itself,
and that can be something unfortunate in biotech, where you know,
if the first thing fails, everyone's all can be. It
can be tempted to say, oh, the technology didn't work,
but in reality, you think about how many different drugs
(24:21):
that pharmac companies test all the time. Right, So I
think really promising technologies need to be afforded that runway
and that ability to really take multiple shots on goal
before you can get the end to really see if
it's working. Right. Well, I mean, if nine of traditionally
developed drugs fail once they get to clinical trials, you
could be way better but still likely to fail on
(24:44):
anyone drug. Yeah, Yeah, even a fifty percent would be huge, right,
but still that means one out of two drugs will fail.
Relative to the world we live in now, a world
where one out of two drugs fail could be a
world where we get more new drugs for less money.
(25:05):
In a minute, the Lightning Round including the worst thing
out being named to the Forbes thirty Under thirty, and
the best thing about accepting that your company might sail.
That's the end of the ads. Now we're going back
to the show. Let's let's close with the Lightning Round.
(25:28):
You personally interviewed over a thousand people when you were
starting your company, as I understand it, which seems very intense.
And I'm sure as if there's anything in your life
outside of work where you've been that intense. Oh, everything
that is a core to my being. If you ask
my spouse, you would say any new game that we
(25:51):
start playing. And I'm very competitive and it's just part
of my being. I iterate, I get a lot of
reps in He always likes to make fun of me
that I have an AI in my head. I'm constantly
learning and improving the model until eventually I become a
lean mean. We've been saying a lot of Katan recently,
and I think if we him fifteen times in a row,
(26:14):
So yeah, I am very intense and thorough in my life.
Is chat GPT overrated or underrated? Both? Actually? I think
it's both over and underrated. It's overrated for some applications
and underrated for others. I think it's overrated for things
(26:34):
where there aren't a lot of information available already on
that thing. I think it's underrated for applications at coding,
where there's already a large body of literature out there.
So it's really good at replicating things that exist, less
good at discovering new things that don't exist. I read
an interview where you said one of the things you've
(26:56):
learned as in running your company is you learn to
be okay with your company dying with your company not
making it, which I found like very surprising and interesting.
Can you just tell me a little bit about that. Yeah,
I mean, I think it gets to really the core
of how we drive our culture, which is I think
that soul for so long companies have been driven through
(27:17):
fear and bravado of you know, we're crushing it, We're
pounding on our talking about how we're crushing it, and
less about emotional vulnerably and introspection and self awareness, and
ultimately I found the thing that really transformed my leadership
style was learning what I had grips over of where
I was really attached to outcomes, And ultimately, I think
(27:37):
for all CEOs, a lot of that is tying meaning
to what happens with the company. If the company fails,
this means something about me as a person, and I
think that stifles a ton of innovation and curiosity and
tends to drive those cultures of fear. So ultimately, the thing,
for example, that got me to stop micromanaging was really
being okay with the company dying, because ultimately, what is
(28:00):
micromanaging if not just fear right or fear or control.
And once you let go of that fear and you
recognize you're just open to learning. You can still really
want the company to succeed, and you can be passionate
about it, but you're no longer thinking, oh, I'm screwed,
or like I'm a failure if this fails, and that
just opens a whole new level of levity and lightness. Nice.
(28:23):
What's the worst thing about being named to the Forbes
thirty Under thirty list? I think they did a photo
shoot where there was a there was a very revealing
split on the dress, and I still get constantly made
fun of by my close friends for that. What's one
example of a thing that went wrong as you were
(28:46):
building the company? Something bad that happened? Oh so many things.
We had a whole period where there was a ton
of attrition and people leaving, and you know, the first
time that happens to a founder can I took it personally,
It's like someone leaving your baby, and you wonder why.
That was actually a huge growth moment for me because
I was for so long trying to put for the
(29:09):
strong face. If it's okay, it's okay. And finally, at
the end of like a month of this, I just
sat in front of the company at an all hands
and I honestly I just broke down in tears. I said,
I feel like I failed you guys. You know I'm
still grieving this. I really don't know what to do.
And it was paradoxically in that moment, most of the
team really rose up to the occasion and I found
(29:31):
support in ways I didn't even know where possible from
the team. Alice saying, is the CEO and co founder
of verge Genomics. Today's show was produced by Edith Russolo.
It was edited by Sarah Nix and Lydia Geancott and
engineered by Amanda ka Wong. You're always looking for more
(29:56):
guests for the show. If there's someone out there working
on an interesting technical problem with big stakes, tell us
about that person. You can email us at problem at
Pushkin dot fm, or you can find me on Twitter
at Jacob Goldstein. I'm Jacob Goldstein and we'll be back
next week with another episode of What's Your Problem.
Pushkin. Over the past few decades, it's become more and
more expensive to develop new drugs. It now costs over
a billion dollars on average to bring a new drug
to market in the United States, and of course drug
companies pass those high development costs onto us in the
(00:36):
form of higher drug prices. This has been going on
for so long that we have sort of gotten used
to it. But when you zoom out, it's strange because,
as I've said before on this show, and as I
will say again on this show, one of the main
things technology does is it makes things more efficient and
(00:57):
therefore cheaper. Over the past few centuries, we've seen technologies
make all kinds of things cheaper, everything from clothes to
food to TVs. So why hasn't new technology made drugs cheaper? Two.
I'm Jacob Goldstein and this is What's Your Problem, the
(01:17):
show where I talk to people who are trying to
make technological progress. My guest today is Alice Zang, co
founder and CEO of verge Genomics. Alice's problem is this,
how do you use artificial intelligence to drive down the
price of discovering and developing new drugs? Why is it
(01:38):
getting more expensive to develop drugs, despite the fact that
we have better technology to do it. Yeah. Absolutely. One
of the reasons is, you know, even though a lot
of the new technologies we've developed have made us better
at testing more drugs faster, but the fundamental problem is
that even if we can get a drug all the
way to clinical trials, which is the last step of
(02:00):
drug development, ninety percent of those drugs still fail. So
if you think about it, we're spending millions on each drug.
Of those drugs are failing at the last and most
expensive stage of drug development. And so really most of
that billion plus dollar figure you hear is due to
the cost of failure. Just to be clear, that figure
(02:22):
more than a billion dollars. It's you've got to include
the cost of all the drugs that don't work exactly,
the ones that do right exactly. So the ones that
do work have to pay for all the ones that fail.
That's the fundamental problem, exactly, And you're setting out to
fix that if you can. Absolutely, we think there's an
opportunity for AI to fundamentally shift really the failure rate,
(02:47):
and the most impactful time to do that really is
the failure in clinical trials. So can we predict before
we go in to these expensive clinical trials genes or
targets or drugs that are more likely to work in humans,
because even a ten percent decrease in that failure rate
could have massive I saw a number of up to
(03:11):
fifteen billion dollars annually in industry cost savings. You could
still be in a universe where most of the drugs
that go into clinical trials fail, but instead of ninety
percent of them failing, seventy percent of them fail, and
that would be a huge win. That would be a
huge efficiency gain. It would save a ton of money, absolutely,
And I think that's something that's underappreciated about AI and
(03:31):
really any technology, is that oftentimes people have this expectation
that this technology is going to absolutely transform a field overnight.
And I think what people don't appreciate is that most
of the time that doesn't happen. It's always step by
step incremental. But even a ten percent change would have
billions of dollars of cost savings and would be a
(03:52):
huge win for patients in the industry worldwide. I like
that frame, actually, I like that frame of maybe AI
can have drugs fail most of the time, but not
as much of the time as they fail. Now, like,
it seems very credible, It seems very plausible. Would you
put it that way? Yeah, it's all life is nothing
but a learning process, Yes, getting less bad at everything.
(04:16):
So I know you were studying to be a doctor
and a researcher not that long ago, a few years
ago before you started your company. Like, tell me how
you went from an mdphd program to starting the company. Well,
my PhD research was actually in using genomic analysis and
computational biology to analyze large scale data sets and find
(04:41):
new drugs that could improve drug development. And we found
that from our very first drug that was predicted from
our algorithms when we put it in mice after they've
been injured, help them walk and recover from that injury,
that nerve injury about four times faster than the leading standard.
And that was just the first drug that was predicted.
(05:03):
And I looked at this technology in this approach and
I thought, Wow, there's so much promise here. You know,
am I really going to just publish this and let
it sit on a bookshelf somewhere, or if I'm not
going to be the one to really develop this to patients,
you know who will, And when I looked out off
the field, I did not see a ton of biotech
(05:25):
or farmer companies that were truly computationally driven. Usually within
pharma companies they might bring in computational biologists to support.
There are scientists or their biologists, but there wasn't really
a genomics computationally driven company at that time. Now there
are many, but at the time there are very few.
And so I actually, you know, it wasn't a binary decision.
(05:47):
People always ask me, how did you make the courageous
decision to leap? It wasn't really like that. I think
what we did first is that we just took three
months three month leave of absence. We joined a program,
an incubator called a y combinator. We as you and
you and well me and my co founder Jason. And
(06:09):
the first question really was, you know, can we even
generate some data that validates that computational biology can predict
targets that work? And then when we saw some data,
the next question was can we even hire people that
want to come on? And the next question was can
we even raise money from people that will care? And
(06:31):
I think that is so such an important lesson because
I think people oftentimes get caught up in just the destination,
you know, is where I want to be? Is this
the career I want to have that they don't take
the first step, And really it's the first step that's
needed to actually get the data to even decide if
it's the appropriate track for you. And did you really
just keep thinking, well, this might not work, but let's
(06:53):
do the next thing. Were you in a place where
you could have gone back to the MD PhD program
for a while. Yeah. I took a leave of a
continuous leave of absence for probably over five years, probably
more than I should have, until the point where a
lot of my friends are like, are you really, are
you really gonna go back? And finally the medical school
(07:13):
is like, you're not really going to come back, let's
just terminate your leave of absence. But it was in
the first few years a really important safety net for
me that gave me the psychological safety to really take
a risk and really pursue a new idea that I
don't know if I would have otherwise. And I think
that's so important. I think for universities to provide is
(07:34):
that to recognize there can be more than one track
for people to do really excellent science and make an
impact more than just becoming a professor. And sometimes that
psychological safety is what's needed to help people find their
ultimate calling too. By the ways, so far, By the way,
(07:55):
what's a very brief definition of computational biology. It's really,
at the end of the day, in my view, just
the use of computers and data sets to understand and
biology better. By the way, what happened to that molecule
that you were testing in mice in grad school? That
(08:17):
seemed useful? I don't know. It's a good question. Actually,
I think the project was taken on by someone else,
but I'm not actually completely sure. So, Okay, you leave
grad school, you start a company you in fact now
(08:37):
have taken You have a bunch of molecules that you're
working on, and that seemed promising. But there's one that
is in clinical trials now right to treat als Luke
Gary's disease, a terrible disease that is very poorly treated.
And I thought that we could talk about the story
(08:57):
of that molecule of that drug as a way to
understand the way your company works. Can you just sort
of take me through the life of that drug? So far? Yeah, absolutely.
I'll start off just by talking about als and why
it's been so hard to discover the right therapy, and
then you know why how we did that differently. So,
(09:19):
as you might know, LS Luke Garrig's disease is a
really horrible disease. What happens is that these neurons called
motor neurons start dying, and most patients experience paralysis and
then death, usually within three to five years of diagnosis.
A very fast progressing disease, and there really aren't any
(09:40):
meaningfully effective treatments that really slow or stop the disease today.
So a very horrible disease with a horrible prognosis and
no available treatments, and why it's been so hard I
think to discover really effective treatments is really just the
complexity of the disease, and really any disease of the brain,
the brain is the most complex organ in the body.
(10:02):
So you end up having a lot of drugs brought
into clinical trials that worked in mice. I always like
to say we've cured LS or can There are many
diseases in mice a thousand times, but none of them
have really worked in humans. So what we did differently
was we started from day one by collecting data from
over a thousand ALS patients as well as controls, and specifically,
(10:24):
we collected samples of brain tissue as well as spinal
cords from these patients that actually passed away from ALS.
So you got samples from a thousand patients who had
died of ALS. How did you do that? So what
we've done over the last seven years is we've signed
partnerships with over twenty one different brain banks, hospitals, labs,
(10:49):
academic centers worldwide that collect these brain tissues. They're usually
donated from patients that have passed away from the disease
and whose families want to contribute to research. Could So
step one basically is get tissue samples from real patients.
And you said controls as well, right, So tissue samples
from healthy people as well, so that you can use
(11:10):
them as a basis of comparison. You have the samples, Now,
what's step two? So step two is that we put
an enormous amount of effort into quality controlling these, So
that's a big underappreciated step. They can be very noisy samples.
And then step three is that we sequence them, so
(11:31):
we profile, what is the expression of all twenty thousand
genes in the genome, and we also sometimes do DNA sequencing,
we look at genetic mutations. We also have a clinical
information about that patient, how long did they have the disease,
when did they die? And that makes for a very rich,
(11:52):
multidimensional data set, and that gives us essentially a global
snapshot of what happened in that patient. H okay, and
you and presumably the sequencing that you're doing on the
patient's tissue samples, you're doing the same sequencing on the controls,
the samples from healthy people. So now you have this
(12:12):
very large data set. What's the next step. So then
you have this snapshot of what happened, and the tricky
part is to figure out what caused it. I often
liken it to a plane has crashed, right, You're looking
through the rubble and you want to figure out how
the plane crashed and how that information can be used
to prevent further planes from crashing. So that's when our
(12:36):
software engineers and data scientists as well as machine learning
scientists come in and we have algorithms essentially to integrate
multiple data types all the way from the RNA, so
how the genes were expressed to genetic mutations to essentially
create a map of disease biology, and within the map
our networks of genes that are all interconnected that we
(12:58):
believe cause disease. And so I like to think about
it like when you're looking through a plane crash the rubble,
you want to find the black box, which I'll help
you figure out the cause of the disease. And by
having all the information, we essentially locate the black boxes
of disease, the targets that are really at the center
of those networks, and then we design drugs against those
(13:20):
targets that we believe can reverse disease. It seems like
differentiating between correlation and causality in this particular setting would
be really hard, right, Like to use the plane metaphor,
if you had a bunch of planes that crash and
a bunch that hadn't crashed, you might say, oh, like
the wings were off all the ones that crashed, and
that's why they crashed. But actually the wings came off
because they crashed, right, and it was something else that
(13:42):
caused the crash. I feel like that would be I mean,
an obvious problem. Yeah, that might be hard. To solve Absolutely,
you hit the nail on the head, and actually the
plane metaphor is a really great one here. For one
of the biggest challenges with looking at tissue from a
patient that already died is that you're getting the crash right.
You're not seeing video of before the crash. You're really
(14:03):
getting the crash. And the challenge is how do you
figure what caused the crash versus as well was just
the effect of the crash, like a burned wing, etc.
And one of the ways we do that is we
combine different data types. So we found that looking at
one type of data, for example, just RNA data is
(14:25):
in particularly helpful, but it's actually looking at where do
you get convergence signal that pulls through multiple types of
data to start revealing more compelling signal. So as an example,
we look at genetic data as well. So genetic data
is useful for looking at cause versus effect because it
contains information about genetic mutations that you were born with
(14:46):
as a baby that then lead to increased risk later
in life for a disease. And that's kind of nature's
human experiment for really cause and effect. And when we
layer that on that information on with the RNA data.
It actually gives us information about how the genetic drivers
are acting in these functional pathways, which is a big
(15:07):
issue actually with just looking at genetic data on its own.
So I wish I had a better I wish I
had a way to actually string that through to the
plane metaphor. But and there's a time for leaving metaphors behind.
Your company uses AI in drug discovery. I appreciate in
a certain way that you haven't said AI yet, but
(15:27):
also I don't want to not talk about it. I
mean in the sort of figuring out what's going on
in this step? Is that well, is that the first
instance in this process where you're using AI? Is it?
We're talking about that here? Yeah, I mean, I think
AI is a really broad term for any kind of
process where the computer is learning from something. So there
(15:48):
are all sorts of applications of AI in this entire process,
for example, how we're integrating the data sets together, how
we're inferring what are the central nodes or the key targets.
I would say the most classical use of A on
the way that most people think of it is then
(16:09):
once we have this network of say one hundred genes,
how do we actually find what the cause is? How
do we find what is the hub or the right
target to hit to turn off or on all hundred
of those genes. And that's where machine learning and AI
comes in handy. In a minute, Alice explains how this
(16:31):
actually works in the case of the ALS drug verges
working on. Now now back to the show. So okay,
Alice and her colleagues at Verge have collected all these
tissue samples from ALS patients. They've used the samples to
(16:52):
generate this huge data set that shows genetic variation and
changes in how genes are expressed, along with lots of
clinical data about the patients, and then they build these
basically these AI models to try to figure out where
in this complicated biological process that's happening in this disease,
where they should try to intervene with a drug, Basically
(17:16):
where they should try and target a drug. I think
of this oftentimes, like if you think of a map
of all the airports in the US, you want to
figure out how to go after the hubs like Chicago
or New York. You don't want to go an airport
in Kansas or I will wouldn't be very effective at
stopping airplane travel in the country. So there's a lot
(17:38):
of different pieces of information that we collect to then
infer what are the best genes that are not only
central within this network, but also there's independent evidence of
a disease causal effect or a relationship to disease. And
so you do all that in this instance, and what
(17:59):
do you figure out? So what the algorithms spit out
is essentially a ranked list of targets, all right, So
these are ranked list of targets that are predicted if
we could dry them, would restore that network back to
levels of healthy people and potentially slow or stop the disease.
And then what we do is we take those targets
(18:20):
and we start testing them in the lab, all right,
So we actually what is kind of cool about the
platform is we get all these targets from human brain
tissue and we also can test them in human brain
cells in the lab. So you get a list it's
basically genes to target. You either it says upregulate or
make this gene express more or make this gene express less.
Is that basically what the AI is out putting exactly, Like,
(18:44):
so how long in the instance of this ALS drug.
How long was the list? More or less, our initial
set of targets was twenty two high confidence targets, and
then we actually then generated another chut choosing updated data
of about thirty more targets as well. And what was
really striking when we tested these targets is that when
(19:07):
we tested them in the lab, we found that on
average over sixty percent of them, though more recently actually
around eighty percent of them actually validated in the lab,
so they actually protected ALS patient cells from dying, which
is very high. So we're really excited that we're actually
seeing very robust validation of the computational predictions, at least
(19:29):
in the lab. Okay, so you have this list, you're
testing it, something like half of them seem promising, you said,
sixty percent seem promising. What happens next? Okay, So what
happens next is that we so we test them in
these human brain cells. We understand the mechanism. One of
the really interesting findings from this ALS program and specific
(19:52):
is that when we looked at the network that we
found in these patient spinal cords, we found a new
cause of disease that was previously unknown so most of
the hypotheses in ALS, where many of them to date,
have really been focused around these protein aggregates, these clumps
of proteins that we can easily observe by ie that
you see in ALS patients. Right, A lot of them
(20:13):
are observational hypotheses. But what we found by looking at
a deeper cut of the data is actually, at baseline,
most of these patients actually had a baseline dysfunction in
their life csomal pathway, which I like to call the
garbage disposal pathway. It's what is critical to clear out
junk from the cell. And because patients were at baseline
(20:36):
vulnerable to these toxic insults, it wasn't so much the
protein clumps that were directly causing it. It was because
they're already vulnerable to these clumps of proteins that their
cells started dying. And is the idea that the gene
you're targeting is causing the cell's garbage disposal to not work, right,
(20:57):
Like you're trying to fix the garbage disposal by targeting
this particular gene. Yeah, it's a central regulator of that pathway.
And it was also a target that was ranked I
think it was ranked number one or number two on
the list. So just to be clear, how how do
you get from you know, so you have fifty or
so things to test, fifty or so targets, something like
(21:23):
thirty of them seem promising. How do you decide which
of those thirty to proceed with? Yeah, so that's a
great question. We get asked that a lot. I think
at that point it's a strategic decision. Right, you were
a startup, Right, we have to be able to develop
things quickly and capital efficiently. So we were lucky in
that sense that one of the top targets was also
(21:47):
a target that already had where the path to developing
a drug was relatively smooth, A lot was known about
that target. We could start doing chemistry and designing molecules
relatively easily, and the target itself had actually been tested
in the clinic for other diseases, not als, but things
(22:07):
like Crohn's disease and surrounds, so we did know there
was some safety data around hitting that target. We do
then for targets where we can't develop all of the targets, right,
we can only take focused bets for targets where there's
a bit more technical risk, Right, It might be a
bit more exotic. People don't really understand how it works.
(22:29):
There's not a lot of tools out there to really
develop drugs against it. That's where we might partner with
a pharma company to develop those targets. And we have
such a collaboration with Eli Lily where we developed our
als target, but actually Lily has the opportunity to essentially
take you targets number three through twenty two plus and
(22:53):
choose four of them to develop themselves. Oh interesting. So
in that way, you're essentially laying off the risk to
this giant pharma company that can afford to make more bets.
I'd say we're distributing the risk and we're allowing us
to really capitalize on the entire opportunity all of the targets,
because it's impossible for any small startup to do, you know,
(23:15):
thirty different programs. And it's actually in line with what
a lot of pharma companies are looking for. A lot
of pharma companies are looking for. What is that novel
target that no one else is working on that's kind
of unexpected, Where if we could really get a competitive
edge in here, this would be really meaningful for a
position within within drug development in the next ten years. Well,
(23:37):
and I mean it also seems compelling because even though
this seems like a more promising way to do drug development,
drug development is hard enough that anyone candidate drug is
probably not going to work, right. Yeah, An any biotechniqus
to be able to have a pipeline and the ability
to withstand I think some failures because I think it's
(24:01):
unrealistic to expect one hundred percent of what you try
will work. But that doesn't reflect on the technology itself,
and that can be something unfortunate in biotech, where you know,
if the first thing fails, everyone's all can be. It
can be tempted to say, oh, the technology didn't work,
but in reality, you think about how many different drugs
(24:21):
that pharmac companies test all the time. Right, So I
think really promising technologies need to be afforded that runway
and that ability to really take multiple shots on goal
before you can get the end to really see if
it's working. Right. Well, I mean, if nine of traditionally
developed drugs fail once they get to clinical trials, you
could be way better but still likely to fail on
(24:44):
anyone drug. Yeah, Yeah, even a fifty percent would be huge, right,
but still that means one out of two drugs will fail.
Relative to the world we live in now, a world
where one out of two drugs fail could be a
world where we get more new drugs for less money.
(25:05):
In a minute, the Lightning Round including the worst thing
out being named to the Forbes thirty Under thirty, and
the best thing about accepting that your company might sail.
That's the end of the ads. Now we're going back
to the show. Let's let's close with the Lightning Round.
(25:28):
You personally interviewed over a thousand people when you were
starting your company, as I understand it, which seems very intense.
And I'm sure as if there's anything in your life
outside of work where you've been that intense. Oh, everything
that is a core to my being. If you ask
my spouse, you would say any new game that we
(25:51):
start playing. And I'm very competitive and it's just part
of my being. I iterate, I get a lot of
reps in He always likes to make fun of me
that I have an AI in my head. I'm constantly
learning and improving the model until eventually I become a
lean mean. We've been saying a lot of Katan recently,
and I think if we him fifteen times in a row,
(26:14):
So yeah, I am very intense and thorough in my life.
Is chat GPT overrated or underrated? Both? Actually? I think
it's both over and underrated. It's overrated for some applications
and underrated for others. I think it's overrated for things
(26:34):
where there aren't a lot of information available already on
that thing. I think it's underrated for applications at coding,
where there's already a large body of literature out there.
So it's really good at replicating things that exist, less
good at discovering new things that don't exist. I read
an interview where you said one of the things you've
(26:56):
learned as in running your company is you learn to
be okay with your company dying with your company not
making it, which I found like very surprising and interesting.
Can you just tell me a little bit about that. Yeah,
I mean, I think it gets to really the core
of how we drive our culture, which is I think
that soul for so long companies have been driven through
(27:17):
fear and bravado of you know, we're crushing it, We're
pounding on our talking about how we're crushing it, and
less about emotional vulnerably and introspection and self awareness, and
ultimately I found the thing that really transformed my leadership
style was learning what I had grips over of where
I was really attached to outcomes, And ultimately, I think
(27:37):
for all CEOs, a lot of that is tying meaning
to what happens with the company. If the company fails,
this means something about me as a person, and I
think that stifles a ton of innovation and curiosity and
tends to drive those cultures of fear. So ultimately, the thing,
for example, that got me to stop micromanaging was really
being okay with the company dying, because ultimately, what is
(28:00):
micromanaging if not just fear right or fear or control.
And once you let go of that fear and you
recognize you're just open to learning. You can still really
want the company to succeed, and you can be passionate
about it, but you're no longer thinking, oh, I'm screwed,
or like I'm a failure if this fails, and that
just opens a whole new level of levity and lightness. Nice.
(28:23):
What's the worst thing about being named to the Forbes
thirty Under thirty list? I think they did a photo
shoot where there was a there was a very revealing
split on the dress, and I still get constantly made
fun of by my close friends for that. What's one
example of a thing that went wrong as you were
(28:46):
building the company? Something bad that happened? Oh so many things.
We had a whole period where there was a ton
of attrition and people leaving, and you know, the first
time that happens to a founder can I took it personally,
It's like someone leaving your baby, and you wonder why.
That was actually a huge growth moment for me because
I was for so long trying to put for the
(29:09):
strong face. If it's okay, it's okay. And finally, at
the end of like a month of this, I just
sat in front of the company at an all hands
and I honestly I just broke down in tears. I said,
I feel like I failed you guys. You know I'm
still grieving this. I really don't know what to do.
And it was paradoxically in that moment, most of the
team really rose up to the occasion and I found
(29:31):
support in ways I didn't even know where possible from
the team. Alice saying, is the CEO and co founder
of verge Genomics. Today's show was produced by Edith Russolo.
It was edited by Sarah Nix and Lydia Geancott and
engineered by Amanda ka Wong. You're always looking for more
(29:56):
guests for the show. If there's someone out there working
on an interesting technical problem with big stakes, tell us
about that person. You can email us at problem at
Pushkin dot fm, or you can find me on Twitter
at Jacob Goldstein. I'm Jacob Goldstein and we'll be back
next week with another episode of What's Your Problem.
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