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]{.underline}
>
> 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]{.underline} [R]{.underline} [O]{.underline} [C]{.underline}
> [E]{.underline} [E]{.underline} [D]{.underline} [I]{.underline}
> [N]{.underline} [G]{.underline} [S]{.underline}
>
> **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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