GM3: Implementing Biomarker Programs – Paul Ridker

GM3: Implementing Biomarker Programs – Paul Ridker


Dan Roden:
So we’ve asked Paul Ridker to come and talk to us because, as he points out, he plays
in the genomics space. But Paul is the father of a laboratory test
that moved from the laboratory to clinical work flow, and we were interested in hearing
sort of the way in which that happened, the stumbling block, CRP. Paul is the father of
CRP, or one of the fathers of CRP. Paul, is that a fair thing to say? Paul Ridker:
It’s a bastard; so it’s okay. [laughter] Male Speaker:
“Success has many parents” no sorry. So thank you for coming and sharing. Paul Ridker:
Thanks. So let me begin, actually, by thanking Dan, in particular, and Teri for encouraging
me to come and a special thanks to Eric, actually, because some of the things I want to talk
about, I think, are very relevant to your slides when you opened with about how this
translational piece occurs. Female Speaker:
[unintelligible] no idea. You’re going to have to [inaudible]  [talking simultaneously] Paul Ridker:
That’s okay. I can just  Female Speaker:
Sorry. Paul Ridker:
do it forward. I’ll be  I’ll figure it out. And I changed my talk, like, three times yesterday
for a variety of reasons. One was because I wanted to  I realized I may be one of the
few people living at the translational end of this. I am a cardiologist and so I kept  whenever
you see parentheses, it’s going to change the slides. So this was “Moving new biomarkers,
genetic or non genetic.” That’s the first bias of mine is that I don’t think there’s
really a difference between a genetic biomarker and non genetic biomarker. This is just a
world view I have, and it translates “from a cardiologist’s perspective.” I added a “cautionary
tale.” You’ll see why. Oh, and then I think I’m the only person who
actually put a conflict slide up here. Pearl, you can demonstrate to the dean I really did
show it. So the other thing was  is that I tailored
this for the payers, but they’re not here. They’re playing golf. [laughter] And so you’ll see that some of this is really
meant for them, but so be it. So a couple of broad observations for some of us who have
lived in this world. The first is misnomers. “Prediction is not prevention.” And we really
often forget that, and I think we heard some of that yesterday. The more cynical part of me  and I can get
much more positive at the end of my talk. I promise. The cynical part is “Many clinicians
will not act even after there is hard evidence that knowing something new improves care.”
That’s something did not get discussed yesterday, but it is just the nature of the beast. “Guidelines”  which were seemingly so important
for payers  “Guidelines usually lag clinical data by many years”  I had “20 years” in
here. I had an earlier slide. I changed it to “many”  “and rarely are evidence based,
particularly those that claim to be so.” Now, I want you to this about that a little bit. “Physician obstacles that translation are
at large and very difficult to surmount.” And the quote at the bottom comes from a cardiology
friend of mine, senior mentor of mine. “All change is for the worse, including change
for the better.” And many, many doctors  yes, you do know who this is. Many, many doctors
think that way. So Dan said, “Please come, Paul.” I really
did enjoy yesterday. And I want to show you that I have been involved in this field, and
it’s remarkable. I haven’t  I never get an opportunity to show it  almost 20 year old
slide. So this is when you actually ran SNPs one
at a time in your lab, and you only had to have 1,400 patients to get a paper in New
England Journal. This was our demonstration in factor V 
well, what became factor V Leiden was higher when people had venous thrombosis and arterial
thrombosis. But I’m really a clinical trialist. And so this observation we thought wasn’t
that important. What we thought was interesting was could we design a randomized, double blind,
placebo controlled trial to test a genetic hypothesis. So we think this was the first NHLBI funded
pharmacogenetic trial ever done. It was published in 2003. We began in the late 1997, 1998.
The idea was to screen people for this new polymorphism, factor V Leiden, and figure
out whether or not they respond to warfarin differently. And so we had people who were positive and
negative for factor V Leiden, recruit all over the country, follow them for recurrent
venous thrombotic events. The punch line of the paper was this. We had a 64 percent reduction
in recurrent venous thrombosis by giving a novel therapy which, by the way, never made
guidelines, which was low dose warfarin instead of giving full dose warfarin. Very low side
effect profile, very effective, therefore not in the guidelines. [laughter] But the point I want to make to you was that
the real trial was based on this. The NIH funded this because of the pharmacogenetics,
and that particular prespecified subgroup, factor V Leiden or the prothrombin mutation,
it just didn’t matter. Both groups equally benefited from being on low dose warfarin
for preventing recurrent venous thrombosis. And that was our first foray into what became
much more commonly now pharmacogenetic clinical trials. I’m going to fast forward. We also run something
called the Women’s Genome Health Study. It’s one of our many large scale GWASs. We tend
to do GWASs inside of randomized trials. One of the issues I want to leave you with is
trials really matter, and I think NHGRI could not fund the trials. I know you don’t have
the budget for that, Eric, but you could think about funding the genetics inside the trials. So this is our WGHS. This, NHLBI. It’s 20,000
women. And the punch line of this one was, on the left, that taking the best known 101
SNPs from the genetic risk score or looking at all the literature, we couldn’t differentiate
very well at all between high and medium and low risk people, but what Jeff told you yesterday
was crucial. So this is the genetic risk score not doing
much, but family history, simple question, “Did mom and dad have a heart attack or stroke
before age 60?” discriminated it very well. And so that tension between “Are the SNPs
picking it up?” “Is it shared environment” or “Are we missing another piece of heritability?”  And so the family history piece, I really
liked very much yesterday, what you’re trying to do in that business, adding that to the
clinical practice. Now, this it is what I was thinking I was
going to present. “How do you move a biomarker from the bench to the clinic in sort of a
thoughtful way from our perspective?” And it’s a little like Passover. There’s four
questions. But “Is there evidence that individuals identified by the biomarker of interest, genetic
or otherwise, are at high risk even when other risk factors are acceptable?” That’s important.
Why measure something new if it just is a surrogate for something else? In the lipid
world, that’s advanced lipid testing versus a total cholesterol/HDL cholesterol ratio.
Am I really getting something new? But the second question is very rarely asked.
I think this is the biggest problem for biomarkers in general. It would be a big problem also
for genetics is “Is there evidence that individuals identified at increased risk due to the biomarker
of interest benefit by receiving a therapy they otherwise would not have received?” Why do a test if you’re not going to do something?
If all I’m going to do is recommend diet, exercises, and smoking cessation because you’re
at high risk for heart attack, I don’t need a test to make that recommendation. Now, because we’re in a genetics setting,
I added the third question which is the obverse of that. Very important for side effect profiling.
“Is there evidence that individuals identified at increased risk due to the biomarker of
interest benefit by avoiding a therapy they otherwise would have received?” If I can predict
a toxicity and we do these clinical trials where we treat everybody, maybe there’s a
way to weed out the problems. The last question I’m going to just  is
up there because I want to show you what we’re doing. But it’s not relevant for diagnostics.
It’s terribly relevant for therapeutics. “Can you alter this pathway?” Knowing a new pathway
exists or biomarker exists is really only partly [unintelligible], and the question
is “Can you change it?” So I do inflammation biology. This is what
we teach the Harvard medical students. Inflammation, atherosclerosis is the same as rheumatoid
arthritis and psoriasis in terms of biology, but why does it affect the joints, the skin,
or the lumen differentially. We publish papers like this. This is interleukin
6 levels predicting future myocardial infarction in otherwise healthy men, baseline levels
predicting future vascular events. And I would argue  some of you saw the paper in Lancet
that the genetics community discovered now that IL 6 mattered because they had Mendelian,
randomization, but these data are actually 16 years old. CRP, which is what Dan talked about, is that
red molecule in the background. This is the cover of Nature, whether or not CRP as a target
for therapy  probably a misreading of the whole field. But I wanted to show you that
this, again, is one of the things where Eric  is the time it takes to move something from
an idea to practice. So this is 1997 that the study started in 1993. This was the original
publication, saying what’s now called hs CRP, predicts MI on the left and stroke on the
right. We would go on to show this. That was in men.
We would do this in women; so generalizability across big NIH cohorts. This is a comparison
of quintiles of CRP on the left measured at baseline against quintiles of LDL cholesterol
on the right, and they’re completely independent of each other. Fast forward. A meta analysis is done. 54
prospective cohort studies. Not one, not two  54. And all of them show the exact same thing.
If you know your CRP level, you can predict coronary heart disease on the left. You can
prediction all vascular deaths on the right. And the most important piece of this is the
biology. The incremental risk determined by knowing something about inflammation, adjusting
for all usual covariates [spelled phonetically], using a standardized model, the metric the
attributable risk for inflammation is at least as large as that of blood pressure and cholesterol.
And the question is why aren’t we actually actioning [spelled phonetically] on this? This is here for Geoff. Again, the Reynolds
Risk Score was developed with my statistical partner Nancy Cook. I kind of get credit for
this. I shouldn’t. She did all the heavy lifting because it was a math problem, not a clinical
problem. But it’s not just that we added CRP to framing him. We also added family history.
And as Geoff and I talked about, I think that’s actually at least equally important in understanding
what’s going on. If you’ve never been to this website, please
do. It’s quite user friendly, and it’s meant to assist in this translational process of
understanding why you might want to actually think about a new biology. But the first question is usually where fields
stop. Usually people will develop something and say, “This predicts risk.” But as I said
before, prediction is not prevention. You have to go to the second step which is very
rarely done. We were cognizant of that, and so we wanted to say to ourselves, “What could
we demonstrate to the clinical community why measuring this biomarker might matter for
clinical outcomes?” what our payers were talking about yesterday in terms of would you actually
want to do it. And we felt very strongly that you needed
to demonstrate clinical benefit from a therapy patients otherwise would not receive. So we
had to design a program of studies saying “Who could we identify at risk with inflammation” 
as our example, biomarker, today  “and could we then change practice?” So we had observed  and, again, data are
very old  that people getting statins had very large risk reductions when inflammation
was present. This is hard endpoint data that’s on the left, and it’s statins lower  this
inflammatory biomarker, the CRP on the right. And so skipping through a lot of other stuff,
we designed this clinical trial called “Jupiter,” and this is what I was referring to yesterday.
And just as a side effect because the payers aren’t here, the two that I spoke to did not
know about the study, and that was instructive to me. So Jupiter is a randomized double blind placebo
controlled trial, asking the question “If I identify people who would never get a statin
under any current guideline in primary prevention because their LDL cholesterol’s already below
the treatment target for a therapy  so you can only get into this trial if your untreated
LDL’s below 130 which is our target for treatment. The average LDL was only 100. But you otherwise
were at risk because we screened for CRP and enrolled those people who had this proinflammatory
response who therefore would have a high risk, and then they were randomly allocated to a
potent statin and placebo, and it’s not a surrogate endpoint trial. It’s a hard endpoint
trial. The question was “Could we prevent heart attacks,
strokes, and cardiovascular death among these people who simply would not get treated under
any structure?” So this was actually for the payers. 55 percent reduction in myocardial
infarction among the group that, A, people thought weren’t even at risk and, B, the drugs
would never work because the biology didn’t fit what it was all about; 48 percent reduction
in stroke, 47 , 50 percent reduction in bypass surgery and angioplasty and, if you do some
health care economics, you can very quickly figure out that’s the big win in terms of
cost. And at 20 percent reduction in all cause
mortality which, in fact, the FDA, USFDA, did not put in the label which we thought
was interesting because that’s been the standard for what people thought really would matter. Now, Canada did action on this. That’s what’s
so interesting to me. Most of these patients were enrolled in the United States and Europe.
Canada had a relatively small enrollment, but the Canadian guidelines changed almost
immediately. So within four months of publication, six months when it came out, the Reynolds
Risk Score, in yellow, was introduced in Canada and, basically, for the last three and a half
years in Canada, anybody with a high LDL, low HDL, or high CRP gets a statin, and that’s
been a very interesting thing to follow. What I want to talk about though is how genetics
is very informative within trials though. So one of the controversies of our study was
why did this work? These are very powerful LDL lowering drugs. They’re also very powerful
anti inflammatory drugs. They do both. So no statin trial can deconvolute that. Well, the genetics are very helpful. This
is work from my colleague Dan Chasman and one of his postdocs Audrey Chu. They were
just published. And one of these is on the left, really for Dan and for Eric in terms
of what we think about in terms of statin pharmacogenetics. So the point I want to make
in the diagram on the right is, yeah, we could  we can predict LDL response by measuring a
certain set of SNPs  and we’ve shown this repeatedly now and others have as well which,
of course, you could also measure the LDL which might get you there just as fast  but
the point I want to make is that the genetic determinants of this statin induced LDL reduction
do not predict the statin induced CRP reduction, and we were able to show the genetic determinants
of the statin induced CRP reduction do not predict the statin induced LDL reduction,
and that was instructive because they’re different pathways. So what we’re trying to understand by using
these trials is can we now take this and ask the fundamental question, “Can we lower inflammation
to lower vascular risk?” And I don’t want to drag you through this too much because
I want to come back to the genetics in a minute, but the way the clinical trial community thinks
about this is “How do you attack the problem?” You have the biology, you have the genetics. And I just want to show you two that share
something in common. You want to do a clinical trial where you take the confounding out.
Now, this comes from someone who lived in observational epidemiology for years, became
somewhat frustrated by it and realized that maybe trials is the way to actually go if
you can afford them. So you want to have a setting where you lower
inflammation but you don’t affect lipids, hemostasis, or thrombosis. You just affect
the one pathway. The first trial uses something called LDM, which is low dose methotrexate.
This is the one that’s been funded by the NHLBI. The study is called CIRT, the Cardiovascular
Inflammation Reduction Trial. We’re taking secondary prevention patients on all usual
therapies, enrolling those with diabetes and metabolic syndrome as a surrogate for inflammation.
And what they’re getting is the standard of care for rheumatoid arthritis, but they don’t
have rheumatoid arthritis. They have coronary disease. We’re trying to see whether lowering
inflammation will lower their risk. And you might say that sounds very odd and gutsy.
The reality is, is that there’s seven observational cohorts; and if you just scan the yellow column,
you see the hazard ratios are all below one for incidence of vascular events. And, again, it’s the attempt to use trials
based on its biology, based on it’s genetics to find out can we alter care. I’m going to skip over this. This is just
the NIH stuff. The primary aim is to test the inflammatory hypothesis of atherothrombosis
by doing this trial. The second one, funded by Novartis, is up
and running already, uses interleukin 1 beta inhibition; again, an approach that lowers
inflammation, doesn’t affect lipids. Here the genetics is terrific. For those of you
who know the IL 1 genetics story, this notion of an endogenous dangerous signal, how modified
LDL affects the NLRP inflammasome, that inflammasome, very important for CRP production among other
things, and that inflammasome, of course, converts caspase, allowing pro IL 1 to go
to IL 1 beta [spelled phonetically]. You get IL 1 beta [spelled phonetically] locally,
and we can now inhibit that directly. So now we have a cardiovascular event trial
being based on genetic observations because it’s now been shown that the earliest linkage
here, the earliest trigger of that NLRP inflammasome in the vascular world, is cholesterol crystals.
And so if early cholesterol crystals are triggering those [spelled phonetically] genetically inherited
issue, then we can actually try to inhibit this. And you can see that it goes through
this pathway to IL 6 to liver [spelled phonetically] and back to CRP to our biomarker. Some of you may or may not know this drug.
The pediatricians might. The Canakinumab is a human monoclonal anti IL 1 beta antibody.
It’s approved in the U.S. and Europe for one of the Orphan Drug Act for rare genetic disorders
that lead to pro hyperproduction of pro IL 1 beta but allows us to do a clinical trial,
trying to neutralize inflammation as we do this, and this is what the drug does  it
lowers IL 6. It lowers CRP. It lowers fibrinogen. It doesn’t do much of thing else. The question
is “Will it lower vascular events?” And so that trial is called CANTOS, the Canakinumab
Anti inflammatory Thrombosis Outcomes Study. We’re running this around the world. There’s
some 17,000  well, right now, there’s about 2,000 patients randomized. Eventually, it
will be 17,200. Three different doses of the study drug against placebo and, again, a hard
endpoint trial. You can imagine within this, we have pre consented
everybody for all possible future genetics but don’t have the money to do the genetics;
so one of the other issues for NHGRI to think about is, within these trials that are very
expensive, small investments to leverage that might be something to think about. But I was talking to Geoff Ginsberg, when
we were in Washington together at another NIH meeting Wednesday, and thinking about
this from a cardiologist’s perspective about “Will genetic screening play a role in patient
focused thrombosis care?” and “Will pharmacogenetics matter?” And it’s funny because I actually
am someone who believes the answer is, absolutely, yes, but we haven’t quite gotten there, and
trying to get there seems to be the critical issue for us. So I wanted to give you a few
examples from the cardiovascular literature. The first is one that, initially, I thought
wasn’t going to help us very much. This has to do with the data about the SLC01B1 polymorphism
that the search investigators, using, again, a clinical trial where they had placebo data,
were able to demonstrate that carriers had a far higher rate of myopathy than did intermediates
and then did the non carriers. And now this has turned out to be simvastatin specific,
and Jeff made the very thoughtful point to me. He asked, “Well, Paul, what happened in
your Jupiter trial?” Because we had rosuvastatin, which is a safer drug. And these are the data,
Geoff, and all you have to know is if there’s nothing going on, and that’s because this
drug doesn’t do it. And Geoff made the insightful comment that I said, “Well, who’s going to
pay for that test?” And he said, “Well, maybe AstraZeneca would, which I thought was actually
a very interesting insight into how to get things to actually move forward because you’re
right. You might want to use this drug because it’s safer if you happen to carry that particular
issue. What I’ve been a little more skeptical about
is that doesn’t seem to be how people think. This is also from that trial. I asked the
question yesterday of one of you about the commercialization of this process. This is
the KIF6 story. This was an assay that was highly promoted to the cardiovascular community,
based on sort of mixed evidence, and we finally did this within, again, a large randomized
double blind placebo controlled trial, and the punch line on the left was the relative
risk reduction with a statin among non carriers was exactly the same as it was among carriers.
There was no difference in the LDL reductions. There was no difference in the CRP reductions.
There was no difference in the event rates, and then there’s no difference in the relative
risk reductions. But the observational data has suggested there might be. And, again,
this is the tension, I think, between someone who moved from the observational world to
the trial world, trying to get rid of some of these issues. So, Eric, this was added for you about your
density maps because I wanted to end on a much more positive note in your quest about
the speed of translation to practice, and the first was, “Don’t be discouraged. It takes
a long time to change practice even when randomized trials exist.” And it’s something that I think
NHGRI just has to realize. You were making fantastic basic science discoveries. That’s
really exciting. You’re in this process of translation. It’ll happen. Number two, “Sure there are bumps, potholes,
and U turns on the translational highway”  but it’s misspelled  “but where else are
you going to drive?” is what it should say. The third is something I’ve also changed my
mind about. “It would be nice in the cardiovascular community to have a killer app, but I don’t
think we actually need it because I don’t think it’s the average patient that we’re
really thinking about anymore.” I mean, it would be nice. But the flip side is, “If
the cost of screening falls far enough”  and the Moore’s [spelled phonetically] Law
suggests that it will  “you don’t need a home run for all patients. You just need
a clear benefit for some even if they’re rare individuals.” And number four is a personal issue for me.
“It really matters for parents and for kids.” Part of the reason I’m here is and I don’t
mind saying this publicly. I have a child with Long QT. It makes me think a lot about
the personal implications of everything you all do versus the research implications of
what people like I do, and that tension is very, very much about what this is sort of
all about. So, Eric, you ended with a quote yesterday,
and I want to show you the quote that sits on my office wall. It’s a little longer than
your quote was because it wasn’t quite as piffy [spelled phonetically]  Eric:
You have a bigger wall. Paul Ridker:
I have a bigger [laughs] wall. [laughter] Yeah, yeah. It’s Harvard. It’s not really
that much bigger. It’s a very small plaque. And I’m just going to read it. Many of you
are familiar with this, but it really is relevant to what you’re trying to do. “It must be considered
that there is nothing more difficult to carry out, nor more doubtful of success, nor more
dangerous to handle, than to initiate a new order of things. For the reformer has enemies
in all those who profit by the old order, and only lukewarm defenders in all those who
would profit by the new order, this lukewarmness arriving partly from fear and partly from
the incredulity of mankind, who do not believe in anything new until they have had an actual
experience of it.” This is, obviously, Machiavelli, talking about
the politics of what it’s like to be President Obama or anything else. But I think it’s the
same issue for all of us. Everyone in this room is dedicated, I think, to trying to bring
innovation to practice. There are many, many, many reasons it’s very hard to do. There’s
many, many people who actually don’t want you to do it, but we all also understand it’s
the only way to actually go. So I’m very pleased to be here, Dan, and I
think that the NHGRI is one of those motors that will drive this process. So, again, thanks
for the opportunity to chat with you this morning. Dan Roden:
Thank you, Paul. Well, we have  we’re running behind as usual, but that’s okay. Well, so
we have time for a couple of questions. David and then Gene [spelled phonetically] [inaudible]
ignoring David; so I’ll let him go first. [laughter] Male Speaker:
Very interesting talk, and I just was struck by your  I think your third conclusion about
the killer app and the average patient. So the data you showed us in the clinical trials
really is data on averaging all patients. And the question is even for an intervention
that doesn’t show an effect when you average all patients, there may be some patients in
there in which that intervention would be terrific, and so how are we going to figure
that out? Paul Ridker:
So this is exactly why this is up here because the extreme phenotype concept from the genetics
world apply to the clinical trials world makes these GWAS within trials very valuable. Again, it’d be nice if I had a genetic test
that said, “Give these people Drug A and these people Drug B.” That’s the average effect
overall. I don’t think it’s going to happen, but if I can find 25 people out of this 15,000
who should never get a statin, either because they get absolutely no LDL reduction so we’re
fooling them clinically or because they have a clear toxicity, it’s going to hurt them,
and I can convert them to somewhere else. I think it’s a great benefit because, again,
at the end of the day, we are physicians, and the patient in front of us matters. And
there may be other patients who get an enormous LDL reduction but, I can tell you, get no
CRP reduction at all. I want them on the statin, but maybe I need to divert them into these
other clinical trials that we’re doing where you’re going to inhibit the inflammation. So I really agree. And part of the tension
is the cost. Again, that’s why the second half of that one is there. If the cost is
low enough then, of course, the  you don’t need that big a benefit to justify the cost,
and this is one of those rare fields where cost could absolutely plummet as we think
this through. Gene Passamani:
Two comments and a suggestion, Paul, and beautiful presentation. My wall has the piece from Churchill
who says, “The Americans always do the right thing after they’ve exhausted all other possibilities.”
And  [laughter] second is that I grew up with a cholesterol
fight. In 1967, Bob Levy and Don Fredrickson published a very nice review, and it was pretty
suggestive that cholesterol was something bad. Well, in 1994, it was finally a conclusive
final trial that finished it. Prior to that, I had to personally respond to a writer in
Washington who said that lowering cholesterol doesn’t help. It may prevent cardiac disease,
but it makes you violent and so you die a violent death. I kid you not. The third point I want to make and more serious
is that it seems to me that all clinical trials designed, henceforth, ought to really have
a consent and an intention of collaborating with folks in genomics to do what you’ve done.
I mean, this is a wonderful opportunity, and we ought to learn from it. Paul Ridker:
So, Gene, I would say two things. You’re absolutely right. The lipid thing, for those who remember
this, is very important to remember because I think the controversy about screening for
genetics, to me, is no different than the controversy we lived through many other times,
and the lipid hypothesis  you remember the Atlantic Monthly articles and the whole thing?
So you’re absolutely right. Trials are tricky. Eight years ago, it was
hard to get the pharmaceutical companies to agree to let you do a GWAS within them. Fast
forward to today, it’s quite easy. They’ve gotten over their anxiety about it. They don’t
necessarily want to pay for it because they haven’t figured out what’s in it for them,
but their fear of it diminishing their market share is not quite what it used to be. So, again, one of my pitches for this was,
you know, a modest amount of money from something like NHGRI to think about genotyping a trial
on top of the hundreds of millions that go into trial, it might be a very good investment. Dan Roden:
Geoff and Howard [spelled phonetically], I think [inaudible] go on [spelled phonetically]. Geoff Ginsburg:
Paul, great remarks. I’m surprised you didn’t mention the fact that CRP was discovered in
1930. [laughter] But along those lines, I wondered if you could
just give us your thoughts on why the road has taken so long for things like CRP and
some of the examples you used, and then you look at oncotype, and the 21 gene predictor
was published in 2004, and within a year or two, we had a test on the market that was
being rapidly taken up by oncologists. Paul Ridker:
Boy, that’s a six beer question. [laughter] I think part of answer here has to do with
this notion of one of the problems that CRP had was we were introducing a new biology
and a new biomarker and a new path of physiology and a new intervention, simultaneously. It
didn’t come in pieces, and I think that was actually part of the problem. I was really hoping to talk to the payers
today because I know exactly which payers did and didn’t pay for this and when they
did and didn’t pay for it. And that was what was yesterday, those guys were around. On the other side, the genetics community,
I think, has done in some ways a better job because at least it’s a zero one outcome.
But the data we saw from one of yesterday about the thienopyridine spike and then fall
is also very instructive, and I stayed away from that example because I don’t know what
I really think. Part of me thinks it’s a great idea and part
of me doesn’t, and those are all good studies with very different conclusions, and that’s
one of the tensions in that field. I don’t have [unintelligible] it’s tough. Female Speaker:
Can I add just one thing to that? I think that there are many examples that perhaps
there are increasing examples more recently of much more rapid pace, and I’ll give you
the example. The IL 28 B was discovered at Duke a year
before it was on the cover of Gastroenterology and being used by all the clinicians. So although
there are some that lag for many years, it’s often because of controversy, conflicting
studies, et cetera. There are many markers that are moving much quicker than that. Male Speaker:
So Howard’s next, but before Howard, I think part of it is also sort of “What do you do
with the result?” Female Speaker:
Could I just  I just want to comment it’s because you’re avoiding therapy, and that’s
a great cost savings. Male Speaker:
[unintelligible] [talking simultaneously] Paul Ridker:
So Dan’s right. One of the problems this had was it led to the suggestion that maybe another
20 million people ought to be on a statin, and we think that’s not a bad idea, but I’m
not sure the payers thought that was remotely a good idea is part of the unspoken thing
here so… Male Speaker:
Howard. Howard:
You mentioned a little bit about that clinical trials are expensive and they’re sitting there
and just the genetics piece [unintelligible]. And I thought your name was familiar, and
I looked back, and we’ve actually published three papers together although we’ve never
met. [laughter] And  Paul Ridker:
That is a Harvard model. [laughter] Howard:
Yeah. And that’s because you’ve made your DNA available to some stuff that Brian Gage
[spelled phonetically] and I were doing and [unintelligible]. So the [unintelligible]
supporting that is a great thing, but there’s also other funding sources, non federal funding
sources for doing these kind of [unintelligible]. So what are your thoughts on how clinical
trialists can make visible your  the potential collaborations like that? Paul Ridker:
Well, it’s actually good news. I mean, Jeff’s here. So most clinical trials in the United States
[are better for us] [spelled phonetically]. These very large ones are done at one of about
five centers, and Duke is probably the largest of them. And the Duke group is very much in
favor of banking everything as are all the major studies. Pearl knows this. Again, this shift in what
I used to call genetic paranoia is pretty much gone. The companies no longer fear this
the way they did just eight to 10 years ago, and so many, many of these big trials now
are routinely collecting  at least putting away [whole blotter buffy code] [spelled phonetically]
and usually are doing some sort back end genotyping. When the study is positive, people tend to
wait and see what the outcome is. But I think, again, Duke, Cleveland Clinic,
Harvard  there’s a [inaudible] a lot of these trials initiating, and most of them now do
have biobanks so  and then we can go to people like you to get some other neat, new things
done. Male Speaker:
Okay. Female Speaker:
So I want to comment, actually, that this is also happening in cancer. So I actually
just put together an application for a large trial, a large study, actually, a genome wide
association to look at response and immune [unintelligible] adverse events in response
to ipilimumab, and BMS is giving us all of the samples that they have to be able to do
that in addition to collecting samples from academic centers. So their either going to give us data or samples,
and then we’re bringing that together with samples from academic datas to be able  centers 
to be able to power the study to do this which for  I don’t know how many people know about
this drug, but it has a lot of adverse events. Not very many people respond. It’s a very
important study. There’s a lot of evidence suggesting that immune mediated drug  immunotherapies
may have inherited variation may contribute. So I just want to point out that sometimes
when you ask the companies, they’ll actually give you the samples. Paul Ridker:
Right. So, again, you’re absolutely right. The trial Canakinumab trial I described, this
interleukin 1 beta issue, Novartis needs it be a win to get a label, but we were able
to negotiate up front that, even if it failed for that purpose, DNA would get handed over.
We would then have to go to, presumably, the NIH to get the money to do the genotyping,
but there’s so much good biology buried into “What are you doing when you inhibit a very
specific part of the inflammatory system in otherwise stable patients?” that I think you’re
absolutely right. If we all did that, we would learn a lot quickly.
And the beauty again of a trial  not to denigrate the observation of epidemiology because that’s
also where we get most of our funding  but because the trials allow you to look at it
in a structured setting against placebo where you can look at before, after, and the drug
effect. And so I think that both of these are very complimentary. Male Speaker:
Okay. I think, well, we can talk all morning. But let’s go on. Pearl is going to talk about
the clinical research interface working group. Maybe, tell us what they do. [end of transcript]

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