ATDEPARTMENT OF HEALTH AND HUMAN SERVICES
FOOD AND DRUG ADMINISTRATION
CENTER FOR DRUG EVALUATION AND RESEARCH
ADVISORY COMMITTEE FOR PHARMACEUTICAL
SCIENCE
CLINICAL PHARMACOLOGY SUBCOMMITTEE
Tuesday, November 18, 2003
8:30 a.m.
Advisors and Consultants Staff Conference Room
5630 Fishers Lane
Rockville, Maryland
PARTICIPANTS
Jurgen Venitz, M.D., Ph.D., Chair
Hilda F. Scharen, M.S., Executive Secretary
MEMBERS
David D'Argenio,
Ph.D.
Marie Davidian,
Ph.D.
Hartmut Derendorf,
Ph.D.
David Flockhart,
M.D., Ph.D.
William J. Jusko,
Ph.D.
Gregory L. Kearns,
Pharm. D., Ph.D.
Howard L. McCleod,
Pharm.D.
Mary V. Relling,
Pharm.D.
Wolfgang Sadee,
Ph.D.
Lewis B. Sheiner,
M.D.
Marc Swadener,
Ed.D.
GUESTS AND GUEST SPEAKERS (NON-VOTING)
Acting Industry
Representative:
Efraim Shek, Ph.D.
Guest Speakers:
Richard Hockett,
M.D.
Pertti Neuvonen,
M.D.
FDA
Shiew-Mei Huang,
Ph.D.
Peter Lee, Ph.D.
Lawrence Lesko,
Ph.D.
C O N T E N T S
Call to Order:
Jurgen Venitz,
M.D., Ph.D. 4
Introduction of the Committee 4
Conflict of Interest Statement:
Hilda F. Scharen,
M.S. 5
Open Public Hearing 8
Introduction:
Lawrence Lesko,
Ph.D. 8
Drug Interactions
Introduction:
Shiew-Mei Huang,
Ph.D. 8
Evaluation of CYP2B6-Based Interactions:
David Flockhart,
M.D., Ph.D. 17
Evaluation of CYP2C8-Based Interactions:
Pertti Neuvonen,
M.D. 40
Committee Discussion 63
Pharmacogenetics: Integration into
New Drug Development
Introduction:
Lawrence Lesko,
Ph.D. 73
Academic Perspectives:
David Flockhart,
M.D., Ph.D. 83
Industry Perspectives:
Richard Hockett,
M.D. 100
Practitioner Perspectives:
Mary V. Relling,
Pharm. D. 129
Committee Discussion 152
Concluding Remarks 170
P R O C E E D
I N G S
Call to Order
DR.
VENITZ: Good morning and welcome
everyone to the second day of the Clinical Pharmacology Subcommittee
Meeting. This is the continuation of
yesterday's topic area. My name is
Jurgen Venitz and I am the Chair. I
would like to start by introducing all the members of the committee and invited
guests around the table.
Introduction of the Committee
DR.
D'ARGENIO: David D'Argenio from the
University of Southern California.
DR.
FLOCKHART: Dave Flockhart from Indiana
University.
DR.
SHEINER: Lewis Sheiner, University of
California, San Francisco.
DR.
SWADENER: Mark Swadener, Boulder,
Colorado.
DR.
JUSKO: William Jusko, University of
Buffalo.
MS.
SCHAREN: Hilda Scharen, FDA, Center for
Drugs, Executive Secretary.
DR.
KEARNS: Greg Kearns, University of
Missouri.
DR.
DERENDORF: Hartmut Derendorf, University
of Florida.
DR.
DAVIDIAN: Marie Davidian, North Carolina
State University.
DR.
SHEK: Efraim Shek, Abbott Laboratories.
DR.
McCLEOD: Howard McCleod, Washington
University.
DR.
RELLING: Mary Relling, St. Jude
Children's Research Hospital, Memphis.
DR.
SADEE: Wolfgang Sadee, Ohio State
University.
DR.
LEE: Peter Lee, COPB, FDA.
DR.
HUANG: Shiew-Mei Huang, Center for
Drugs, Office of Clinical Pharmacology and Biopharmaceutics.
DR.
LESKO: Larry Lesko from FDA, Office of
Clinical Pharmacology and Biopharmaceutics.
DR.
NEUVONEN: Pertti Neuvonen from the
University of Helsinki, Finland.
DR.
HOCKETT: Rick Hockett, Eli Lilly.
DR.
VENITZ: Thank you, everyone. Let me turn over the microphone to Ms. Hilda
Scharen. She is going to read the
conflict-of-interest statement.
Conflict of Interest Statement
MS.
SCHAREN: The following announcement
addresses the issue of conflict of interest with respect to this meeting and is
made a part of the record to preclude even the appearance of such at this
meeting.
The
topics of today's meeting are issues of particular matters of broad
applicability. Unlike issues before a
committee in which a particular product is discussed, issues of particular
matters of broad applicability involve many industrial sponsors and academic
institutions.
All
special government employees have been screened for their financial interests
as they may apply to the general topics at hand. Because they have reported interests in
pharmaceutical companies, the Food and Drug Administration has granted
general-matters waivers of broad applicability to the following SGEs which
permits them to participate in today's discussion; Dr. David D'Argenio, Dr.
Marie Davidian, Dr. Hartmut Derendorf, Dr. David Flockhart, Dr. William Jusko,
Dr. Gregory Kearns, Dr. Howard McCleod, Dr. Mary Relling, Dr. Wolfgang Sadee,
Dr. Jurgen Venitz.
A
copy of the waiver statements may be obtained by submitting a written request
to the agency's Freedom of Information Office, Room 12A30, of the Parklawn
Building. Because general topics could
involve so many firms and institutions, it is not prudent to recite all
potential conflicts of interest but, because of the general nature of today's
discussion, these potential conflicts are mitigated.
We
would also like to note for the record that Dr. Efraim Shek is participating in
today's meeting as an acting, non-voting, industry representative.
In
the event that the discussions involve any other products or firms not already
on the agenda for which FDA participants have a financial interest, the
participants' involvement and their exclusion will be noted for the record.
With
respect to all other participants, we ask, in the interest of fairness, that
they address any current or previous financial involvement with any firm whose
product they may wish to comment upon.
Thank
you.
DR.
VENITZ: Thank you, Hilda.
Two
housekeeping issues before we get started.
You may have noticed in the original agenda for the second, we had a
topic on Pediatric Population PK Template.
Due to time constraints, that topic had to be deferred to our next
meeting or one of our next meetings.
Open Public Hearing
I
have also been informed that we won't have any presenters at the open public hearing
today so we might be able to get an early adjournment.
Having
said that, I would like to ask Dr. Lesko to introduce the topics for today and
give us our charge.
Introduction
DR.
LESKO: Thank you, Jurgen. I am not going to do much with the first
topic, cytochrome. I will Dr. Shiew-Mei
Huang do that and then, after that, I will introduce the pharmacogenetic
topic. So let me turn it over to
Shiew-Mei.
DRUG INTERACTIONS
Introduction
DR.
HUANG: Good morning.
[Slide.]
The
first topic this morning, we will talk about CYP2B6 and CYP2C8 drug
interactions.
[Slide.]
Recall,
at the last April meeting of this committee I have discussed that the CDER Drug
Interaction Working Group is revising the guidance, the In Vivo Drug
Interactions Guidance, which was published in 1999. Because of the emerging technologies and
tools, available, we have additional information which prompted us to update
this guidance which is about three-years old.
We
are going to use information that is obtained from various workshops
cosponsored by the agency or the information that was published in the PhRMA
Position Paper or from internal research from the reviewers about industry
practices and literature data.
As
I discussed last time, we would like to propose to include the information on
classification of CYP3A inhibitors in this revised draft guidance which will be
published for public comment again so that when we have drugs that are
substrates of 3A, we will be able to prioritize our study and we will be able
to label drugs that are strong or moderate inhibitors in the labeling to
facilitate the priorities of the interaction or clinical significance of
interactions in the drug label.
We
also discussed that we are seeing increasing submissions that interactions are
based on P-glycoprotein. Based on our
discussion in April, the majority of the committee members agree that the
digoxin is a good substrate for P-glycoprotein although it is also a substrate
for other transporters such as organic anion-transporting peptide. Still, right now, it is probably the best
substrate to study because the clinical significance of the interaction
outcome.
In
addition, in this '99 guidance, we will also include in vitro evaluation
technologies discussing various substrates, inhibitors, inducers for key
cytochrome P450 enzymes. I will discuss
that a little bit more. In keeping with
the impending publication of the Final Rule of Physician Labeling, we will also
discuss case examples indicating certain drug interactions that may be put into
the Highlights Section of the new Physician Labeling in addition to an
additional section of drug interaction in the labeling.
[Slide.]
I
just want to briefly discuss the current practices on cytochrome-P450-based
interactions. In the in vitro
evaluation, our reviewers have been recommending and industry has been
consistently performing the evaluation of these key enzymes; cytochrome
P450-1A2, 2C9, 2C19, 2D6 and 3A, both for reaction phenotyping, determining the
metabolic pathway of the new molecular entities. In addition, these other enzymes, 2A6, 2B6,
2C8, 2E1 often are also evaluated.
For
enzyme-modulating effects for inhibition; again those five key enzymes have
been most consistently evaluated--if not, our reviewers would provide
feedback--also, for induction, since 2D6 has not been shown to be induced. These are the four enzymes, plus some of the
2B6, 2C8 increasingly have been studied in this in vitro evaluation.
As
far as in vivo or clinical human interaction studies, again, our reviewers have
communicated and the sponsor has been conducting the studies to evaluate other
drug effects on the new molecular entity and the drug's effect on others. They are often prioritized based on the in
vitro evaluation of cytochrome P450.
For
example, if the reaction phenotyping is
indicating 3A as a major enzyme, there is usually a study involving a
strong inhibitor of 3A. If this compound
is shown to be inhibiting certain enzymes, then the effect on others with
appropriate probe drugs are often conducting.
Increasingly, we have seen both in vitro and in vivo evaluation of
P-glycoprotein-based interactions using various substrates in vitro, with
digoxin, or in vivo with digoxin, fexofenadine, as a substrate.
Depending
on the drugs or previously known similar compounds, other pathways such as
phase-II metabolizing enzymes or sudden peptide transport or if it is renally
secreted, certain compounds that are inhibiting renal active secretion have
also been evaluated in various submissions.
[Slide.]
So
why do we want to discuss CYP2C8 today?
The various cases of rhabdomyolosis involving gemfibrozil in statins;
there are data to show that monotherapy of gemfibrozil and statins, on their
own, they have shown some dose or concentration-related increase in the
incidence of myopathy or rhabdomyolosis.
So this could be a pharmacodynamic interaction. However, we are seeing reports in the
pharmacokinetics of statins that have been changed because of coadministration
of gemfibrozil--I show cases there--since gemfibrozil does not appear to
interact with these statins, via CYP3A, even some of the statins with 3A
substrates.
There
is a possibility of other enzymes or transporters that are being affected by
gemfibrozil such as CYP2C8, 2C9, UGT, glucuronosyltransferases or organic
anion-transporting peptides.
[Slide.]
For
example, just look at the sample of literature data. Many of these were published by Dr. Neuvonen
and, later on, he will elaborate on each study results more in detail. You can see here the examples from statins
such as fluvastatin, a 2C9 substrate here.
It didn't show an interaction with gemfibrozil.
Rosuvastatin,
as shown yesterday by one of our presenters, there is a two-fold increase. Simvastatin acid, lovastatin acid and
cerivastatin, there are various degrees of increase in area under the curve
when gemfibrozil was given together.
These were in healthy volunteers.
Another, rosiglitazone, a 2C8 substrate, repaglinide, also as 2C8
substrate also so a different degree of interaction. Here, with repaglinide, it is up to more than
an eight-fold increase when gemfibrozil is given.
As
a comparison, trimethoprim, which, in the literature is also shown to be
affecting 2C8, has a relatively smaller effect on rosiglitazone.
[Slide.]
In
our submissions, we have seen recently compounds such as Drug A which has been
shown to be metabolized by CYP2C8. The
major cytochrome P450s, 3A, 2C9, 2C19, 2D6, may not appear to affect this
metabolism. So what do we do if we would
like to know its interaction potential with this drug. Especially as we discussed yesterday, certain
safety biomarkers such as QT prolongation have been increasing evaluated when
drugs are submitted for approval.
If
we need to evaluate QT prolongation, we either use supertherapeutic dose or we
try to stress the system using enzyme-inhibitors to increase the exposure and
try to anticipate the worst-case scenario.
In that case, what can we do to increase the exposure to see what is the
maximum exposure that will happen, assuming this is the case, what inhibitors
are available for us to evaluate.
Or
in another case, Drug B, which has been shown to inhibit CYP2C8 in vitro, what
are the ideal or probe substrates of 2C8 that we can evaluate this drug's
effect on other drugs? So this is about
2C8.
[Slide.]
So
why are we interested in CYP2B6 which we are discussing today? There are recent studies on efavirens and
bupropion which have shown that 2B6 is the key or the principal enzyme
responsible for efavirens metabolism and one of the key pathways for
bupropion. There are recent data on
inducers of 2B6 such as some HIV protease inhibitors, dietary supplements such
as St. John's wort.
Our
submission with Drug C is sometimes metabolized by 2B6 in vitro. So, again, we would like to see the clinical
significance of other drugs' effects on it, what kind of inhibitors are
available there for us to evaluate their clinical significance.
[Slide.]
Today,
we have invited two experts in the field;
Dr. David Flockhart to talk about CYP2B6 and drug interactions. Dr. Flockhart and his colleagues at Indiana
University have recently published research data on efavirenz metabolism and
will give us a review in this field.
We
also have Dr. Pertti Neuvonen from University of Helsinki. Dr. Neuvonen and his colleagues have
published numerous articles characterizing strong inhibitors such as
ketoconazole, itraconazole, on various probe substrates of 3A to estimate their
extent of interaction. He has published
a lot of grapefruit-juice-related interaction and, more recently, he has
published various gemfibrozil and statin interaction data, and also in vitro
evaluation of various substrates and inhibitors and inducers.
[Slide.]
The
issues for them to discuss and for the committee to consider are what is the
clinical significance of 2B6- and 2C8-based interactions and are there tools
available, are there pro-inhibitors for the clinical evaluation of 2B6- or
2C8-based interaction, or do we have substrates that their interactions are
mostly based on 2C8.
Some
of the examples that may be shown later may
have a lot of possible transporters involved and we would like to know
whether there are good inhibitors and substrates that will be able to provide
us useful information particular to these two enzymes. Also, maybe there are other areas that we
need to focus on based on this particular evaluation.
[Slide.]
These
enzymes are felt to be important from our working-group discussion and this is
just to show you the big group of our Interaction Working Group members from
our Office of Clinical Pharmacology and Biopharmaceutics, members from the
Office of Pharmaceutical Science, members from--used to be from CBER, Center
for Biologics, and also from Office of the Commissioner who wants to see what
our current evaluation is and the
labeling impact, whether these are consistent with the new proposed rule and
how would this facilitate the healthcare providers and patients to use the
labeling depending on how we will address the interaction issues in the label.
With
that, I would like to introduce Dr. David Flockhart to discuss 2B6-related
interactions.
Evaluation of CYP2B6-Based Interactions
DR.
FLOCKHART: Thank you, Shiew-Mei. It is a great pleasure to be here this
morning, particularly, I must say, on the same podium as Dr. Neuvonen whose
work I have followed for a long time. We
have actually published together and collaborated but we have never met until
yesterday evening.
[Slide.]
I
am going to talk about cytochrome P450 2B6.
Those of you who know me will know that I really don't know much about
this. But it is a subject of a great
deal of interest in our Division of Clinical Pharmacology at Indiana and the
work in 2B6 is led by Zeruesenay Desta.
Dr. Desta has currently a series of projects aimed at defining probes
and inhibitors of this important enzyme.
So I am going to talk a little bit about our thinking about new ways of
evaluating it.
[Slide.]
I
am going to talk about some data on expression because that is historically
important in terms of understanding why we have not spent a lot of time on this
cytochrome up until now and then talk about some potential substrates, both in
vitro substrates and substrates that might be used in the clinic, and then talk
about inhibitors in the same kind of context, ones that might be used in test
tubes and ones that might be used in people.
2B6
is a heavily inducible enzyme. I think
that is one thing that is really obvious from its study at this point. So interest in inducers of it is as
important, and there may be a large number, actually, of important, clinically
important, interactions with this enzyme that result in low concentrations,
particularly of HIV medications, that we, as yet, are--well, we are not unaware
of but we, as yet, don't understand in terms of the mechanism.
[Slide.]
I
think the main reason, as is the case with many isoforms, and this would have
been the case in the past even for the two Cs, all of them, 2C9, 2C19 and 2C8,
is that the early antibodies that we always talk about, and I am referring in
particular to the classic paper published by Shimada and Guengerich ten or
fifteen years ago now which first documented by Western Blot the amount of
different P450s in the liver.
On
that, the amount of 2B6 expression was very low. It actually isn't shown in their diagrams but
was estimated in the test to be a minor component and less than 1 percent of
the total P450. Therefore, and as
recently as two or three years ago, in conversations with Grant Wilkinson at
Vanderbilt, he was absolutely convinced that it played a tiny role in human
drug metabolism.
[Slide.]
This
was why Grant thought that. This is
taken from a review article that Dr. Desta and I have put together, but if you
look just at the detection percent on the left here, from a series of studies
published in the late 1990s--well, I guess throughout the 1990s--there is a
relatively small n in the studies. So
these are all individual livers but, in a significant number, you can't even
pick up the enzyme at all.
If
you just recall, you will see the rough numbers here. So, 1 to 2 picamoles per milligram of
protein, which is not a lot of P450, in the liver was detected except in one
study, this one from Japan, in which 19 picamoles were picked up.
This
has a lot to do with the specificity and sensitivity of the antibodies we were
using at the time. But the number of
studies indicates the potential interest in this isoform.
More
recently, and you will notice that the dates of these references on the right
are later--this is up until the present--in every liver tested, or most livers
tested in these studies, you can actually pick up the enzyme. Not all, though. And this may relate to genetic polymorphisms
that have been described but not terribly well characterized to this point.
But
you will notice, on the last slide, I talked about 1 to 2 picamoles being
present. The average, in these
studies--I haven't gone through the somewhat disingenuous exercise of trying to
average all these things, but you see that it is significantly higher, probably
a lot higher, with the newer antibodies and there are some that are
significantly higher.
Also,
I think now people in the field would agree there is a consensus that we have a
specific antibody. When you study using
these antibodies, the variability--there is a huge variability in protein
expression but also in RNA expression.
The RNA expression data is currently confusing because Aaron Schutz and
other people at Mary Relling's institution have shown quite nicely there are
multiple splice variants of this enzyme that might contribute to variations in
its activity. So we have yet to sort out
really confidence assays for the RNA.
But, suffice it to say, the amount is considerably more than we thought
it was originally.
[Slide.]
So
new mono and polyclonal antibodies of higher sensitivity and specificity have
made it clear that there is a greater frequency of detection. I think, in all the livers we have ever
tested now, the enzyme is there, and there is more of it and it looks rather
less than 0.1 percent. It averages about
6 percent of the total liver with absolute maximum amounts that are really
quite significant, presumably in livers that are turned on or people that are
turned on for one reason or another, up to 25 to 44 percent.
[Slide.]
This
is taken from the paper that Shiew-Mei referred to which is our in vitro study
of efavirenz metabolism. A couple of
points about this. Pharmacologists
always have to put up diagrams, structures, but there is an important unusual
group on this, this triethylene planar group, monoplanar, group, out here which
is common to a number of substrates of cytochrome P450 2B6. I am going to show you data that basically
show that this is the dominant route, this is the main route, by which
efavirenz is metabolized in people.
A
minor route here we have recently shown is mainly cytochrome P450 3A, but this,
in people, is about one-hundredth this route.
So this route is the dominant means of clearing this drug from the body. It is an 8-hydroxylation in the 8 position
down here whereas the 3A-mediated metabolism is A7. It catalyzes the 7-hydroxylation right
here. You count from this side, so this
is 7. This is 8 down here.
There
is also metabolism of this, of the metabolite, of the metabolite, although less
quickly to the 8,14-dihydroxy.
[Slide.]
Let
me just show you some of the data that supports this. These are data simply showing the clearance
of efavirenz, itself, from an in vitro incubation. So this is the disappearance of the
parent. They might have parent left, if
you like, and you see the only one isoform under these conditions which is 1
micromolar efavirenz, approximately the concentration reached at steady state
during normal dosing of about 600 milligrams a day. Only one isoform reduced it.
So
these are the data that initially got us interested in it. This is fairly comprehensive. It does include 2C8, both 3A isoforms. There is a difference between 3A4 and 3A5 out
here which has held up in subsequent studies.
3A5 seems to be a more efficient catalyst of efavirenz metabolism than
3A4.
[Slide.]
Our
very first clinical data--this is the first time I have shown this--phase I and
phase II here; phase I is in the absence of rifampin and phase II is after 10
days of rifampin treatment. You do see a
decrease in bioavailability and an increase in the rate of metabolism of
efavirenz in vivo.
This
is something that you see with cytochrome P450 2B6 but you also see it,
obviously, with cytochrome P450 3A, 2C9, 2C19, a number of other isoforms.
We
are currently conducting a study of about 100 people in which we are
trying to determine at what point in this curve it would be intelligent to
conduct a phenotyping study; in other words, one that might allow us to do a
single point determination to study a large number of people in order to get
some sense of the clinical variability of this enzyme in vivo. That might be, and we don't know the answer
to this yet, a urinary ratio of efficacy to 8-hydroxyefavirenz or it might be a
serum ratio. But we don't have those
data yet and I can't talk about it.
[Slide.]
This
is a similar drug. This was published in
January of this year. It is a drug with
a number, DPC963 but you will notice a similar structure up here. I am simply putting up this complicated slide
to make the point that 2B6 catalyzes metabolism of an efavirenz analogue as
well.
You
will note that this drug also is metabolized notably by 3A as well. But, again, the dominant route to metabolism
is by 2B6. All this is saying, really,
is that there are a number of related drugs that are metabolized by the same
pathway.
[Slide.]
2B6
is also a low-affinity catalyst of S-mephenytoin metabolism to Nirvanol. This is from a paper published in 1996 that
we became interested in. I say low
affinity because mephenytoin, which we can't use without and IND anymore--it is
off the market in the United States, unfortunately. It is a valuable probe drug, obviously, for
cytochrome P450 2C19, but, in this study, the metabolism of mephenytoin not to
is 4-hydroxy metabolite, which is 2C19-mediated reaction, but to Nirvanol which
is the demethylation reaction of mephenytoin which was studied. These authors showed that only one isoform
did this.
This
was the only data in this paper, but we became interested in this idea because
we had been interested in 2C9 team and actually had done a study which we
published in 1992 showing that S-mephenytoin and omeprazole could be used as
probes for that.
So,
what we wanted to do at the time, was to take this large 200-person study, take
their urine and see if we could actually do 2B6 phenotyping from the same
urine. Unfortunately, that turned out
not to be the case.
[Slide.]
This
is largely because of work done by a very smart Korean post-doc, Jim Ko, who
showed at the time, before we actually got into wasting these valuable urine
samples, that this same reaction, the N-demethylation of mephenytoin at this
concentration can be carried out by two isoforms, 2B6, but also 2C9.
He
went on to show in subsequent studies that the high-affinity catalyst was 2C9
and not 2B6. So it remains unclear at
the moment whether or not one can use mephenytoin as a probe for 2B6. Personally, I think it is rather compromised.
[Slide.]
I
am going to skip this. This basically
just shows the metabolism of mephenytoin.
[Slide.]
These
are our data suggesting the R-mephenytoin might be a substrate probe for
2B6. Certainly, in vitro, possibly, in
the future as one isoform that does it again.
These data are rather thin in the sense that they only are recombinant
enzyme data. We haven't done careful
studies because, at the time--this is 1996--we didn't have any confident in
vitro inhibitors of 2B6 that were specific and only recently have we been able
to have those.
[Slide.]
Now,
there are a number of inhibitors that are now published clear and obvious. Not all of them, unfortunately, are
specific. It is very clear that both
paroxetine and sertraline can inhibit this isoform using in particular bupropion as a probe, the
hydroxylation of bupropion as a probe.
Antiretrovirals
including nelfinavir and ritonavir are potent inhibitors. Both ticlopidine and clopidogrel have been
shown by our group to inhibit 2C8.
Clopidogrel is metabolized primarily by it. We and others have shown also that thioTEPA,
the chemotherapy agent, is an inhibitor of cytochrome P450 2B6. We are pretty confident that that happens in
vivo and we have some data to indicate that it is fairly specific in vitro.
[Slide.]
These
are some of those data suggesting that this is a specific P450 inhibitor in
vitro. This is just a percent of control
activity with a series of cytochrome P450 isoforms with a series of different
probes. This is 100 percent so
everything should be here. But when you
coincubate thioTEPA, I believe at 1 micromolar in this experiment, you see a
decrease principally in 2B6 although there is a little inhibition of 1A2 as
well.
When
you look at this carefully, and you do a dose-response to thioTEPA, and these
are data that we published, I think, three years ago now, you see that 2B6 is
preferentially inhibited compared to the others. There are decreases in all of these but the
potent inhibition with an IC50 or 5 micromolar which is well below, actually,
the concentration that this drug reached in vivo, it is here.
So,
because of these data, we believe that thioTEPA can be used as an in vitro
inhibitor if the conditions are done right and this low concentration can be
used as a specific in vitro inhibitor of this enzyme. That is an important tool to allow us to
study it further.
It
is the case, obviously, that the thioTEPA is a chemotherapeutic agent and you
can't just give thioTEPA to normal volunteers.
So it is not something that we are going to be able to use in vivo.
[Slide.]
This
just shows the potency. We used
S-mephenytoin metabolism at high concentrations, relatively high
concentrations, to be inhibited by thioTEPA.
These are just Dixon plots indicating that you see linear kinetics and
potent inhibition.
[Slide.]
Cyclophosphamide
has also been described to be metabolized by this enzyme and it was first
described really carefully by a series of nice studies done by Irv Wainer and
his group at Georgetown University and in Montreal when Irv was there. This is the structure of
cyclophosphamide. Its metabolism to its
principal active metabolite which is 4-hydroxycyclophosphamide is carried out
primarily by 2B6--that is why it is in the red--but also by these isoforms. A number of groups including David Waxman's
group and a number of others have contributed to these studies as has John
Slattery's group at Seattle.
[Slide.]
So
the difficult position we are in is we have these in vitro data. It is not really--it wasn't really clear, how
much of this actually occurs via 2B6 in vivo.
But we have noted a study from Holland by Huitema in 2000. What this is is a study of a sequential
treatment in cancer patients with cyclophosphamide and thioTEPA.
In
this situation here, what you are looking at is concentration of drug against
time in two different sequences. So first,
in this situation, cyclophosphamide is given prior to thioTEPA and you see the
normal kinetics that you would expect of cyclophosphamide. I just want to point out that the
concentration of the parent drug is notably higher than that of the metabolite
which is in the squares below.
On
the other hand, if you coadminister thioTEPA, you give it I.V. at the same
time, you see a notable decrease in the red in the parent concentration and a
notable increase in the metabolite concentration. So--I'm sorry; I got that the completely
wrong way around. This is the parent
here, which goes up, and the metabolite goes down. So this is an inhibition of cyclophosphamide
metabolism, not an induction.
So
we think, because of these data, that thioTEPA is acting to inhibit 2B6 in vivo
and resulting in a change in cyclophosphamide pharmacokinetics.
[Slide.]
As
I indicated a moment ago, there are a lot of inducers of this enzyme. In fact, many of the substrates of this
enzyme, we have not yet found one that doesn't seem to auto-induce its own
metabolism. Rifampin, hyperforin,
phenobarb, ritonavir, phenytoin, carbamazepine, all induce. We are familiar with these as ligands for PXR
and sometimes CAR.
The
HMG-CoA reductase inhibitors, interestingly, have been shown in some situations
to induce 2B6 metabolism as have nevirapine induces its own metabolism as does
efavirenz. Clotrimazole has been shown
in vitro to as well and there is recently a clinical study indicating that
artemisinin induces the metabolism of bupropion.
[Slide.]
So,
overall, 2B6 is a significant contributor to hepatic CYP expression. The number of substrates is growing and I
anticipate that the number of submissions to the agency will grow although
Shiew-Mei tells me that there are a lot more substrates coming over the FDA's
desk that are 2C8 than there are 2B6.
Efavirenz
and bupropion, we believe, are specific in vitro probes. I haven't spent much time talking about
bupropion because we haven't studied it much ourselves. But there is a reason we haven't studied it
and that is because we felt early on that it was pretty clear that its
dominant route of metabolism is via 3A
and not through 2B6. Although it does
have a fairly specific 2B6 route of metabolism, the hydroxylation, most
bupropion is via another route. So this
compromises its utility as a probe.
ThioTEPA
is a specific inhibitor of 2B6, we believe.
There is clearly no evaluable specific inhibitor yet of 2B6 in vivo that
we can use and we really need one, you know, to be able to prove for sure that
a lot of reactions are occurring via this enzyme in vivo.
Lastly,
we do think efavirenz is a potentially valuable in vivo probe for the activity
of more currently evaluating that.
Thanks
for your attention. I would be glad to
take a couple of questions.
DR.
VENITZ: Thank you, David.
Any
specific questions for David? Shiew-Mei?
DR.
HUANG: You listed ritonavir as a 2B6
inhibitor and later on as an inducer.
This is similar to the situation with ritonavir with 3A.
DR.
FLOCKHART: Yes.
DR.
HUANG: Ritonavir is shown to
self-induce. It is an inducer for
3A. It is an inhibitor of 3A. Although the 10-day or 14-day study, most of
the studies with ritonavir have shown an inhibition effect.
DR.
FLOCKHART: Yes.
DR.
HUANG: That is the basis for ritonavir
and nelfinavir.
DR.
FLOCKHART: The nelfinavir with ritonavir
is inhibition.
DR.
HUANG: Right. I wonder if, for 2B6, do we know about what
is the net effect?
DR.
FLOCKHART: No. It is clearly in vitro. Several groups now--I think three groups have
shown that it is an inducer in vitro. In
our hands, it is a good inhibitor in vitro but we don't know in vivo. I guess, with ritonavir, you have to be a
little careful about that to net. After
about ten days or two weeks, it is clearly an inhibitor but there are periods
in between when it would be a net inducer.
DR.
SADEE: David, I assume that this is all
hepatic activity.
DR.
FLOCKHART: The data that I am presenting
is hepatic activity. It is clearly
present. I cut out a slide showing some
of our work and the work from Mikael Akoban's group and Aaron Schutz' work
indicating that it present in a lot of tissues.
It is not just an hepatic enzyme.
DR.
SADEE: Because that could also make a
big difference in terms of inducibility.
If there is also a lot of extrahepatic activity, the inducibility will--
DR.
FLOCKHART: Absolutely. Notably, in my business, it is present in the
breast. It is present in muscle. It is present in CNS. It is a very widely distributed isoform which
may have all kinds of interesting implications.
It is also an effective catalyst of a lot of endogenous things like
testosterone, estradiol and so on.
DR.
SADEE: It could be present in tumor as
well.
DR.
FLOCKHART: It is present in some
tumors. That has been shown, active in
tumors, RNA protein and activity.
DR.
SADEE: Another question. The variability; is this caused by maybe
pathophysiology? Is there anything known
about particular states of liver disease?
DR.
FLOCKHART: I would really be going out
on the edge to suggest that, Wolfgang, at the moment. But it clearly a very inducible enzyme. You can turn it on very easily. It seems to be less inhibitable, at least in
our hands. It is something that is very
sensitive to the PXR and CAR-inducing mechanisms and maybe others.
DR.
SADEE: One more comment, and it is
semantics, basically. I always feel very
uncomfortable about specific inhibitors.
DR.
FLOCKHART: Yes; you never know until you
have studied for infinity.
DR.
SADEE: That is why it is so--just for
official use, I would strongly recommend using "selective."
DR.
FLOCKHART: Selective; okay. I have tried to use the term "relatively
specific."
DR.
LESKO: David, what do we know about the
distribution of 2B6 activity in the population and what is the range of
expression or activity, say, from low to high?
Is it like 3A4, for example, or is it like something else?
DR.
FLOCKHART: The problem is we don't
really have a good probe at the moment in vivo, so I think we are conducting a
study right now that should give us a handle on that using efavirenz. There are studies that have been done by Ed
Lecluse and others indicating there is a fair amount of variability, ten- to
twenty-fold variability, in bupropion hydroxylation in people.
But
my problem with that is that some of that could be influenced by 3A activity in
alternative routes. So, in this
particular setting, 2B6 variability, I think we really don't have the data yet,
Larry. I would be reluctant to
extrapolate the in vitro variability in livers although, of course, that is
about the variability you see in 3A.
DR.
HUANG: We can elaborate on the
discussion later on but I just want to follow on Wolfgang's discussion on
nonselective inhibitors. Recently, we
have been discussing drug interactions whereby you want to use two drugs to
inhibit two major, equally major, pathways in order to create a worst-case
scenario.
DR.
FLOCKHART: A really bad thing, to create
a really bad thing.
DR.
HUANG: A really bad case. So it may not be a bad idea to use a
nonspecific inhibitors where you could inhibit one major pathway and the other
one that you are also concerned with.
Here,
you have listed several that are not specific.
For example, ritonavir has various pathways.
DR.
FLOCKHART: Right.
DR.
HUANG: But if you know this drug is not
metabolized by all the other pathways, and we know that when it is a strong
inhibitor--
DR.
FLOCKHART: That is a very, very good
point. So Bob Temple has, many times,
and I am sure both you and Larry have made the point that, if you want to study
the worst possible interaction with 3A, you have got to kill the thing with
ketoconazole. So one could make the
case, if something is metabolized, both by 3A and 3B6, that you could
coadminister a drug that inhibits both, like Ticlid, like ticlopidine, which is
a fairly effective inhibitor of both drugs.
Ketoconazole,
actually, interestingly, at high concentrations, you have to use a fair amount
of ketokonazole but it seems to kill 2B6 as well if you go high enough.
DR.
HUANG: Just to clarify; for ticlodipine,
is it the parent drug that is active for both, or is the metabolite.
DR.
FLOCKHART: The parent drug is the
inhibitor.
DR.
HUANG: For both; okay.
DR.
FLOCKHART: Yes.
DR.
HUANG: Thanks.
DR.
SADEE: I have one more question,
David. If the variability of 2B6 is as
high as it appears to be and the variability in 3A4, for instance, also, so you
would have a substrate for both. Then,
in one person, there would be a 3A4 substrate.
In another person, it may be the 2B6 substrate and the other enzyme may
play no role. So I am just wondering
about labeling this or presenting the information that this is a substrate for
both enzymes and, in reality, in individuals, there may be other--
DR.
FLOCKHART: I guess that might be the
case. I don't have data yet,
Wolfgang. I think that generically I
would agree with you. I think there may
be people for whom there is very little 3A activity and 2B6 would be the
dominant route. My bias, at the moment,
and it is a bias based on not much data, but I will share the data, is that 2B6
is really dominantly the enzyme for efavirenz.
Even
when you turn on with rifampin, you don't see a lot of 3A contribution. The basis for that is the big difference in
affinity between the two isoforms for efavirenz and the fact in the urine of
the patient that I showed you that was induced, we see the 2B6 hydroxylation,
the 8-hydroxylation route, really turned on.
There is a lot of that metabolite in the urine and very little of the
7-hydroxymetabolite although that is increased as well.
So
I think, in that situation, when it is really turned on, there is more 3A. But it is still a dominantly a 2B6 drug.
DR.
VENITZ: Any further questions? Thank you, David.
Our
next speaker is Dr. Neuvonen. He is
going to share with us his experiences with 2C8.
Evaluation of CYP2C8-Based Interaction
DR.
NEUVONEN: Thank you, Mr. Chairman, dear
colleagues and committee members.
[Slide.]
In
my talk about CYP2C8 and drug interactions, I will review substrates,
inhibitors and inducers of 2C8, some in vivo interaction studies and finally
present some suggestions for in vitro and in vivo studies.
[Slide.]
CYP2C8
is highly expressed in the liver. The
protein content of 2C8 is on the same level as that of 2C9 and clearly than
that 2C19. There is lots of
interindividual variation in the protein content of 2C8 and 2C8 seems not to be
detectable in the intestine.
[Slide.]
Many
drugs are substrates for 2C8. In vitro
studies, paclitaxel, amodiaquine and torsemide have been used. 6-alpha-hydroxy paclitaxel is a 2C8-mediated
reaction and amodiaquine is metabolized mainly by 2C8. Torsemide is metabolized both by 2C9 and 2C8
but, in some conditions, this can be used as a marker substrate.
In
vivo studies, cerivastatin, repaglinide and rosiglitazone have been used as
substrates. Also many other compounds
are substrates for 2C8. For example,
many of the substrates of CYP3A4 are also substrates for 2C8. But the relative contribution of different
CYP enzymes may depend on the substrate concentration used, for example, in in
vitro studies.
[Slide.]
This
slide shows the relationship between amodiaquine metabolism and paclitaxel,
6-alpha-hydroxylase activity. As can be
seen, amodiaquine clearance and formation of N-desethyl-amodiaquine correlate
very well with the activity of paclitaxel 6-alpha-hydroxylase. This was a study where microsomes from ten
human livers were used.
[Slide.]
Trimethoprim
is a competitive of 2C8. It has a Ki
value of about 32 micromolar and it is relatively selective up to 100
micromolar concentration.
[Slide.]
As
can be seen here, the inhibition of other CYP enzymes is very little, up to the
concentration of 100 micromolar.
[Slide.]
However,
when higher concentrations are used, and here are shown 250 and 500 micromolar
concentrations, trimethoprim inhibits, for example, 2D6, 3A4, 2C19, 2C9, 1A2
enzymes.
[Slide.]
Quercetin
is a competitive and potent inhibitor of 2C8.
It has a Ki value of about 2 micromolar but quercetin is also a potent
inhibitor of 1A2. So it is a
nonselective inhibitor of 2C8.
Glitazones
are potent inhibitors of 2C8.
Gemfibrozil is nonselective but it seems to work both in vitro and in
vivo. There are also many other
nonselective inhibitors; for example, many substrates of 3A4 seem to be
inhibitors of 2C8.
[Slide.]
Here
are shown Ki values of some glitazones.
Rosiglitazone and pioglitazone are relatively selective for 2C8 whereas
trogliazone inhibits more 2C9.
[Slide.]
Some
of the so-called selective probe inhibitors used as a diagnostic inhibitors in
in vitro studies are also inhibitors of 2C8 at the concentrations generally
used. For example, ketoconazole at the
concentration of 1 micromolar considerably inhibits activity of 2C8. Ketoconazole is a noncompetitive inhibitor
with an apparent Ki value of 2.5 micromolar.
So
data regarding ketoconazole--let's say that the inhibition data where it has
been used as an inhibitor of CYP2A isoforms may include also inhibition of
2C9. Also DDC is a significant inhibitor
of 2C8.
[Slide.]
2C8
is clearly inducible. In vitro rifampin
is a more potent inducer of 2C8 than of 2C19 or 2C9 and rifampin is more potent
as an inducer of 2C8 than, for example, phenobarbital or dexamethasone. In in vivo studies, rifampin clearly
decreases, for example, the AUC of repaglinide which is a substrate of
2C8. It decreases the AUC roughly by 60
percent. Of course, here, maybe the
induction of 3A4 contributes to the finding, but probably it is best to measure
extent to induction of 2C8.
[Slide.]
In
the following, I will present some in vivo interaction studies where
gemfibrozil and some statins or oral antidiabetics have been administered. All these studies are randomized crossover
studies in healthy volunteers where gemfibrozil or placebo or a comparator have
been given for three to four days. Then,
on Day 3, a single dose of either cerivastatin, simvastatin, lovastatin,
repaglinide or rosiglitazone has been administered.
[Slide.]
Here
are data on the effect of the gemfibrozil on cerivastatin. As can be seen here, gemfibrozil greatly
increases the AUC of unchanged cerivastatin.
The AUC was increased on average five or sixfold and in 110 healthy
subjects the increase was tenfold. Also,
the concentrations of cerivastatin, lactone or M1 metabolite, which is formed
by CYP3A4, are greatly increased by gemfibrozil whereas the concentration of
M23 metabolite is drastically decreased.
This M23 metabolite is formed by CYP2C8.
I
think that this pharmacokinetic interaction greatly contributes to this
toxicity of the gemfibrozil/cerivastatin
combination which has been previously found.
[Slide.]
Gemfibrozil
inhibits cerivastatin metabolism also in vitro, the formation of 23 metabolite
is clearly reduced by gemfibrozil.
[Slide.]
Here
are shown the effect of gemfibrozil of the pharmacokinetics of simvastatin or
simvastatin acid. Gemfibrozil increased
AUC of simvastatin acid about two, threefold, whereas the AUC of the parent
simvastatin was unchanged.
[Slide.]
Here
is shown the role of CYP enzymes in simvastatin metabolism and it can be seen
that simvastatin acid is metabolized by CYP3A4 but also partially by 2C8. It seems that gemfibrozil could inhibit this
2C8-mediated partway. Of course, there
are also some alternative explanations for the finding.
[Slide.]
Gemfibrozil
also increases the AUC of lovastatin acid whereas the AUC of parent lovastatin
remains unchanged. Bezafibrate had no
effect.
[Slide.]
Here
are shown the effect of gemfibrozil, itraconazole and their combination on the
concentrations of repaglinide and its M1 metabolite. Repaglinide is a short-acting oral
hypoglycemic agent. Plasma
concentrations of unchanged repaglinide were increased greatly by gemfibrozil
whereas itraconazole had only a minor effect on plasma concentrations of
repaglinide. The combination of
gemfibrozil and itraconazole drastically increased plasma concentrations of
repaglinide.
Gemfibrozil
increased greatly the concentration in one metabolite which is formed mainly by
CYP3A4 and, as expected, itraconazole greatly reduced it.
[Slide.]
Here
are shown the effect of two CYP3A4 inhibitors and gemfibrozil on the AUC of
repaglinide. The data regarding
clarithromycin are derived from another study.
Clarithromycin and itraconazole both increased the AUC roughly 40
percent. Gemfibrozil increased it on
average eight-fold and the combination of the gemfibrozil and itraconazole
about twenty-fold. Of course, the flat
glucose-lowering effect was clearly increased along with these increased
concentrations.
[Slide.]
Here
are shown the effect of gemfibrozil of rosiglitazone. The AUC of rosiglitazone was about two,
three-fold--increased two, three-fold, by gemfibrozil and both the Cmax and
half-life were increased.
[Slide.]
In
the final two figures, I will suggest some possibilities for in vitro and in
vivo interaction studies, in vitro human liver microsomes or recombinant human
2C8 enzymes can be used. Paclitaxel and
amodiaquine seem to be well suitable substrates. Torsemide is useful only with recombinant 2C8
because also 2C9 is metabolizing torsemide and forming just the same
metabolites.
Trimethoprim,
quercetin and pioglitazone or rosiglitazone can be used as inhibitors and
rifampin is useful as an inducer.
[Slide.]
For
in vivo studies, repaglinide can be used as a probe compound, probe
substrate. Also, rosiglitazone is
useful. Cerivastatin would be also
useful but, of course, it may be difficult to get for in vivo studies. Amodiaquine is probably too toxic to be used
in interaction studies.
Gemfibrozil
can be used as an inhibitor. Of course,
one should remember that it is nonselective.
It inhibits, for example, 2C9 and also some transporters, at least
OATP2. Trimethoprim is more selective
but it is not very potent. By now, there
have been only very few in vivo data about trimethoprim as an inhibitor of 2C8.
Pioglitazone
and rosiglitazone could also be possible inhibitors. Rifampin is a useful inducer but, in
conclusion, further studies are needed to find optimal probe substrates and probe
inhibitors, particularly for in vivo interaction studies with 2C8.
Thank
you.
DR.
VENITZ: Thank you, Dr. Neuvonen. Any questions? Mary?
DR.
RELLING: So some of those interactions
that you described at the end with gemfibrozil were remarkably potent
interactions. So do you suspect that
there are other mechanisms involved besides just CYP2C8, with gemfibrozil, for
example? That was a 1900 percent effect
on AUC.
DR.
NEUVONEN: I agree that there may be also
other possibilities. For example, the
role of OATP2 inhibition should be clarified in these interactions. But surprisingly all substrates of 2C8 we
have studied by now together with gemfibrozil, there has been a significant
interaction with gemfibrozil and those substrates.
DR.
RELLING: That is in proportion to their
relative KM's or Ki's roughly.
DR.
NEUVONEN: Not very well. So it is not sure if it is a parent
gemfibrozil or some of its metabolites, for example. Of course, we are not aware of the liver
concentration of gemfibrozil.
DR.
HUANG: I was just going to add to
it. I guess many of these drugs that you
study as a substrate, with gemfibrozil as a 2C8 substrate, the concentration of
2C8 may vary among these drugs and so KM may not be the only determining
factor.
But
I want to comment on do we know anything about gemfibrozil dose and the effect
on some of the statins? Do we know any
dose effect.
DR.
NEUVONEN: We have used the standard
dose, 1200 milligrams per day and we have not studied possible dose-effect
correlation. So I have no answer to that
at this time.
DR.
HUANG: I was wondering, is there an
interaction between itraconazole and gemfibrozil?
DR.
NEUVONEN: Between itraconazole and--
DR.
HUANG: Itraconazole and gemfibrozil.
DR.
NEUVONEN: I am not aware of it. We have not studied it.
DR.
HUANG: Okay, because the nineteen-fold
increase was only when itraconazole was added.
DR.
NEUVONEN: Actually, I would like to
correct my previous answer. Of course,
we measured the concentration of itraconazole too in these studies. If I remember correctly, it was, perhaps,
that plasma concentrations of gemfibrozil were decreased.
DR.
HUANG: Decreased.
DR.
NEUVONEN: Yes. It has been reported in the publication. If I remember correctly; yes. We thought that it could be a displacement
from protein binding or something like that, but we have no final--
DR.
HUANG: I was just wondering, the higher
effects of gemfibrozil on repaglinide when itraconazole was additionally added
to the regimen, was it due to its effect of gemfibrozil or just added other
mechanisms of interaction because
itraconazole, itself, doesn't really affect repaglinide.
DR.
NEUVONEN: In the case of repaglinide, I
guess, or at least one explanation could be, that repaglinide is metabolized by
2C8 and 3A4. If both of these metabolic
enzymes will be blocked, then this could explain more than the additive
interaction observed with these compounds.
DR.
VENITZ: Dr. Derendorf?
DR.
DERENDORF: You mentioned quercitin as a
potent inhibitor. I would assume that
data comes from in vitro studies. When
you give quercitin, you hardly find any in the blood. It gets converted to the conjugate. So is there any information on the quercitin
conjugates or any in vivo interaction data.
DR.
NEUVONEN: Actually, we have no
experience of our own with quercitin.
These data are based only on the literature. So I have not seen any in vivo studies with
it.
DR.
VENITZ: Larry?
DR.
LESKO: What is the nature of the
relationship between the in vitro data and the in vivo data on the substrates
and inhibitors? In other words, is there
a qualitative rank order that, if I have a sensitive substrate in vitro, I
would see the same sensitivity in vivo in the comparative sense, or, conversely,
if I had a weak inhibitor in vitro, would it serve as a weak inhibitor in an in
vivo situation for the same substrate.
DR.
NEUVONEN: You mean, basically, now with
2C8 enzyme?
DR.
LESKO: With 2C8.
DR.
NEUVONEN: Actually, our data with
trimethoprim--well, based on our in vitro data, we calculated, if I remember
correctly, that in vivo it should inhibit roughly 20/70 percent 2C8
activity. I think that the in vivo data,
actually, we have in press in line with these findings. So trimethoprim increases the AUC of
of repaglinide but not as much as
gemfibrozil.
DR.
VENITZ: David?
DR.
FLOCKHART: Two things. I think I would like to congratulate you for
just doing the experiment with both itraconazole and gemfibrozil. As Mary points out, it is a big effect. But I think this is relevant to the kind of
evolution of the guidances about drug interactions. We have all been talking about multiple drug
interactions. I think many of us have
been saying for many years that, while the real world is people are taking
many, many different drugs, we have been studying one-on-one drug interactions.
So
I would just like to emphasize the point that we need to move into a mode, and
I know Larry is aware of this problem, of studying more multiple-drug
interactions. There has been data,
really, for twenty-five years indicating that, in the elderly, they really get
into bad adverse drug reactions once they are over five or six medications, at
least in the V.A. system. I think that
is important.
I
would also--since you are here and in this country, I would like to thank you
again for coming and for the large contribution that your group has made to our
understanding of these things over many years.
Many, many times we, in the U.S., have talked around doing studies,
thinking of doing things. Your group has
actually been the one that has actually done it.
DR.
NEUVONEN: Thank you. Actually, one point I would like to add is
that we should not look too much at the mean increases but just to look at the
interindividual variation in the extent of interaction because I guess that
just those adverse effects are coming from those patients who are most
sensitive and, therefore, the variability in the extent of interaction should
be overreported.
For
example, in the case of cerivastatin, there were, even in the material of ten
homogeneous students, an increase of 10 in 1, so what is the variation in a
typical population.
DR.
KEARNS: I think you just answered the
question I was going to ask. It is
remarkable. Not only have you come a
long way, but you have managed to at least read my mind a bit. But, my point is from a regulatory
perspective. To me, and maybe this is
just a very simple way of thinking about it, but it is the constitutive
expression of the enzyme in a patient that will determine the extent of the interaction.
So,
from a regulatory standpoint, when you are contemplating putting in labeling
about an interaction and you may be basing that on mean data, how do you
reconcile that with respect to a prudent warning. If it is a drug that has a huge therapeutic
index, it makes no difference. But if it
is a drug that is used to treat cancer or other narrow-therapeutic-index drugs,
it is a big issue.
So,
to my friends at FDA, how are you going to deal with that?
DR.
LEE: May I answer that? In the last two advisory meetings, we
actually proposed a method to look at the probability of an adverse event due
to the drug-drug interaction. So we
would look at the PK safety relationship and calculate, based on the distribution
of PK change--and calculate what will be the probably of an adverse event.
So
we would not only look at the mean value but also look at the patients who are
on the extreme.
DR.
SHEINER: That's the right thing to do
except that now your data requirements go way up because you are now talking
about estimating sort of tails of the distribution, not that they are not the
most important. They are because we are
concerned about 1 in 100, 1 in 1000, events.
There are a series of things. But
the amount of data you need to actually get a confident estimate of something
like the tail area is really, really nasty.
It is not just like twice as much.
Have
I got that right?
DR.
HUANG: Just to add to that, I think at
the last advisory committee meeting we presented a case where we are estimating
the percent population that may have QT prolongation more than 30 milliseconds
due to drug interactions or due to renal disease because that particular
example, the drug is both metabolized and renally excreted. So, actually, the assimilation also shown was
a percentage of population which would result in QT prolongation more than 30
milliseconds where you have both renal failure, a certain creatinine-clearance
range, and having ketoconazole. So we
are approach that quantitative approach.
We haven't done the multi drugs yet but we are doing two different
conditions to estimate that. We have not
applied widely but we are starting to.
DR.
KEARNS: I understand that. That is laudable. But, again, and I hate to go back to the QT
discussion because it is always painful for me, but ketoconazole is an
IKR-channel inhibitor. Until you can
factor in the intrinsic ability of that interacting substrate to have its own
pharmacologic effect that may produce an adverse effect, then the kinetic piece
is just part of it.
As
Dr. Sheiner just mentioned, then the n goes up way big to factor out maybe the
pharmacodynamic piece of it.
DR.
HUANG: Yes; our reviewers take note of
that and actually this was in the consideration when we look at the data on
some of the inhibitors that we would recommend in order to increase the
exposure of drugs that we are evaluating for QT prolongation.
DR.
KEARNS: Still on the point that Greg
raised, I don't know if you had something in mind as an alternative but, yes,
in fact, averages are used, or mean values are used, along with some other
considerations, I suppose, in making recommendations in the label. Is what you are asking related to the way
this information is expressed in the label?
For
example, is it leading to expression of ranges of let's say
area-under-the-curve increases? How else
can you do it, I guess, is what I am sort of trying to get to. What are some alternatives to the way it is
done currently?
DR.
KEARNS: I wish I knew. But what is troubling me sitting here as a
pediatric pharmacology person is that, if we look at developmental expression,
activity of the enzymes changes over time.
There are not a lot of drug-interaction studies in children to see, at
three months of age, if you look at the P450-based interaction and the extent
of it, how far do you move it kinetically, compared to when the enzyme is fully
expressed.
Again,
it boils down to the therapeutic range so there is this clinical need for
people to generalize and to put interactions on tables and charts or to
memorize the important ones. I think, at
some level, that is good. It is like a
warning. But, at another level, if the
pharmacist refuses to fill the prescription because there is a drug interaction
in the label, then patients can be deprived of therapy where the interaction
for a given person may not exist in a meaningful way.
So
I don't know the answer, but it is a problem.
DR.
HUANG: That is why at least one of the
approaches that we are taking is to warn about the most significant interaction. That is why we are trying to put in the
labeling that you are dealing with a drug with a strong inhibitor or, if this
drug is given with a strong inhibitor what you should do.
Hopefully,
this will be caught up in the computer system where you can search for only
strong inhibitors and that is where you put maybe three flags instead of one to
make a difference between all these interactions that will come up as a warning
when patients are--I think that is the first step. At least that is what we are trying to do to
minimize the trivial interactions and flag the important ones.
DR.
SHEINER: Not to dwell too much on the
technical side, but means are bad descriptors for distributions that are highly
skewed. This is probably what you have
got here. It depends on which way you
look at it. If you look at AUC increase
and it goes up twenty-fold, that is a huge skew to that side.
If
you just flip it upside down and say you are looking at the amount of active
enzyme or something like that, then that is going towards zero and that
actually compresses the thing.
So
there usually is some reasonable transformation, whether reciprocal or square
root or whatever, that will allow you to get a more symmetrical distribution
and then allow you to maybe make a little bit more confident statement about
what fraction of people are beyond a certain limit. That is sort of a very simple type of a
thing.
The
other point is progress can be made here because these are really population
issues. In other words, we study the population
and if we can know what the distribution of various isoforms of the enzymes and
so on are, and we can know what the distribution--perhaps this is a little bit
tougher--of the sensitivity of individuals if it varies.
If
it doesn't vary, if it is just a matter of this particular enzyme has this
particular inhibition potential from that drug, then maybe we can get a lot
from sort of these pooling data across multiple sources rather than having
every manufacturer have to go out and get his panel of people and go and do the
same thing over and over again.
DR.
HUANG: Just to add another point. We are starting to--at least for extreme
cases, we have started to put it in labeling; for example, Strattera, which was
talked about yesterday, or last week, actually.
In the labeling, we actually talk about CYP2D6 inhibitors effects on an
extensive metabolizer versus a poor metabolizer.
So
I guess, in the past, we just mentioned, it is a D26 substrate and with a 2D6
inhibitor, you may need to be aware of the adverse events and--we didn't say
dose adjustment. But, in poor
metabolizers, we do not expect to have an interaction. So I think this needs to be taken into
consideration. We have started to put
this information on the labeling so, at least in the extreme cases, where we
know that a poor metabolizer, you don't expect an interaction. That we are putting in.
The
subjects with intermediate metabolized activity, then you may see variable
interaction, extent of interaction. I
think we are starting to see this and I think this may be discussed more in the
later session. But at least we try to
address one aspect.
DR.
LEE: Just to follow up Dr. Sheiner's
suggestion. Are you suggesting that if
we see a sort of increase of AUC or PK due to an inhibitor we can verify the
distribution of the increase to a population PK type of analysis using the
pooled data?
DR.
SHEINER: No; I wasn't saying that. I am not exactly sure how you could verify
anything. I was just saying that when
you think about how you describe--let's say even in the label, how you describe
what you are likely to run into. I am
saying if you have a very skewed distribution, the mean is not a good
descriptor of what is going on.
It
is sort of like we saw yesterday with the QT interval. We can't get at the individual parts of the
heart and their conduction and their repolarization but the mean there is
really insensitive to the fact that you have got heterogeneity which is what
the issue there is.
It
is the same thing here. You have got heterogeneity
in the population as to how much enzyme they have got. So X amount of drug will be a lot of problem
for somebody but not much for someone else.
You want to find some way of A, estimating what is important, and B,
expressing it in such a way that people can understand it. All I am saying is taking the average may not
be what you want to do.
DR.
VENITZ: I think you have got a lot of
general comments back on drug-drug interaction.
Let me get back to what you guys what us to talk about which is 2B6 and
2C8.
Committee Discussion
DR.
VENITZ: The question put in front of the
committee is what our recommendations would be, as to committee support, given the state of the art
in our knowledge on 2B6 and 2C8. I think
you are primarily interested in in vitro substrates, in vitro inhibitors and in
vivo substrates and in vivo inhibitors.
What
is the committee's feedback or response to that question?
David,
do you want to summarize?
DR.
FLOCKHART: Just for 2B6, I think we have
a decent couple of substrates in vitro.
We have got efavirenz and we have bupropion. I think in some settings, mephenytoin is a
reasonable substrate probe as well. As
inhibitors, the only specific in vitro one, selective in vitro one--excuse
me--is thioTEPA that I am aware of. I
don't think we have specific inducers and I don't think we have validated in
vivo probes.
DR.
VENITZ: That was my conclusion, too,
listening to David. Any additional
comments on 2B6? I am looking at Dr.
Neuvonen. Maybe you want to summarize
what your recommendations would be with respect to 2C8 in vitro inhibitors, in
vitro substrates, in vivo inhibitors and in vivo substrates.
DR.
NEUVONEN: In vivo assay substrate, I
would recommend repaglinide because it seems to the most sensitive of those
compounds which are easily available. Of
course, rosiglitazone can also be used, but it may be not so sensitive a
marker.
As
inhibitors, I would like to use gemfibrozil even with great reservations
regarding its mechanism of action because it seems to be so potent. But trimethoprim is more selective and
actually I have no data regarding pioglitazone and rosiglitazone. They may be in the future more useful but
actually further data are needed.
DR.
VENITZ: Any additional comments by
anyone on the committee?
DR.
SHEINER: How do you usually sort of
probe for these things? We have seen
some exquisite experiments in which you have done area under the curve and
things like that which you can't argue with that. But, in a typical situation where you are
trying to--I am thinking again about gathering information on populations; what
do you do to decide whether somebody has or has not got a given enzyme or some
drug does or doesn't inhibit another one in a sort of a survey sense.
You
can't do intensive PK studies, crossover studies, in that many people. So what are the techniques you try to use to
decide what these distributions are?
DR.
FLOCKHART: I think there are techniques,
but they haven't been used a huge amount, Lew.
There are a number of not necessarily recent, but there are a number of
studies over the years where people have looked in large databases to look for
well-known interactions. I am thinking
of things like interactions between ACE inhibitors and potassium, those kinds
of things, the things that are fairly well documented, and looking in large
populations to see how real they really are.
DR.
SHEINER: How would you know?
DR.
FLOCKHART: If you have the mechanism
biologically understood, you can go into a large database like the Reagan Strafe
Institute database at Indiana and look at the number of people who actually
coprescribe those two things who actually get hyperkalemia.
That
kind of activity is valuable, I think.
We haven't done enough of it. But
increasingly, as we move towards being able to use databases like that
more--for two reasons. One is there are
more of them. Two is the data in them is
becoming more reliable. Three, I guess,
is they are becoming more accessible. So
I think those kinds of estimates are things that are not something that we talk
about or use widely, certainly in medical practice, at the moment but it is the
kind of data that really ought to be integrated into a doctor's thinking about
coprescribing drugs.
DR.
VENITZ: Shiew-Mei?
DR.
HUANG: Just a clarifying question. Dr. Neuvonen, you mentioned, during your
talk, that there are quite a few CYP3A substrates, that they are also CYP2C8
inhibitors. Are you talking about some
of the 2C8 inhibitors or just some of the substrates that we have not evaluated
as inhibitors?
DR.
NEUVONEN: If I remember correctly, there
was a study published in British Journal of Clinical Pharmacology some two or
three years ago where they showed that many of the typical substrates of 3A4
were inhibitors of 2C8 so that when they are used in vitro, concentrations
which were roughly five times the KM volumes, regarding the 3A4 enzyme, these
compounds caused nearly total inhibitor of 2C8.
I guess it was a paper by Ung et al.
I can't remember exactly.
DR.
HUANG: Thanks.
DR.
SADEE: I just have a general
question. When preclinical data are
being submitted, are all these P450s covered in the preclinical data that are
submitted to the FDA or is it mandatory now?
What is the status?
DR.
HUANG: I guess you meant nonclinical
human microsomal data.
DR.
SADEE: Right.
DR.
HUANG: For reaction phenotyping, for
metabolic pathway, in addition to the five critical enzymes, 1A2, 2C9, 2C19,
2D6 and 3A, most of the time, for reaction phenotyping, we also see 2A6, 2B6,
2C8 and 2E1 data. For inhibitors, the
five are the ones that we most consistently see. Sometimes, we also see 2B6 and 2C8.
For
induction, it is 3A is the majority that we look at. In addition, some of the 2C9 and 2C19. Increasingly we are seeing 2B6 and 2C8 in
addition to 1A2.
DR.
SADEE: So there is no guideline as to
what preferably would have to be presented?
DR.
HUANG: In the past, we have stressed
those five that I mentioned earlier because it constitutes 90 percent of
the metabolism of most drugs as metabolized by CYP enzyme. But, increasingly, the tools are available as
we discussed today when we have more specific probes and we have inhibitors in
vitro available. We are going to include
those in our guidance on what substrates, conditions, were studies so that the
study will be valid to be able to be evaluated.
However,
in vivo, based on today's discussion, we are probably not ready to make a
strong recommendation until we have a better idea. I guess some of the substrates, we might be
able to recommend, and some of the inhibitors, especially in light of possibly
inhibiting multiple pathways. So, ever
if they are nonspecific enzymes, they might be able to be useful in certain
conditions.
DR.
SHEINER: I have got to get back to
Greg's question. How is that going to
translate in labeling? What are you
going to say when you find that there is a possibility that lots of different
drugs taken together could make a big difference in the metabolism of something
else.
DR.
FLOCKHART: I don't think you are going
to do that. It is going to be
guided. So, for example, at the moment,
pick a drug, Versed, midazolam. We have
in the label that you see a big change with ketoconazole, erythromycin,
clarithromycin. That is totally
appropriate. It is the main metabolic
route.
But
what we are seeing here, really, is that increasingly companies, for good
reason, are coming up with drugs that avoid one isoform for genetic reasons and
for drug-interaction reasons. That is to
everyone's benefit, probably, because they have alternative routes when one is
cut down. But I think the next level of
sophistication here is really to be able to say, okay, I know this drug is a
3A, 2D6 drug and what happens if I put in ritonavir, which kills both
enzymes. That is the logical sequel to
Bob Temple's saying the worst interaction would be keto. Well, for that drug, the worse interaction
may be ritonavir, something that kills both.
DR.
HUANG: I just want to add that looking
at these interactions, some of them are multiple interactions, some of them are
specific to drug interaction. The
utility is at least twofold. One is to
help us in designing our study and to evaluate the safety database. For example, as shown yesterday, we look at
the most stressed system where the exposure would increase because of
multiple--right now, we are talking about one at a time--multiple drug
interaction.
So
what kind of exposure do we need to evaluate?
So that is what these interactions can provide us, and the other one is
the labeling that has been discussed where we have different degrees of
labeling depending on the severity of interactions. Sometimes, we contraindicate or sometimes we
modify the dose or dosing interval to accommodate a certain drug interaction.
We
have not given specific instructions when multiple drugs are given
together. Right now, it is still
individual drugs.
DR.
VENITZ: Any final comments on the
metabolic drug interactions? Mary?
DR.
RELLING: Just that, based on what Lew is
saying, the most important thing is to carefully describe what has been done to
determine which enzymes are involved in the disposition of the drug. We can't predict five years from now what
potent 3A inhibitors or PGP inhibitors or 2C8 inhibitors may come on the market
that we don't know about and we have to
trust pharmacists and physicians to keep educating themselves, to keep
providing public sources of what those inhibitors and inducers are. But you can't expect the manufacturer to list
all the drug interacting agents at the time the drug is approved. But you can expect them to carefully list
what has been tested and what hasn't and give a guesstimate of KMs or
affinities so somebody can come up with--a knowledgeable person can come up
with recommendations of how to avoid or modify drugs.
DR.
SHEINER: I really like that. So the dossier, so to speak, is about your
drug.
DR.
RELLING: Yes.
DR.
SHEINER: And not about all the other
ones.
DR.
RELLING: You are responsible for your
drug.
DR.
SHEINER: Right.
DR.
NEUVONEN: I would like to add to the
previous, that when studying the contribution of different CYP enzymes in
vitro, I hope that the substrate concentration used is as close to that in vivo
as possible because the contribution of different enzymes may be quite
different at different concentrations. I
think there have been some artificial data previously based on those kinds of
errors.
DR.
VENITZ: Final words on drug
interactions? Thank you.
We
are moving to our next topic and our last topic for today,
pharmacogenetics. I am going to ask
Larry to give us the introduction.
Pharmacogenetics: Integration into
New Drug Development
DR.
LESKO: Thank you.
[Slide.]
We
are in the home stretch talking about a related topic but still somewhat
different. I want to introduce the topic
of pharmacogenetics and integration into new drug development. This is actually the first public advisory
committee in which these issues, I think, have been discussed in a general way,
although we have had other meetings that have discussed specific pharmacogenetic
issues.
This
is really the beginning of a discussion on this topic. I anticipate we will have many more of them
within this committee and, perhaps, some others. So today is really a starting point to open
up the discussion of where we ought to be going with pharmacogenetics as it
matures in the context of drug development.
I
think of drug development as not only what a sponsor does during the research
phase in getting an NDA put together but drug development also includes the
regulatory decision stage as well so a lot of what we are talking about
encompasses that entire scope.
[Slide.]
I
mentioned yesterday that pharmacogenomics is one of the key areas in the FDA's
new strategic plan that came out in August.
As part of that strategic plan, there are some target goals for the
development of guidances related to this topic for the purpose of advancing
pharmacogenomics in drug development and its use in public health.
We
had a workshop last week on the first of these guidances that was released on November
1. It was called Genomic Data
Submissions. This DIA workshop was
intended to gather public comment on this draft guidance and also to raise
issues related to the integration of this information in drug development and
how it might be submitted to the FDA in one of various pathways depending on
the criteria that define it.
Dr.
McClellan opened up the conference, and this quote is taken from his
presentation which reflects the strategic plan and the interest that he has as
well as our Center Director and that is we need to speed up the use of genomics
to help make our medicines safer and more effective.
Part
of speeding that up is to provide guidance to the industry, particularly in an
area that is evolving where there is a lot of uncertainty as to how the FDA
views this data and how it is going to use it.
So this was the first of several guidances which are targeted for the
genomics area. Two more are targeted for
2004. One of them is a general
pharmacogenomics guidance which will touch upon the issues I will introduce
today.
[Slide.]
Pharmacogenomics,
or pharmacogenetics, is a broad area so I want to try to narrow the discussion
a little bit and thus I will define pharmacogenomics as a tool, a tool to
segment phenotypes based on genotypes.
Pharmacogenomics, in and of itself, doesn't necessarily cause bad things
to happen or good things, but it is a way of finding out information about
patients. What we do with that
information is, of course, what we want to discuss.
The
focus is on interindividual variability in pharmacokinetics. We can also talk about pharmacodynamics but
not for today. The problem is basically
one dose given to many genomes results in different degrees of variability and
different degrees of exposure; that is, the patients.
For
the purposes of today, let's define phenotype as an exposure metric--for
example, area under the curve--or pharmacokinetic parameters such as intrinsic
clearance, and let's define genotype as some inherited variation in
drug-metabolizing enzymes.
[Slide.]
The
problem is interindividual variability.
This is a major obstacle for effective therapeutics, as we all
know. This variability predisposes
people to risk. We give the same dose to
many patients. We have some that react
fine, some that have adverse events and some that don't react at all. So there is a wide spectrum of patients. Part of that is thought to be related to the
genetic characteristics that affect the metabolic activity.
It
has become quite common in clinical pharmacology to conduct studies routinely
during drug development to focus on the so-called intrinsic and extrinsic
factors that affect PK. These include
the well-known ones of demographics such as age, gender, ethnicity and race, the
diseases, hepatic and renal, and, as we just discussed, the whole spectrum of
drug interactions.
What
we do with the information is look at the potential need for dose adjustments
based on changes in exposure, usually, sometimes changes in exposure and
response. Then, based on that change in
exposure under the special-population situation, we recommend adjusted doses
that we think will provide exposure that is considered safe and effective.
Where
we have come to is that genotypes have become known to influence exposure and
these influences are as large, if not greater than, the factors that we
routinely consider in the clinical pharmacology area of drug development. I am talking about the factors that relate to
the alleles of the common enzymes that have polymorphic aspects of the drug
metabolism.
[Slide.]
This
is not necessarily new. Everyone here is
familiar with the well-known polymorphisms and drug metabolism. We discussed TPMT extensively in our first
two meetings and 2D6 is well known, responsible for a high percentage of the
drugs in the marketplace and 2C9, less drugs, but some significant drugs with a
high incidence of adverse events such as a warfarin.
So
the evidence is growing. There is more
and more information appearing in the literature on the importance of genetic
factors, both retrospective analysis and prospective studies. While all this is not new, what has changed
in the landscape recently is the potential that we have to deal with the
variability.
[Slide.]
Tests
for the cytochrome P450 genotypes have become more widely available,
potentially, in the future, FDA approved, and, if available, and if sensitive
and specific enough, these tests can be used as an adjunct tool, not much
different than blood levels of drugs for individualizing doses of drugs that
are substrates for these enzymes. The
value of this type of information is that, unlike therapeutic drug monitoring,
this can be done in advance of giving the drug as opposed to after
administration of the drug.
Likewise,
the evidence of clinical utility of these tests is increasing both in the
published literature. Oftentimes, years
back, it was retrospective but, more recently, in prospective literature. This is not equivocal evidence, necessarily,
and there is a lot of debate about what level of evidence underpins the
clinical utility. This is another area
that is still evolving.
[Slide.]
Related
to regulations, we have labeling regulations that talk about evidence that is
necessary to support the safe and effective use of the drug. This includes dosing adjustments in selected
subgroups of the larger population. In
any case, that labeling should describe this evidence and identify tests or
actions that are needed for the selection and monitoring of patients who need
the drug.
This,
if we interpret it in the context of pharmacogenetics, would also lead one to
conclude that a genetic test, if suitably validated analytically and
clinically, would be a valuable adjunct for label information.
[Slide.]
So
the problem that we have to solve, not today but in the next coming year, let's
say, is I think we need a systematic way of thinking about pharmacogenomics in
drug development; for example, a type of decision tree. When are pharmacogenomic studies important
based on some prior in vitro studies, let's say, of drug metabolism? What phases of development might this
information be efficiently and effectively gathered?
What
types of studies ought to be designed and conducted? How should these results be interpreted and,
probably most importantly, at the end of the day, how do we put these results
in the label and translate it for the benefit of practitioners and patients?
[Slide.]
One
example of a possible strategy, just to start somewhere; let's say we had in
vitro data that indicated a pathway of drug metabolism was the major pathway
for clearance of the drug and that pathway has known polymorphisms. One might think about determining the
differences in pharmacokinetics in the important genotypes in phase I healthy
volunteers and then, taking that pharmacokinetic information and assessing its
significance in terms of differences using some exposure-response relationships
involving biomarkers or clinical endpoints.
That
may be where things stop. Maybe there is
some significance, but one might think about including complete or partial DNA
collection in phase II trials and/or phase III trials in patients. One could design this collection as a
prospective sparse-sample strategy with formal population PK analysis looking
at genotype as a covariate as we have done before with other covariates in the
area of, for example, age or race or ethnicity.
One
can also look at retrospective analysis of genotype associations with clinical
and safety endpoints and then, from this data, collectively conclude that this
is or isn't an important variable in the drug-concentration response
relationship. There may be other ways to
gather this information but that is the purposes of opening up this
discussion. But this is one starting
point.
Lastly,
labeling products with the information; conceptually, it seems like it would be
similar to other special populations defined by other factors.
[Slide.]
Then,
finally, there are the questions that we want to put on the table for the
committee. The way we planned this
session is basically to begin to hear what the issues are and, thus, we have
asked the presenters to look at this issue from three different perspectives.
Dr.
Flockhart will look at it from his experience in academic research,
significantly in the area of 2D6 and some other areas. We have asked Dr. Hockett to come from his
experience with developing atomoxetine and what the issues were in that program
in terms of what we know about that. And
then, thirdly, we asked Dr. Relling to present a clinical view as a
clinician--a new drug came on the market that is a substrate for one of these
enzymes; in the future, what would you like to know about it.
With
those three perspectives, then, we hope we get the issues on the table for
discussion and the two questions that we have here, are the approaches
presented to study the influence of pharmacogenetics on exposure response
sufficient and appropriate. It may
actually be a premature question because we don't really have a lot of
approaches and it is okay with me if we end up just discussing the issues that
might lead us to answer that question in the future.
I
think the second question is important; are there criteria or approaches that
the agency should consider recommending to sponsors. Again, this may be premature but I think,
overall, if we have a good discussion on the issues surrounding the question
and the problem we are trying to solve, I think it would be very beneficial to
our thinking and, perhaps, we can come back to these questions at a later time
for more specific recommendations.
DR.
VENITZ: Thank you, Larry.
Let
me ask David to come back and take the podium and give us the academician's
perspective.
Academic Perspectives
DR.
FLOCKHART: I am going to talk about two
things, really.
[Slide.]
One
is a large picture of how we might approach this process and the second thing
is Shiew-Mei asked me specifically to talk about--this is pretty funny--2D6
while I was here. What I heard on the
phone was 2B6. So I spent a lot of tie
developing my 2B6 presentation before and I didn't realize she also wanted me
to talk about 2D6.
So, can you clean up your accent a
little bit.
The
other thing that Larry has not talked about and I think he does deserve a fair
amount of credit for, and the Office, in general, does, and that is for what I
think is a real kind of series of acts of leadership that led to the labeling
changes for the TPMT enzymes. That is
something we have known about for a long time, but the recent Committee on
Pediatric Oncology basic approval of what this committee would have
recommended, I think, is a real step forward.
Now
I think we have to approach other things and so 2D6 came up logically as a next
subject. I like to think about big
decisions like this in diagrams and some of you are aware of this, pyramids and
other things. I have tried to be a
little bit more organized this time and presented this way of making decisions
as a target, a circular target.
The
idea here is that you go from the middle out towards the wider world of
healthcare professionals prescribing and patients being treated. You start in the middle with a valid genetic
test which is really the basis after you have decided that there is a real
distinction, of course, that that test can make.
And
then I think you could argue that we may even need a guidance on this. I think there are a lot of things about a
genetic test that we assume but which are not written down in code and there is
a fair amount of confusion about.
Howard, among others, has educated many of us about how many snips in the
human genome are wrong and how many we haven't picked up.
I
think the characteristics of a genetic test and the series of hoops such as
genetic tests might have to jump through from a regulatory point of view are
important things that might be the subject of a guidance.
Outside
that, once you have that, there is obviously the correlation between that and
phenotype. Larry just really alluded to
this series of discussions. How do you
do that? There are lots of ways of doing
it. You can do it retrospectively. You can do it prospectively in a very highly
expensive and organized way or you can do it using random sampling. There are lots of efficient ways to do this. But which are the ones we trust and which are
the ones we think we should seal with you like the imprimatur of the FDA in
terms of a good way for a company to do a test like this. That might also be a subject for a guidance.
Then,
beyond a simple correlation of genotype and phenotype, there is the real world,
the real dirty world, of drug interactions, diseases, races, genders and really
large clinical trials. The genetic tests
that we come up with must be robust enough to survive in that environment. I think one might come up with
recommendations for how to do that as well.
I
am not sure this last one needs to be here.
This is economic assessment. But
it is something that is in people's mind all the time. It has been done for TPMT. It has been done recently, several times,
including I just saw an article this morning, yet another article, about 2C19
and Helicobacter pylori, Greg, demonstrating its economic effectiveness.
But
I think this is important to the people who are doing the testing. It is important to healthcare professionals
and it is certainly important, I think, to pharmaceutical companies, what is
the value of these tests in the larger picture.
That is also potentially a subject at least for discussion.
[Slide.]
A
way of thinking about this is--and this is an old diagram that I have just
reorganized a little bit. If you think
about the population treated with a drug--and here I have just got the Y axis,
really. This could be a
unidimensional. But this is a schematic
representation of a population treated, an average drug, where about a third of
people don't have a response. So this is
no response and this is a response on the upper side.
What
we are really doing here is coming up with a genetic variant that would divide
these people up one way or another.
[Slide.]
In
an absolutely ideal situation, you would have this, an ideal parameter
separation where the relative risk between the two things is huge. Unfortunately, there may be situations where
this is the case. I am thinking potentially
of hemochromatosis and a number of other situations like that.
[Slide.]
But,
in fact, in reality, in my experience, anyway, there is hardly ever--maybe I
should never say never, but this hardly ever happens and you are nearly always
dealing with a messy situation like this.
So it becomes important to have a parameter that makes this distinction,
that separates these two things.
I
think, myself, this is probably a disease-specific parameter. I say a disease rather than a drug or a
population because, for many, many diseases, there are separators already. I work in breast cancer. You can predict a person's response to
therapy for breast cancer with a large number of things; the stage of the
tumor, the grade of the tumor, the number of lymph nodes, the age of the
woman. We routinely put this into
regular clinical decision making in terms of what we are going to do with women
who have breast cancer.
A
genetic test that is going to improve on that has to survive in that
decision-making matrix. It has to be
something that will improve it. I think
it is not enough to say it would just survive.
It has got to improve it.
So
what do we do here? I am really just
putting this up for a matter of discussion
It is one thing to call it just statistically significant. The clinicians amongst us would say you need
to do more than that. It has got to be
clinically as well as statistically significant. But we spend a huge amount of our time just
testing for the p-value and really not thinking enough about more clinically
relevant statistics like the relative risk or, in fact, the absolute risk
between these two things.
As
clinicians, certainly as someone who teaches clinical pharmacology, I try and
encourage our residents and interns and medical students to think in terms of
absolute risk because it is a more valuable thing in many contexts and, indeed,
to think about the number needed to test or the number needed to treat. So the number of patients you would need to
treat, to come up with a significant outcome, or, in this case, the number of patients
you would need to test in order to come up with someone who really had a
significant difference on one side or the other; what is the parameter we
should use?
I
am not standing here saying we should use one or the other. I am saying we should have an intelligent and
informed discussion about how we do this.
I, personally, am biased towards thinking this is a disease-specific
thing and that, in breast cancer, I could give you the relative risk caused by
four lymph nodes. I could give you the
relative risk brought about by a woman being aged--having a stage 3 tumor.
I
know those numbers. Therefore, if I had
an equivalent change caused by a genetic test, I would think that might be
something valuable. Something that was
less than that would not be as useful.
[Slide.]
I
am going to change tracks completely.
That ends my general statements because Shiew-Mei asked me to talk about
2B6--I mean, 2D6. The specific question
that Shiew-Mei asked me to address was the question of distinction between the
extremes. So, I guess, in some ways, it
is related to the same thing.
[Slide.]
Just
to summarize very quickly about 2D6, we know it is absent in 7 percent of
Caucasians. Fascinatingly and
interestingly, it is hyperactive in 30 percent of East Africans including
Ethiopian and Saudi Arabians and a number of people in Spain. It ketolyses the primary metabolism of a
large number of drugs which is why we are talking about it, really, and is
potently inhibited by a large number of equally interesting drugs.
[Slide.]
This
is frozen? This slide didn't come out in
the handout? It is a big figure.
[Slide.]
Just
to make some points about this. These
are old data from the Swedish group.
Debrisoquine is a probe for 2D6 activity. This is the number of subjects. We can clearly distinguish these people
because they are two logs different from the mean over here. So poor metabolizers are, in general, a
completely separate phenotypic group.
There is a cutoff here. There is
also a cutoff up here and, for the very fast people, these are actually, I am
increasingly coming to believe, very distinguishable as well.
We
had someone recently who destroyed codeine at a rate, really, that was almost
100 times someone in the middle here. So
there are unusual people at the extremes out here but it is not really, if we
are honest about it--like, there is nobody in here. This is something like a thousand
subjects. So, inevitably, if you
increase this to a million subjects, there would be people in here who it is
hard to distinguish.
If
you are talking about 2D6, this is 7 percent of the population. But this is much more of the population. This is well over 30, 40 percent of the
population. There are people who are
intermediate metabolizers of one kind or another. So the difficult question for a company is
what do you do about these people. Do
you make any kind of dosing recommendation at all or do you just leave that
there.
Now,
the case for making any recommendation would be that there would be, if there
is a difference in pharmacokinetics that is real in this group, and secondly
that there is a large number of people in that group. What I am going to say is two things. I am going to say that the answer to this is
really sometimes it is worth it but not always.
[Slide.]
This
is from Michael Eichelbaum's data in a paper published with Esmeier and a
number of others in 1997. It is a very
bad slide, I'm afraid, but it basically shows that this is ultrarapid
metabolizers and poor metabolizers by genotype here. You can see that there are a group of people
who you genotypically predict to be in the middle but, nearly always, they
overlap with these people over here. So,
for this given genotype here, which is a star-1-star-1 genotype--this is the
old nomenclature; I'm sorry--it overlaps over here whereas this also overlaps.
There
are a group of people, the star-10s here, who are intermediate. But, certainly, when this was published, six
or seven years ago, there weren't clear ways of distinguishing this group.
Since
this was published, and I am missing the allele slide that I had, we have
really relatively ethnic-specific alleles, the star-10 allele among Asians--I
say relatively, because it is not absolutely.
You can pick up star-10 in Caucasians and you can pick up star-10 in
Africans, but it is a relatively Asian allele.
Star-17
is an African allele. Andrea Guideker
and Greg Kearns' group has shown the importance of star-29 in African-Americans
as well. So it is possible that it is
able to define people--it is possible now to define people more who are in this
group and we can discuss that a little bit.
[Slide.]
Now
here is the difficulty. These are also
data from the Swedish group. So this is
the number of functional alleles against nortriptyline concentration. You are simply looking at concentration on a
normal, not a log, scale against time.
So this is the number of functional alleles. A poor metabolizer would have a rate and a
half-life like this.
But
you notice that, if one allele is deficient--so if this would be a star-4
heterozygote, for example, someone who had one knocked-out allele, one
completely dead, nonfunctional, completely inactive half of the DNA and the
other is perfectly active, and that person has a very slightly different
pharmacokinetic profile from this person, but a very notably different
pharmacokinetic profile from someone who has two alleles knocked out.
This
is true for a number of drugs but not all.
So this is a situation where, if you change from two active alleles to
one, you see a significant change. This
is a substrate-specific thing, I believe, and there are substrates where, if
you go from two to one, you don't see much change.
[Slide.]
But
we, and others, have modeled these kinds of data. So, if you look at the number of functional
alleles at a low dose, 25 milligrams, you see people come into the therapeutic
range and, at a middling dose, you see people exceed the therapeutic range and,
at the 75 milligrams TID dose, you see people go way above the therapeutic
range and people who have two or three functional alleles fall nicely in the
therapeutic range.
[Slide.]
Now,
these kinds of data have been used by the Europeans to come up with dosage
guidelines. This is just a diagram from
the omega document on dosing nortriptyline.
So this is doses of nortriptyline recommended for different 2D6
phenotypes and genotypes in Europe. So
this, again, is the same debrisoquine diagram that I showed you, number of
subjects, rate of metabolism in the inverse.
The poor metabolizers are over here.
The
genetic variants are indicated in these cartoon forms. The X is a knocked-out allele, so that would
be here and here, and the multiple-copy alleles are over here. And the doses predicted from the model by the
European group--I am trying not to designate any particular person because
there were so many people involved in doing this--were a 500-milligram dose,
100 to 150-milligram dose, or 10 to 20-milligram dose. So this is a ten-fold difference, a
fifty-fold difference, from one end to the other of nortriptyline dose
according to the phenotype and genotype.
Obviously,
what these people have done here is they have made a recommendation in the
middle, even though I showed you a moment ago that there is not a huge
difference between the pharmacokinetics of nortriptyline in a heterozygote
compared with someone over here. But
they have gone ahead and done it anyway because this variation is so large.
So
the important question, I think, for us, is are there substrates where we
should do a similar thing.
[Slide.]
I
am just putting these questions out. So
two recommendations. These are really
both recommendations for discussion. In
the long-term, over the next several meetings, we should define and make clear
a disease-specific parameter that is a target for useful pharmacokinetic tests
and, secondly, for these three isoforms, at least, and I would recommend that
these be the first addressed, we recommend a genotype and phenotypic test that
defines this. We, at least, can get into
this discussion.
Personally,
I am not here yet. I haven't got this
really clear in my mind and I am not sure how we would recommend doing this but
it is an important thing that is worth discussing.
So
I will stop there and I think I might sit down as well. If there are any points-of-information
questions that people have, I would be glad to deal with that.
DR.
VENITZ: Are there any information
questions for David before we get into our discussion?
DR.
SADEE: With the heterozygotes, it is not
clear why they would be, necessarily, closer to the homozygous null carriers.
DR.
FLOCKHART: You are right. It is not clear. It is an observation.
DR.
SADEE: So, most likely, the ones that
one finds to have this, the other allele has something wrong with it, too, that
may be less well expressed.
DR.
FLOCKHART: You mean, it is not a
knock-out? The other allele is--
DR.
SADEE: No; one is a null allele and the
other one would be less well expressed in some fashion.
DR.
FLOCKHART: Conceivably an interaction
because of the absence of one allele, you mean.
DR.
SADEE: Yes; or the ones that you find
have relatively poor metabolism. It is
just there is another genotype that affects this that we don't know about.
DR.
FLOCKHART: What you are talking about
is, in this situation, where one allele is dead.
DR.
SADEE: Right.
DR.
FLOCKHART: And this situation is where
both alleles are dead.
DR.
SADEE: Yes.
DR.
FLOCKHART: So what--
DR.
SADEE: Then the gene, the allele that is
not dead, is somehow impaired and that may be a polymorphism that is not
described.
DR.
FLOCKHART: Oh; I see what you
meant. I'm sorry. We might be missing one here in this
particular setting. That is possible
there; yes.
DR.
HOCKETT: If you get more than a few
patients, that can't be the explanation because there aren't that many alleles
that decrease function a little bit that would give you that picture. So it is going to depend how many patients
went into the formation of this graph.
DR.
FLOCKHART: And how many alleles,
actually, because we have done so much on 2D6.
I mean, we are still beating up new alleles. We have 43, 44 new alleles. Really, if all of them were tested here, and
I don't know that they were, but the vast majority--these are people who know
what they are doing, I think, in general, Sweden--the vast majority would have
been tested here so it is possible that the average--that if this were one
patient, which it is, that that could be the case. But if this were a population average, and I
think you could plot a population average like this, it would be hard to
explain it that way.
DR.
VENITZ: Any other questions? Thank you, David.
Then
our next speaker is Dr. Hockett. He is
going to give us the industry perspective discussing a recently approved drug.
Industry Perspective
DR.
HOCKETT: Good morning.
[Slide.]
I
appreciate the chance to address the committee with an industry perspective but
I caution you, there is no way I can give you an overall industry perspective
so you have to take this in light of what this would be consideration of one
person at Eli Lilly.
[Slide.]
Like
David, I am going to start off with a few general comment. I am actually going to get on my soapbox for
a couple of slides. I think there have
been a couple of difficulties for the field in pharmacogenomics and I will go
through those.
The
title is a case study of Strattera. I
will talk a little bit about Strattera because that is the most recent example
of where a genetic test has been put in the label, at least a mention of one. Then I will talk, again, about some more
generalized thing about pharmacogenomics and how we think they are going to
apply and what I think, from my perspective, would be nice to see as far as
CYP2D6 if it would have been required in the Strattera label.
The
first problem that I think the industry has had and, thankfully, it is getting
less and less as we progress, is illustrated on this slide where there are far
too many definitions of what we are talking about; pharmacogenomic,
pharmacogenetic, applied genetics, applied genomics. I reminds me a little of the
"po-tay-to"/"po-tah-to" argument as to how you pronounce
this.
I
have seen the slide of David's that now says that pharmacogenomic and
pharmacogenetic are actually just a two-snip change of the same
terminology. I didn't steal that from
him but he has done that. In fact, you
can see several groups. EMEA, which is
the FDA equivalent in Europe, has got a very broad definition. The PWG, which is a loose consortium of
pharmaceutical companies and biotech groups called the Pharmacogenomic Working
Group, actually has split the definition.
Why they have chosen, and I am with this group, to split hairs is still
unknown to me. It is not very
helpful. Even at Lilly, we have
subdivided this. In pharmacogenomics, we
have a little bit narrower view. It
really means we want to understand the genetic influences of how people respond
to drugs.
None
of those are right or wrong, but you can say it leads to confusion in the
field.
[Slide.]
The
second problem that we have had is pharmacogenomics has been hyped, I think, an
overamount in the field. We are not
going to have a choice. We are actually
going to have to do this. The field,
pharmaceutical companies, will be dragged, kicking and screaming if we don't
help lead the way.
I
illustrate this from this U.S. News and World Report that actually fell on my
doorstep in January of this year where the cover of this said, "This drug
is for you." There have been
several magazines that do this.
Interestingly, if you open up this and look at the article, the gist of
this was that we are all going to run around with our human genetic sequence on
a card about the size of a credit card.
That will allow physicians to figure out which diseases you are going to
get, which drugs he can give you to prevent those diseases you are susceptible
to and, if you get a disease that wasn't predicted, what drug.
Boy,
that may happen. But it isn't going to
happen anytime soon. There are multiple
problems, not the least of which, how much is it going to cost me to sequence a
single person. The first time we did it
was several billion. We are probably a
log-fold or two less than that now, but, even if it was a million dollars, how
many of us are actually going to have the sequence done.
Second
off, even if I could sequence everybody in this room, I don't know how to
interpret all the variation yet. There
is not enough data for me to understand disease susceptibility versus drugs.
In
fact, I have put a collection of my favorite hyped sayings for pharmacogenomics
here. I am not going to go through
those, but some are rather interesting such as, "Applying pharmacogenomics
to drug development will cut cycle times to 1.5 to 2 years." I don't see that ever happening. I just think that is not going to be true and
I think we are deluding ourselves.
[Slide.]
What
this combination has done is what I would like to illustrate on this
slide. This is, in applying new
technologies, you have this gentleman with the telescope and let's equate that
with pharmacogenomics. He hasn't got
his eye on the prize. He is looking in the wrong place. I think this has deflected what we should be
talking about in pharmacogenomics, when he has missed the comet over here in
the sky.
This
comet, I think, for pharmacogenomics, is developing new genetic biomarkers that
will allow us to predict how people are going to respond to drugs, not we are
going to change cycle times, not that I am going to be able to predict
everything. But, in certain instances,
we are going to develop specific biomarkers that are going to help us do it.
[Slide.]
Now
I say this because at Lilly I sat down with my colleagues and we developed a
list of how we are to apply genetics to drug development. We really apply that in three areas; in the
discovery arena, in preclinical toxicology, where we give these drugs to
animals and try to make sure that they don't destroy a whole bunch of organs
when they then go into humans, and then in the clinical side.
You
can see there are lots of different things but, in reality, we have two key
activities and two key activities only.
The first one of these is to identify and understand targets. We want to use genetics to try to figure out
where there is the next available drug target for an unmet medical need. Then the second one is to develop human
biomarkers where I can actually predict, then, who should be on a particular
drug, either for a positive reason--they are going to have efficacy--or a
negative reason--to avoid toxicity or adverse events. That is what we are going to talk about.
[Slide.]
What
I have listed here, then, are the broad categories where genetics is going to
be applied in medicine currently. We
have two big areas called disease-susceptibility biomarkers and drug-activity
biomarkers.
Now,
the disease-susceptibility ones are those that would predict you are going to
come down with a genetic disease. You
are familiar with several of these, especially under the single disease genes
of Mendelian inheritance. This is where
I think I would differ with David when he said the absolute distance between a
genetic event and a response never happens.
It actually does in the Mendelian inheritance like sickle-cell anemia.
If
you get two copies of the disease, you have the disease and, if you don't, you
don't. But, other than that, he and I
agree precisely. However, in complex
diseases, that is much less the way it is.
If you take Alzhemier's disease and Apo4, it has got a fairly large
relative risk but it does not separate the population at all, and we will come
back to that.
Then
the drug-activity biomarkers which some would call the true pharmacogenomic
biomarkers. This is where I think, as a
drug company, we need to spend all our time.
I have put the one in green that we are talking about today those things
that happen when you have defects or variants in metabolic enzymes and that
leads to changes in PK profiles and can lead sometimes to profound toxicities.
[Slide.]
I
have put a list of things on here where we, as a drug company, would choose to
include genetics in drug development.
Contrary to some prevailing opinions in at least the lay press, we don't
like to give drugs to people who are going to respond badly. It is not very cost effective for us to have
adverse events and severe toxicities. So
we are very much in favor of trying to identify those individuals and keeping
them off our drugs.
We
may get into the discussion are we willing to subdivide our market, et
cetera. That is actually an entirely
different topic. But you can see we are
planning to apply this very early in discovery and all through clinical
development; phase I studies of a particular type, mainly in the PK variety,
Phase II and III if we can use to figure out who is going to respond either
positively or negatively to our drugs.
[Slide.]
For
Strattera, it is primarily metabolized by CYP2D6. You can see there are profound differences in
the plasma clearance, a ten-fold difference if you are poor metabolizer. In fact, the AUC has got a ten-fold
difference, ten-fold higher in this case, if you are a poor metabolizer and the
half-life is significantly extended.
Obviously,
we were interested and concerned about this.
Did this lead to safety concerns or just did it have tolerability or
efficacy issues. That, obviously, the
interplay between those things, would have profound implications for the
label. So, if it developed a very severe
toxicity, it may become a label requirement.
If it is simply a tolerance issue, it might not be. And you will see that is, indeed, what
happened.
[Slide.]
When
you look at the clearance of Strattera, and this is the number of patients, and
the plasma clearance here, this very much looks like the metabolizer status
that Dave showed on one slide and I am going to show in just a minute where you
have got the poor metabolizers down here in black. You have got the extensive metabolizers here
or the wild-type variants and then the ultrametabolizers here. It looks very much the same for Strattera as
it does for any kind of drug.
[Slide.]
I
am going to show you just one slide of data.
It came from a single study. It
is the best data that I think illustrates the point. We did some initial clinical pharmacology
studies to look at what the maximum dose was.
We looked at some CYP2D6 genotypes obtained under double-blind
conditions. Therefore, the clinicians
are now going to start patients on a dose of the drug not knowing what their
genotype is. Then are then going to adjust
the dose based on toxicity, tolerability and efficacy.
In
the end, then, we are going to compare EMs to PMs and see where they ended up
and where there are large changes in the ultimate dose they were given for
efficacy, toxicity and tolerability and were there any differences between EMs
and PMs.
[Slide.]
So
that is what happened. It is illustrated
on this slide where you can see the extensive metabolizers are in green, the
poor metabolizers are in purple. The
bottom is weeks of therapy and the Y axis is the mean dose in milligrams per
kilogram per day. You can see the
comparison between EMs and PMs is essentially there is no difference.
So,
without understanding EM to PM differences in prescribing these drugs, they
actually ended up on the same dose which means we haven't got a profound
toxicity problem with PMs in Strattera.
[Slide.]
To
summarize several different kinds of studies on this slide, there were some
adverse-event discontinuations in all studies.
In fact, poor metabolizers had a slightly higher level than extensive
metabolizers except they were based on insomnia and irritability not on
profound toxicity. So, in the end, what
we really had was a tolerability question and not a safety question.
There
was a slight hint of efficacy increase in PMs especially on an ADHDH response
scale compared to EMs, but we didn't have enough patients in there to make that
terribly profound and, obviously, there weren't enough patients to affect the
label.
[Slide.]
So,
in negotiations with the FDA, CYP2D6 was put in the label. In fact, it occurs seven times in the
Strattera label in the Pharmacokinetics Section, Adverse Events Sections,
Drug-Drug Interaction Sections and the Laboratory Testing Section. But it is not a requirement because there is
no profound safety issue dealing with CYP2D6 in Strattera.
Here
is one of the verbatim quotes. Actually,
this has been mentioned already today where it talks about the incidence of
poor metabolizers, et cetera, as well as having to pay attention to the
alternate drugs that may induce a poor metabolizer status.
[Slide.]
Obviously,
as far as Lilly was concerned, that is almost a non-event in
pharmacogenomics. It is in our
label. We were happy to have it
mentioned in the label. For medical
reasons, we don't mind people testing.
But it didn't make any sense to require it because there wasn't a
toxicity issue and we agreed and we came to terms.
Obviously,
that doesn't give you a whole lot to talk about and so I am going to expand
this a little bit in how do you define PM status and how actually, if you do
have one that is required, would you put it in the label.
For
some of the P450s, it is actually pretty easy because there are a couple of
alleles. Dave has already alluded to
CYP2D6. It is more problematic. There are actually 44 alleles defined, as he
already said. This is a typo. Actually, there are 21 alleles that have been
defined that have absent activity. The
vast majority of those are at such low frequency that they probably shouldn't
be routinely ordered and we will come back to that in just a second.
There
are at least two that are classified as decreased or intermediate alleles,
star-10, star-29, star-17 and then a duplication exists in this where you can
have more than two copies of the gene, et cetera.
[Slide.]
All
of that leads to some problems. In fact,
as Dave has already alluded--and he and I didn't talk about our presentations
beforehand and they ended up being remarkably similar--you have vast
differences in ethnic groups.
Here
are Caucasians that have a 5 to 7 percent incidence of poor metabolizers. You have Asians where the poor metabolizers
are actually less than 1 percent. But
then they have a significant number of intermediate metabolizers. In fact, there can even be differences among
Asian groups.
And
then there are a bunch of ethnic groups that we don't have any data, or at
least there is no published data, on what this means. What this is going to come down is you are
probably going to have different recommendations based on different ethnic
groups and different alleles that need to be ordered if you are talking about a
different ethnic population.
[Slide.]
Just
like Dave, here is my requisite phenotype/genotype slide. It is rather complicated but I think there
are about three or four important points to make on this slide. On the bottom, here, is the metabolic ratio
which, in this case, is a dextromethorphan/dextrorphan ratio. On the Y axis is the genotype under the star
allele nomenclature. Here, there are
three, or the amplified status, two, one and no functional alleles.
Then
you have got the designations here of where the ratio is in relation to the
genotype. The first important point, as
Dave has already pointed out, there seems to be relatively good separation of
poor metabolizers. I have seen at least
a dozen or eighteen different studies that show the same kind of thing. It is relatively easy and there is a decent
phenotype/genotype correlation for poor metabolizers.
For
the rest of these, there actually isn't, in my mind, a very good separation, in
fact, if you have got one allele. There
is a huge overlap if you have got two functional alleles, at least for
dextromethorphan. Obviously, this kind
of decision has to be data driven and, if you get additional data, especially
with different drugs that separate them, that is true.
Even
if you look in places where they have only one functional star-10 allele,
which, in this case, is this star-4, star-10, where they have three patients
here, those people are clearly not over here in the intermediate. They are well within the
extensive-metabolizer status for this drug.
So
I would agree with Dave. It is not very
clear. Then, if you look under the
ultrametabolizers up here, I think, for this case, they have such an overlap
that it is not useful to distinguish between those two.
[Slide.]
Obviously,
then, you have a decision; are you going to require a phenotype or a
genotype. I have put just a few things
up here. It is not exhaustive. There are some advantages to going to either
side. From a drug-development
perspective, I would prefer a genotype.
The reason for that is I can measure it at once, as Dave said, before I
give any drug and I can actually measure a bunch of alleles at a time and get
more than one drug, or more than one metabolic status for one particular enzyme
out of this.
In
fact, we are developing a chip at Lilly where I am going to be able to look at
120 different genes all at one time for a relatively inexpensive cost and,
obviously, then, we are going to prefer to do that kind of thing.
[Slide.]
I
have just a couple more slides and then I will end because I think I am getting
close. There are, then, a bunch of other
considerations that come into play when you decide to do a genetic test. I can't do justice to this topic. It is probably worth an hour's presentation
in and of itself.
But
I am going to touch on two of them, and those are the first two on the list
here. The ethical, legal and social
implications of this can be rather profound.
It really has to do with the population's reticence at doing genetic
testing and their fear that something bad is going to happen to them, like
insurance is revoked or they are going to be labeled in some way if they do a
genetic test.
For
this reason, I am in favor of having something like a metabolic enzyme be one
of the first tests that are propagated here because it doesn't have the
disease-association status that some other things such as complex disease would
have and will potentially be swallowed by our public much easier than a
different kind of test.
[Slide.]
Obviously,
that is a profound thing that we have to get over. We also have to make sure that we educate
them properly and try to get rid of the hype for what this can do and talk more
about what it actually is going to be practical to do on that kind of scheme.
[Slide.]
Then
the last one is the utility of the information and biomarker. Once again, these are very similar to the
slides that Dave already showed. This is
the best case; if I have got a genotypic variation and a response, I get
absolute discrimination between the two.
I agree, that almost never happens.
We
will be lucky if we can get them that have this kind of separation. This would probably be acceptable. If they are like this, I don't think they are
even going to be instituted or accepted.
The example we have, I think, the best example we have for where it is a
poor separation is the Apo-E4 variant that causes at least 50 percent of
Alzheimer's disease in the Caucasian population. But it has such poor separation between those
that are going to get Alzheimer's disease and those that are not, that I don't
know what to tell the patient if they come down with an E4; you are at slight
increased risk to get Alzheimer's but certainly not guaranteed. That kind of test, obviously, is not going to
be very widely accepted.
[Slide.]
The
last slide I have is CYP2D6 recommendations.
I think the PM genotype predicts the PM phenotype in roughly 99 percent
of cases. That is at least shown very
well in two very large studies that have been published. Since there are 21 alleles that actually
cause a null phenotype, you would think that is very challenging but, if you
look at these very large studies, they do it with only about five or six. Those are the most frequent ones that are
found in these populations. Here is the
listing of these more frequent alleles that pick up about 99 percent of this.
I
think, to avoid confusion, the FDA should specify that you can do both
phenotype and genotype as acceptable methods for defining this PM status, but I
think this should include a recommendation for what is minimal genotyping, a
minimal number of alleles that would be acceptable for that kind of genotyping,
similar to the list that is supported in the literature.
I
don't think the genotypic designations of UM, IM and EM have--they have
distinguishable phenotypes on a population basis but not on an individual
patient basis and, therefore, I am not actually in favor of indicating them by
current data. The important point here
is current data. If we generate specific
instances where you have a separation between the two, obviously, it has to be
a data-driven decision.
Then,
recapitulating what has happened with the Strattera label, genotyping for these
mutants is warranted only when a compound's margin of safety is exceeded in
poor metabolizers and, if it is, then I fully am in support of it actually
being a requirement in the label.
[Slide.]
The
last one of these things I just have is we are all in favor of getting the
right targets, the right drugs, into the right patients. Within our education program, though, we have
to make sure we convey what we think is the appropriate time line for
this. Unfortunately, my guess for this
appropriate time line is after I am done, actually, practicing in this
field. I think it is going to be dozens
of years before we get to that ubiquitous type.
With
that, I will stop, take general questions, if you like, but I think there is
going to be a discussion in the end.
DR.
VENITZ: Thank you, Dr. Hockett.
Any
specific questions, informational questions, about his presentation?
DR.
FLOCKHART: Just one question, Rick,
within an excellent presentation. But
the graph you showed of Strattera, the population-distribution graph, you had
shaded the UMs, the ultra-rapid metabolizers.
DR.
HOCKETT: Yes.
DR.
FLOCKHART: How had you defined
them? There was a big overlap, but how
did you define them?
DR.
HOCKETT: Whether they had three or more
copies of a functional allele which had been the star-2 allele. That is how they were defined. I have seen descriptions of thirteen copies,
but we never saw anybody over four copies.
It was always three or four.
DR.
FLOCKHART: That is one family in Sweden.
DR.
HOCKETT: Yes; right. Exactly.
Generally, I don't see that number.
It is usually just three or four, it appears.
DR.
KEARNS: Rick, when you did your
presentation, you had a slide that suggested when Lilly might include
pharmacogenetics in--did you find, in the PK data for Strattera, that having
2D6 genotype was useful in examining your PK data?
DR.
HOCKETT: Yes. It clearly helped distinguish who was--we had
a very high correlation between the genotype/phenotype. So when there was a poor metabolizer by PK,
it came up poor metabolizer by genotype as well.
DR.
KEARNS: So do you think it is reasonable
and, I guess, where I am going here is in pediatric studies, in particular,
where the numbers of subjects in a PK study may be smaller then in an adult
phase I or phase II, that, when there is a drug that is metabolized by a polymorphically
expressed enzyme, having that genotype data, assuming it does correlate with
phenotype, can be useful in separating out, perhaps, is there an age effect on
the disposition of the drug.
DR.
HOCKETT: Let me answer that, or respond
to that, in two ways. We are developing
a program at Lilly where we are going to be doing metabolic-enzyme and
transporter testing out of every phase I patient who comes through our clinical
trials. That will include pediatrics
because we think it will help us understand the PK.
The
only hesitation I would have is in definition of what useful means. I think there is going to be a scientific
useful and a drug-development useful.
Generally, we don't have enough patients with enough PK outliers to know
precisely what is going on and say, with absolute certainty, that it is due to
a particular genotype.
We
don't necessarily need that to understand if we have to worry about it in phase
II and phase III. So if you allow me
that distinction between absolutely scientifically proving and then figuring
out what we have to follow in phase II and phase III, then I would agree that
it will be useful for us to understand and have the genotypes on these
individuals.
DR.
SHEINER: I am not questioning the
conclusions you drew about the drug you spoke about where you found that this
difference in metabolism didn't reflect in the difference in outcome, but I do
want to discourage the use of the design wherein you conclude that some genetic
difference or anything else is not important because you find that physicians
ultimately don't wind up adjusting doses differently in the two groups.
The
medical profession has a long and glorious history, not only of not noticing
what harms they do but actively promoting harmful therapies. So I don't think that is a sensitive way to
design a study although I am sympathetic with the notion of saying, what are
the practical consequences as opposed to the sort of theoretical ones. But I think we can probably come up with a
better design to try to see whether something actually makes a difference than
that one.
DR.
HOCKETT: Point taken.
DR.
HUANG: Either you are going to do a
prospective study or retrospective genotyping if you are going to have another
2D6 drug with what we know about, the more alleles that we know are null
alleles, which the assay may not be available before. What would be your minimum alleles that you
would like to test in order to conclude that the genotype may not have an
effect on your adverse events.
DR.
HOCKETT: Do you mean in 2D6? I would put it at six or seven, which is what
we typically measure, although, by the middle of next year, I think it will be
a moot point. The chip we are going to
build is going to test 40 or 42 alleles for 2D6 and we won't have to worry about
that.
DR.
HUANG: Even those that are available,
because I just wanted--because not all chips have all the alleles. What are the essential ones on based on, in
your opinion, expert opinion?
DR.
HOCKETT: Which is available? The ones that were listed--
DR.
HUANG: It doesn't matter, available or
not. What are the key ones?
DR.
HOCKETT: The key ones are the ones that
are most frequent that you are going to see.
So, in 2D6, it will be three, four, five, six, nine and you might add a
couple of others like 16 or 15. But it
is really a frequency question. So we
typically do about six or seven. Those
are the ones that I would continue to really look at.
DR.
HUANG: Because you cited two large
studies. I assume they are mostly a
Caucasian population.
DR.
HOCKETT: Yes. The poor metabolizer status in things like
Asians and African-Americans, we will add one or two alleles depending on those
although, for CYP2D6, right now, I don't think an intermediate status is--and
the poor metabolizers for Asians are basically the same alleles as Caucasians
but just less frequent.
So
that is why I say it is six or seven. We
would add a 17 or a 21 for African Americans or Japanese and then those are the
ones that we frequently look at. We are
still running about a 99 percent genotype/phenotype correlation in everything
we have seen.
DR.
FLOCKHART: Could I just amplify that a
little bit because this is an important point.
We routinely add, on the basis of ethnicity--we don't have your chip
yet, so we conserve our resources by looking for star-10 in Asians and star-17
and 29 in Africans.
There
is a problem with that in thinking about this because we can't really separate
those phenotypes yet. So a
star-17/star-17 homozygote strictly is not distinguishable in most studies of
most drugs from an extensive metabolizer.
Key
in this decision-making algorithm is whether there is a gene-dose effect. If there is a really clear intermediate
group, and I think there probably are drugs where that is the case. Tamoxifin is one of them where you do see
that. But, in general, I think that is
going to be a really hard thing. Whether
we actually recommend it, I think, depends on whether there is a phenotypic
difference.
So
I think, in general, I would agree completely with Rick. You need relatively small, five or six, to do
it. But I would also agree with Rick in
that the point is going to be moot in a couple of years when we will have lots
of ways of doing it.
DR.
VENITZ: Wolfgang?
DR.
SADEE: I do come back to the issue of
the heterozygous, even the patients with two "normal" alleles. The spread is so large that it is, in some
cases, convenient to say they are poor metabolizers and intermediate
metabolizers. But half of the
intermediate metabolizers are very close to the poor metabolizers and the
ratios that you can see here in the genotype and phenotype plots are such that
one would worry about a fairly large percentage of the patients having a very
slow metabolism even though they are not classified as poor metabolizers.
So,
again, it would appear that there are yet unrecognized polymorphisms probably
in the promoter regions and other regions that contribute to this or whatever
else factors contribute to that. So, in
some cases, it may be useful to just say, here is the group of poor
metabolizers and those are going to be the only ones who are at risk. But that may be few cases because the
intermediate metabolizers may have such a poor metabolism that they are also at
risk.
So
how do we deal with that?
DR.
HOCKETT: The one thing that is not
contained in the genotype/phenotype graph I showed you was a reproducibility
among a series of individuals. So you
are dealing with a single determination here.
I would have bet that there is a fair amount of variability within the
group that you can swap them from one position to another within their own
distribution graph.
Therefore,
I don't know how to interpret those that are close to poor metabolizer status
as to whether or not, if you measured them repeatedly, they are always in that
position. I would bet they are not, but
Dave might be able to answer that question because I have never seen that kind
of data.
DR.
SADEE: I think that is a key question
because, if they are just in the same position, then this is some intrinsic
factor. If that is extremely variable,
then all bets are off.
DR.
HOCKETT: As long as the variability
doesn't flip them over to the poor metabolizer on the other side of the ratio.
DR.
VENITZ: Larry?
DR.
LESKO: Rick, I wanted to ask about the
early study in the clin-pharm area. Was
this study done by enrolling X number of subjects and then retrospectively
looking at their genotype to figure out the difference in pharmacokinetics or
was it prospectively enrolled to get suitable numbers in each of the genotypes
that you were interested in.
DR.
HOCKETT: No. Every study that was done with Strattera for
genotyping was done retrospectively. We
did collect some things prospectively, but we kept them double-blinded to try
to answer the question in a different way.
I should say that there were a couple of late phase III trials where
they separated the individuals based on poor metabolizer status, but the early
stuff was all done retrospectively.
DR.
LESKO: Do you think that is the most
efficient way to do it?
DR.
HOCKETT: No, but that was our first
foray into one of these drugs that was going to be necessary. I think we have learned a fair amount. I think we would change our approach
slightly.
DR.
LESKO: What do you think would be more
efficient?
DR.
HOCKETT: This is going to open up
another can of worms. I think,
prospectively, it would be, especially if we find that there is a toxicity that
we have to identify or deal with with poor metabolizers. Then we have to gear up to make sure we get
an FDA-approved test when our drug is released, is the most efficient, because,
for us to be able to sell a drug that requires a test, at least my
understanding is you are going to require at least a fair amount of work going
down the road to an FDA-approvable assay for that to happen. That has, then, got to be done in
parallel. Otherwise, I can't sell my
drug.
DR.
LESKO: I was sort of coming from another
standpoint, the increased cost of screening people to get suitable numbers of
genotypes versus just sort of increasing the enrollment in a study and hoping
that the breakout occurs--
DR.
HOCKETT: Yes; it is far most
cost-effective to screen people even if it is $300 or $400 than to enroll
them. The average cost in most clinical
trials is about, what, $10,000 a patient to carry them through a clinical
trial. So if I can screen a bunch to
keep that number down, I am much better off.
DR.
VENITZ: Thank you, again.
Our
last presentation for this meeting is Dr. Relling. She is going to give us the practitioner's
perspective for pharmacogenetic testing.
Practitioner Perspectives
DR.
RELLING: Good morning.
[Slide.]
I
think it has been implicit in what we have all been saying that obviously there
are some drugs where the therapeutic range is so wide we don't need to know
anything about how to prescribe them and we are willing to give a very high
population dose to everybody in order to achieve a high probability of efficacy
and a low probability of toxicity, and that it is for drugs with narrow
therapeutic ranges.
Of
course, anticancer drugs definitely fall in this range where the dose that one
needs to achieve a reasonable probability of efficacy is so close to the dose
that achieves serious toxicity that anything that we can do to help us to individual
doses in any given patient is something that we would try to have.
[Slide.]
So
let's go ahead and make the assumption that getting the right dose of the drug
for the disease being treated is important.
Of course, sometimes, that can be true, but there may be other
approaches to titrating the dosage besides doing something like genetic
testing.
So,
in cases where that might be problematic is, of course, the probability of
response and the adverse effects should be related in some way to drug exposure
and titrating dose may not be optimal.
Either the disease would be too serious to risk a period of
undertreatment, and I think the this we just heard about, for example, ADHD,
might be a disease where it is not so serious if the patient goes a few weeks
with a suboptimally controlled disease whereas there are other diseases where
spending even a few weeks at suboptimal control could compromise overall
long-term outcome, that the adverse effects are so serious that it is not
ethical to risk then and that you are really bound to do whatever you can to
adjust the dose as accurately as possible from Day 1 or that the response or
the adverse effects are delayed to too difficult to monitor.
Too
difficult to monitor, for example, might be something extremely expensive or
extremely invasive, Swann-Ganz catheters or some implantable device that just
wouldn't be reasonable for following patients long-term or that, really, there
is nothing that you can monitor while you are seeing the patient week after
week or month after month to give you a clue as to what might be going on with
long-term adverse effects.
Again,
we have, in cancer, got examples of that that comes from our association
between the cumulative incidence of a very late adverse effect, the development
of irradiation-induced brain tumors whose onset didn't occur until five years
after the start of radiotherapy, so that was over six years after the start of
treatment for acute lymphoblastic leukemia that was related to a single genetic
polymorphism and a single gene, this TPMP or thiopurine methyltransferase gene.
So,
obviously, there is nothing that would could monitor during this period of
therapy when patients were receiving their thiopurine daily for two-and-a-half
to three years that would give us any clue that the patient would ultimately
develop a life-threatening secondary brain tumor. So that is an example of a late effect that
we need something earlier to monitor to figure out how to adjust doses.
[Slide.]
In
the diseases that we treat at St. Jude, the most common pediatric tumor is
acute lymphoblastic leukemia. I think
some of the phenotypes that we monitor in this disease are illustrative of how
we have to go about monitoring therapy.
So ALL is treated with, as I said, two-and-a-half to three years of
almost daily chemotherapy with anywhere from five to eight drugs almost all of
which cause myelosuppression and those patients are monitored weekly for their
blood counts. Myelosuppression is
something that we can monitor and sometimes make dose adjustments in therapy to
prevent that myelosuppression, at least in the following week or ten days.
Vincristine-induced
peripheral neuropathy is another example that has a relatively short onset
adverse effect. It is possible to adjust
the doses of vincristine to try and avoid that adverse effect as patients are being
treated. As we start going out, the
onset starts getting longer and the relationship to therapy more
complicated. So the use of
glucocorticoids like prednisone and dexamethasone have been associated with the
development of avascular necrosis but exactly when it happens, what its onset
is, what the best way to prevent it is, is not clear so that now we are left,
when a patient has symptoms or MRI imaging indicating vascular necrosis, we cut
the dose or we stop the dose but we have no idea if it is right thing to do in
terms of long-term overall outcome of that disease and cure.
Methotrexate
neurotoxicity can be quite delayed.
Sterility, long-term obesity might be five, six, ten, fifteen years
after the start of therapy. Ultimately,
whether the patient is cured is a decision that can't be made until you are at
least five years from the diagnosis of the disease and the development of
secondary tumors is also one that is three, four, five, six, seven years out.
So
monitoring therapy during the period of treatment isn't feasible in this case
and having anything to help us adjust doses prospectively would be worthwhile.
[Slide.]
Also,
to make the point we are all making, that we recognize this has to be made in
the context of other factors that we know affect drug pharmacokinetics and
pharmacodynamics. So, as there are some
drugs for which renal function might really be the most important determinant
of exposure and it is likely that there are not strong polymorphisms, for
example, in drug-metabolizing enzymes that could be important but whatever the
environmental or nongenetic influence on drug disposition, it does have to all
interact with the patient's constitutive genetic state.
[Slide.]
What
I have been struck with is the conversations we have just been having is we are
focusing where the light is shining. We
are focusing on the polymorphisms that we already know are important, like
CYP2D6 and TPMT. But I guess I am a
strong believer that I do think we will discover additional genetic
polymorphisms in the next ten, twenty, thirty years that we currently have no
idea are important, so that to make decisions about drug development based on
phase I studies doesn't seem to me to be an option. There has to be DNA collection throughout all
phases of drug therapy.
I
have been told that Dr. Sheiner is someone that likes us to think in a sort of
organized way about decision-making so I am trying to use this as a little bit
of a platform for what do I want to know, how sure do I need to be and what am
I willing to assume as a clinician who wants to have prescribing information
for pharmacogenetics.
[Slide.]
I
want to know whether specific genetic polymorphisms influence the probability
of response or adverse effects. Whereas
there can be twin studies or family studies that indicate a genetic component
in drug response, I think we are talking about wanted to identify individual
genetic defects that may be problematic.
So we are talking about specific proposal polymorphisms.
[Slide.]
And
we want to have some idea of how the polymorphisms affect drug response, by
interfering with protein products involved in absorption, distribution,
metabolism, excretion or the response or pharmacodynamics to the drugs. That is because of the point we made earlier
that, in order to have an idea of how to put this in the context of drug
interactions and diseases, we have to have an idea of what the underlying
mechanism is involved.
So
if it is a genetic polymorphism and a drug-metabolizing enzyme, then I should
have heightened sensitivity to the administration of any other drugs that are
substrates for those same enzymes and that providing this information in the
context of all that information, the nongenetic information, is important.
[Slide.]
Also,
to give me a little bit of information in the labeling about what doses or
routes of the drugs were tested when pharmacogenetic information was collected
so that, in situations where doses are relatively low or exposures are long, a
24-hour infusion instead of a two-minute I.V. push, the effect of the drug
saturating an enzyme or a protein product could be quite different.
So
let me understand a little bit about how the studies were done. And the same would be true in terms of
predicting how relevant polymorphisms and hepatic metabolism would be helpful
to know if there is oral or prolonged exposure versus very short acute
exposures.
[Slide.]
What
am I willing to assume? We have kind of
been talking about this all morning. The
in vitro data and preclinical data can be helpful so even if the clinical
information isn't strongly supportive of an effect, having the basic
information about what enzymes are involved in the metabolism or the handling
of a medical is helpful if only for doing things like predicting three and four
drug interactions. As we heard about
this morning, three and four drugs is a whole lot different than just two drugs
interacting, to help the prescribers use the information that we know about the
effects of polymorphisms from other drugs on the drug of interest.
Again,
by using basic principles of pharmacology, the clinician may be able to make a
more sophisticated decision about how to use the medication by providing that
information.
[Slide.]
This
is what I think was mentioned earlier also, this European group has tried to
get together and come up with some dosage recommendations that would be
reasonable to put into place now for some drugs that are substrates for 2C9,
2C19 and CYP2D6. They have come up with
recommended starting doses for a number of drugs in poor metabolizers and
extensive metabolizers, and, in once case, where there were sufficient data, in
the ultrarapid metabolizes.
Having
this kind of information, again, although the clinician would have to be
careful, by knowing about how the medication is handled, how the drug us dosed
relative to the concentrations that are likely to saturate these protein
products, you might willing to state that an ultrarapid metabolizer receiving
another drug in this class might be deserving of a higher dose even though
there might not be clinical data specifically testing that drug at those higher
doses in those genotypes.
[Slide.]
What
do I want to know? I do want to know the
frequency of the specific genotypes in at least the three largest ethnic racial
groups, understanding that Hispanics are, in many cases, a larger ethnic group
but that they are going to be somewhere in between these three groups in terms
of allele frequencies, in general.
You
basically want to know the frequency of the common homozygous genotype,
heterozygotes and those that are homozygous variant or defective.
[Slide.]
Giving
allele frequencies is another possibility that I think most clinicians are not
really comfortable going through Hardy-Weinberg calculations. So I think clinicians are going to be more
comfortable with knowing the frequency of the genotypes rather than allele
frequencies.
[Slide.]
We
have talked about the difference between phenotype and genotype. While it is true that phenotype is the bottom
line, phenotype can be influenced by concurrent drugs, by diet, by disease. This information could be important to put in
the label as long as it is clear to the clinician that that is the truth whereas,
of course, the patient's germ-line DNA is the patient's germ-line DNA with the
possible exception of stem-cell-transplant survivors whose blood DNA is not
going to be their germ-line DNA.
It
has the advantage that it must only be studied once, although, again, with the
caveat that the technology could improve so that genotype might need to be
repeated in the future as technology improves.
It has already been mentioned that genotype is probably more susceptible
to false negatives than phenotype is just by virtue of the fact that probably
no genotyping test is going to capture all inactivating alleles or mutations.
[Slide.]
Some
concepts about genotyping tests that I think we have to educate ourselves
about, that there are multiple types of variant and wild-type alleles for every
gene. We have already heard about those. We have already talked about false negatives,
and that the number of false negatives really depends on the proportion of the
inactivating variants that a genetic test is going to account for.
I
think, in my mind, this is going to be the responsibility of the person
providing the test results to indication what variants they test for and, given
current data, what proportion of inactivating variants their test covers, and
that putting that in a label is probably not feasible because that is a piece
of biology that is going to change rapidly over time. So I don't think we should hold manufacturers
of individual drugs to that standard.
[Slide.]
That
patients can be heterozygotes. Clinicians
are going to get back results that will indicate more than one mutation in some
cases. Again, the better the
interpretation of the test, the less information has to go in the label and the
less we have to worry about clinicians being able to understand this. I do think, again, this is going to be the
responsibility of the people providing genetic tests to say, here is what the
raw genetic results are. We are willing
to interpret the haplotype likelihoods this way and so there is a 95 percent
chance that this result means that the patient is a heterozygote and there is 1
in 100 chance it means that this patient is homozygous-deficient.
[Slide.]
Again,
some knowledge of genetics and molecular biology will be helpful as
pharmacogenetics gets incorporated into labels.
We have heard people debating about the role of assessing heterozygotes
but I do think, in most of these cases, is it going to be a reasonable
assumption that heterozygote phenotypes are usually in between the two
homozygous genotypes and that, although there may not be strong clinical data
for that particular drug indicating a different dose is indicated in
heterozygotes versus homozygotes of one genotype or the other, given a patient
has other concurrent drugs, given a patient might have other altered routes of
metabolism or excretion, it is reasonable for the clinician to make some
assumptions about heterozygotes and so provide the clinician with that
information.
We
have already talked a lot about gene duplications and how a gene-duplicated
allele along with a heterozygote variant allele could confound
interpretation. Again, I would put more
of that responsibility on the provider of the test result and not that kind of
detailed information being requested in the label, necessarily.
[Slide.]
Again,
the more information the clinician has about how they understand how these
different mechanisms of genetic variants might affect the expression of a
protein product will be better if the clinician understands that a gene
deletion obviously means the gene can't be expressed at all. There is no controversy, that an early stop
codon means there absolutely can't be any protein, that gene duplication means
there might be more active protein and that things like conserved amino-acid
substitutions or promoter polymorphisms are likely to have a less significant
effect, that will be helpful but, again, interpretation of the genetic results
should take care of most of these relatively complicated decisions.
[Slide.]
As
I am writing all this down, I am thinking, is this too much to expect of
clinicians? It may be, but I do think
that there are plenty of examples where we expect a high degree of
sophistication in clinicians in being able to prescribe drugs. Now, with the availability of ematinib, the
922 translocation in peripheral blood or bone marrow really needs to be
followed to see how it is progressing within a patient.
That
can be assessed several different ways, by cytogenetic tests, by FISH, by
RT-PCR, and there may be a lot of clinicians who don't understand the
subtleties between the way that those tests work. But that doesn't mean that we don't expect
them to have some idea of how to follow diseases in these patients.
There
are many drugs for which G6PD deficiency is either a warning or a
contraindication and most clinicians don't understand how those tests are
done. They don't know whether they are
phenotype or they are genotype, that we are expecting them to try to get them,
to try to utilize them, to try to prevent adverse effects for patients
prescribed some of those drugs in some cases.
I
noticed in the Hepatic Dosing FDA Guidelines, the Child-Pugh score is used
repeatedly to describe how to interpret the liver dysfunction in patients. I would wager to guess that there are many
clinicians using drugs for which the Child-Pugh score is described in the label
but they don't understand exactly how to calculate that or what those numbers
mean, and we can go on and on.
So
I do think it is a lot to expect of clinicians but I don't think that that
means it shouldn't be done.
[Slide.]
What
else do I want to know? I don't want to
know a lot of the details about phenotype but at least tell me whether it is a
blood test or a urine test, give me a little idea of the direction of the
phenotype, so that could be AUC, that could be enzyme activity, and how, at
least, directionally, it relates to the genotype and give me some idea of what
interferes with the phenotyping test so I know whether it is reasonable to try
on the patient.
For
genotyping, we have already mentioned at least an idea of the number of
inactivating variants, their approximate frequencies and it would be
helpful--again, this could be provided by the person providing the genotyping
test to understand what proportion of inactivating variants their genotyping
test accounts for in at least the major racial ethnic groups.
[Slide.]
Also,
that negative results can be very helpful, so just understanding that a drug
has been tested to see whether it is a substrate for different genetically
regulated polymorphism gene products and knowing that it is negative may be
helpful and that that information should be included where possible.
[Slide.]
How
sure do I need to be? I think it is
helpful to just provide examples of real data and I guess I would prefer that
we leave the option somewhat open as to exactly what kinds of data are
presented. Knowing the average or
standard deviation or the median plus-or-minus the confidence interval for the
dose in three genotypes, homozygote, wild-type, heterozygote and homozygous
variant at some specific doses.
For
example, given here are some doses. That
can be helpful. Understanding the
frequency of a serious toxicity like QT widening along with confidence
intervals in patients of different genotypes.
Given a dose, a fixed dose, what proportion of patients displayed
evidence of response or what proportion of patients displayed evidence of
toxicity?
[Slide.]
The
literature is filled with these kinds of examples that I think would be helpful
in the labeling.
[Slide.]
This
is an example of the frequency of the median and confidence intervals for
severity of mucositis in patients who are homozygous CC, heterozygote or
homozygous TT for an enzyme involved in folate metabolism who are given
methotrexate as transplant preparative regime.
[Slide.]
This
is an example of the warfarin milligram-per-day dose in patients who were
titrated to achieve a target INR. One
can see the degree of overlap among the genotpyes, see that there is overlap
but that there will be differences in the median and range of doses tolerated
by patients in those various genotypes.
This
is the proportion of patients cured based on their 2C19 genotype in the wild
type versus heterozygote versus homozygous variant genotypes treated with a
standard dose of omeprazole.
[Slide.]
So
our favorite gene polymorphism TPMT; this shows the difference in enzyme
activity of frequency distribution and the mean tolerated weekly dose of 6
mercaptopurine in the 1 percent of patients who are homozygote mutant, the
10 percent who are variant heterozygote and the 90 percent who are
homozygous wild-type, the same polymorphism, the cumulative incidence of
requiring a dosage decrease based on myelosuppression in the homozygous
variant, heterozygote and wild-type patients along with confidence intervals
for that cumulative incidence.
I
think any of that kind of information is information that clinicians can
interpret if they want to understand how to best prescribe medications in their
patients.
[Slide.]
In
terms of the labeling sections that may be relevant for clinicians, I think
that we have heard about most of these today, that the Clinical Pharmacology
Section is very important to provide general background information, a little
bit of information about what doses of drug were used, what concentrations were
used in in vitro studies, a little bit about how the studies were done, where
relevant to put information in the Warnings, Precautions and Adverse Reactions
and Overdosage Section, and to provide some information on dosage and
administration, especially given that what is right there right now includes
information on dosage adjustments, given degrees of renal dysfunction and
hepatic dysfunction which often have far less ability to discriminate doses
that have been true for many pharmacogenetic polymorphisms that have been
associated with different doses and adding information on what has been
observed in different genotypes for dose of a drug, I think, in that section is
important.
[Slide.]
The
other principle that I think has been illustrated already in a couple of labels
that have been approved by the FDA, having just cross references among sections
I think is a good idea. So, if there is
something about genetic polymorphisms in clinical pharmacology, it can state,
"Please see the Adverse Reaction Section for additional information on
dosing of these drugs."
[Slide.]
A
couple of miscellaneous items in terms of terminology that I think should be
considered, that it should be allowed to use colloquial terms where it is
relevant and people may be familiar, so extensive and poor metabolizers, fast
and slow acetylators, that is fine to use if they are already out there in the
medical literature. To try to avoid the
word "mutant" if possible.
Most people don't like to hear themselves referred to as mutants--
although I don't mind at all, and I am homozygous variant for all kinds of
things--that the terms variant and defective are more neutral and probably
descriptive.
Avoid
the word "normal" if possible and use wild-type or describe what the
effect is on the phenotype, high activity, normal expression. All of these star HUGO nomenclature
designations that those of us in the field throw around are not going to be
very interesting to most clinicians.
If
they can be easily mapped to the wild-type, common or variant-defective allele
in the label or at least in the genetic test, I think that will be helpful to
prescribers, but we are going to have to deal with the fact that this is
confusing, that there may be several HUGO designations for a wild-type allele
and lots for defective or variant alleles.
[Slide.]
I
apologize because I have still got a couple of typos in here. In terms of a decision-tree, it is really
nothing other than what we have been talking about. If the ability to titrate the dose
intraindividually is apparent without compromising the patient, that, just
based on response, then I don't think we really have to look very much further
on how to prescribe the drug intelligently.
But,
if not, and if the drug is complicated by late effects or invasive monitoring
or, as I mentioned, very serious diseases where under or overtreatment is not
an option, then are there other simple lab tests that can be used or, like Bill
Evans used to say, "If you can use shoe size, you use shoe
size." You use what works.
If
that is not an option, and pharmacogenetic tests are available and an option,
then, yes; they should be used and I don't really think we have to decide on
phenotype versus genotype. I think both
kinds of information should be provided to prescribers.
So
I will stop there and be happy to take questions.
DR.
VENITZ: Thank you, Mary.
Any
questions for Mary? Let's get started on
the discussion,
Committee Discussion
DR.
VENITZ: You all have the questions that
Larry and the FDA are asking us; are the approaches presented to study the
influence of pharmacogenetics on exposure response sufficient and appropriate
and a follow up question, are there any other criteria or approaches that FDA should
consider recommending to sponsors?
So
I will open the floor for general discussion as well as any questions that you
might have for Mary's presentation.
DR.
SHEINER: Mary, can you just flash up the
one slide again? Is that possible, or is it gone? Has it disappeared? I think it was maybe the first or second one.
That's
it. I just wanted to say, Mary, that I
knew that anti-cancer drugs were dangerous but I didn't know that you could get
more than 100 percent toxicity at a high dose.
DR.
McCLEOD: It is more than one toxicity.
DR.
SHEINER: I wanted to say I am pleased
that you used the three questions that I have asked, but that was actually more
in the line of, if you are going to do an investigation, sort of a learning
study or a confirming study, because when you get to decisions things get a bit
more complicated and you need utility functions and stuff like that, sort of
like that, sort of that how certain you need to be becomes what is it worth to
you. So life gets a lot more complicated.
But
I did want to say I really like the way you sort of put it all together
there. The problem is you had an awful
lot of, "What do I want to know?"
We have got to do some kind of distillation. Maybe some people can handle it and, as you
say, the expectation is the people in the field taking care of people will have
to be able to respond to these things, but we have got to distill it down. That was a lot of, "What do you want to
know?"
And
you went so far as saying, "And I want to see some real data." I know that is you and I know that is us, but
it is a big demand and, you make the label huge that way, you may find that you
get an unintended consequence which is nobody pays attention to it, which is
already a problem with labels. They go
on and on.
The
other point I just wanted to make about your last slide when you said, if you
can titrate, then maybe you just should titrate. One of the things we shouldn't lose sight of
is, even though therapeutic drug monitoring is not as good as effect, if the
issue is a pharmacokinetic change, and if you are worried about this drug is
inducing and that drug is blocking, and so on, in the end it comes down to what
is the drug level. It may be the easiest
thing to do is just to find out and not have to worry about all those details.
DR.
RELLING: Yes. I put drug levels as phenotype.
DR.
SHEK: Again, looking at this decision
tree and looking at the adjusted dose, with regard to practicality, I would
assume, for injectables it might be easy but how practical is it with the
dosage that industry is putting that you can adjust the dose. Do we have also to look at more flexibility
there which might have its own economic impact where you have more variability
in the dosage, strains that a supplier, a manufacturer, will come up with to
enable you to do it.
Although
maybe drugs, they wouldn't have the toxicity, but I believe that side effects
might have an impact on compliance and might have an impact on efficacy, people
are not complying because of the side effects.
I don't know how much flexibility is there, whether the industry has to
respond and come up with more dosage flexibilities.
DR.
RELLING: As somebody in pediatrics, we
deal with this all the time. I mean, we
just have to come up with different dosages based on formulations. But there are examples where I am sure there
is pressure on the industry to come up with more formulations.
Again,
I don't think we can let the fact that different doses may be required in
different patients be the reason not to have individualized doses. We have got to figure out a way to do it.
DR.
HUANG: Going back to your decision tree,
and your question whether we can titrate to the response and, if so, then you
adjust dose accordingly. All the
examples that you have shown, which one do you think the clinician will not
answer, "I can adjust the dose?"
For example, some of the warfarin and this whole list of tricyclics
where the physician was saying, "No; I cannot adjust and I am going to go
to the left," or the majority would go to the right, where they say,
"We could adjust according to the response."
DR.
RELLING: I will let somebody else handle
tricyclics. My impression is that
under-treatment of psychiatric disorders is a major problem, the fact that
there is this assumption that there is a huge proportion of the population that
just intrinsically don't respond and nobody knows why and it is only a
trial-and-error period of six to eight weeks.
I think that causes unbelievable morbidity in this country right now.
There
might be a lot clinicians who say they can do that, but having better
information about how to come up with a good starting dose, I would think would
be critical in that area. Almost every
anti-cancer drug is a drug that can't be titrated based on response accurately
or reliably. I don't know--let everybody
else put in their favorite compounds. I
guess there are others where it is not problematic. Insulin is one where you can titrate to
response.
DR.
McCLEOD: I think the warfarin example is
a good example of why you can titrate to response but it is not good
enough. The cohort data that David
Veenstra and others have published identify that the people with the homozygous
variant genotype were able to be titrated to a good INR. It took an average of 94 days and we all
know, in the area, that is the first 70 days that are most critical for
preventing clot post arthritic--or hip replacement or in the case of atrial
fibrillation.
So
it can be done, just not in a timely enough manner to prevent some events. How many of those events is arguable. In other situations, it is not as big a
deal. If you have a mild rheumatoid
arthritis and you want to get the methotrexate dose right, you have a few weeks
to get it wrong. It is inconvenient and
patients don't like it, but it is not life-threatening or associated with high
morbidity.
So
I think maybe that decision tree needs to go how soon you need to get it right
because, if you need to get it right quickly, then it may be that a lab test
will be more appropriate and can be done, as Rick mentioned, before you ever
give the drug as opposed to having to wait and respond.
DR.
SADEE: I think, looking at all the data,
there is a fundamental problem in that we have a few polymorphisms in the P450s
that are clear. They abrogate the
function of a protein and that is useful in a fraction of the patients. But then there is additional variation that
is really very, very large. So you
cannot say we cannot titrate the dose on the basis of genotypic information
because it may only take care of a very small fraction of the problem.
Maybe
it is useful to just think about the fact that the cytochromes that are highly
polymorphic are a very unusual example in that it hardly ever happens in any
other gene that nonmutations, mutations such as abrogate the function
altogether of a protein, accumulate to such high levels, let's say 30, 40
percent of allele frequencies in some cases.
So that is a very unusual situation.
If
you do a genomewide study and those studies have been published now, then
polymorphisms in promoter regions, polymorphisms affect the stability of mRNA
processing, splicing, et cetera, are probably five times as prevalent or maybe
even ten times as prevalent as those that affect protein function. That is where most of the research has been
going.
So
I agree with Mary's statement that anything we do should make sure that the
polymorphisms that we put into any labeling are seen as just maybe the ones
that we know right now, that there is room for additional polymorphisms that
can be 100 KB upstream of a gene, nobody has ever looked at it and they are
extremely important, could affect the expression tenfold, easily.
So
these polymorphisms may appear over the next few years. So whatever we do needs to be predicated by
the sense that we actually only know a very small portion. Lew, you said we have to distill it down, and
that is correct. But we can only distill
down if we know from where we are distilling down.
And
I think we are still, even in the cases of 2D6, quite a way from knowing all
the important variations that occur, not even to talk about epistasis, compart
heterozygosity, haplotype information, you name it. All those are complicating factors that you
definitely want to touch after having distilled down, but you have to know it
ahead of time. There is no good method
to determine whether two functionally important polymorphisms are on the same
allele or on the opposite allele except for maybe the methods you are using
now, and they work pretty well. But
nobody is using it.
So
there is a lot of uncertainty and that is the difficulty of what we are dealing
with, to distill down from an entirely incomplete piece of information to
something that then is supposed to educate us how to use dosages. That will be rather difficult.
DR.
VENITZ: Let me add something and maybe
reiterate something that Lew had mentioned early on and this goes back to my
favorite utility function implicit in both of your presentations. If I look at Strattera, the reason why you
ultimately didn't care about the phenotype is because you were worried about
insomnia. The reason why Mary cares
about it is because her toxicities are life-threatening, at least potentially.
If
you had to pick the perfect pharmacogenetic test or the perfect scenario where
it might be useful, you want to pick something where the stakes are very
high. Either the stakes may be the
consequences of toxicity or the consequences of lack of efficacy. That is why I think oncology is a perfect
area for that because the stakes are very high.
A
lot of other diseases or indications you may find, yes, there are relevant
genotypic differences that are reflected and genotypic differences that you can
measure in terms of exposure of response, but the consequences, clinically
speaking, are insignificant. Those are
the ones where there is very little at stake and it is very difficult to
convince practitioners that are already having a tough time translating all the
nice research that we are doing into practice.
It is very difficult for us to convince them to actually change
anything.
So,
in terms of strategic planning on the FDA side, I would focus on the scenarios
where there is lots at stake as opposed to picking the ones we know a lot about
but clinically the relevance is limited at best.
DR.
SHEINER: There is also an interesting
signal-noise issue. It is right that we
focus on the poster children, the big effects and so on, sort of to get
people's consciousness up about what is going on. But I remember, and maybe I am showing my age
too much here--I remember 30 years ago digoxin.
This was the classic drug to adjust for renal function.
Yet,
when we looked at a huge number of patients receiving digoxin, they were mostly
old and their creatinines were around 2 because they were old and their kidneys
were not working as well as young people.
But the number of people with renal disease in a random population in a
hospital ward was rather small and, if you just did the sort of standard
statistical test and asked, did renal function help when you put it into the
regression. Very little information
about how you ought to dose that drug in practice was conferred by knowing the
creatinine.
Now,
that is not true. The person with a
creatinine of 10, obviously, you learned a lot.
But they were very, very rare so you couldn't get it to show up. Now, does that mean we should be sort of
segregating out the outliers and saying, "But that is who we really care
about," or does it mean we really want to talk about average behavior.
These
are all issues that don't really come up as scientists because you are trying
to push the knowledge forward but do come up very much in a regulatory
agency. How much do you hold people's
feet to the fire? How much do you put in
the label? How much do you prevent
things from happening. It is very tough
to answer because, again, you need population data. You need somebody is going to be realistic
about the way they evaluate it rather than somebody who has got a flag to wave
or an ax to grind.
It
is just starting here. You found a
couple--I think it was brilliant of Larry to limit this discussion to metabolic
enzymes of a certain type. But this is
opening a Pandora's box of thousands of possible genetic variants and their
implications for pharmacodynamics, pharmacokinetics, lord knows what else and
just the thought of how you were going to deal with in some way in which you do
pay attention to the important ones and not to the unimportant ones is really
almost daunting.
DR.
SADEE: I think focussing on the ones
where it really makes a difference, the dosing, and you mentioned tricyclics,
but also the treatment of psychosis, of first-case psychosis, is a real problem
because, if it is not treated properly, it may cause damage for the rest of the
life of that particular patient.
And
yet you do not know whether a drug is effective until maybe six or eight
weeks. At least that is the conventional
wisdom. There may be better techniques. So if you underdose because there is a high
metabolizer, for instance, you wouldn't know about this and these patients
would be damaged for life. So I think
that is another situation where it is extremely critical to get the dosage
right.
With
respect to all these multiple polymorphisms and unknown factors that we are
talking about, I think we must be aware of also the increasing knowledge about
epigenetic changes and accommodate of the modeling which is exploding into our
face. There may be absolutely no
polymorphism and it still may be epigenetic; that is to say, there is a stable
genetic change in the gene that you may not see by the normal genotyping where
the gene is silenced or where the comatin is remodeled.
That
appears to be malleable even though it was thought to be once a gene is
silenced, that will be for life but it can be reversed. So these are epigenetic changes we do not
even touch upon and they may be also huge in their effect.
DR.
LESKO: Just to elaborate a little bit on
"the stakes are high issue."
The current let's call it "model" in drug development is to
look at covariates that affect pharmacokinetics early on and then react to that
in one form or another in terms of drug dosing.
I
guess I am trying to get to maybe a better understanding of why the issue of
stakes are high would be any different in a genetic or genotype-defined
population than the stakes are high for any drug in which we study routinely
hepatic disease, renal disease, and so on.
I
understand it is only interpretation but how does it differ as a cofactor that
might become something that is a routine factor to study in drug development
with the decision about what to do about it later on. It almost sounds like, "I don't want to
study this cofactor unless the stakes are really high."
But
it is part of understanding the basic informational content of the clinical
pharmacology of the drug, so I sort of want to pursue that thinking a little
bit.
DR.
VENITZ: Fundamentally, I don't think it
makes any difference. What I was
referring to is how you can translate that into actually changing the
practice. If you pick the low-hanging
fruits, you have a better chance of convincing people that this is actually
important. Otherwise, we are going to
swamp labels with pharmacogenetic information that, in reality, is not going to
be used.
You
heard what Lew was saying about distilling information. I am talking about information may not even
be relevant so distilling it to the point that it doesn't even appear on the
label.
DR.
SHEINER: I think it is very
different. The difference is that how
many drug-eliminating organs are there.
There is the kidney, the lungs and the liver and that is about it. So there are only a few things you need to
look at. We lump all hepatic diseases
together. Maybe we shouldn't, but we do.
So
it was doable. We are now entering a
realm where the number of possible things you could have to look at just keeps
on multiplying. Not only does that
produce terrible problems in false positives and the ability to extract from
100-people's worth of data when you have got a thousand covariates which one
makes a difference. We have got to be
much more intelligent about this.
A
drug that is excreted unchanged, you have got to look at the kidneys. Basically, it is how well do they work. It doesn't matter what disease has caused
them to not work so well. It is a doable
containable problem. This is not. So that is the difference. There is no conceptual difference but the
difference is we are in a very different universe. We are in a thousand-dimensional universe. And everything changes.
DR.
LESKO: So what do we do about it. It gets to another question I was thinking
about and it is that when studies like this would be conducted, they might be
conducted, for example, in a phase I healthy volunteer population. Typically, that information, whether it is
drug interactions or anything else is extrapolated to many other populations
for the purposes of adjusting doses without a lot of consideration of issues
other than the differences in exposure.
So
I guess what I was wondering is, as we have gone through, actually two days and
maybe, Greg, you touched upon this is if you had a genotypic difference
demonstrated in a test population which would be a phase I healthy-volunteer
population, as you extrapolate that knowledge to other populations, where would
it become more important or less important?
For
example, in the elderly where you have maybe in an extensive metabolizing group
slower metabolism so the differences become closer, genotype doesn't make much
difference. In young kids, maybe the
development process doesn't make much difference.
Does
genotype interact with other covariates that are out there in that little
circle that Mary showed? What do we know
about those sorts of issues?
DR.
RELLING: I think that the Strattera
example is interesting. I would like to
know a lot more information. Where you
see this incredible bimodal distribution, in some estimate of I think it was a
parent oral clearance, I don't know where those doses that were tested were relative
to the doses that were actually used in the chronic dosing over weeks that you
showed us where it ended up that there was no difference in the delivered dose.
I
don't know what was titrated week by week in order to decide whether to go up
or down on the dose. But, yes;
presumably those other nongenetic factors--it may just include simple things
like what dose you are working at, which is the other way of saying what AUC
are you working at, which is kind of what Larry just said. If you are very, very old and everybody has
lousy clearance, you may wipe out the importance of a polymorphism. If you are very, very young and everybody has
beautiful clearance, you may wipe out the effect of a polymorphism.
So
that is why I am afraid, even though I know that that was a lot of slides of
information that I want, I think to really use the information smartly, you are
going to need to have a fair amount of information and you are going to need to
assume a pretty high level of functioning about understanding of pharmacology
and pharmacokinetics to use the information optimally.
Anything
you do to make it real simple so it looks like the package inserts we have now
is going to wipe out so much of the complexity that really helps clarify the
information that it will be misleading.
I have thought about why does the label have to be manageable? Nobody reads it anyway. It is all on the Web. Why not make it huge. Why not make it fully referenced, fully
graphicized? Put a lot of information
there. Make it the world's best review
article on the drug.
Now
that is all electronic, what does it matter how big it is? And then put in everything that affects it
including drugs and age and renal function and liver function and put in tons
of information.
DR.
VENITZ: Any other comments or
recommendations? Larry, do you want to
wrap things up?
Concluding Remarks
DR.
LESKO: I think we are getting near the
end and getting pretty tired. I think we
have been overwhelmed by information from the last two days and it has been
extremely valuable to us to get the comments and input that we have.
As
usual, we have to distill a lot of what we heard over the last two days and try
to take each of the four, five different projects we brought to this committee
and move them forward to the next level.
I
guess I will just close by expressing my thanks and appreciation to the
committee for their input into the topics.
I would express thanks to the guest presenters that we had. They added a lot to the meeting. Appreciate that. And thanks to all of the FDA presenters that
were able to put on the presentations during the course of the last two days.
So,
as always, it has been a very good experience and a learning experience. Thank you.
DR.
VENITZ: Let me add my thanks to the
invited guests for coming that far, to the committee members for freeing their time and for the FDA staff for
organizing it. Let's adjourn the
meeting. Have a safe trip home.
[Whereupon,
at 12:00 p.m., the meeting was adjourned.]
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