- Welcome, everyone
here and everybody watching from home
to the Promise of Precision Medicine.
We are going to explore today
how individualized treatment
will revolutionize
healthcare as we know it.
My name is Linda Henry.
I'm the managing director of
the Boston Globe Media Partners
which also built a media
company called Stat News
which reports from the
frontiers of life science.
Stat created the great
graphics we will see today
as they have been diving
deeply into precision medicine.
As a little bit of housekeeping,
this is a 60 minute session
which will start with a discussion
amongst our fantastic
panelists that we have today.
Then, we'll open up to
questions from the audience
and I will be very precise
and end this at 1:30.
So for those of you who are interested
in attending President Trump's speech,
you will have a half an hour
still to get downstairs.
Now, it is my pleasure to
introduce this world-class panel
that we all get to learn with today.
So Nancy Brown is the CEO of
the American Heart Association.
Jay Flatley is the Executive
Chairman of Illumina.
Dr. Scott Gottlieb is the 23rd
United States Commissioner
of the Food and Drug Administration.
Vasant Narasimhan is a
CEO Designate at Novartis.
As of Monday, maybe he'll be CEO.
And Dr. Tan Chorh Chuan,
the Minister of Health of Singapore.
Welcome and thank you all
for being with us here today.
They all have incredible
experience and credentials
and expertise and I encourage
you to look at their bios
on TopLink to learn more about them.
So it is so interesting to have
this conversation right now.
About 42% of all drugs
currently in development
and 73% of oncology drugs
are targeted therapies.
Bio-pharma companies nearly
doubled R and D investment
in precision medicine
in the past five years.
That is projected to increase
another 33% in the next five years.
The number of precision
medicines in development
will increase 69% in the next five years.
So there is a lot going on right now.
I want to start by talking
about the expansion of precision medicine.
Vas, our ability, to use your
words which I really loved,
you talked about our ability
to generate, interrogate,
and gain insights from data.
It's rapidly increasing.
Can you explain to us
how this is accelerating
precision medicine?
- Yeah, sure
and thanks for the opportunity
to speak with all of you.
When you look at it,
there's a couple of trends that
are happening all around us.
One, the computing power
that we have at our disposal
has increased dramatically,
such that we can actually
take on so much data.
The second is now we're able
to tie together datasets,
genomics, proteomics,
phenotype, the biomarkers
that actually enable us to
generate deeper insights
into human biology.
It's another accelerator.
Then, the third is our
analytical power is improved.
Our ability to tie datasets
together and then analyze them
to get to the underlying
determinants of disease.
When I look at it, I think
there's two big opportunities.
One is can we mine our clinical data
to find new drug targets
that we wouldn't have identified before?
We've seen that already
in mining our large-scale
clinical trials with proteomics.
The second, and it may be
the more tractable one,
is how do we find through
precision medicine
high-responding patient populations?
It's worked beautifully in cancer
with when we do have
genetically-derived tumors.
It's worked now with gene
therapies for eye disease.
The question becomes beyond that,
can we move into other areas
where we can identify
high-responding patient populations
and target the medicines
in clinical development
through clinical trials
so we get the patients
who get the biggest benefit at the end
when we actually launch these medicines?
- Wow, great.
Nancy, there are some limits to this data
that Vas is talking about
in terms of the population
that we're getting this data from?
- Yes, absolutely.
I think Vas has rightly identified
the power and the potential of using data
and the intersection of
high-compute capability
with all of this data that
exists in many different places.
The concern that I would raise
is that the individualized data
is from very select populations.
If we really are going to focus
on high-responding patients,
then we need to have data
that's representative of all people.
If you look at who participates
now in clinical trials,
who perhaps has participated
in these longstanding population studies,
we would find that not all
populations are represented.
So in addition to thinking about
how we interrogate the data
that exists today and how
we create data frameworks
that can allow this data to
come together and be analyzed
among and across these vast datasets,
we must do more to recruit individuals
who will be willing to share their data
so that all populations are
represented because if not,
the promise of precision
medicine will not be realized.
- Chorh Chuan, you are dealing
with this issue directly.
Can you tell us about how
you're helping with this?
- Yes, I think it's very
important for us to ensure
that the study populations
that go into precision medicine
are sufficiently diverse.
In 2016, a survey of the
genome-wide association studies
show that of 2500 studies,
only 19%, one-nine percent
of the participants were
of non-European descent.
- [Linda] Wow.
- Of these 19%, the majority were Asians.
This matters because in the
underrepresented participants'
ethnic groups in these study populations,
genetic markers might be wrongly assigned
to disease related just because
they're underrepresented.
Therefore, you run the
risk that by creating
more precision medicine
for one group, you in fact,
create imprecision medicine
for another group or groups.
So a lot of what I think
needs to be done for us
to pool larger sets of more
diverse patient representation
so that we can be more
confident of the validity
of the predictive power of the bar markers
in different populations.
- So more diverse data will
enable the precision medicine
to be more precise to the individual?
- Across populations, ethnic groups.
- Jay, you've lived at the intersection
of biology and technology.
Even with all this data
and once we do get it better
balanced in terms of diversity,
there's still a lot that we don't know.
Can you talk to us about some
of the limits of the data?
- Sure.
So let me start by saying
that the underlying tools
that have kind of powered
the discovery engine
around genomics and precision
medicine have increased
in power dramatically over
the last couple of decades
and have been used quite
effectively by governments,
research agencies, and
commercial companies as well
to create a vast knowledge
about what's going on
in the human genome and that
of other plants and animals.
But if you think about
how much we actually know
versus how much we think there is,
that estimate's very hard to come up with,
but people think it's still in
the single-digits somewhere.
Some say low single digits,
some high single-digits.
So we have a lot of work to do here.
The way that work needs to get
done is through big science.
That big science, I think,
is beginning to happen
around the globe in the form
of what we call population
genomics programs.
One great example of that is
the Genome England program
where they're sequencing 100000 genomes
through the clinical side of the NHS.
All of Us program in the US.
There's a program in France.
China, obviously, has a
precision medicine program.
Many others around the world
are beginning to come out of the ground
and so the combination of those projects,
if we can figure out how
data gets shared between them
which we've been talking
a bit about already,
will exponentially increase
the power of those datasets
and kind of achieve what I think
is the most fundamental objective
and that's to improve the
clinical utility of the genome.
- Great.
So Scott, you've talked about
how clinical trials are done
and how they have to be
changed in order to catch up
with the rapid advancements
that we're having in precision medicine.
Can you talk to us about
some of your initiatives?
- I think I would start off by saying
first we have to decide what
we mean by precision medicine.
I think on the one hand,
we have the ability
to change the underlying
mechanism of disease
and that's one component
of what I think about
when I think of precision medicine.
When you think about changing
the underlying mechanism of disease,
you think about things like
gene therapy and Cas9/CRISPR.
In those constructs, you're
able to often observe
the efficacy very early in small trials.
Sometimes these technologies
are so efficacious
that they're
super-efficacious, if you will.
Some of the things that we've
seen in early development,
you're producing almost too
much levels of certain proteins
that you're trying to replace
in some of the genetic diseases
that are being targeted
with these technologies
and that's been in some
of the early literature.
So the issues that the
agency's gonna confront
are gonna be less about
determining efficacy
where there's a strong proof of principal
and you can observe efficacy early
and more focused on longterm
durability and safety
and product issues, issues
related to the off-target effects
of these technologies
and what the implications are longer term.
So I think that's where some of the focus
on how we're going to
try to steer the design
of clinical trials in these
realms is going to play out.
We're gonna be putting out some guidance.
We've talked about it early this year.
I think we're really
at an inflection point
right now where we're at a point in time
where we're sort of
defining the modern rules
for how these technologies
are gonna be regulated.
We're gonna be looking at
accelerated approval end points
for earlier approval on
questions of efficacy
with more vigorous longterm followup
in some of these constructs
where we have authority
to do that under accelerated approval.
You're seeing a lot of engineering
around the product-specific issues,
in part for commercial reasons,
where you see companies
changing how they develop
some of these technologies
because there might be stacked
royalties and other things,
patents, that are blocking.
So it makes the issue
of looking at the product
issues more complex.
The other thing you think about
when you think of precision medicine
is instead of changing
the underlying mechanism,
manipulating the underlying
mechanism of disease,
and there you think
about the traditional drug
diagnostic combination
and drugs that try to target
a mechanism of disease
in a pre-specified group
and I think there it's incumbent upon us
to think of clinical trial
designs to make it easier
to pre-specify those
groups and to target them
where you can observe a strong
efficacy signal earlier.
So you think of things like
tissue-agnostic approvals
and master protocols and
drug diagnostic combinations
trying to get the diagnostic
approved alongside the drug.
We put out a couple of guidance documents
articulating those pathways as well,
but I think they're
distinct sets of challenges,
if you will,
and I think they need to
be thought of differently.
- There was a precision
medicine conversation here
that Chorh Chuan and I
were at yesterday I think
and one part of the
conversation was talking about
approving the process as
opposed to the specific therapy,
so the process by which
the patient is served
versus the actual dosage amount
or combination amount that had to be,
which is the way things
have done in the past.
So instead of testing and
approving each dosage amount,
approving the process
by which it is achieved.
- I think, you know, we
wouldn't necessarily regulate
how things are applied
in clinical practice.
I think where you might see a construct
where you're regulating a
treatment system would be
if you're trying to approve a drug
and a diagnostic in combination.
It's been a little bit more challenging
to define the pathway on how
to get regulatory approval
for the diagnostic at the same time
that you get regulatory
approval for the drug
in part because a lot of
these diagnostic tests
have been promulgated as
laboratory-developed tests,
so they haven't been brought
through a traditional regulatory process.
I think that, I've talked
about the fact that I think
it's incumbent upon us
to think differently
about how we regulate
diagnostics and I think it's time
that the agency needs to work
with Congress and stakeholders
to develop very specific,
targeted legislation
that would give us a
unique set of authorities
to regulate diagnostics properly.
My view is that the old 510(k)-PMA pathway
and it's a little bit jargon-y,
but there's a technical audience,
but the traditional pathway
for approving medical devices
doesn't really fit well
with modern diagnostics.
We need very well-fashioned authorities
when it comes to diagnostics.
- Vas, what has your
experience been with this?
- Your first question on
the process and the product,
we recently licensed a cell
therapy through the FDA
and this was a very precise therapy
where we take a patient's
cells, we reprogram the cells
for B-cell cancers, and
then we infuse them back.
It's a completely
different construct, right?
It's sort of a living drug
where the patient's own cells
are part of the production process.
As Scott points out, this is a
completely different paradigm
and then I think on the diagnostics side,
I think with the power of digital
and can you really do digital diagnosis
when you think about the
power of liquid biopsies
and can we detect cancers earlier?
I personally think that
we're at the limits right now
of what the human mind can ascertain
in terms of biomarker signatures.
We're gonna have to have a
machine looking across a panel
of signatures and identify a signature
that would then give us
a patient sub-population.
Asthma is probably not asthma.
Severe asthma is probably
not severe asthma.
There's probably a whole
range of sub-populations.
Then, we need, of course, the regulations
that would allow us then
to bring that, a drug,
with that signature or that diagnostic
through clinical trials
which we'd still have to
do prospectively to market.
- The other way to interpret
your question is exactly,
I know you picked up
first, which is and then
that's why I did the
bifurcation in my comments
which is on the biological side,
the process is the
production of the biologic
in a lot of these cases where
we have precision medicine
like CAR-T, like gene therapy.
That's where a lot of focus
of the regulatory process is
on the manufacturer of these
personalized treatments
because that's where a
lot of the complexity is
and that's where a lot of
the things can go wrong.
You called it cell therapy.
We call it cell-based gene therapy.
(laughing)
- Correct my language then.
- Take a note.
- I'll defer to you how
you describe your products.
(laughs)
- Nancy, I want to go back to data
because this is really what
precision medicine relies on.
You've talked about how
with larger datasets
we can improve care for data.
We talked about the importance
of diversity in our dataset as well
and because large datasets
help physicians understand
how targeted treatments have worked
for genetically-similar
patients in the past
and helped doctors uncover treatments
that they may not have thought of,
but assembling the large datasets
requires healthcare systems
to share information.
How do we solve the
current barriers to data?
- I think one of the most
important things to remember
on data is what we're trying to do
is create in essence the public trust
that there is this promise of
more personalized treatments
for patients and what patients want
is to live a healthful, satisfied life.
So health systems feel
that they are the fiduciary
of patient data and they are
and we've got to find a way
to inspire the patient voice
in all of this because what
gets lost, I think, oftentimes
is it's about process
and it's a very technical conversation,
but at the end of the day,
what we're trying to do
is assimilate this mass amount of data
on behalf of patients and the public.
The other thing that we
haven't touched on yet
as it relates to data is what we believe
is very important data that
must accompany the data
from health system EHRs,
from clinical trials
and longterm population studies,
and that's environmental
data, exposure data,
as well as data on how people
are living their lives.
One way to push the system,
we think, is to inspire people
to be willing to donate their own data
from their own wearable devices,
from new sensing devices
that are not invasive sensing devices.
If we could create this
groundswell of people that say,
"Hey, I want something different
for myself and my family
"than I have today and I'm
willing to donate my data
"to make that happen
"and by the way, that's my
data in your health system
"and I would like that to
be part of this as well."
That's what we're trying to do
at the American Heart Association.
It's what Francis is
trying to do at the NIH.
I think the system has to be pushed
by the people who are
the ultimate beneficiary.
- You talked about the
word trust and Chorh Chuan,
you have talked about the
importance of trust as well.
This includes rules on who
gets access to this data
that Nancy was just
talking about collecting.
- I think it's very important for us
to have very early engagement
to build their trust
because the promise of
precision medicine is
when we can get more and
more different types of data,
clinical data, genetic data,
data linked to research,
environmental data, they are
specific for an individual.
Then, in aggregate, we have
a pool of data that allows us
to see patterns and associations
that allow us to create a more
continually learning system.
But that means that we have
patient-identifiable data
across many, many different types of data
which is specific to the patient.
So I think people will be naturally
and understandably
concerned about privacy,
about security of the data,
and also about who gets
access to this data,
and whether there are protections
against genetic discrimination,
particularly in employment and insurance.
I think these are difficult issues,
but they need to be the
subject of an early engagement
of the populations
because this is, I think,
the direction in which the
entire field is going to move.
- Great.
Jay, you had thoughts on this as well,
on what Nancy was saying
about getting more people
to share their data.
How do we drive the
behavior of data sharing
and how do we get past the challenges
of safeguarding that data?
The challenges that we've seen here
that people tend to
hoard what data they have
either for economic reasons,
intellectual property reasons,
or geographic boundaries.
Very often you'll find
countries have rules about data
not leaving the geographic
boundary of that country.
So we've been involved in
efforts to try to figure out
ways to kind of break down those barriers.
There's a whole host of things
that have to happen here
including things like standards
around phenotypic data.
You find some of that locally.
Centralized health
systems tend to do better
than places like the United
States in that regard.
Then, also the underlying technology
about how you actually
federate these databases
and, if you have to keep data
geographically separated,
how you have high-performance
query systems
that can interrogate
across these databases
and how you exchange economic value.
So there's been some and
there's ongoing research
on using technologies like blockchain
to try to track royalties.
In fact, there was an
announcement yesterday, I think,
that Amazon is beginning
to work on this notion.
There are other companies
in the blockchain sphere
beginning to do this as well.
I think that'll help break
down some of the concerns
about do I get the economic value
out of all the work and
dollars that we invested
to create my particular dataset?
There's also some standards
being formed like GA4GH
which are beginning to
create the kinds of standards
we need globally to put
these datasets together.
- Vas, in order to get
beyond the treatment,
to actually get to the
preventative care aspect
of precision medicine, how do we establish
a continually-learning infrastructure
with realtime knowledge?
- Yes, I think it's gonna take time
to really get the signatures and data
that we'd need to enable this.
We have examples, for
instance, many companies
are now running trials
in Alzheimer's disease
where we look for a
particular gene signature,
the APOE e4 gene and we know that patients
who are homozygous have
a much higher likelihood
to get Alzheimer's.
So you treat early and then
you continuously watch.
So that's a very simple example.
It gets much tougher.
We've been doing work, as many other have,
at looking at circulating tumor DNA
and other signatures for early cancer.
You can do the continuous
monitoring in that instance,
but how do you know if you're
treating in an instance
where you don't need to treat?
Is there the positive and
negative predictive value
of such tests 'cause we
know the body is constantly
in a homeostasis versus cancer?
So I think there's gonna
have to be a conversation
about how much do we really want to know
about our own health and
when do we want to know it?
Because it's only valuable to know it
if we can do something about it
and do something about it with
a high degree of conviction
and with a high-degree of data.
At least, that's how we've
been thinking about it.
- So this predictive modeling
that we're getting to,
the preventing the diseases
that we know we can help?
- That we know comp...
There's, I think, a great
example that I like in asthma
where we know atopic march
happens in these kids.
We know if you have asthma very early on
and you treat early,
you can potentially prevent atopic march.
So could we identify those
kids who would benefit
and then prevent the asthma
from ever becoming severe?
Those are the kinds of use
cases that would make sense.
- So Scott, one of the other
big areas of precision medicine
that we have to talk
about is affordability.
How can we bend the curve on
the price of precision medicine
knowing that we've had an
incredible drop in the price
of genome sequencing which
is one of the first steps,
but what else can we do?
- Picking up on what Vas was saying,
I think doing clinical
trials for primary prevention
are particularly hard and costly.
You often times need very
large, randomized trials
and you're looking over
long periods of time.
I think if we want to try
to bring the cost down
of primary prevention, we need to think of
how we design those clinical
trials better to use data
to better pre-specify the
population that might be at risk
so maybe you don't have to
look over as many patients
and look out over as
long of a period of time.
Also, have better ways
to predict how products
could increase the background
rate for certain risks.
So, for example, if you're
gonna give a drug to someone
over a long period of time
to try to prevent the onset of Alzheimer's
'cause they might be at risk
for Alzheimer's disease,
you want to make sure
that prolonged exposure
to that product isn't gonna
increase your background rate
of cardiovascular risk, but if you want
to discharge the risk
of slightly increasing
someone's propensity
to have a cardiovascular disease
through the administration of a product
that's gonna be administered
over a long time,
that's a very large clinical trial
and it's a very large
placebo control trial.
So we need better ways
to predict those risks
and to pre-specify the populations at risk
so that we can bring the
cost of development down.
- If I could just add one point?
- Please.
- It's part of the reason,
since we have a expert group in the room,
it's part of the reason why companies move
to the adjuvant setting
in cancer because we know
this is a patient already had
cancer, cancer is resected.
Now, can you prevent the
recurrence of the cancer?
Getting into primary cancer prevention,
then the numbers get very daunting.
So to Scott's point, I mean,
if we could really specify
who would benefit in a
much more specific way,
then I think the opportunity
to do these studies increases.
- Yeah, most of the developments
in secondary prevention
for that reason because someone
who already had an event,
if you already had a heart attack,
you're at higher risk for
having a second heart attack.
It's easier to demonstrate
that you can reduce the secondary risk.
There are constructs though
where we have studied drugs
in secondary prevention or
in more-at-risk populations
and then extrapolated into
lower-risk populations.
The classic example being ACE
inhibitors in heart failure
where we started with
Class IV heart failure
and marched our way down 'cause
we allowed for extrapolation
from a regulatory standpoint.
So there are models for doing that.
I think we need to perfect those models.
- Linda, I might add that
this is my favorite chart.
- Great.
(laughing)
- That the price of
sequencing has gone down
by two to three orders of
magnitude in the last 10 years
depending upon when you start and end
and that we've signaled
that there's another order
of magnitude decrease
in the price of sequencing gonna happen
over the next reasonable
short number of years.
- Yeah?
- I think if you look at
two big topics discussed
in this forum on health,
one is precision medicine data
is value-based healthcare.
I think if you can find a
greater convergence and overlap
between the two, it could
actually be very powerful.
For example, within
the portfolio of things
that are being done,
are there precision medicine applications
that can help improve
health while reducing costs?
For example, by reducing the
more ineffectual treatments.
How many patients you need to treat...
For all the patients that you treat,
how many people actually get a benefit
and how many people
don't have any benefit?
I was just reading a report that said
that for the top 10 bestselling drugs,
for every person that
benefits from the drug,
between three to 24 people
actually show no improvement.
So I think there are many opportunities
for us to reduce costs in other areas
so as to create more budgetary headroom
for the expensive types of treatments
that inevitably will come out
which will be targeted, very
small numbers of patients.
- Yeah, the system will pay
more for a certain benefit
(laughing)
than an uncertain benefit.
- That's reasonable.
- One of the themes that
I've heard through this
and is also the theme
of the entire Fourth Industrial Revolution
that's been a theme of the forum
is the amount of
collaboration that is going
into driving precision medicine
forward, the interactivity.
Vas, do you want to talk to us about...
You're at the forefront of,
you're incorporating AI into this
and you're working with
technology companies.
- Yeah, actually, but before,
I want to just make a plug.
Francis Collins is in the room
and we are actually working
in many coalitions with public
and private partnerships
to try, and particularly in cancer care,
to bring the datasets
together, to get much smarter
about how we can actually
improve patient care.
That's just one example,
but I think that's very
positive for society.
On artificial intelligence, I mean,
clearly there's a lot of opportunity.
I think, particularly to organize the data
and actually generate certain insights.
The big challenge will be can we use AI
like many of the partnerships
we have and others do
to speed up drug discovery
and speed up clinical development?
I think it's still early days.
I think we always have to remember with AI
that when we do image analysis,
there's a training dataset
you can train the algorithm on
and then you can look at the images.
In drug discovery, there's
not a great training dataset.
Probably can't find drugs
for some of these conditions.
It will undoubtedly speed up our ability.
I like Garry Kasparov's concept
that when you take the chess master,
when you take a smart person
and put them with a smart machine,
you can be a smart person
or a smart machine.
So I think we're gonna have to
get used to having a culture
where you have smart people
working with AI to power drug development.
- I might add onto that the
power of partnerships is so key
and we've really focused on that.
At the American Heart Association,
we've created a data discovery platform
that now has 10 million data points.
The American Heart Association and Amazon
have created that marketplace.
But we announced most recently
on the issue of drug
development a new partnership
between the American Heart Association
and the Lawrence Livermore
National Laboratories
which has one of the world's
fastest supercomputers.
The idea is can we use
the fantastic scientists
of the American Heart
Association who are represented
in academic medical centers
throughout the world
and the artificial
intelligence capabilities
to do more simulation and modeling
of how drug molecules
attach to the wrong protein
to try to get this issue of
side effects off the table.
We think we're in a proof
of concept phase right now,
but we're very excited
about this use and application
of artificial intelligence
because it is the mind and
the machine coming together
in a way with this
super-computing capability
that is soon-to-be 10 to the 18th power
which is unbelievable.
- Just to pick up on that point,
it kind of reinforces the need
to try to develop good natural
history models of diseases.
It's amazing we don't have
good natural history models.
We still expose patients to
placebo in hypertension trials
to see what happens with
untreated hypertension.
- Yeah.
- We should know what
happens with untreated--
- [Nancy] We know what happens
with untreated hypertension.
(laughing)
- Right.
You still need a placebo arm
'cause you're looking for safety,
but we haven't been
able to model that yet.
I think that's a big
opportunity for investment
and we've made some investments,
particularly in rare diseases,
but I think looking harder at that,
that's gonna enable
the opportunity for AI.
- I want to open it up for
questions in the audience
'cause I know that there's a lot
of really interesting
perspectives that we have.
I think that there's one over there.
- Hi.
So for somebody starting
a Phase 1-2 study today,
it's gonna take quite a while
to get to the NDA stage.
What should they be thinking about
given things are changing?
- Is that directed at me?
(laughing)
- We assume so.
(laughing)
- Well, first of all, I would
challenge just the concept.
I think you've seen some...
I think as we move into a realm
where the mechanistic understanding
of how the drug's gonna
behave in a certain disease
is well understood and
as we see drug developers
targeting more rare diseases
and significant unmet medical needs,
I think you've seen very
efficient development programs
because you can get proof
of concept very early.
Then, it becomes a question of looking
at longterm safety issues
where you're not gonna be able
to discharge all the risk pre-market
and you have to have very
efficient tools post-market
to continue to look at safety issues.
We now have those tools
thanks to new legislation.
I would think about how you
could try to develop drugs
under that construct
which is sort of the theme
of this panel: trying to better
target development programs
so that you're gonna be
delivering a more-certain benefit
so that regulators, not just
the FDA, but EMA as well,
have more confidence on the
clinical efficacy questions
and then it becomes a question
of trying to look at longterm risk.
- Could I also chime in?
I think the power of all these
technologies is incredible,
but it's also incredible to me
when I look across biotechnology trials
and often the basics aren't done right.
So my first advice would
be to get the dose right,
make sure you characterize
the patient population,
understand your target product profile,
what you're trying to actually develop,
and understand your endpoints.
Of course, you can use AI
to inform better endpoints,
but I think right now with the
energy which is all positive,
sometimes there's a miss on
understanding those basics.
Those basics ultimately are
what Scott's teams first look at
before they consider these
higher-end technologies.
- I'll just add, I mean,
I've been on both sides of
this now, as some people know,
working on the venture
capital side before.
I've heard...
I've seen companies be reluctant
to come in and talk to the agency
for fear that they're gonna get advice
that's then gonna
encumber them in some way
in terms of how they
wanna develop the product.
I think more often than not,
particularly in very novel settings,
it's beneficial to come
in and talk to the agency
because first of all, you might get advice
that's gonna help you avoid problems,
but if you find out that the
agency doesn't understand
a certain disease well or isn't in sync
with your understanding of the technology,
it gives you an opportunity
to education them.
I've seen that be done
very effectively as well.
So I think that it's
rarely a bad proposition
to come in and talk to
us, even pre-clinically.
We see a lot of people doing that,
especially on the seeper side of the house
with some of the novel gene
therapy and CAR-T and Cas9.
- Question right here.
- Hi.
I just wanted to tell you more
about a project that we have in Israel.
I work for Mr. Morris Kahn,
the founder of Amdocs.
What he did in Israel, he
founded the Morris Kahn
and Maccabi Health Institute for Science.
Basically, in Israel,
from the time you are
born 'til you pass away,
you are at the same
health insurance company,
so they have your data
from the time you were born 'til you die.
Usually, the family stays
in the same health insurance company.
So what Mr. Kahn did, he basically,
he started it as a philanthropy venture
'cause he wanted to provide
scientists with the data
so they can work on the data
that Maccabi health insurance
has and just to help science.
Mark, I think it would be
better if you explain it.
- Is there a question?
(giggles)
Is there a question?
- I don't have a question,
but we've actually got information
from about two and a half million patients
which we've had for 22 years
and this big data has given
us a tremendous resource.
We've provided the base for
interrogating this database
and we've actually come up
with some very interesting
findings for research.
It's a very interesting and
valuable tool for research.
The advantage we have in
Israel is that we can actually,
we don't have the problem
of using the information that we have.
- Great.
Can we have a question please?
Right back there.
Thank you.
- Yeah, thank you.
Great comment on the panel.
Really, a great topic, precision medicine.
I was wondering social determinants
are very important of
the outcome of medicine.
How do you see them
integrated in the concept
of precision medicine the
social determinants aspect?
Also, precise prevention.
If the panel will comment on
precise prevention in medicine?
- I'd be happy to jump in
on the social determinants of health.
I think it goes back
to the very beginning.
At the end of all of this
technical discussion,
it's about real people who
live in real communities
that may or may not have
access to a healthcare system
or to healthcare workers.
We know and understand things
like levels of education,
access to healthy foods,
access to medications,
access to treatment are going
to ultimately impact health
outcomes in the world.
We can't forget as we're
thinking about high technology
and wonderful ways to deliver
more precise treatments
to patients, that all
of these societal issues
must still be addressed.
I think this idea of
understanding the impact
of people who are more
disadvantaged as it relates
to access to things that
will help them be healthier
and what that does ultimately
to their overall health
and wellbeing is something
that must be studied.
It's something that must be focused on,
and I think we're
recognizing more and more,
food is an easy thing to talk about.
If you look at the impact
of people who over-consume
high-fat, high-sodium,
high-added-sugar diets
on overall health and wellbeing
when we talk about what
we're able to study
from a primary prevention point of view,
we know very clearly.
Skyrocketing obesity, type
2 diabetes, and hypertension
which cause heart attacks and strokes.
So we understand this
and we shouldn't forget
that important part of this
ultimate drive that we all have
to deliver more precise treatments
to the right person at the right time.
- Chorh Chuan, you have thoughts on this?
- I think part of the
promise and potential
is we're able to integrate social data,
environmental exposure data
together with the
clinical and genetic data
and if we are able to do that,
then we have some additional
ways in which we can help
to identify people at particular risk
of specific problems and conditions
and presumably be able then
to target the interventions.
In some cases, interventions
may not be drugs
or the conventional treatments,
but social interventions.
- Great.
That's good.
Right here.
- [Man] I'm an endocrinologist
working in Delhi.
So I have two questions.
- Hold on one second.
We'll just give you the mic.
- One is that we develop these
drugs in a very long process.
They undergo first animal
trials, then patient trials,
then finally they're marketed.
How is that that after they
are marketed, some drugs like,
for example, PPAR antagonist
like rosiglitazone
like, for example, some
of the anti-obesity drugs
that suddenly then you
discover after their marketing
that they have side effects
they have to shut down?
- I can start
and I guess we'll see if
Scott wants to jump in.
(laughing)
Of course, when we do the clinical trials,
we power them based on what we see
in each stage of development.
So in Phase 1, we see signals,
Phase 2, we see signals.
Then, Phase 3, the studies
are powered to detect
of course, efficacy and then safety
in particular signals that
we've seen in the development.
Now, what happens post-licensure is,
and many in the case of diabetes trials,
you're talking about exposing
15000 to 30000 patients.
So you will have a
certain ability to detect,
but I can tell you.
My background originally
was a vaccine developer.
You have to expose
sometimes 70000 children
to actually find a very rare
case of intussusception.
So it's no different when we
think about diabetes drugs.
Once we expose millions of
people, we will find signals.
Now, it's incumbent upon
companies to collect that data,
report it to regulators, and
keep updating the labels.
Then, sometimes a signal pops
up after enough exposure,
especially after longer-term exposure
in some of the cases you
cite that then has to change
how the drug is used.
So that's how this happens,
but there should be great confidence
because it's a very rigorous process.
I mean, we process millions
of safety cases every year.
We have to report them within 14 days
and if it's a serious
case, within seven days
to all regulators and
particularly the FDA and EMA.
- I think over time, our
expectation of safety
around products has increased
and I think that that's very reasonable.
Our expectation of safety
from the cars we drive have increased.
I think everyone would
rather drive a used 2005 car
than a brand new 1995 car
because cars are much safer now.
So we're looking to discharge.
Societally, we're looking to
discharge more remote risks
in the clinical development program.
That's why you see trying
to discharge the risk
of secondary cardiovascular side effects
in diabetes trials now.
I think you referenced that.
We've made a decision that's reasonable
even though it's gonna
add to time and cost
because our expectation
of safety's greater.
In a world where there's a
lot of already-good therapies,
you want to make sure the
incremental technology
is gonna deliver benefit.
I think where we haven't caught up though
with our expectation of
a higher sense of safety
around new products is the tools
to try to discharge some of these risks.
I think that's where better investments
in regulatory science to try to look
at discharging these risks
through something other
than just randomizing 70000 patients
would yield a lot of dividends.
- Yeah, especially that
drug dapagliflozin.
See, you did the drug trials
because they were at
the end, hard to treat,
but they found out such a useful benefit.
30% of the population improving
cardiovascular benefit.
So that's a positive thing for this.
- I will avoid being drug
specific if not necessary.
(laughing)
- We have another question over here.
- Francis Collins from NIH.
Appreciate the shout out from Vas
about the importance of partnerships
'cause we are at a space, I think,
where pre-competitive opportunities
are really all around us.
I want to ask you Vas, in
terms of what's happening now
with the development of these
large-scale cohort studies.
Jay already mentioned the All of Us study
which will launch this
spring in the United States.
We'll enroll a million
participants, very diverse,
with baseline information about
their social circumstances.
They'll answer lots of questions.
We'll have blood samples
and urine samples.
We'll have genomic data.
We'll have their electronic health records
and their pre-consented for
recontact for participation
in clinical studies of all
sorts: by industry, by academia,
maybe about prevention,
maybe about devices,
maybe about drugs.
So from your perspective
representing a very successful
pharmaceutical company,
what would you want to see as
far as the sort of platform
that would be developed
by studies like that?
I might just say 'cause this is Davos
and this shouldn't just be about the US.
At last count, there are
over 50 such cohort studies
in the world that are enrolling
at least 100000 participants,
all of whom are gonna gather
in Durham, North Carolina,
in the next couple of months
to try to figure out how to share data
and to have data standards
and make this available
to be a real research engine.
So give us a little idea about
what would be most critical
to include as far as the
parameters of such studies?
- Yeah, so thanks for
the question, Francis.
I would think of three things
just quickly off the top of my head.
I think, one, it's the power
of actually understanding
the natural history of
many of these diseases.
I think that the truth is
while biotechnology has come a long way
and the science of medicine,
we have a lot to learn
about many of these diseases.
There's so many conditions
where we treat them
as a monolith, but there's probably
a lot more complexity that's out there.
I always like to think that
there's millions of proteins
that you could target in the body
and we can target about 400.
So there's a lot we
haven't figured out yet.
So I think one is that.
I think the second is what I really hope
that those platform studies
bring is a connection
between phenotype, genotype,
biomarker over time
because I think one of
the things, big learnings,
from the large genomic
studies is without phenotype
and without some of the other parameters,
it's actually very difficult
to really interpret what's going on.
So if we have strong and good
data that we can interpret,
that will surely fuel drug development.
The final element I'm most
excited about honestly
is the patient element.
It's still very hard to
enroll clinical studies.
Suddenly, with these
platforms that you describe,
Verily is working on 'em,
you suddenly democratize this
because suddenly you
go directly to patients
who are already engaged and interested,
want to become part of clinical research,
and now we can reach them
which is a big advance
from what we currently do
which is randomly selecting investigators
and randomly hoping people might show up
to those investigators with
our inclusion criteria.
If we could streamline that
process, it would be big.
- I just have a general sort
of add on to that comment.
That is that we think
it's critically important
that we deeply connect the clinical world
to the research world
and we create this
virtuous learning system.
So many of the clinical
results that happen today
through the physicians' offices
don't ever close the loop
and those datasets don't ever become
subsequently available for research.
So I think some of these
larger-scale projects
that are getting started are
driven by the clinical needs
and to the extent you can
database that information
and create the right privacy controls,
you can then use that
collective information
for research purposes
and complete the cycle.
We think that's really
important going forward.
- Chorh Chuan, did you
have some thoughts on that?
- No.
- No, okay.
Next question.
Right here.
- Thank you.
Majid Jafar, Crescent Petroleum
and also the Loulou Foundation
which is a rare disease
medical research foundation.
Almost building onto Francis' question.
So the technology's clearly there
and the data is being gathered,
but what about the administrative
or structural hurdles
to really getting forward
and also the differences
because in the US,
you've got the challenge
of sharing across hospitals
and insurance companies?
In Europe, you may have
national health providers,
but they're pretty slow
to uptake and innovate
notwithstanding initiatives
like Genomics England.
So what would you see,
whoever wants to chime in,
as the key bottlenecks, the
key challenges to overcome,
and some ideas to achieve that?
- Nancy, this is something
that you've talked about.
- Sure.
- You've talked about,
I love your example of
if there's an update for Facebook,
you get it sent to your phone
and everybody updates it,
but the change the
procedures for physicians,
it takes years to get it through.
So this is sort of similar to that?
- Yeah, absolutely.
I think this idea of creating
a data-sharing culture
is really key.
Francis mentioned one example
of how bringing people together
to inspire this movement will happen.
You mentioned you were from
a rare disease organization.
I think no one better than
rare disease organizations
power patients who are not
having access to what they need
to come and march to
getting what they need.
I really believe at the end of the day,
it's not about the technology.
It is about an ethical
framework and patient privacy
and making sure that all
of the types of things
that can assure that
individual's data is protected
is what we have to be able
to illustrate and demonstrate
and to some extent to fix.
You look at what banks
and credit card companies
are able to do around privacy.
There are privacy breaches
that really worry people.
On the other hand, they've
created this wonderful framework.
Who would've thought,
you used the 1990 car.
Like in 1990, would you imagine ever
that you're gonna take
a picture of a check
you have to deposit
and it's gonna show up
in your bank account.
You're gonna trust and
everyone's gonna trust
that that's gonna be actual money
that you can then go spend.
So my own thought is it's
not about the technology.
There's probably more technology
allowing this kind of data sharing
than is even needed at this point.
It's not about a willingness
of players to collaborate
because I think there is
this worldwide sense of collaboration.
It's about the fiduciary
responsibility that people feel.
It is, from my point of view,
gonna require a groundswell
of the ultimate beneficiary,
patients and the public,
to say we want to do this
because it's going to benefit
us and better outcomes
and we're gonna have to test it
and make sure the security's there.
- Can I maybe just chip in again?
- Yes.
- To say that it is a complicated
issues, but in some cases,
the laws and the
regulations get in the way
because it's really a balance
between the societal
benefit versus the risks.
Many of our privacy laws, we
developed at the time, I think,
when the benefits were not
quite so apparent then.
So say in the case of Singapore,
some of these are being
refined so that it would then
create a more facilitatory
environment for sharing of data
and for common good.
I think this will also help to accelerate
the data-sharing type of
culture within institutions.
- Could I just say one thing?
- I'm sorry, Jay was
gonna jump in, I'm sorry.
- I was gonna say I think
another element of this
is the move toward consumer-managed health
and this is to become
critically important.
Many people want to have control
of their information themselves
and be able to get it
directly from their physician,
look at that information.
That's beginning to
happen, but ever so slowly.
Our view, of course, is that
consumers have the right
to their data, even their genomic data
even though people have tried
to stand in the way of that
and that over time, that
needs to be a forcing function
to accelerate the changes
in the health system
and the medical treatment
system in particular
which tends to adopt
change very, very slowly.
- Yeah, the other part of this equation is
trying to get the information into forms
where it's gonna influence
prescribing decisions by providers.
We've seen historically that
labels are slow to update
with new information.
We're looking at ways to be
more proactive with that.
The Cures Act gave us some
authorities to do that,
to reduce the barriers to
supplemental indications.
We're looking to initiate
proactive updates
on old generic drug labels, for instance,
initiatives like that.
I think where I'm a little
bit more perplexed is
that the provider
community has in some cases
been slow to update their
own clinical guidelines
apart from the regulatory process.
You see clinical guidelines
that are sometimes out of date,
particularly, we're
looking at opioids now.
We're talking to the provider communities
about building into the label,
creating clinical guidelines
that would delineate how long
the duration of use should be
for opioids for different
clinical indications.
If we had those kinds
of clinical guidelines
developed by the provider community,
we could then incorporate
that into drug labeling
and the health systems and
other providers can use that
as a way to try to control dispensing.
So it's one example of
where clinical guidelines
get married to the regulatory process,
but they don't exist and
I'm a little perplexed
why the provider community
hasn't created those.
Just to step back a little
bit, we do have the ability,
the legal authority, if
there are clinical guidelines
developed by expert groups,
we have the ability to incorporate
that into drug labeling.
Then, it can become part
of prescribing
recommendations that we give.
- Oh, I was just, on the guidelines,
as the writer and author of guidelines
for cardiovascular and
stroke care in America.
- [Scott] Cardiovascular
wasn't very up there.
- Thank you, Scott.
(laughing)
I was just gonna say, I think
that the other thing coming,
you can debate whether
they're too fast or too slow
because we recognize that
when a guideline is changed,
it changes clinical practice in America.
In November, we issued new
hypertension guidelines
that redefined hypertension
and 100 million people
woke up the next day as hypertensive.
So you can't,
just like you're not gonna
approve a drug with haste,
we're gonna be very careful
as we make sure we're looking
at all of the evidence.
That being said, I think this thought
about how precision medicine
will really challenge,
if clinical trials change,
then guideline development has to change.
We're going to need to be
very quick in thinking about
how we compare an old way
of doing clinical trials
to a new way of doing clinical trials
as we're writing guidelines
is one thing I would mention.
Then, the second thing I would mention is
guidelines can move quickly
when new technologies are created.
Earlier this week at our
International Stroke Conference,
we issued new guidelines for
the acute treatment of stroke
because of the new clot retrievers
that quickly went through clinical trial,
quickly got FDA approval
in the United States,
and the guidelines were
changed in record time
so that more patients could benefit.
I think this is yet another piece
of the continuum that has to change.
- So I'm gonna go to lightning round
because we only have a few minutes left.
I want to hear what your
biggest prediction is,
sort of your most wildest prediction
for precision medicine is.
I want to start with you Chorh Chuan.
- Well, so my biggest dream.
- [Linda] Yes.
- Is that we will be able to
work with a patient community
to aggregate all this data.
So that we'd be able to more
precisely target people at risk
with treatments and
with preventative care.
- Yeah, I tend to think
about this as a future state
and it's always the one we envision
of where we want to get to.
I think that ambition is that each of us
have our genomic information,
our germline data in the database.
We have our microbiome profiled.
We didn't talk much about that today,
but it's an incredibly-interesting,
emerging new area to study.
That we understand what's
going on in our blood,
and that when drugs are prescribed,
you have all of this information.
If it's cancer, you're
sequencing the tumor
or you're doing a liquid
biopsy to read out that tumor
and then some deep learning algorithm
is what can integrate all that information
in ways that humans won't
be able to any longer
and essentially becomes
an assist to the physician
in doing the diagnosis.
- Not sure I can pick one.
I'll give you two.
One, I think AI is gonna power
our drug development
processes pretty soon.
We're gonna be able to
prospectively define
high-efficacy patient populations.
The second is that
blockchain at some point
will lead to interoperability
of clinical data
which will completely transform
how we do clinical trials.
- I would just say building on that,
the power of digital phenotyping
and creating real value
for all of this other
data that really matters,
environmental data,
personally-collected realtime health data.
That that will, powered with the things
that Vas just mentioned,
really change the way
patients are treated.
- I think we're at an
inflection point right now
when it comes to gene therapy.
Similar to what we looked
at maybe even the 1990s
when you had early
antibodies on the market,
but they were murine and
they were derived from mice
and they weren't very
effective because of that.
Then, there was an inflection point
where we gained the technology
to make those antibodies
humanized and then fully human.
Then, all of a sudden,
antibody-based drugs
went from being something
that didn't work very well
to being important therapeutic products.
I think we're at an
inflection point right now
where gene therapy is gonna
become a more common form
of product development
and the inflection point
was the development of
vectors that are reliable,
that can reliably deliver the gene.
I liken the development
of the current vectors
to development of the
humanized antibodies.
MIT recently put out some data.
I don't know if they're right or wrong,
but they looked out on the next, I think,
about six or seven years in terms
of how many approvals
there'll be in gene therapy.
I think they predicted 40.
They're predicting a run rate by 2020
similar to what we see in terms
of the development of
antibody-based drugs.
Again, there's gonna be a
lot of uncertainties here.
I think the longterm issues
are gonna be the ones
that are most complex for the agency
and for patients and for providers,
but a lot of opportunity as well.
- So what I'm hearing is a future
where there's a lot of
the emerging technologies
are merged into precision medicine
and that we have much better care.
Within that, we have enough
people participating,
we have one of the things that
we didn't talk enough about
was ensuring equal access
to this sort of therapy,
and being able to really treat people
with the care that they need
and not based on who they are
in a way that not only
cures, but to be able to get
to a place where we can
do preventative modeling
so that we can stomp it
before it affects 'em.
Any last thoughts?
Lightning round.
- I think one area we
didn't really talk about
is the huge change management
that will be necessary
among the medical community
because the way they're
gonna practice will change.
They would have to trust in
AI decisions, support systems.
The way they deliver
drugs, treat patients.
So a lot of working with
the medical community
and the medical schools to
prepare the new workforce
of the future, I think, will be essential.
- Yeah, we didn't talk a
lot about diagnostics here,
mostly therapeutics, but I
think in the diagnostics space,
it's becoming deeply
challenged for funding.
There's very few new companies here
and the reason is that there
are significant challenges
around reimbursement and
regulations, speed of approval.
Many of these small, innovative companies
just can't get through that
process in a timely enough way
to fund their way through.
So we're not seeing these new companies
start up and be successful.
It's a big, big problem.
I think we need more dynamic processes
in the regulatory and
reimbursement systems.
- I would say just from
a patient perspective
how we make these precision
medicines available
to more and more patients around the world
because in the long
run, the curative power
of these therapies or
transformative powers is incredible,
but it's only gonna
matter for global health
if we have broad access.
- Broad access, great.
- That requires really
looking at this risk continuum
and who's taking the risk.
The company, the insurer,
the healthcare system,
the healthcare provider, the patient,
and how along the way do we
shift that risk a little bit
so everyone can benefit?
- I would just echo that I think
when you have that capability
to alter the underlying
trajectory of a disease,
you worry about a technology gap.
I think that...
You look at oncology, for example,
and the gap between what goes
on in communities sometimes
and what goes on in the academic centers
is growing wider in
certain clinical settings.
So you have to focus on that to make sure
that you're providing equal access
and equal opportunity for patients.
- Thank you, all.
I have learned so much
from all of you today
and the great questions from the audience.
(applauding)
45 seconds to spare.
