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Chris Austin: Okay. Yes. I don't know if all of you -- I
know some of you do, but you may all not realize how much -- at least a large part of NCATS
was incubated within Genome. I got to Genome in 2002 and left, well, when NCATS got formed
in 2011, so I was there nine years. And really, a fair amount of what I'm going to show you
was sort of a little marsupial in the pouch of Genome, and when NCATS got formed, we kind
of jumped out and are kind of now on our own. But NCATS is only about two years old. So
we are, I often like to say, we're like a toddler who's still learning to walk. We have
a lot of potential, but we're still making a mess. And we hope, eventually, we will do
something to really meet our potential.
So, this is the problem that we work in, and this is the slide I show every time I give
a talk. And it's -- which is that -- this is a problem I know that you think about a
lot, too, which, to me, it really is the biomedical research question of our era, that is, that
we live in this really paradoxical time, right? Where we know more about ourselves and health
and disease, perhaps exemplified by the Genome Project, but by fundamental advances in general,
but at the same time, we are not making a lot of headway with improvement in health
outcomes. And if you haven't read -- if you want to be depressed, you can -- if you're
not depressed enough from reading The Post or something, you can read -- this is an IOM
report that came out about a year ago that was pithily titled "Shorter Lives, Poorer
Health," and it talks about just how unnecessarily unhealthy Americans are. And a lot of this
is because -- certainly not all, but a lot of this is because we are really bad at transitioning
discoveries over here into health outcomes. And there's a lot of outcomes as a result
of this or knock-on effects that -- the drug device diagnostic development system is a
mess, since I left Merck about 10 years ago, the pharma industry has laid off, I don't
know, Lon [spelled phonetically] could probably tell us the up-to-date numbers, but something
north of 200,000 people. And a lot of those were researchers, because of this problem.
The clinical trial system in this country is a mess, to put it mildly, and even when
we do discover interventions that improve people's health in clinical trials, our system
is actually terrible at getting those transitioned to all the patients who could benefit from
them. And so, as a result, people are unhealthier than they should be, and not to put too fine
a point on it, funders of the enterprise, public and private, have either gotten impatient
with us or have lost patience with us completely. And you see this, both in the flight of capital
from the life science markets and the private sector, and the difficulty of getting across
what NIH does in the public sector. And those of you been around NIH know, just in the time
I've been here, last 11 years, that conversation has changed quite dramatically. I think it's
fixable, but I think we have to recognize it's a problem.
This is the other thing that keeps me up late at night. I don't know if those of you know
this, anybody who works in the drug development field will know this. I show this because
it will be -- have particular currency for you. I know Eric likes showing the Moore's
Law and sequencing costs graph. Well, in the world that I live in, we don't have Moore's
Law, we have Eroom's Law, which is Moore's Law spelled backwards. And it's spelled backwards
because of the dramatically negative productivity growth in therapeutic development over the
last 60 years, that is, that the cost -- that the number of drugs produced per billion dollars
has gone down monotonically by half every nine years since 1950.
So, just think about that. This is the reason why PhaRMA's going bankrupt, right? There
is no way you can keep this going as a business. And it's also why people are unhealthier than
they should be. And just in case you're wondering, this number, if you extrapolate out, this
goes to zero in 2040, so we'll probably all be retired by then, but going to zero is not
good for any of us, because we're all going to have Alzheimer's disease by then, and it
would be nice to have a treatment, which ain't going to happen at the current rate.
So, one of the -- and one of the things I find most frightening about this is you think
about the epical changes in biology of medicine that have happened since 1950, and none of
them has affected this slope. It's really quite terrifying, when you think about it.
So one of NCATS' jobs is to try to make this flatten out and eventually go back up. I'm
going to make the argument that a lot of this is a scientific problem. The translational
space is remarkably empirical. We really do not understand most of what we do in this
space, and I think as somebody who comes from a mechanistic background, it's obvious, when
you move into this space, that it's much more like medicine, clinical medicine, than it
is science. And those of you that are docs will know what I'm talking about.
Okay. So this is another graph I show all of the time. You know, there's a norm excessive
[spelled phonetically] problem, where's the opportunity, you know. You know that's when
I need an updated graph here that goes -- a number of Mendelian disorders identified,
it's now, you know, north of 4,500, so here are all these rare diseases, the molecular
basis of which, thanks to you all, we now know. And I don't know, when are we supposed
to know all of them, thanks to you guys? Probably another -- what's the number now? Five years
or something? Yeah.
So, and -- but whatever it is, you know, if you go back, you know, 15 years ago, the number
was less than 50. And now it's 4,500, 5,000 or so. So this ought to give us opportunity
to intervene. And of course we have this. I always love showing this. You guys still
showing this, the lollipop graph? I know Teri shows it.
[laughter]
But I love this for all the reasons that, you know -- this represents opportunity, right?
Targets, potentially. And I always -- I want to go back and show this. This is -- those
of you who have known me for a while know, this is the graph I -- this is the slide I
always used to show when I first got to Genome, that the idea was that we were going to create
this translation toolbox when I got to Genome. That was my job, was to be the translation
guy at the Genome Institute. And it is interesting to reflect on what we were going to put into
this. You know, one was, by the time I got here HapMap had started, right? And ENCODE
had started, and NNGC [spelled phonetically] had started as well. So we have, you know,
heritable variation, we have functional and structural elements, we have cDNAs, and we
started -- one of the first things I did was to work on transcriptome reference sets, but
this was back in 2003, so it was really archaic technology, MPSS technology, if you remember
any of that back then, creating sRNAs, which I'll tell a little more about, this Knockout
Mouse Project, which was one of the things that I started along with a bunch of other
people here. And then anybody remember this? Human base? That was something that never
happened as -- that's an interesting -- if you get Eric drunk enough, he'll tell you
why this didn't happen. It's basically a political issue.
And then we put this into the toolbox as well, and when I started talking about this, nobody
knew what those things were. And it was really because, if you think about all of them except
small molecules, they all operate at the gene level, the locus level, or the mRNA level,
but one of the things that the Genome Project told us, of course, is it's really just protein,
number of proteins that confer, not number of genes that confers organismal complexity
in health and disease, so we needed a tool which allows us to manipulate Mother Nature
at the level that Mother Nature works, which is the protein level. So, small molecules
work at the protein level.
Okay, so I love this one. Anybody, who do you think said this? Come on, there's got
to be one person, and it's got to be her, right? And you know, here's an example that
we all love to quote, you know, and how many of these are there? I don't know. But it's
one of the things that, you know, we're interested in, you're interested in, in making happen
a lot more often. However, I always like to point out that what I've learned as a neurologist,
and then as a geneticist, was a very hard lesson, which is that this -- so even you
cardiologists in the room want to know what this is.
[laughter]
That's a brain, first of all. And that big black thing, that's not normal. That's a stroke.
But I also learned as a geneticist, that this, which is the original paper from Linus Pauling
on the cause of sickle cell disease in 1949, does not equal this. And just because you
can figure out why something is broken, doesn't mean that you can fix it. It is a completely
different exercise. And it requires completely different skill set, et cetera. And whenever
I go anyplace -- and you probably get heckled with this, too -- I go places, and I talk
about how the genome is going to help us develop new drugs, and there's always somebody who
says, "Look, buddy. 1949. You still ain't got a drug based on this insight. So don't
talk to me about all these new genetic targets until you fix this one." Now we're working
on that -- I'll show you that in a second -- but it is a fair point.
So, what is the NCATS mission in all of this maelstrom? This is the mission. And I just
want to emphasize a couple of words. One is the word "catalyst." You know, we're 1.8 percent
of the NIH budget. Eric reminds me that -- or maybe it was Rudy -- that we're not bigger
than you guys -- ho, ho, ho -- as of, I guess, this year, because of one very large program
that you've all heard of, the CTSA program. But to catalyze -- the only reason we're catalysts
is that we're disease agnostic, like you all, and if you're going to talk about translation
to improve health, then we need to demonstrate improvements in health by working at particular
diseases. And because we have all 7,000 diseases, like you all, we have to come up with improvements
in. We have to do everything we do collaboratively. So it's one of the reasons that NCATS is really
fundamentally different from most of the other ICs. So we focus on innovative methods and
technologies -- hat's really what we're about -- that will enhance the development testing
of limitation of diagnostics and therapeutics and were disease agnostic.
Now when I became director, I actually changed this mission a little bit. I tried to do it
officially, but as Eric discovered when he tried to do the re-org of NHGRI that he eventually
succeeded in doing, simply to change the words in a mission statement of one IC, of one operational
division in one cabinet department, actually requires an act of Congress. So, I was told
don't try to change the mission statement, but I do think about it differently. So, look,
there's two problems I have with this. One, it's just diagnostics and therapeutics, and
I really think about interventions broadly, behavioral interventions, et cetera. Secondly,
it's not good enough to just make diagnostics and therapeutics. You've actually got to show
that they do something. So what we really are focused on is interventions that tangibly
improve human health. That is a much bigger mission than this, which is much more limited.
That's a very important distinction.
Okay, so I love this graph, and a lot of you probably have seen me, or figured, because
a lot of you have probably seen me show this before. This was actually made by Francis'
office. It's an organizational chart of the NIH. Now, this -- I like to think about this
as a silo, as an aerial view of the silos that make up NIH, really. But you notice in
the middle here, you know, NCATS is supposed to be a horse of a different color here, but
it's also supposed to be a convener, an adapter, occupier of the tragedy of the commons; it's
all the kinds of things that we think about ourselves as. And it really is true, that
probably a lot like you all, and this is not by mistake, not by happenstance, you know,
we don't think about the translational space in a parcellated way. We don't think about
what's different about diseases, we think about what's common to diseases and what's
common about the translational space. And we're big on integration and connections,
because we're actually confident that that's the way Mother Nature designed the human body,
you know, knee bone connected to the leg bone and all that. And if we approach the translational
problem that way, we will have better success.
But because of where NCATS works, and perhaps best exemplified by the fact that NCATS, unlike
any other institute, doesn't do any basic research. We really start at target validation.
You know, I'm a geneticist by training. We don't do any basic research. But as a result,
you know, we're sort of right-shifted, if you want to think of it that way, in the development
spectrum, and so we have much tighter and more systematic interactions with disease
advocacy groups and non-profits, and FDA, pharma, biotech, VC [spelled phonetically],
all those kinds of things.
Okay, so I mentioned this before, but it's important to understand. If you're not familiar
with this translational vernacular, it's worth knowing about. It has some problems, but it's
worth having in your lexicon, because it is something that people use. And I actually
do find it reasonably helpful.
So the important thing is that NCATS works across this space. So what am I talking about
here? That T1, in most people's vernacular, goes for my target, a punitive target, through
target validation to an intervention which is shown to be potentially useful and a proof
of concept trial, say a Phase IIA trial if it's a regulatory relevant intervention. Then
to go from a proof of concept to approval is T2. So that's a bigger population, Phase
III, et cetera.
However, that's really where the next phase of translation starts. So you haven't actually
gotten to health, because you're working in this hothouse environment of a clinical trial.
People in the wild, in their wild-type environment, eating all kinds of crazy things and doing
all kinds of crazy things, that they, you know, let them do in the clinical trials,
does the intervention actually work? And how do you get it to people who -- all the people
who could benefit from it? And then if you get it to all the people who benefit from
it, do you actually show that you had an effect on population health?
So whenever I'm asked to explain, you know, give me an example of this, so my old company
made a drug called Vioxx, you may have heard of, and it really had its downfall in T3.
It actually got to approval quite nicely. The problem came when it went to many, many,
many more patients than had been in the clinical trials, and you see this a lot, and my favorite
example of the T4 problem is of post-menopausal estrogen. So you're all aware of the fact
that in this space and in this space, this looked brilliant, you know, great, you know,
biological rationale, looked great in all the animal studies, all the clinical studies.
It got very widely adopted. And then it turns out, given the results of the women's health
study, we were actually killing people. So it's important to not stop here, or here,
or here, or here until we get the health. And so it's a really important issue.
Now I will grant you that probably most of what we would do with Genome is either down
here or maybe down here or here, you know, in the middle, maybe not so much, but, you
know, that'd be interesting -- it's something that, you know, we've been talking about with
Genome, and maybe you'll see this as I go on.
Okay, so sometimes people ask me, "Well, you know, translation's pretty straightforward,
right? So what's your problem? You know, what are the problems that you guys work on?" And
these are some of the problems that we work on. And if you look at all of these problems,
they are the reasons that intervention development fails, either in the pre-clinical stage, the
first three, or four, depending on your point of view, or in the clinical stage, or later
translational stages.
And you'll notice that there's no disease listed here. And so this gets to a point that
I mentioned before, that these are all problems that are everybody's problem in general, but
nobody's problem in particular. I mean, what I see is responsible for these things, which
is why nobody solved them. I mean, they are scientific and organizational problems. They're
nobody's problem in particular, so they're tragedy of the common's problems, if you're
familiar with that term. So virtually everything NCATS does is in the tragedy of the commons
space.
And if I thought about, you know, things that really intersect with what you do at Genome,
and what I used to do, these red ones are particularly relevant, you know, why do interventions
have toxic effects when they're not supposed to, why do they not have efficacies that they're
supposed to, how to de-risk undruggable targets, data interoperability, biomarker qualification,
clinical diagnostic criteria, how do we find the appropriate patients to get an intervention
to, pharmacogenomics if you want to call it that.
Okay, so what's within NCATS, there's -- you can divide this roughly into three pieces.
Eighty percent of the budget is in the CTSA program. And if you don't know what that is,
I'll tell you in a second. About 15 percent of it or so is in rare diseases, and I -- there's
a lot of reasons for this. Part of it is because rare diseases are most often multi-system
diseases, and so they don't fit nicely in any one institute. I also like thinking of
it as, not to be pejorative, I like to think about them as sort of model organisms of the
translational space. If you're going to try to work out novel ways of doing translation,
you don't want to start with type 2 diabetes. You want to start with a relatively simple
system. And rare diseases represent that. And then we have about 5 percent of the effort,
maybe a little more, is focused on technology development without a specific disease implication,
you know, as approximate outcome.
So one of the things that I'm, you know -- sort of, when you become director of an institute,
and those of you who run things will know this, you know, you just have to have things
that people can remember and put on their t-shirts, and you know, stuff like that. So
this is what I've been beating into the heads of everybody at NCATS, is that it's not good
enough to simply develop new ways to solve that long laundry list of problems that I
gave you. We have to demonstrate in individual use cases that they actually work better than
the previous methods, or else nobody should believe us. But if they do work better, then
we can't assume that everyone will stop old ways of doing things and all of a sudden see
the light and start doing what they're supposed to, and whether these are scientists or physicians
or patients. And so we have a lot of emphasis on dissemination science as well, which, as
you know, is probably a science in its own right.
Okay. So I'm going to start at the clinical part and work backwards now. So the division
of clinical innovation -- so this is currently, it's mainly the CTSA program. And here's the
vision. This will not surprise you that the first is development, demonstration, implementation
of methods of technologies -- and this next word is important -- logarithmically improve
the efficiency of clinical research. Translational problems are so large that it is not good
enough to think about arithmetic improvements. I think about this really, you know, I think
about everything in terms of genetics and genomics because it's my background. Just
think, if sequencing technologies had gotten 10 percent better, 20 percent better, 30 percent
better, wow, great, you know, we'd be toast. We wouldn't be anywhere. It's only because
people at the institute said, "No, no, no. Logarithmic improvements, $1,000 genome,"
you know, and so -- because that's suitable to the scale of the problem we're dealing
with here.
Secondly, there's a whole other area that we could talk about if you're interested.
What I've realized, taking on this job after being away from medicine for almost 20 years,
is that what I grew up with, the tradition of clinical investigation and phenotyping,
which was the heart of what people did in academic medical centers, a lot of that has
withered because the relentless pressure on reimbursements and academic departments having
to make money, et cetera, et cetera. And so the -- if you think about trying to make genotype/phenotype
correlations, we were now really good, thanks to you all, at doing genotypes. We are actually
terrible at doing phenotypes, and we've actually lost a whole generation of people who never
really learned how to do this. And so it's something we're doing within the CTSA program.
So that gets into the training programs that we spend a lot of time doing.
I think what we really need to have over the long-term is something that I think about
a lot, is a robust academic discipline of translational research, which is going to
have different metrics. It's not going to be papers in a covered cell. They're going
to be different metrics, and something I talk to the CTSA heads a lot. And then we're really
big on novels of engagement -- models for engagement of the various communities that
we work with. And really, if you think about this, you know, why do we do this, you know,
translational research by its name means we are carrying something from a place to a place,
which indicates that where we're going ought to be of interest to somebody. So unless we
know what that person is interested in, whether it's a clinical scientist, or a practitioner,
or somebody in the community, or whatever, a patient advocacy group, what have you, we're
not going to understand what those problems are, so we like to have those partners involved
in every project we do from the beginning.
So, this is a CTSA program, it's a national consortium of medical research institutions.
This is the current map. It's probably one of these, I would guess most or all of the
institutions that you come from if you come from an academic center, so 62 of them now.
These are legacy -- large parts of these are legacy GCRC programs, if you remember -- if
you know what that is. And we're still dealing with some of that legacy. But the vision here
is that this program will go from being what they had been for the last 20 or 30 years,
or 40 or 50 years, in some cases, which are essentially glorified core facilities, to
do clinical research within academic medical centers. Which is not bad, it's just not enough.
It's not commensurate to the scale of the problem, to a national network for translational
medicine.
And actually this committee that I'm going back to talk to after I leave here is a new
steering committee, which is focused very much on this question, so that one would be
able to recruit for clinical trials across this network through a common electronic health
record infrastructure, have a common IRB structure, be able to recruit PIs because most of the
key opinion leaders are at these places as well, have innovative clinical trial designs
imbedded into the metrics of the program, and overall improve the efficiency and quality
of clinical research. And I'm hoping that a few years from now, when I come back, I'm
going to be able to talk to Lon, and Lon will not say to me, you know, "We're going overseas
to do our clinical trials. Because we would love to do them here, but you guys are so
inefficient and so costly that we can't afford to do it." That's what I hear from pharmas
all the time. I probably don't need to tell you what the statistics are in NIH-funded
trials. They're actually quite bad, to put it mildly, so there's a lot of room for improvement
here. And we're going to use the CTSAs to drive that.
Part of the help here in doing this was an IOM report that came out about six months
ago on the CTSA program; if you're interested in it, you could read it. But the important
-- one of the important things was to really strengthen leadership of the program by NCATS
and to focus on clear deliverables and outcomes. There's a committee that we have that's a
working group of our counsel, which is helping us with this. And you can look for names that
you recognize. I just want to point out too, Ron Bartek, some of you probably know, who
runs the Friedreich's Research Alliance, and Lynn Marks who runs a clinical research at
GSK, as well as other folks that you probably know, Gary Gibbons, among others. So stay
tuned on that. They're going to report in May at our council, so it will be interesting
to see what they come up with. And what I've asked them specifically to do is to focus
on outcomes, clear outcomes that address critical translational, general translational questions
to improve the quality and efficiency of translational research through the CTSA program. How do
we do that?
Okay, so if we move backwards now, from the clinical space into the pre-clinical space,
this is all of what I built when I was at Genome, actually, so some of you have probably
seen this, and it really hasn't changed much in the last couple of years. And the idea
here is that most academic investigators who want to get into the translational space just
don't have the experience, the expertise, the facilities, the knowledge, et cetera,
to do this. And so what we did when I came to Genome now almost -- over 10 years ago,
the idea was that we would set up an industry standard, industry scale, translational operation,
which would recruit mainly people from biotech and pharma, but the model is that all of those
people would work in project teams and they would work collaboratively with academic investigators
who were disease experts or target experts. So though this is our intramural program,
it is the weirdest intramural program in NIH because it has no tenure, it has no tenure
track, it has no independent PIs, everybody works in project teams, every project is a
collaboration with somebody somewhere in the world. So it's a very unusual program.
So what happens is, collaborators come to us, stuck at various stages, and they go into
one or another of these programs, and what comes out the bottom are deliverables, and
those deliverables are either a physical entity, a drug, a lead, a repurposed drug, an siRNA
probe and/or data that goes in public domain. But what -- and those deliverables move the
project forward down the translational pipeline. But the other thing is, we like every project
to be dual-use project. So they tell us -- they not only move a project forward, but they
have a paradigm technology development component so they tell us how to do one or more of these
processes better. I probably don't need to remind you, but the current success rate going
from here to here is about 0.1 percent, takes about 13, 14, 15 years, and costs, depending
on how you do the math, between $2 billion and $6 billion. So, as I often say, something
which fails 99.9 percent of the time cannot be said to be optimized, so we are spending
a lot of time working on this problem.
And if you ask why -- how is NCATS different from all the pharmas, like the pharma I used
to work in, there's a critical difference. And that critical difference is we do not
have a short-term commercial imperative. I could tell you, every person in every pharma
you go to, almost every one of them, they know what the problems are. But you can't
support the research operation doing that science. Somebody's got to do that science.
And it can't really -- it can't be done in a for-profit environment. The other thing
is that we work on the 95 percent of targets and diseases that are not de-risked enough
to work on in a private sector environment.
Okay, so every project is a collaboration with people all over the country. These are
the places that these folks came from. Just imagine, the model here is, in order to get
hired, you have to know the state of the art at one or more of these places, but the state
of the art is terrible, so I don't want you put in state of the art, maybe 99.9 percent
failure rate, right? So you get hired, you got to tell me what the state of the art is,
but you got to tell me how to do it better. And when you bring in 150 people, which we
have from all these different places, and you set them loose that way to focus on science
only, and do it in a collaborative way where they are tied in with academic opinion leaders,
you can make a lot of headway quite rapidly.
So this is the first center that I started, back in 2003. It currently has 200 collaborators
in the target to lead space [unintelligible] screening, informatics, MedCan focused on
sort of one minus pharma, the universe of targets and diseases that exists. You subtract
out what BioPharma works on, that's what we work on. And the mission is chemical siRNA
probes and new technologies.
And just to give you a couple examples on how we've done this. So, one of the things
that we've started doing a number of years ago was to make genome-wide RNAi screening
actually work. Those of you who have done this know that you can -- it's actually quite
easy to go out and buy an siRNA library, but the results you get for the most part are
junk. And I have data that would curl your hair to show it to you. And I didn't bring
these, but you can often pick out individual genes that will allow you to move forward,
but overall, the data are simply not reliable. And when we look -- the more that we looked
into this, the more actually shocked we were by this.
So we started this center about five years ago: to work to reduce the practice, doing
genome-wide RNAi screening; secondly, to develop a collaborative resource to do individual
projects; and thirdly, to create the first public-sector database of RNAi data. It's
amazing, 10 years after Genome Project, there is no such database, right, where you can
go in and look at every gene in the genome knocked down, one at a time, and be able to
mine those data. It's quite remarkable. And the reason is that the companies who make
the siRNA oligos would not agree to make the data public.
So, being kind of a -- using our convincing power, and Genome's familiar with this, we
were able to get Life Technologies to agree to do this. And so just last December, we
had a couple of great papers, this one -- and I don't show this just because Bob's here
-- there's a really great paper on mitophagy, genes involved in mitophagy and Parkinson's
disease that was in Nature, and then the same month was this press release having gene silencing
data available for the first time. So you can now get into -- these data are all in
PubChem, we're putting them elsewhere, too -- but get in and look at every oligo and
what its effect is in these screening systems. And just the one little tickler I'll give
you is that the results are absolutely not -- do not correlate with the genes that the
companies say are being knocked out. They are oligo-specific. They are sequence-specific.
But those sequences have almost nothing to do with the genes that are identified. So
I'll just leave that with you, and you can -- and we figured out why that is, too. I
don't have time to get into it, but it's a really interesting story.
Okay, so if I then move on to the small molecules side, we did exactly the same thing on the
small molecules side. This is a project we started years ago with Ellen Sidransky, who
you know is a tenure investigator at the Genome Institute, working on small molecule chaperones
for misfolded glucocerebrosidase in Gaucher disease without -- just skip through about
six years of work, we've identified compounds that bind these, that bind to mutant glucocerebrosidase,
are biochemical inhibitors. So they bind and inhibit the enzyme in a tube, in a cell-free
system, but in a cell-based system, they actually increase glucocerebrosidase activity, exactly
as you would expect with a chaperone, and these have now been licensed to two different
companies, Biogen and LTI, for further development, and we're now working on the alpha-synuclein
connection to see how these compounds might affect alpha-synuclein levels.
Another thing which -- another technology development area is working on the area of
drug combinations. You're all aware that there's a lot of promise in drug combinations, a lot
of diseases which can't be treated with single drugs. It's been very hard to do efficient
screening of combination for a variety of reasons. A number of companies tried to do
this, Combinatorix, which a lot of you probably remember, they -- again, really smart people,
but they were limited by the technology available at the time. So -- and also the need to make
money, because they were a company.
So we had the opportunity now, about 10 years later, to say, "Well, gosh, you know, isn't
there a better way to do this?" And so without skipping through all the data, what this required
was getting a higher value library of small molecules, which I'll show you in a second,
an effective plating process based on acoustic dispensing rather than contact dispensing,
and automated data analysis methods and really sophisticated bioinformatics. And bottom line
is, is that's not been done. And the first example of this was just published last month
in a paper with Lou Staudt, looking at Lou's most favorite type of lymphoma. And you can
go read the paper. It's quite a beautiful paper but it's -- what it's doing is identifying
compounds that might be synergistic with ibrutinib, which is one of the typical compounds used
in this kind of besolymphoma. And we're applying this to literally 20 other projects now. And
the reason this is possible is that we spent about two years on technology development:
getting the compound acquisition, the dispensing, the compound management, the informatics right.
One of the things that also made this possible is we'd spent five years before that developing
this, which was a complete non-redundant list of every compound ever approved for human
use worldwide. Amazingly, when we started doing this back in 2007, I thought there must
be such a list, you know, a complete non-redundant list of every compound approved for human
use, right? It's got to be available. Just Google it. Took them five years to come up
with this, variety of reasons; this sort of reads like a Tom Clancy novel, why it was
so hard, but it's all in this paper. The important thing is, and this is really a -- this is
a page right out of the genome book, we did this once, to high quality, and then we made
all the data public. So it's now in a public database, anybody can access it, we've got
a physical collection of compounds that we collaborate with people all the time to screen.
Moving on to another problem now, another problem in the translational space is unanticipated
toxicity. The data are a little bit old now, but toxicity accounts for about a third of
failures when you're trying to develop a novel drug. And one of the reasons is that toxicity
is really -- toxicity testing, for the most part, is really stuck in the 1950s. That is,
what you do is, if you have a novel drug or a novel chemical, you expose an animal to
a certain amount of compound, has a certain tissue dose, and then you pretty much close
your eyes and wait for what's called an apical endpoint. That is an observable endpoint.
The animal gets cancer, it dies, something really obvious. And then you say, "Hmm. That
was bad. We shouldn't do that again. Kill that compound and go on to the next one."
But you very seldom know why it actually caused that problem. So the next time, you don't
get any better at predicting which one is going to be effective. And this gets back
to the supericism [spelled phonetically] problem we were talking about before.
So about six or seven years ago, we, and the EPA, and the FDA, and the National Toxicology
program came up with this idea that, well, you know, could you do the mother of all systems
biology experiments to solve this problem. And metaphorically what we're doing, and this
will be near and dear to your hearts because I know this is the way you all think too,
we thought, "Well, okay, instead of giving these chemicals," which, you know, drugs are
environmental chemicals, "to the rat and then watching what happens, we're going to metaphorically
dissect the rat or the human into its component cell types, and then we're going to dissect
that into its pathways within those component cell types. We're going to treat all of those
pathways within those cell types with all of those different chemicals, look at the
effects on the pathways in the cell types and phenotypes, and then computationally put
the rat back together again."
So we're actually doing now 10,000 different drugs and chemicals in triplicate 15 point
dose response in a different pathway assay every week. So it's about 600,000 data points
every week. All of this goes into the public datum in the public domain. You can access
it. And we're gradually coming up with predictive models using the historical data that we have
from animals to try to predict eventually what compounds might have adverse effects.
So this is the collaboration that interestingly has gotten me involved in a world that I never
knew before, which is the EPA and the NTP, the people who run the super fund sites, but
they're very much the same problems, they're just chemicals that have an adverse effect
on human health.
There's somewhat different -- for those of you who are chemists, there's somewhat different
kinds of chemicals. And this program called Tox21, and these are the goals: identify patterns
of compound-induced biological response, to identify the toxicity or disease pathways,
and eventually develop predictive models for biological response in humans. And this just
makes the data point -- it makes the point that the last six months we've deposited 33
million data points. That is a data point in this case is a chemical with a given concentration
in a pathway into PubChem.
Something that I knew would be interesting for you is that we've, also in this project,
begun to think, well, we know that humans and animals vary quite a bit in their response
to chemicals, otherwise known as pharmacogenetics, right? But in the chemical world, in the environmental
chemical world, you see the same thing. So the concept was, hmm, well, could we use -- could
we study -- would it be possible to study the effect of human-inherited variation on
response to environmental chemicals and drugs. So what we did was, we took the 1,086 lymphoblastoid
cell lines from the 1,000 Genomes Project, and we screened them in a 15 point dose response
across about 250 different chemicals. So, imagine that. So now we're doing 250 chemicals,
in duplicate, at 15 concentrations times 1,086 different cell lines.
There's a kind of thing you can do when you have big honking robots and you don't have
a drug you have to make at the end. You can really do these kinds of massive experiments.
And then you can use response to the chemical or lack thereof -- this is a simple cytotoxicity
assay in this case -- as a quantitative trait that allows you to map the loci responsible
for differential sensitivity to the drug or the chemical. And we did a lot of work before
this to test whether this crazy idea could even work and whether we would have the power;
turns out it did, it did work, and the paper's now in preparation. But another thing we did
was, again was take a page out of your book, was to say, "Well, gosh, you know, this a
huge amount of data. And we have some really smart informatics people, but, you know, wouldn't
this be a great thing for a challenge?"
So we teamed up with DREAM, who were the challenge people, and with Sage Bionetworks, Steve Friend's
operation, and we did this challenge with them to use crowdsourcing to better predict
the toxicity of chemicals, both the chemical response and the genetic loci responsible.
And this paper is now -- I should tell you, the winner of these, which actually the same
team from UT Southwestern, the winner of this challenge didn't get money, they got a guaranteed
paper in Nature Biotechnology. And we got about 100 submissions to each of these as
a result of that without offering anybody any money, which I thought was interesting,
just the paper and bragging rights, which is important.
Another thing we're doing, which I threw in here for Dede's [spelled phonetically] benefit,
because I know she's a techno-geek, and I say that with all affection as a fellow techno-geek,
another thing we're doing in the toxicology world, which you may have heard of, is this
tissue chips for drug screening project. This is a classic NCATS project in a couple of
ways. First, it addresses a critical bottleneck in the translational process. That is, how
do you test for toxicity? Secondly, is a novel kind of collaboration. This is a collaboration
with DARPA. And thirdly, it's focused on logarithmic improvements. This is focused on fundamentally
changing the way that we test for effects of drugs.
A different approach from the Tox21 approach, which is a very reductionist pathways/systems
biology base; this is an experimental approach using microfluidics. So the idea here is that
we would have, instead of a modeling in cells, we would model in three-dimensional organoids
of, in this case, 10 different human organs. And these would all be represented on microfluidic
platforms that would allow us to infuse, theoretically, artificial blood, into, say, an artificial
intestine which would get -- then the drug would either get absorbed or not, go through
a microfluidic channel to an artificial liver and get metabolized or not, and then go, say,
to an artificial kidney and have a toxic effect or not. So that's the model.
And so when this started about two years ago, NIH funded 19 awards and 10 different systems,
and these are just two examples. This is a lung chip from the Wyss and a blood-brain
barrier chip from some folks at Vanderbilt. And then DARPA focused -- had two rather large
awards focused on the microfluidics and the engineering. I must say, when I first started
working on this, I thought this was completely nuts. I thought, "There's just no way they're
going to make this work." I must say, I was wrong. At least -- [laughs] -- and I'm glad
to say I was wrong. So why was I wrong? It's because I underestimated the value of convergence.
So this is a convergence of IPS-ESL [spelled phonetically] technology, biosensor technology,
microfluidic technology, and tissue printing, ES/IS cell [spelled phonetically] technology.
All of these are milestone-driven projects. Every one of them, without exception, is ahead
of its milestones. And so what we're doing now, is now that the individual microsystems
of the organs seem to be working reasonably well with the positive controls, they're starting
to be put together in groups of two or three.
If I move on to a more therapeutically-directed program, this is a program that we started
in 2009, again, when I was at Genome. It focused on rare diseases, and the model here was the
same model as the NCGC model, that is, these are obligatory collaborations between academic
or small company investigators who have expertise in a disease or a target, have partially developed
an intervention in this case, and they collaborate with NCATS' intramural scientists who have
expertise in drug development, to move these projects to the point where they are adoptable
by an outside organization, usually a biotech or a pharma, for completion of development,
usually after IIA. And so these are the kinds of things the trend does. If you look at all
of these things, they are very difficult to do or impossible to do within a normal academic
environment for a lot of reasons. It's hard to get grant support to do this. These are
very expensive. They're not hypothesis-driven programs, et cetera, et cetera, et cetera.
So these are all things that TRND does. Essentially they go from where the NCGC ends, which is
a lead, to first-in-human trials. If we can offload these to a company first, before that,
because they're de-risked enough, great, but often that doesn't happen.
So this is a portfolio, and I don't want you to read this, I just want you to see if you
can see it, the therapeutic area's very broad. Therapeutic area's anything from hematologic
disease to cancer to infectious disease, nerve degenerate disease, et cetera. Why do we do
that? Because we're interested in, remember, general principles which will allow us to
improve the efficiency of the translational space. In order to have general principles,
you have to go across therapeutic areas, or else, by definition, you can't argue it's
generalizable.
Now look at the collaborators. About half of them are academics, half of them are small
companies. There are three, actually, with Genome And these all went through peer review,
I should say. They were not picked by me. So I think this says something about the quality
of people at Genome, because there is a lot of competition to get into this program. And
so I'm just going to very quickly tell you what they are and who they're from.
So this is a project on a rare myopathy with Bill Gull [spelled phonetically]. This is
a classic rare disease to a single gene mutation of G and E, it's a disorder of the sialic
acid pathway. The important thing is, in this case, Bill was stuck because he didn't have
the capacity or the knowledge to do all the IND-enabling tox studies and had no natural
history studies, so he didn't know how to approach this problem. So we teamed up with
Bill, and actually, within a year, we're off of clinical hold and into patients.
This project you may have heard about, because it's gotten a lot of notoriety in the press.
This is a very unusual development project with a drug which has been used as an excipient
in the past, never an active drug, given interthecally. The collaborators here are, all of these folks,
including Bill Pavan from Genome, is one of the people involved in cloning the NPC [spelled
phonetically] gene about a decade ago, or maybe a little more, along with three other
universities, four ICs and two companies involved in this, including J&J, who makes this. And
the important thing is, we went through all of these milestones in a rather rapid fashion,
and this trial is now in progress at the clinical center as we speak. One of the important things
is, one of the reasons this worked so well is that the disease foundations were involved
from the very beginning, so they were very clued in and hooked into what was going on
here.
This is a kind of leukemia that you probably heard about from probably Lou. He's been working
on this a large amount of his career. This is a fusion that results in a particular rare
type of leukemia, and through an earlier NCGC project, we'd actually identified this compound,
which is an old Roche compound, believe it or not, which seemed to have an effect on
improving or inhibiting this -- the action of this fusion protein. And so this program
is now moved into TRND as well.
This one I show not because it's an NHGRI program, but because I want to get back to
that Linus Pauling paper, that this is -- if this works, this will be the first drug developed
against the mechanism of sickle cell disease. So this is -- but it's a classic example of
why these projects are so hard to do. So this is a collaboration with a company called SRX.
It's a virtual company in Boston. This compound right here, any of you who are chemists, you'd
be having a stroke at the moment because that is a really nasty-looking compound if you're
a chemist. It also has a really problematic, potentially, mechanism. It binds irreversibly
to sickle hemoglobin. When it does that, it shifts the oxygen dissociation curve to the
left and allows the sickle [unintelligible] to get through the postcapillary venules,
the hypoxic environment of the postcapillary venules without sickling. However, the concern
was if it covalently binds the sickle hemoglobin, does it covalently bind every protein in the
proteome and cause all sorts of problems. Now it doesn't, but that was one of the reasons
why this company could not get this program -- could not get support for this program.
It's also regulatory issues and clinical risk issues. But we partnered with this company,
and within a year, got all the work done, all the tox work, the CMC work, and the regulatory
work to be in the patients, and now it's been through two -- a Phase I and a Phase IIA trial,
and it's now in a Phase IIB trial now. And the biomarkers are great, that's all I can
say. So we're really excited about this.
Okay, so I'm going to skip through this one. Okay. I'm just going to finish with ORDR.
So you're all aware of this because Genome has been a partner of ORDR since it started.
So ORDR is now part of NCATS. And there are a number of things I just want to point out.
One is, this Rare Disease Clinical Research Network. It's a rather remarkable network
of 17 different consortia, working at over 200 institutions. Important things about these
are that they don't work on individual diseases. They insist on grouping diseases, that is
either by cell type or by pathway or something, so as you don't work on one disease at a time.
And secondly, every one of them has to have a patient advocacy group as a critical and
integral member of the consortium. And this repository is something that we're actually
very excited about, too, as one of those things that's sort of similar to the -- when you
think about the problem of the old days of doing genetic linkage studies. You remember
how it was very hard to get funding to acquire and phenotype patients to do linkage studies?
And it really held back the field? That's what's going on in therapeutic development
now, so it's one of the things we're working on. And this Genetic and Rare Disease Information
Center that of course has been funded with Genome for many years now, and this is an
information center that gets about 500 calls a month, generally from patients and parents
who have just gotten a very bad diagnosis, to help them with what this disease is, how
to find, you know, advocacy groups and practitioners, experts in the field, et cetera.
These are the consortia that are part of the Rare Disease Clinical Research Network, and
if you just look at the names of a few of these, like the Lysosomal Disease Network,
these work on the 40 lysosomal diseases, so this is an organelle; there are other ones
that work on phenotypes. This is rare kidney stones, and then this one is a clinical syndrome,
nephrotic syndrome, and then this one is more of a syndrome as well, autonomic rare diseases
of the nervous system.
So I just want to finish with this, that overview of what NCATS does. So a lot of you know Steve
Groft, who has been a real icon in this community for the last 30 years, was actually at the
FDA when the Orphan Drug Act was passed in 1983, and shortly thereafter left the FDA
to go to what was then the Department of Education and Welfare to get orphan products started
and orphan research started within HHS, what's now HHS. He retired on Saturday. And so this
is a big change for us. Those of you who know Steve know he is an absolutely extraordinary
visionary and advocate for rare diseases, rare disease research, understanding, and
treatment. He is not replaceable, clearly, and so we are going to have him stay on with
us as probably a half-time consultant, because he's really critical for what we're doing.
But we are going to be recruiting for a replacement for Steve, and so if you know folks who are
in the rare disease community, I'd like to hear about them when we put the ad out. What
I'm looking for is really to go genome-wide for this problem. And the way I describe this
is, you know, Steve and his incredible efforts have focused attention on the problem of rare
diseases. But, for the most part, they've been on individual diseases. And we now have
the opportunity to globalize this question. Because rare diseases, there are 6,000 independent
diseases, but of course they're connected to each other in ways that we don't understand.
And so the next director of this office, I want somebody -- it to be somebody who thinks
of the rare disease problem as a problem, and probably has a heavy interest in knowledge
and informatics and thinks about systems, not on individual diseases. So it's really
an opportunity to take this whole effort to the next level.
However, it's important, and there have been some questions, actually, "Oh, gosh, is NCATS
going to abandon rare diseases because Steve's leaving?" Give me a break. No [laughs], we're
not. As a matter of fact, we are -- as I've often told Steve, I think in many ways NCATS
is a validation of all the things Steve has done over the last 30 years, so it's going
to continue its important work, but I love this picture, as Steve rides into the sunset,
those of you that are physicians will know why he is riding a zebra. If you don't know
what that is, I will ask you to talk to Bob Nussbaum, he can tell you. And with that,
we are done.
So I just leave you with this. You can have these slides. Certainly I'm glad to hear from
you about anything, but if you're interested in any of these projects, any of these areas,
I want you to have the contact information for the people who do these. And I'd be glad
to take a few questions before I run back and try to deal with our steering committee.
Eric Green: Okay. Thank you, Chris. Jim.
Male Speaker: That was great. I was just wondering. It seems
like a lot of problems with therapeutics boils down to engineering and delivery, right? I
mean, that's why we look for small molecules because, well, we can get them into places.
Do you have a concerted effort, looking at ways of targeting things and --
Chris Austin: Yeah, I actually should have said this because
it's -- so, yes, one of the things, and you probably didn't see it because I flipped through
it so fast on the NCGC slide, the only way we're going to get to predictability and be
able to target is to understand what the general principles are that govern small molecule
target interactions. We don't understand that. It's really interesting to think about. If
you think about genetics, if we did not understand sense and antisense, that A goes with T, G
goes with C, if we didn't understand that, how would you do genetics? But we don't understand
that in this space, so everything's empirical.
So because this space is so much more complicated, there are three-dimensional structures that
are floppy and change shape and all of that stuff, we really need to generate massive
amounts of data and then work backwards to identify what those principles are, what those
patterns are. And that's a lot of what the NCGC now is going to be able to do. It's been
sort of distracted from doing this for a variety of reasons for the last couple of years.
The other thing that we're doing is working a lot with the structural biologists to see
how we can marry those a little bit better. It's made a little bit more difficult by the
fact that the PSI has gone away, but we'll deal with that. We're also working very closely
with engineers both at DARPA and at pharmas about novel ways to identify compounds more
efficiently. But I would say, overall, it's a matter of understanding what the general
principles are. You know, I often say that, you know, the robots that we have can screen
-- they screen -- the big one screens about 3 million wells a week, which is great. But
the fact that we are screening 3 million wells to find a compound that might work is prima
facie evidence that we have no idea what we're looking for. So, eventually, I want to put
the screeners out of business, because we'll be able to target them.
Male Speaker: So, thanks, Chris. Behind your Eroom's Law
there, of course as you know very well, there's a sort of flat rate --
Chris Austin: Yeah.
Male Speaker: -- of new drugs.
Chris Austin: Yep.
Male Speaker: The problem is, well, that that's flat, but
also the problem is that the cost is going up --
Chris Austin: Yep.
Male Speaker: -- and that's where the overall slope is low.
Chris Austin: Yep.
Male Speaker: Part of the reason that cost is going up so
much is because things are failing really late in the process.
Chris Austin: Yep.
Male Speaker: And part of the reason there is because 15
years before, the things they chose to work on were never going to work.
Chris Austin: Right.
Male Speaker: Right? The targets were never going to get
you there because they weren't relevant to the human disease they were being developed
for for a lot of money.
Chris Austin: Yep.
Male Speaker: So, with all that logic, it seems to me you're
positioned to do something nobody else on the planet could be, that is sitting amongst
the other institutes with access to the extramural expertise who know more about those targets
than anybody else.
Chris Austin: Right.
Male Speaker: Anywhere.
Chris Austin: Right.
Male Speaker: What are you doing in that regard, to really
draw out the --
Chris Austin: Yeah.
Male Speaker: -- expertise that exists --
Chris Austin: Yeah.
Male Speaker: -- extramurally and amongst the institutes.
Chris Austin: Yeah, it's a great question, and it is -- you're
absolutely right, it is a unique advantage that we have, and we use it a lot. We also
have the FDA on speed dial, and that helps. Doesn't help with the target validation in
question, but it helps with others. You might have noticed this AMP thing. I know you're
part of this, this Advanced Medicines Partnership that came out last week. It's really a target
validation effort, is really what it is.
And so we are very deeply engaged in conversations with the institutes about doing this in individual
projects. I was just at a meeting not long ago on Alzheimer's disease in this arena.
And I would say the other thing we're focused on is general enabling validation technologies
and how do we make those work better. And the genome-wide RNAi thing is a perfect example.
I think that will be a great technology now, now that it's been worked out how to do it.
Male Speaker: So I realized you inherited 40-year GCRC and
several years of CTSA.
Chris Austin: Yeah.
Male Speaker: But I would like to encourage you to keep
your logarithm measure with that group. Having been amongst two CTSAs, the goal is not always
logarithmic advances. The goal is sometimes sustaining the 40-year --
Chris Austin: Yes.
Male Speaker: -- legacy. So I would just encourage you to
keep pressing on with that --
Chris Austin: Yeah.
Male Speaker: -- because we all need it.
Chris Austin: Thank you so much. Can I ask where you work?
What were the two places?
Male Speaker: No.
[laughter]
Chris Austin: No? Yeah, so you were -- as Eric --
Male Speaker: I'll tell you off line. We're in an open session.
Chris Austin: Oh, yeah, okay. [laughs] As Eric was telling
me before, we are -- we are pushing that agenda quite aggressively now, because it absolutely
has to be done, for all kinds of reasons. The opportunity is huge. And so I've got a
lot of people mad. And so as Eric said, "Hmm, you must be doing something useful if you
got the people mad." So, yeah, it is, and I think the good thing about it is that the
PIs really understand the opportunity here. But they have -- what I often discovered they
were lacking is clear -- a clear mission from NIH about what they were supposed to do. And
so I would say the vast majority of them were really excited about this.
Male Speaker: I think Lon's comment about this really comes
back to part of the problem too, is, you know, working on things that never should have been
started in the first place.
Chris Austin: Yeah.
Male Speaker: And you know, the old, if you have a hammer,
everything looks like a nail -- well, the answer to every question is the acid that's
running in my lap.
[laughter]
Male Speaker: You don't just throw it [spelled phonetically],
so, you know, we need to get passed that.
Chris Austin: Yep.
Male Speaker: Like you said, it's almost going to be mission-led.
And certainly at University of North Carolina and at Washington University, where I was
previously --
Chris Austin: Yeah.
Male Speaker: -- there was a lot of good things happening
Chris Austin: Yeah.
Male Speaker: -- and a lot of it wasn't.
Chris Austin: Yeah.
Eric Green: So we have time [spelled phonetically] for
one last question --
Chris Austin: Yep.
Eric Green: -- just before you go. So you want to say
anything about some of the conversations we've had, thinking about, as we're looking at opportunities,
and we are --
Chris Austin: Yeah.
Eric Green: -- funding programs moving into the clinical
arena --
Chris Austin: Yeah.
Eric Green: -- or the application of genomics, and often
if they're at institutions that have CTSAs.
Chris Austin: Yes. So one of the things that I'm most excited
about, as you might imagine, is that the very things that I think are needed in the genomic
medicine space actually at least are theoretically available through the CTSA program. It really
is complementary to what I did at Genome and a lot of the early pre-clinical things that
we do at NCATS. And so I think these questions of not only genetic disease therapeutic development,
which is kind of a minor part of what we do, but on a more fundamental level, for instance,
how is it that genetic circuits are put together as analyzed through the lens of knocking down
every gene one at a time? There's a lot of really cool experiments to do in that space.
You think about one of the major problems that we work in the clinical space, which
is, how do you identify people via biomarkers, which could be genetic biomarkers, for treatment,
and then test those hypotheses in a rigorous but efficient way? Those are things which
are of great interest to many of the people at the institutions which have CTSAs. So if
we can harness that, then I think there's some really remarkable things that we can
do together.
I think from my own point of view, the problem -- the limitation at this point is not Genome,
it's us, you know, because we have some work to get our own house in order to be good partners
with you. But I really think there are enormous things that we should do together, and it
will not surprise you to learn that as somebody who has spent nine months in Genome, this
is how I think.
Male Speaker: Nine years.
Chris Austin: Yeah, nine years. Thank you. And nine months.
Male Speaker: There you go.
Eric Green: Okay, any last, quick questions before Chris
has to race out the door? Okay.
Chris Austin: Good.
Eric Green: Thank you.
Chris Austin: Thank you so much.
Eric Green: Okay, Teri, you're up next.