ATDEPARTMENT OF HEALTH AND HUMAN SERVICES

> FOOD AND DRUG ADMINISTRATION
>
> CENTER FOR DRUG EVALUATION AND RESEARCH
>
> **ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE**
>
> **CLINICAL PHARMACOLOGY SUBCOMMITTEE**
>
> Monday, November 17, 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.
>
> Wolfgang Sadee, Ph.D.
>
> Lewis B. Sheiner, M.D.
>
> Marc Swadener, Ed.D.
>
> Efraim Shek, Ph.D., Acting Industry Representative
>
> GUEST SPEAKER:
>
> Peter Bonate, Ph.D.
>
> FDA STAFF:
>
> Hae-Young Ahn, Ph.D.
>
> Albert Chen, Ph.D.
>
> Joga Gobburu, Ph.D.
>
> Peter Hinderling, M.D.
>
> Shiew-Mei Huang, Ph.D.
>
> Leslie Kenna, Ph.D.
>
> Peter Lee, Ph.D.
>
> Lawrence Lesko, Ph.D.
>
> Stella Machado, Ph.D.
>
> Ameeta Parekh, Ph.D.
>
> William Rodriguez, M.D.
>
> [C O N T E N T S]{.underline}
>
> Call to Order, Jurgen Venitz, M.D., Ph.D. 4
>
> Conflict of Interest Statement, Hilda F. Scharen, M.S. 6
>
> Introduction to the Meeting, Lawrence Lesko, Ph.D. 8
>
> Quantitative Analysis Using Exposure Response:
>
> Proposal for End-of-Phase-2A (EOP2A) Meetings,
>
> Lawrence Lesko, Ph.D. 19
>
> Issues Proposed to be Discussed at EOP2A
>
> and their Impact, Peter Lee, Ph.D. 37
>
> Case Studies:
>
> Ameeta Parekh, Ph.D. 46
>
> Hae-Young Ahn, Ph.D. 65
>
> Joga Gobburu, Ph.D. 75
>
> Committee Discussion 90
>
> PK/PD (QT) Study Design: Points to Consider,
>
> Peter Lee, Ph.D. 125
>
> Use of Clinical Trial Simulation (CTS)
>
> for PK/PD Studies, Peter Bonate, Ph.D. 130
>
> Case Studies, Leslie Kenna, Ph.D. 172
>
> Committee Discussion 201
>
> Pediatric Bridging: Pediatric Decision Tree:
>
> Introduction, Lawrence Lesko, Ph.D. 212
>
> Case Studies:
>
> Peter Hinderling, M.D. 216
>
> Albert Chen Ph.D. 242
>
> Methods for Determining Similarity of Exposure Response Between
> Pediatric and Adult Populations,
>
> Stella Machado, Ph.D. 259
>
> Research Experience in the Use of Pediatric
>
> Decision Tree, Gregory Kearns, Pharm.D., Ph.D. 278
>
> Regulatory Experience in Using the Pediatric
>
> Decision Tree, Bill Rodriguez, M.D. 278
>
> Committee Discussion 304
>
> [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 and Opening Remarks**
>
> DR. VENITZ: Good morning, everyone. Welcome to the Clinical
> Pharmacology Subcommittee Meeting. As you know, we have a full agenda
> both for today as well as for tomorrow. So, I would like for us to get
> started by introducing the members and the FDA staffers around the
> table before Ms. Scharen introduces the conflict of interest.
>
> My name is Jurgen Venitz. I am the chair of the committee and I am an
> associate professor at Virginia Commonwealth University.
>
> DR. D\'ARGENIO: My name is David D\'Aregnio. I am professor of
> biomedical engineering at the University of Southern California.
>
> DR. FLOCKHART: My name is Dave Flockhart. I am a professor of
> medicine, genetics and pharmacology at Indiana University.
>
> DR. SHEINER: I am Lewis Sheiner, clinical pharmacologist from the
> UCSF.
>
> DR. SWADENER: Marc Swadener, from Boulder, Colorado.
>
> DR. JUSKO: William Jusko, Department of Pharmaceutical Sciences,
> University at Buffalo.
>
> MS. SCHAREN: Hilda Scharen, FDA, Center for Drugs.
>
> DR. KEARNS: Greg Kearns, clinical pharmacologist from Children\'s
> University Hospital in Kansas City, Missouri.
>
> DR. DERENDORF: Hartmut Derendorf, Department of Pharmaceutics,
> University of Florida.
>
> DR. DAVIDIAN: Marie Davidian, Department of Statistics, North Carolina
> State University.
>
> DR. SHEK: Efraim Shek, Abbott Laboratories, the industrial
> representative.
>
> DR. MCCLEOD: Howard McCleod, clinical pharmacologist, Washington
> University in St. Louis.
>
> DR. HUANG: Shiew-Mei Huang, Deputy Director for Science, Office of
> Pharmacology and Biopharmaceutics, CDER.
>
> DR. LEE: Peter Lee, Associate Director, Pharmacometrics, Office of
> Clinical Pharmacology and Biopharmaceutics.
>
> DR. LESKO: Good morning. Larry Lesko, Director of the Office of
> Clinical Pharmacology and Biopharmaceutics.
>
> DR. VENITZ: Thank you. Let me turn over the microphone to Ms. Hilda
> Scharen. She is the executive committee secretary and she will provide
> us with 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 part of
> the record to preclude even the appearance of such at this meeting.
> The topics of today\'s meeting are issues of broad applicability.
> Unlike issues before a committee in which a particular product is
> discussed, issues of broader 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
> 12A-30 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 discussions, the potential
> conflicts are mitigated. We would 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 discussions involve any other products or firms not
> already on the agenda for which FDA participants have a financial
> interest, the participant\'s 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. As you can tell from the agenda, we have three
> main topics for discussion today, end- of-phase-2A meetings; PK/PD
> modeling of QTc prolongation; and pediatrics. The person who put the
> agenda together, Dr. Larry Lesko, is going to introduce the topics for
> the meeting and the outcomes that he would like for us to achieve.
> Larry?
>
> **Introduction to the Meeting**
>
> DR. LESKO: Thank you, Jurgen.
>
> \[Slide\]
>
> Good morning and welcome back to another Clinical Pharmacology
> Subcommittee. In particular, I would like to welcome some new members,
> Dr. D\'Argenio and Dr. Davidian. Thanks for joining us and bringing
> some expertise in you areas to our working subcommittee.
>
> \[Slide\]
>
> What I am going to do today is really introduce the topics for today
> but I am also going to review the topics that we covered in the first
> two meetings, and link those to today\'s topics to try to illustrate
> the continuity in issues that we have been bringing before this
> advisory committee.
>
> \[Slide\]
>
> So, let me start by saying that this is the third meeting of the
> Clinical Pharmacology Subcommittee. As you can see, it has been about
> 12 to 13 months since our first meeting, back in October of 2002. We
> had our next meeting in April of 2003 and this represents our third
> meeting.
>
> I have to say that the input of this group has had a significant
> impact on the progress that we have made in each of the general topic
> areas that I first introduced back in October of 2002, those four or
> five broad areas. As I go through a kind of synopsis or review of what
> we have done to date, you will appreciate where that input is coming
> into play.
>
> \[Slide\]
>
> Back in October I had indicated that a major emphasis of this
> committee is going to be risk, and I subdivided risk into risk
> assessment which we defined as a quantitative or science-based
> estimate of risk in a special population who is either under- or
> over-exposed to drug treatment. This, of course, relates to dosing
> adjustments that are pertinent to labeling of a drug product.
>
> The second broad area of risk was risk management, and that was
> defined as taking action to reduce the risk through appropriate label
> language related to dosing adjustments. As you recall from our prior
> meetings, we talked about a two-fold approach to dosing adjustment.
> One is identifying the magnitude of the risk involved with under- and
> over-exposure and then trying to determine an appropriate dosing
> adjustment to minimize that risk.
>
> \[Slide\]
>
> It isn\'t by accident that have covered these topics so far. In fact,
> approximately on August 30 of this year, the FDA\'s new strategic plan
> was released. It is on the website. One of the key parts of that
> strategic plan that relates to the objectives of this group\--the key
> element of FDA\'s new strategic plan is efficient risk management.
> Secondly, to use the best biomedical science to achieve our health
> policy goals. Third, to make new treatments and technology less risky
> with greater predictability and less time from concept to bedside. I
> would say all the topics we will talk about come under the umbrella of
> the strategic plan, and in particular these elements of it.
>
> \[Slide\]
>
> So, let\'s talk about the scope of topics that we have covered to date
> and will continue to discuss: Quantitative risk analysis using
> exposure-response regulations; pediatric PK and analysis of the FDA
> pediatric database; pharmacogenetics\--we have talked about
> improvements in existing therapies and at the last meeting we
> introduced the topic of metabolism- and transport-based drug
> interactions.
>
> \[Slide\]
>
> Now let\'s take a look at each of those topics and see what we have
> accomplished to date and where we are going today. Well, basically,
> the methodologies that we presented to this committee both in October
> and April have basically resulted in a finalized, systematic
> pharmacometric methodology to apply to dose adjustments. We are and we
> have applied the methodology to both assessment of efficacy and safety
> biomarkers; in some cases clinical endpoints; and it has been helpful
> as a methodology or an approach to assess risk-benefit.
>
> We are currently integrating the methodologies we talked about at our
> meetings into the routine NDA reviews and will in the future in early
> meetings with sponsors that I will talk about when we get to the
> end-of-phase-2A meeting.
>
> We talked on several occasions about the utility function. This
> continues to be a work in progress. The approaches that we have
> discussed at prior meetings have raised awareness and also the issues.
> I think our next step as a work in progress is to have some future
> further dialogue with our physicians and statisticians. There still
> remains an unresolved issue, namely, how to determine the appropriate
> utility function for relative efficacy and safety endpoints.
>
> \[Slide\]
>
> At today\'s meeting, thinking of the broad topic area, what we are
> going to do is talk about a new proposal for an end-of-phase-2A
> meeting between FDA and industry. What we would like to do is discuss
> topics at this meeting that revolve around the evaluation of exposure
> response and prospective dose selection.
>
> We are going to show you some case studies of exposure-response
> analysis. These come from the NDA reviews but we think they are models
> for the type of analysis that we can conduct at the end-of-phase-2A.
> The idea is to look at these models and get a feeling for how the
> analysis at an earlier stage in drug development would have benefitted
> the quality of the new drug application.
>
> \[Slide\]
>
> Also related to exposure response we will be talking about a
> methodology for evaluating QT. This has become a major issue, as many
> people are aware. We will talk about points to consider for PK/PD or
> PK-QT study design. We will talk about the use of clinical trial
> simulation to optimize the study design for this evaluation, and we
> will show you some case studies illustrating pharmacometric
> considerations arising from NDA review of QT data. We are beginning to
> get a lot of experience with this but, looking ahead, what ought to be
> the important aspects of study designs for the next study that might
> be conducted?
>
> We have talked about pediatric PK and the analysis of our FDA
> database. We basically have completed the PK, as we call it, study
> design template, and we have utilized it in interactions with sponsors
> as an alternative to determining full sample strategies in looking at
> the PK in pediatrics.
>
> We have further work in progress on simulation to further optimize the
> number of samples, the sampling times and number of
> patients\--basically the design of the study, and that is an ongoing
> work.
>
> Last time in particular we talked about our pediatric database
> analyses. We are going to look at the database retrospectively. We
> presented some ideas on that. We got your input on it. But that has
> been a challenge for us, and it hasn\'t been a very successful
> initiative.
>
> Over the last three or four months what we found is many incomplete
> data sets for the analysis that we want to undertake. We have
> non-optimal study designs because they weren\'t designed for the type
> of analysis we wanted to conduct. We haven\'t given up however. We
> have begun to look at the database more selectively, picking on drugs
> for case-by-case analysis and comparing pediatric and adult data for
> similarities and differences in exposure response. We have picked
> drugs were there is a more full data set and we will probably bring
> some of that information forward in the future. However, today we will
> talk more about that this afternoon.
>
> \[Slide\]
>
> So, today\'s meeting topic, number three, we want to revisit the
> clinical pharmacology principles of the pediatric decision tree with
> some case studies. This is a decision tree which is always evolving as
> new information becomes available. But you will see in the decision
> tree that there is a point at which we talk about comparing
> similarities and exposure-response relationships between adults and
> pediatric patients. We haven\'t really adopted any methodology to
> compare that similarity so today we will present a method to be used
> in the determination of similarity of exposure-response relationships.
>
> You are also going to hear some perspectives. There will be new
> perspectives. You will hear an FDA perspective from the medical side
> and you will hear an academic perspective from the clinical
> pharmacology side. Both of them will be based upon experiences with
> the pediatric decision tree and applying it in the development of
> pediatric drugs.
>
> \[Slide\]
>
> We have talked about pharmacogenetics, and the emphasis has been on
> the improvement in existing therapies or approved drugs. We focused
> for the most part on polymorphism in metabolizing enzymes that
> determine variability in drug exposure. We are going to stay in this
> area for a while. Our emphasis in prior meetings had been on TPMT and
> the polymorphism that affects dose response for the thiopurines.
>
> Since we met in April we have had additional discussions of the TPMT
> issue and the possible modifications of the thiopurine labels. We
> presented a lot of the information that we presented to this
> committee, including the input of the committee, to another
> subcommittee, which was the Pediatrics Subcommittee of the Oncology
> Drug Advisory Committee, in July of 2003. It was a very interesting
> meeting, very helpful in raising some issues that related to do we
> need this test; what is it going to cost patients; what is its
> predictive value and quality, and so on and so forth. We worked
> through those issues and at the end of the day this subcommittee
> recommended including pharmacogenetic information in a revision of the
> label for thiopurines.
>
> One of the issues that was discussed in July was whether or not this
> test should be required before receiving drug, or the information put
> in the label for informational purposes to be used by the physician
> and the patient in certain circumstances. The recommendation of the
> committee was that the test should not be required as a prerequisite
> for receiving the thiopurines.
>
> \[Slide\]
>
> So, at today\'s meeting we are going to shift the discussion of the
> question of the pharmacogenetics a bit. We are going to focus on what
> should be done in new drug development for substrates that are
> metabolites primarily by polymorphic enzymes. We have talked about
> approved drugs to some degree.
>
> We are going to hear three expert perspectives, an academic, an
> industry and a clinical view. Discussion will influence
> recommendations that we are going to be putting in another guidance
> that is under development. We call it the General Pharmacogenetics
> Guidance. It is going to be worked on and released probably sometime
> in the first half of 2004. This topic will be an important part of
> that guidance. So, we look forward to your input on this issue.
>
> \[Slide\]
>
> Finally, we had talked about metabolism- and transport-based
> interactions with just an introduction to the topic at our last
> meeting. It was intended to be really a foundation for subsequent
> discussion which will continue today. So, we wanted to bring to the
> committee an increased awareness of what we think are some new
> mechanisms of drug interactions that are becoming, to us at least,
> clinically important, and what do we do about them during the course
> of drug development.
>
> Coincident with that, we have a revision of the Drug Interaction
> Guidance in progress, and many of the discussions and issues that we
> will discuss in front of this committee will make their way into the
> revision of that guidance.
>
> \[Slide\]
>
> So, what are we going to hear today? We are going to hear more
> specifics on this issue. We are going to be asking what should be done
> in the consideration of these new drug interactions of emerging
> importance. We will be hearing different views on the topic and we
> will be focusing on two metabolic sorts of drug interactions related
> to 2B6 and 2C8. Again, the discussion will impact future regulatory
> advice on these issues.
>
> \[Slide\]
>
> In summary, I have really broken down today\'s meeting into five
> separate topics where we will be asking for your input and advice. I
> won\'t go over the specific questions right now. We will introduce
> those as we get to the specific topic. Again, we are looking forward
> to today. We are confident, as we have been in other committee
> meetings, that your input is going to be important to us and we are
> always trying to refine our thinking about these topics.
>
> So, that is basically an introduction, a framework for today\'s
> meeting. Looking at the agenda, I am next on the agenda so maybe I
> will just slide into my next presentation but that, hopefully, will
> give you a feeling for what we are going to try to accomplish today.
>
> **Proposal for End-of-Phase-2A (EOP2A) Meetings**
>
> \[Slide\]
>
> Let me pause, take a breath and say that we are moving into the first
> topic of quantitative analysis using exposure response. What I am
> introducing today really for the first time, or discussing it in a
> public forum, is a proposal for the end-of-phase-2A two-way meetings.
> This relates to analyzing exposure response, not at the NDA stage
> necessarily but at an earlier point in time in drug development.
>
> I am going to walk through this proposal and then that is going to be
> supplemented by other presentations. Dr. Peter Lee will give an
> example of some of the issues that will be discussed at this meeting
> and possible impact, and then will present some case studies and you
> will have to use your imagination a bit because these are case studies
> that we drew from our NDA reviews but we want to sort of transpose
> them in time and have you think about the possibilities and the impact
> that this analysis might have had, had they occurred at an
> end-of-phase-2A meeting.
>
> \[Slide\]
>
> Let me start the story of this proposal with the current situation in
> new drug development. This is from the FDA strategic plan. What it
> shows is really an alarming change in the drug development process.
> There are a couple of things on here but the main point of this slide
> probably is that very thin white line that you see there, which is the
> number of NMEs filed with the agency over the last ten years or so.
>
> You can see from a high in 1995 of about 50 NMEs, we are down to 2002
> at about 20. It hasn\'t gotten any better so far in 2003. Recently I
> read in the \"Pink Sheet\" that the number of INDs filed is at a
> record 11-year low. So, something is going on in the drug development
> process and many people are looking at this, including the agency, to
> try to figure out what is going on and how this trend might be
> improved.
>
> \[Slide\]
>
> So, the question comes down to what problems need solving in this
> current situation of drug development. We have seen estimates from
> Tufts and other places that it costs 800 million dollars to develop a
> new drug. The agency is concerned about this expense given the return
> on investment that we have seen in the new drug development process.
> This figure is high. It includes not only the actual direct cost of
> developing a drug but also the indirect cost of lost opportunities.
>
> Almost 50 percent of phase 3 trials don\'t succeed. That is, they fail
> to show their target evidence of efficacy or safety issues emerge.
> This figure comes really from the PhRMA FDA website. Throwing figures
> like this around, I think you realize that this is very much drug
> dependent. It is higher in certain diseases like depression; it might
> be lower in other diseases like antimicrobial drugs.
>
> Only 20 percent of new drugs entering clinical testing are approved.
> So, four out of five don\'t make it for various reasons, whether it be
> safety, efficacy, manufacturing problems, pharmacokinetics. This, in
> some form or fashion, underpins the situation we have in drug
> development.
>
> \[Slide\]
>
> I mentioned that strategic plan that Dr. McClellan released in August
> of this year. There is a point in that strategic plan that focuses on
> new drug development and the need for greater productivity. He
> recommends that steps be taken to reduce the time, cost and
> uncertainty of developing new drugs and he identified this as an
> important public health policy.
>
> \[Slide\]
>
> Well, that brought us around to a specific suggestion that might fall
> into that goal in the strategic plan which we call the end-of-phase-2A
> meeting. It is kind of a general term that we have given to this
> proposal. It isn\'t intended to exclude the possibility of meetings at
> other points prior to the 2A period in drug development. We could
> have, for example, an end-of-phase-1 meeting but, for convenience, we
> had to give this a name and we called it the end-of-phase-2A meeting,
> and I am going to tell you a little bit about it.
>
> The hypothesis for this proposal is that meetings with sponsors early
> in the drug development process will focus greater attention on the
> analysis, in particular, of exposure-response information. We think it
> will improve dose selection and study design for subsequent clinical
> trials.
>
> We have had prior discussion of this hypothesis with Dr. McClellan,
> Drs. Woodcock and Jenkins, and you can see how we have begun to sort
> of get the dialogue going internally at FDA with the Office of New
> Drug Office Directors, the Division Directors and, most recently, we
> presented this proposal and some case studies at a CDER all-hands
> guidance training in which we had several guidances on the agenda, but
> we talked about the April, 2003 Exposure-Response Guidance and linked
> that to this particular proposal. So, it has been an evolving concept
> and what I am presenting today is really a collective input of many of
> the internal thought leaders here, at the FDA.
>
> \[Slide\]
>
> There are a couple of things driving the hypothesis that I mentioned
> about these early phase meetings. One of them is expressed in this
> quote by Dr. Temple. This was from a DIA meeting in June. He said
> there is more to do with regard to dose choice from exposure-response
> studies and there is much to be gained from better use of biomarkers
> and more efficient study designs for phase 3 trials.
>
> It is hard to argue with that but the question was where do we have
> the dialogue on this? Where do we have an interaction with the
> company? The end-of-phase-2A meetings aren\'t the place to have this
> because drug development dose selection phase 3 trials are pretty much
> set at that point and there is not a lot of time to discuss either
> biomarkers or dose-response data. So, there was a missing gap.
>
> \[Slide\]
>
> We have three guidances that drive this hypothesis about early
> meetings. The most recent one was from April of 2003,
> exposure-response relationships. We talked a lot about regulatory
> applications in study design and data analysis. But we also had behind
> that two previous guidances on clinical evidence of effectiveness and
> dose-response information. So, taken together, these are the
> principles\--probably as good as they can get right now I think\--of
> best practices in exposure response. Like a lot of guidances, however,
> they have to be interpreted and, for interpreting those, having
> meetings with industry is a good place to do it.
>
> \[Slide\]
>
> So, as a philosophical point, FDA is interested in good dose-response
> analyses. There are some data driving this hypothesis as well. We
> conducted an informal review of exposure-response data in over 100
> NDAs submitted between \'95 and 2001. The purpose of this review was
> to try to form a foundation for what this meeting is going to
> accomplish, where we identified missing data related to the quality of
> submissions and approval rates. We were looking for the extensiveness
> of dose-response data, dose selection process, how many studies were
> conducted, and so on.
>
> We also did a prospective evaluation of over ten NDAs submitted in
> 2002 and 2003. What we tried to do here was evaluate the impact of the
> review, in other words, what happened at the NDA stage with the
> analysis of exposure-response information. Were problems uncovered?
> Were doses considered inappropriate? We asked the question of whether
> or not this type of review\--the review at the NDA stage\--if it had
> been carried out earlier in the IND period in conjunction with the
> sponsor, would it have saved time; would it have saved costs; would it
> have saved review cycles when it came to the NDA?
>
> \[Slide\]
>
> Some of the results of exposure-response reanalysis in that collection
> or cohort of ten studies showed us the following: That we could avoid
> reanalysis of exposure-response data, potential requests from other
> disciplines to conduct additional clinical trials. That is, we
> reanalyzed the exposure-response data. We integrated data across
> several studies and avoided the need for additional clinical trials.
>
> We found that this reanalysis resulted in the approval of lower doses
> or different dosage regimens than that proposed by the sponsor for a
> variety of reasons including safety. We identified missing data on
> specific doses or in special populations, including drug-drug
> interactions that impacted review time. So, these are all significant
> findings of what a reanalysis at the NDA stage found. Again, can we
> move this forward into the end-of-phase-2A and achieve the same
> objective but earlier and result in a higher quality application?
>
> \[Slide\]
>
> There is an additional goal which we struggled with in terms of
> resources here at the FDA, and that is efficient and effective use of
> our resources. We feel that interactions with sponsors early in the
> drug development process provide not only an opportunity to improve
> things but to provide advice on development of information of exposure
> response and other clinical pharmacology issues, rather than waiting
> until the NDA is in and identifying problems\--drug interactions that
> may not have been conducted; special populations that may have been
> ignored. Yes, we can deal with those but that involves labeling and
> very careful labeling. But having these discussions early about the
> overall clinical pharmacology development plan, exposure-response
> relationships, dose selection and dose choices we think is an
> efficient and effective way to develop drugs.
>
> \[Slide\]
>
> Now, let me talk a little bit about the timing of the meeting so we
> are clear on what we are talking about here. What this slide shows
> basically is the general scheme of things as it currently exists.
> Typically, sponsors will request\--these are all voluntary requests,
> by the way and they are not required meetings\--pre-IND meetings.
>
> The next junction at which FDA and industry has a formal get-together
> is the end of phase 2. Sometimes there is a pre-NDA meeting. Sometimes
> there are labeling discussions and then an action letter. So, you can
> see the wide gap that occurs here between the pre-IND and the
> end-of-phase-2A.
>
> What we are proposing is a meeting that occurs in between these. We
> call it the end-of-phase-2A. As I mentioned at the beginning, I don\'t
> want to exclude the possibility that we can have a meeting at the end
> of phase 1. This will be very drug specific, what we know at the time.
> We are trying to focus on the information that is available in this
> time frame of drug development. If you meet too early you have an
> incomplete data set and the meeting becomes filled with a lot of
> uncertainty. If you meet too late in this scheme the drug development
> plans are already cast in stone and it is hard to change them. So,
> what we are trying to do is find a balance in this drug development
> scheme, going from preclinical to submission, for where is the optimal
> time to have the interactions with sponsors for the reasons that I
> described,
>
> \[Slide\]
>
> The rationale for the meeting time, end-of-phase-2A, is that we think
> that it is at this point that there is basically complete information
> on preclinical pharmacology and exposure response complete in the
> sense of having healthy volunteer studies, drug dose tolerance
> studies, things like that. So, we have the safety data in healthy
> volunteers. We have some efficacy data depending on the drug at that
> point in time. We have some initial efficacy or proof of concept data
> from the early phase-2A studies, and we have safety data in patients,
> albeit a relatively small database.
>
> This is generally, although not always, prior to the so-called conduct
> of registration of label studies, that is, studies that a sponsor may
> conduct on special populations, drug interactions, food studies,
> perhaps some formulation studies. So, taken together, this information
> represents a fairly rich database for an early meeting with sponsors
> and an opportunity to analyze exposure response in particular.
>
> What we would also like to add to this, as we talked about in this
> meeting, is emerging issues. There is a lot of uncertainty about
> integrating things like pharmacogenetics in the drug development, but
> we think this would be an ideal place to talk about things like this
> as well as other topics, such as the use of trial design simulation,
> and so on. So, this is the rationale for it as to why we picked the
> end-of-phase-2A.
>
> \[Slide\]
>
> We also think this is an opportunity to advance the idea that
> mechanistic and quantitative methods of analysis of exposure response
> would be beneficial. We envision that this meeting would involve
> significant modeling and simulation to analyze and integrate
> exposure-response data across studies and explore dose choices for
> both 2B and phase 3 studies.
>
> We think this will be a point at which we can discuss the design of
> studies using computer-assisted clinical trial simulation, and these
> are relatively new technologies that we think should be applied in
> this context. This is a good time for us to talk with the sponsor
> about the design of PK studies to efficiently identify covariates
> affecting exposure response in later clinical studies, things like
> number of patients, sample times, things of that sort.
>
> Also, if you think about all the special populations and drug
> interaction studies that are conducted, those have to be interpreted
> as to whether or not a dose adjustment is needed. So, we think this
> would be a good time to begin to talk about therapeutic equivalence
> boundaries that would be based upon exposure response or help
> interpret the outcomes of these special population drug interaction
> studies as to whether a dose adjustment is appropriate or whether it
> isn\'t, and this will help I think near the end of the drug
> development process with the labeling discussions that we have.
>
> \[Slide\]
>
> Somebody asked about what is the difference between this meeting and
> the traditional meeting that we have with sponsors called the
> end-of-phase-2. Well, I think there are some major differences. For
> one thing, by the end of phase 2 the sponsor has pretty much made a
> final decision on the choice of doses or dose ranges for phase 3.
> Final formulations are developed and it is difficult at that point to
> change things without affecting significantly the time frame for the
> drug development program.
>
> The end-of-phase-2 meeting is a formal meeting, very formal. The goal
> of that meeting is to discuss study design for phase 3; clinical
> endpoints; heavy emphasis on statistics; and basically leading up to
> what is the evidence one needs for approval in terms of the adequate
> and well-controlled trials. Also at the end of phase 2, for most part
> many, if not all the special populations and drug interaction studies
> are complete. So, the opportunity to influence the key parts of drug
> development pretty much have gone by the board at this point.
>
> The end-of-phase-2A meeting, in contrast, will focus on some decision
> points in the development program. The meeting will be a bit informal
> as well. I don\'t mean informal from the standpoint that we don\'t
> take minutes or we don\'t keep track of the meeting, but I mean
> informal in the sense that there is a larger degree of uncertainty at
> the end of phase 2A than at the end of phase 2 because of the lesser
> amount of information, and we recognize that.
>
> \[Slide\]
>
> One of the questions we have and would appreciate some comments on is
> we have limited resources to conduct these meetings. We are going to
> begin them fairly soon. One of the discussions that we had internally,
> and that whole list of discussions I mentioned to you, is if we have
> limited resources where would the impact of these types of meetings be
> greatest. Would it be a first in class drug or one where there is
> significant therapeutic advancement where the importance of getting
> doses is particularly emphatic? Or, in contrast, is it one where we
> understand the pathophysiology of the disease and the pharmacology so
> that we can call upon a lot of the experience to enhance the
> interactions with the sponsor?
>
> We think it would depend on the completeness of the background
> package. I will talk a little bit about that. There is another debate
> about whether this would be for an experienced sponsor or one with
> less experience in terms of the value of these interactions. So, this
> is something we are going to have to sort out. We have in our mind a
> target for these types of meetings to probably have no more than two
> per month with our current resources and as a way of introducing this
> as a pilot project.
>
> \[Slide\]
>
> Let me tell you about the plan for this meeting. We are going to draft
> a guidance for industry. You have in the package that was sent to you
> today a concept paper on this meeting which goes into a lot more
> detail.
>
> The guidance will talk about background objectives, examples of
> topics, the usual process things for setting up the meeting. These
> meetings, like many meetings with sponsors, are going to be voluntary,
> relatively informal and, most important, interdisciplinary. This is
> not a clinical pharmacology meeting; it is a meeting that will involve
> resources from ourselves in clin. pharm., but also the medical and
> biostatisticians in our review divisions. We would like to evaluate
> the impact of this meeting after some years of experience. We are
> trying to think in maybe two or three years we need to look at some
> metrics for how the impact might be assessed.
>
> \[Slide\]
>
> So, in summary in introducing this new proposal for an end-of-phase-2A
> meeting, we think the meeting will serve to decrease uncertainty in
> further drug development, for example in phase 3. Uncertainty, we
> think, leads to some of the problems that I mentioned in the beginning
> in terms of the drug development process today.
>
> We think there is opportunity to do more quantitative analysis of
> exposure-response data to define better the dose ranging for
> subsequent clinical trials. We think it is a good time to identify
> missing information or discuss necessary information prior to
> submission of the NDA to reduce issues that come up at that point in
> the process. We think at the end of the day, after some years of
> experience, we will find this improves the informational quality of
> NDAs and minimizes the delays in NDA review, for example second and
> third review cycles that may be related to dose selection or issues of
> efficacy and safety.
>
> \[Slide\]
>
> So, what is it we are looking for today? You are going to hear a
> story, as I said, about some of the issues we see coming up at this
> meeting and then some case studies. What we would like is some comment
> on the goals of this meeting. Do you think they are appropriate? As
> importantly, what do you see as some obstacles to achieving these
> goals?
>
> You are going to see some analytic methods employed in these case
> studies using exposure-response examples from our NDA review. Think
> about these methodologies, how can they be improved; what should we be
> thinking about in terms of getting even more from the analyses?
>
> Do you have any thoughts on metrics? What are the metrics that would
> be used to measure the impact or success of this initiative? That
> would be important as to whether or not we continue with it beyond the
> pilot period of a couple of years.
>
> So, that is the end-of-phase-2A meeting. I will turn it back to the
> chair but we are going to continue discussing this and drill down into
> some more detail, but if there are any questions I can answer about
> the overall concept.
>
> DR. VENITZ: Any comments or questions for Dr. Lesko before we proceed?
>
> \[No response\]
>
> DR. LESKO: I am going to turn it over to Peter who will continue the
> discussion and talk about some of the issues that we think will come
> up.
>
> **Issues Proposed to be Discussed at**
>
> **EOP2A and their Impact**
>
> DR. LEE: Thank you, Larry.
>
> \[Slide\]
>
> I think later today we are going to hear several examples that will
> illustrate a potential benefit of discussing exposure response at an
> early clinical development stage, specifically at the end-of-phase-2A
> meetings. But what I would like to do now is go over some of the
> potential topics that we think will be useful to discuss with the
> sponsor early on.
>
> \[Slide\]
>
> As Larry has mentioned, we have informally looked at ten NDAs where
> the exposure-response information has made significant impact on
> regulatory decisions. In some of the NDAs the exposure response was
> used to approve a lower dose or a different dose than was proposed
> initially by the sponsor. In some cases the exposure response was used
> to avoid any additional clinical studies, especially efficacy and
> safety studies in the submissions. Finally, you saw that
> exposure-response information has been used to identify the desired
> missing doses and also special population studies.
>
> \[Slide\]
>
> So, we thought that if this type of analysis, exposure-response
> analysis, were done early on during drug development we might
> definitely save review time and besides it may improve the efficiency
> of the drug development process. So, one of the general goals for the
> end-of-phase-2A meeting that we propose is to discuss
> exposure-response issues. We hope that by this type of discussion we
> can make impact on the decision-making about the design and analysis
> or exposure-response study early in the drug development process.
>
> Also, we think that we could discuss the strategy in dose choices and
> special population studies. We also hope to be able to analyze by
> quantitative analysis, for example, modeling simulation and clinical
> trial simulation so that we can integrate relevant preclinical and
> clinical exposure-response data and, hopefully, close the gap between
> what is known at the end-of-phase-2A meeting and what will be applied
> in designing the phase 2B and phase 3 studies.
>
> \[Slide\]
>
> So, here are some of the discussion points. A discussion point that we
> thought would be useful at an end-of-phase-2A meeting\--and what I
> will do in the next few slides is go over each of these discussion
> points one at a time and also talk about the potential impact of these
> discussions.
>
> \[Slide\]
>
> The first topic for the end-of-phase-2A could be the dose range
> strategy. In the examples that you will be hearing today, in most of
> those cases a suboptimal dose was selected in the original NDA which
> would lead to either lack of efficacy of the drug in the phase 3
> studies or adverse events. Therefore, I think it would be useful in an
> end-of-phase-2A meeting to discuss the rationale for dose selections
> in a planned study, and this can range from the first dose to an
> efficacy and safety study. Definitely, this will depend on the
> preclinical and clearance evidence for the effectiveness and safety of
> the drugs.
>
> We could also discuss the drug development strategy which could be a
> sequence of studies that lead to the doses actually in the final
> efficacy and safety studies. We could also talk about the design of
> individual exposure-response studies.
>
> \[Slide\]
>
> The second topic we propose to discuss at an end-of-phase-2A meeting
> is exposure response to support efficacy and safety. In the
> Exposure-Response Guidance that was just recently published early this
> year, we discuss the utility of exposure-response information to
> support efficacy and safety. Of course, this could be on a
> case-by-case basis so it would be useful for the sponsor to come in to
> discuss early on the quantity and quality of exposure-response data
> that might be used to support efficacy and safety. We will also talk
> about the potential design of an exposure-response study that may lead
> to supporting information.
>
> Another useful topic to talk about is the modeling and simulation
> methodology that may be used to analyze the exposure-response study
> and to generate supporting information.
>
> \[Slide\]
>
> Another topic to talk about at the end-of-phase-2A meeting would be
> dose adjustment in special populations. Quite often during the NDA
> review there are quite intensive negotiations regarding labeling
> language, which usually leads to either a delay of review, NDA review,
> or in some cases leads to a phase 4 commitment. So, we thought it
> would be useful, again, to talk about the dose adjustment decision
> tree early on during the drug development process; and also talk about
> a required clinical pharmacology study that would support dose
> adjustment with special populations; also the analysis of exposure
> response and perhaps also talk about an alternative population PK
> study design that may replace the traditional intensive clinical
> pharmacology study supporting special populations and drug-drug
> interactions.
>
> \[Slide\]
>
> The next topic that we would talk about is the design of efficacy and
> safety studies. The objective here is to focus on the likelihood of
> getting the right doses, and also explore some of the \"what if\"
> scenarios and to look at the study robustness and the study power.
>
> We can look at a variety of study design factors, such as dose range
> selections, inclusion and exclusion criteria, the inclusion of special
> populations and PK design, sampling scheme, and so on and so forth.
>
> We could also talk about an alternative study design methodology, such
> as an adaptive design, a different titration scheme or even a new
> study design such as a concentration-control study design. Definitely,
> because of the complexity of the issue, clinical trial simulation
> could be used to design the efficacy and safety trials.
>
> \[Slide\]
>
> Another topic we could talk about at an end-of-phase-2A meeting is the
> population PK/PD study design. At this time, only about 50 percent of
> the full NDAs contain population PK analysis, however, quite
> frequently the objective of this analysis was not very clear and a lot
> of times the population PK studies were not designed prospectively,
> which will lead to the result becoming non-conclusive. Therefore, it
> would be useful, again, to discuss the objective of the population PK
> study early on and prospectively design a study so that the
> information can be useful to support labeling regarding special
> populations as well as drug-drug interactions.
>
> \[Slide\]
>
> Another important topic that we thought would be useful to discuss is
> the QT study design. QT has become a very important topic and has
> attracted a lot of attention recently because of several drugs being
> withdrawn from the market due to the QT prolongation property. As you
> know, the issue here is the large variability of circadian variation
> of QT.
>
> There are other issues such as the baseline correction methods, and so
> on and so forth. Therefore, it would be helpful, again, to discuss the
> study design issue early on, perhaps using clinical trial simulation
> to optimize study design as well. We will be giving several examples
> later on today to illustrate how the clinical trial simulation can be
> used to design the studies.
>
> \[Slide\]
>
> So, today we are going to hear many examples on topic 1. This morning
> we will be hearing three different cases where exposure response was
> used to support dose selection strategy or to support efficacy and
> safety. Later this afternoon we will be hearing two presentations
> regarding the use of clinical trial simulation to support PK-QT study
> design. With that, I will turn it back to Jurgen.
>
> DR. VENITZ: Again, any comments or questions before we proceed to the
> case studies?
>
> DR. SHEK: I have one.
>
> DR. VENITZ: Go ahead.
>
> DR. SHEK: It is my personal belief and I believe most of the industry
> will welcome any productive and effective interaction with the agency
> during the drug development process. But specifically, those ten NDAs
> that you were looking at in 2002 and 2003, how many of those were
> successful the first time and went through, you know, the first
> review, and how many of those failed completely?
>
> DR. LEE: Yes, specifically, we looked at the ten NDAs that either
> received not approvable or approvable. So, all those ten NDAs did not
> get approved status in the first round.
>
> DR. SHEK: None of them?
>
> DR. LEE: No.
>
> DR. VENITZ: Larry?
>
> DR. LESKO: I was just going to add on to the answer Peter gave and say
> that one of the issues that has been talked about is the number of
> review cycles on NDAs. I believe some information was released by the
> agency that indicated that the reasons for multiple review cycles are
> most of the time safety issues. I don\'t remember the exact percent.
> The second reason is issues having to do with efficacy. The third
> reason is CMC issues. It breaks down by percentage in that rank order,
> although, as I say, I can\'t remember which is which.
>
> The question we had was were those multiple review cycles related to
> issues revolving around dose response, and I don\'t believe we
> answered that question because it was too complex a question to link
> to the one issue of dose response. But it is probably multiple
> issues\--risk-benefit considerations, but I think the dose response
> issues were part of the answer, not the complete answer for those
> multiple review cycles. But that is one of the ideas of what we would
> like to actually improve, and maybe it is one of the metrics that we
> would like to look at in the next couple of years, in those cases
> where we have these meetings, has that resulted in approval on the
> first cycle or reduction in delays to the second and third cycles.
>
> DR. VENITZ: Any other comments?
>
> \[No response\]
>
> Then, let me introduce Dr. Parekh. Ameeta is going to give us the
> first case that illustrates the potential use of end-of-phase-2A
> meetings. Ameeta?
>
> **Case Studies**
>
> DR. PAREKH: Good morning, everyone. Before I start, I was noting some
> of the words that Larry had in his presentation. He was talking about
> moving on with the new technologies. Just on a lighter note, I was
> working on my slides over the weekend, trying to do some spell checks.
> It was interesting, I had some British spellings and some American
> spellings, especially on a word like \"learnt\" versus \"learned.\"
> So, I was updating my slides and in my panic I brought in this with
> the updated slides; this with the updated slides; and just as a
> security measure I sent myself an e-mail with an attachment. Well, I
> also just took this because my kids said, \"mom, you never know.\" I
> came in today. The network wasn\'t working so I didn\'t have my
> e-mail. I asked John to use this to update the computer. It didn\'t
> accept this. For some reason it didn\'t read this.
>
> \[Laughter\]
>
> So, you never know what might work. So, I had four and one of them
> worked, and it was the good old well-tested in the clinical trials
> technology that did work.
>
> \[Slide\]
>
> Larry has already laid out the CDER plan for the end-of-phase-2A
> meetings, the focus being on a more rational approach to utilizing the
> exposure-response data early on during the drug development, mainly
> for dose selection, dose optimization and dosage adjustment. As Larry
> also mentioned, it is an interdisciplinary kind of role that these
> aspects play. It is not just solely clinical pharmacology and us. So,
> it is the clinical division and at times even the chemistry reviewers
> and pharm. tox. as well.
>
> What we are going to do is we are going to share some case studies
> with you and, as Larry mentioned, these case studies are not really
> derived from the end-of-phase-2A meetings. These are derived from the
> NDA examples, for instance, but the principles and the concepts that
> will be discussed in these cases do lend themselves very appropriately
> to the general framework of the end-of-phase-2A.
>
> \[Slide\]
>
> Larry talked about the different milestones during drug development,
> the different time frames when we meet with the sponsors to discuss
> the drug development, with some companies more, with some a little
> less. It depends on the companies. So, I am not going to really
> emphasize the milestones, the different stages of drug development too
> much.
>
> I do want to dwell more on the different stages of the review cycle,
> the clinical, pharmacology and biopharmaceutics role in the review
> process, and what the reviewers go through and what questions they ask
> while they are reviewing the NDA, with special attention to the
> exposure-response relationships and, of course, exemplified with some
> case studies and the bottom line upshot of all this, the lessons
> learned.
>
> \[Slide\]
>
> Again, I am not going to focus on all the different stages of drug
> development but certainly I would like to draw your attention to this
> region, here, which is basically the NDA submission. The NDA comes in;
> we look at the NDA, the volumes, and we look for the primary
> components in order to file the NDA. If those primary components are
> in the packages that are submitted, the NDA gets filed. Interestingly,
> at that point how well exposure response is evaluated is not one of
> the components. So, there are certain things that we look for that
> makes the NDA reviewable. We file the NDA and then it goes through the
> review cycle.
>
> \[Slide\]
>
> Basically, what I am going to focus on is in this circle, here, which
> is that the NDA gets filed. It is the review and the focus is what
> goes into the label if it does get approved. Of course, the bottom
> line is the action letter that goes back to the sponsor.
>
> \[Slide\]
>
> So, I would like to zoom in on this circle, here, the stages of
> clinical pharmacology and biopharmaceutics review. I classified the
> three components into three broad components, the NDA review, the
> label and the action letter.
>
> \[Slide\]
>
> Let\'s zoom in on the NDA review. What are the different stages of the
> clinical pharmacology and biopharmaceutics reviewer in the trenches?
> What do they go through? I would acknowledge Dr. Sheiner and one of
> his earlier papers, the question-based approach. We do take the
> question-based approach to reviewing an NDA.
>
> Basically, when a reviewer starts the review of an NDA we do ask a
> series of very logical questions and each one is inter-linked with the
> other, the bottom line being the big umbrella that Larry talked about
> earlier, risk assessment, risk management, dosage adjustment.
>
> How was the dose determined? Again, it is interdisciplinary; it is not
> just us. We do work with the clinical divisions on this. When you
> think of how the dose was determined, an obvious question that comes
> up is what is the exposure-response relationship? When you think of
> exposure-response relationship, you think in terms of both safety and
> efficacy. What is the most useful thing for determining or getting a
> good feel for the exposure-response relationship? It is choosing the
> right dose, the right starting dose in relation to where the profile
> is in terms of its efficacy as well as its safety. So, you can\'t be
> just blind-sided by let\'s get the biggest dose on the market so it
> beats placebo.
>
> There is another downside to it, and that is what are you going to
> lose; what are you going to give up should there be several doses so
> that the patients have the option of titrating up or down? Or, another
> aspect, which is really primarily clinical pharmacology, is
> extrinsic/intrinsic factors. How will the exposure change? Will the
> patients have an option for a lower dose given that, for example, they
> would be taking the drug with, say, ketoconazole and it is a 3A4
> substrate? So, things such as that is where we come in.
>
> \[Slide\]
>
> Once you have a good feel for the exposure-response relationship, both
> in terms of safety as well as efficacy, the obvious questions asked
> are what are the effects of extrinsic factors and what are the effects
> of intrinsic factors? When we consider these things, it is interesting
> how to us, I guess because of the number of NDAs we see, things just
> are so obvious or maybe the hindsight is 20/20. You would think a 3A4
> substrate is an important inhibitor study. There are times when the
> right studies are not done, and that is an example where we can help
> during the early development so that time is not lost towards the end.
> Is the dose of the important inhibitor done right, or will that become
> one of the approvable issues? So, things such as those could be useful
> and discussed during the end-of-phase-2A meeting. Of course, if you
> have the option for dose adjustments, is the pharmacokinetic dose
> proportional? That is where we come in as well.
>
> Peter mentioned earlier cardiac repolarization. The QT effects have
> taken on a big role in current drug development. These are also safety
> issues but we also look at the exposure response with the effects on
> the QT prolongation, and there is going to be an extensive discussion
> of that later on.
>
> Again, designing the QT studies\--we have a concept paper out. It
> talks about phase 1 studies but even in those are phase 1 studies
> there are certain aspects that you need to understand very well about
> a drug. For example, the concept paper talks about super-therapeutic
> doses. What are the relevant super-therapeutic doses? You need to know
> a little bit more about the drug. Again, that is where we can help
> out. For example, is a positive control used? Is a placebo used?
> Again, there is going to be more discussion on that later.
>
> Some biopharmaceutics aspects become important towards the end of the
> review cycle as well. Are appropriate bioequivalence studies done?
> Minor as it may seem, some QT aspects can become, you know, a little
> bit of a discussion issue towards the end, as well as the stability
> out there, things such as that.
>
> \[Slide\]
>
> Once we get all this information and we understand all this, the
> relevant information from all these studies and our understanding goes
> into the label. We try and make all this information in the label in a
> decipherable form as much as possible. Basically, what it translates
> to is what doses should be approved? What is the optimal dosing
> regimen? What is the right patient population? What are the extrinsic
> and intrinsic variables for which dosage adjustment might be needed?
> Again, it is interdisciplinary and it is not just clinical
> pharmacology and biopharmaceutics. We do interact with the other
> disciplines extensively to make these decisions at the end.
>
> Again, if intrinsic/extrinsic factors result in exposure changes, how
> critical are these? Should it go into precautions, warnings or even
> contraindications for that matter? Again, another aspect that has
> become quite important lately is the QT prolongation, the cardiac
> electrophysiology of the drug.
>
> The bottom line for all this is the action letter and it could be
> approval. If everything falls in place you could write a very good
> label. It could be approval with some phase 4 if the phase 4 could add
> value to the label, and the examples that Peter mentioned, approvable
> or non-approval\--that could be very common as well, depending on what
> is missing from the whole picture.
>
> \[Slide\]
>
> I will discuss a couple of case studies. Basically they make slight
> subtle different points, optimizing dose and dosing regimen, case A.
> Case B, selection and dose adjustment.
>
> \[Slide\]
>
> Starting with drug A, it is an injection formulation. Interestingly,
> the dose finding was done by the sponsor. A very nice dose-finding
> study was conducted. However, it was done on a short-term period, and
> that was fine. It was done on, say, X days. The efficacy was evaluated
> over 3X days, and this may be very common. You don\'t do three-year
> dose-finding studies. You do some short-term dose-finding studies and
> then you go into the clinical trial.
>
> Interestingly in this case, the dose finding that was done over an X
> period of time was done with a dosing regimen that was more frequent
> than the 3X time. You would think, you know, it would be okay
> depending on where you are on the exposure response with respect to
> efficacy. If you are way up, you know, a little change in
> concentration shouldn\'t make a difference. However, if you are not,
> then you need to very carefully evaluate what doses you are studying
> in this whole long-term period, and the observation was loss of
> efficacy over time.
>
> \[Slide\]
>
> We did have some exposure-response data. As this profile shows for
> drug A, the concentrations that would provide, say, 90 percent of the
> patients with efficacy was about 10. Interestingly, 10 was about the
> concentration that was targeted and it was studied in the phase 2
> dose-finding study.
>
> So, if you look at the profile here and if the doses were here you
> would think that if the frequency of the dosing is not the same as the
> dose-finding study then, you know, even if it drops from here to here
> it wouldn\'t really lose too much. However, you are at the threshold
> of efficacy here. If you are targeting 90 percent of the patients with
> efficacy, you don\'t really have much room to slide. Basically, that
> is what was observed.
>
> \[Slide\]
>
> Here are a little more specifics on drug A. The dosing was on day 1,
> day 15, day 29 and then monthly thereafter. So, if the dose finding
> was done in this region, here, you would think that efficacy was
> achieved mainly because of the more frequent administration here. But
> as time progressed there was loss of efficacy and, as you can see,
> there were patients that were going below the 10 targeted exposure.
> The reason you would think again hindsight is 20/20, you would think
> they could have done some simulations. But, you know, it is easier
> said than done I guess at the end of the NDA cycle.
>
> \[Slide\]
>
> Here is another example where we think we could have maybe helped out
> with some simulations and some decision-making. When we looked closer
> at the concentration distribution and if you just focus on the four
> boxes, right here is the concentration distribution at day 29. This is
> month 2. This is month 4 and this is month 6. If you look at this X
> axis with 10 as the target concentration, you can see that all these
> patients at month 1 were above those concentrations so obviously
> efficacy was achieved and 90 percent or more of the patients did
> achieve efficacy. However, as time progressed there were several
> patients who lost efficacy.
>
> \[Slide\]
>
> Simulations suggested higher or more frequent doses could achieve and
> maintain therapeutic drug concentrations based on the
> exposure-response relationships. Of course, you do want to factor in
> the side effects. So, of course, factoring that in, higher doses or
> more frequent doses could have helped. So, need for appropriate dose
> and dosing regimen selection could be where we could have contributed
> early on in the drug development.
>
> \[Slide\]
>
> Moving on to drug B, I do want to add that drug B is not a particular
> drug. What I have done here is I have taken several issues from more
> than one drug. I have combined it into this supposed drug B just to
> make the point. So, it is a new drug. The critical issues related to
> exposure response, in this case dose selection and dose adjustment due
> to intrinsic and extrinsic factors.
>
> \[Slide\]
>
> This is the dose-response relationship that is available to us based
> on phase 2/phase 3 data. When you look at this profile you would be
> tempted to go over the highest possible dose, which is maybe 200. So,
> the temptation to pursue the highest possible dose has to be balanced
> off with what you are giving up. If you are going from 100 to 200 you
> are not really gaining that much in terms of efficacy, but what are
> you losing? Even if you go down to 50, going from 50 to 100 you are
> gaining a little bit but at what cost? I would even go down further.
> How about this? This may be better than placebo. It is not as good as
> 50. But, you know, some patients may benefit from that and maybe we
> need to consider some extrinsic/intrinsic factors where even these
> strengths here could be approvable.
>
> So, looking at all this in and of itself is not sufficient. Again, as
> I mentioned earlier, in choosing the doses it is very useful to know
> the shape. Here you have the shape of the efficacy curve, but you also
> need to know the location of this curve in relation to the adverse
> events.
>
> Here is the adverse event profile for different adverse events,
> several studies, phase 2/phase 3. As you can see, for up to 50 you
> don\'t see much difference in terms of adverse events compared to
> placebo, but as you go higher you do see an increase in adverse
> events. How do you balance this off? Thinking in terms of the utility
> function\--we don\'t have that yet but thinking in terms of the
> utility function, you wonder how severe are these adverse events.
> Would it be reasonable even to approve this dose? Again, it depends on
> the utility function or the severity in terms of risk-benefit
> analysis.
>
> So, again, going from 100 to 200 you do need to factor all this in. It
> may be prudent to cover lower doses just so that the patients have
> options. So, there were dose-related adverse events. What if, in this
> day and age, it is dose-related QT effects? Again, bringing in the
> utility function, how critical is this 200 dose? What if it is
> dose-related QT events? Should it even be approved, the 200 mg dose?
> So, all these aspects were considered in drug B.
>
> At this point, when you have a good feel for the exposure response for
> efficacy as well as safety, the next obvious question that we asked is
> what is the effect of extrinsic/intrinsic factors? If there are
> changes in exposures, big changes in exposures, don\'t you think there
> should be more than one strength available to the patients so that
> patients can start at, say, 25 mg, right here, and have the option of
> taking it with, say, ketoconazole if it is a 3A4 substrate so that the
> exposure does give you some room for safety as well as efficacy?
>
> \[Slide\]
>
> Then you target an exposure profile. That is the exposure profile; you
> want to keep a balance of safety and efficacy. You see what happens
> with intrinsic factors. In this case, say for hepatic impaired
> patients, the exposure went up. You can have a lower dose in these
> hepatic patients.
>
> It could be something worse in an intrinsic scenario and in that case
> you may want to consider a much lower dose, and is that strength
> available with stability data? I mean, should that come at the end or
> should that be thought through early on because you don\'t want a
> small thing like that to be a show stopper. In this case, for
> instance, you want to consider not maybe just lowering of a dose but
> even the dosing interval. So, things such as this did lead to dose
> adjustment for drug B.
>
> \[Slide\]
>
> In conclusion for drug B, exposure-response analysis suggested that
> more than one dose should be considered for optimal balance between
> safety and efficacy. Based on the changes in exposure due to these
> factors, dosage adjustment was recommended in the label. And,
> considering these outcomes early in drug development can help plan
> appropriate clin. pharm. studies, say for example, the drug-drug
> interaction studies. We often go back and say, well, you have done the
> study with 200 mg ketoconazole; you should do it with 40 mg
> ketoconazole.
>
> \[Slide\]
>
> So, things such as that are minor but they can become important issues
> with respect to safety and labeling at the end. Based on experience
> for changes due to extrinsic and intrinsic factors, sponsors may
> consider additional strengths for marketing and have appropriate work
> done for these lower strengths.
>
> \[Slide\]
>
> The concluding slide is basically that exposure-response information
> is at the heart of determination of the optimal drug with respect to
> good safety and efficacy, and the cases have exemplified that. In
> conclusion, it is important that carefully and timely consideration be
> given to these assessments, and that emphasis be laid on
> exposure-response analysis for both safety and efficacy and also
> extrinsic/intrinsic factors. Thanks.
>
> DR. VENITZ: Thank you, Ameeta. Any specific questions?
>
> DR. JUSKO: Dr. Parekh, I wasn\'t clear, for drug A were you showing us
> the results of a phase 2A study? It seemed like there was a large
> number of patients. Are you saying that the manufacturer did not
> recognize this drop in concentrations and did not deal with it
> appropriately?
>
> DR. PAREKH: Again going back, we don\'t have any cases with
> end-of-phase-2A type of setting. What I presented in those two cases
> is based on phase 2B and phase 3 data where there was available to us
> some exposure-response information. Based on that, if at least phase 2
> data could be evaluated early on maybe a better assessment could be
> made on dose selection, dose titration or dosing regimens for example.
> But the two examples that I gave are definitely not phase 2A because
> we haven\'t really implemented phase 2A yet. But certainly
> end-of-phase-2B is where we can get some of the data. So, there were
> good dose-finding studies done but the exposure response was not
> evaluated as well as we think so it could have helped the sponsor as
> well as us.
>
> DR. LESKO: Bill, I think that point is actually relevant because one
> of the things we are trying to look at from the NDA is to sort of
> sequentially go back and take information from what we know and see if
> our analysis of earlier data would have led to different conclusions
> than the sponsor actually did. Because one of the realities of
> end-of-phase-2A is, yes, you are going to have relatively small
> studies compared to phase 3 and whether that information, depending on
> a case-by-case, is going to be enough to do effective analyses of dose
> response to go forward with or not depends.
>
> We won\'t always have the extent of information that Ameeta presented
> from that particular NDA, but our experience in going back and saying
> let\'s not look at the phase 3 data; let\'s look at what we knew\--you
> know, try to mirror a real example, still seems to show that we would
> come up with some valuable analyses and maybe different
> recommendations. But that is something we have to learn and get
> through.
>
> DR. VENITZ: Any further questions?
>
> \[No response\]
>
> Thanks again, Ameeta. Our next speaker is Hae-Young Ahn. She is going
> to talk about another example involving a drug that was recently
> reviewed.
>
> DR. AHN: Hi. This is Hae-Young Ahn.
>
> \[Slide\]
>
> I will discuss two studies with rosuvastatin. Since rosuvastatin is
> approved I don\'t have to blind the drug name. At this moment I would
> like to discuss the role of exposure-response evaluation in drug
> development and regulatory decisions using rosuvastatin.
>
> \[Slide\]
>
> The background of rosuvastatin\--it is a synthetic lipid-lowering
> agent. Its mechanism of action is competitive inhibition of HMG-CoA
> reductase. Its pharmacokinetics is as follows: Its absolute
> bioavailability is about 20 percent in the Caucasian population, and
> food decreases Cmax about 20 percent, however, it does not alter the
> exposure of AUC. It is not metabolized extensively. However, 10
> percent of a radio-labeled dose is recovered as a metabolite. A major
> metabolite is formed by 2C9. Rosuvastatin is primarily excreted in the
> feces and the elimination half-life is 19 hours.
>
> \[Slide\]
>
> Japanese and Chinese ancestry have two-fold AUC that of the Caucasian
> population; patients with severe renal impairment have three-fold
> higher compared to healthy volunteers. And, there were significant
> drug-drug interactions. Cyclosporine increased the levels of
> rosuvastatin about seven-fold. Gemfibrozil increased exposure about
> two-fold.
>
> \[Slide\]
>
> The original NDA was submitted in June, 2001. The sponsor proposed
> doses of 10 mg, 20 mg, 40 mg and 80 mg. In May, 2002 an approvable
> letter was issued to the company by the agency. In the letter it was
> stated that 80 mg was not approvable because of little added benefit
> over the 40 mg. This small added benefit does not outweigh the risk of
> myopathy and renal concerns. The letter stated that 10 mg, 20 mg and
> 40 mg are approvable.
>
> Before the NDA was approved the following issues should be addressed
> by the sponsor: The first was additional safety data on 20 mg and 40
> mg because the number of patients in clinical trials were not adequate
> to provide assurance of the safety of either 20 mg or 40 mg. And, the
> company had to address the renal issues because safety monitoring in
> clinical trials was not adequate to determine the nature of the renal
> toxicity. Finally, the agency believed the clinical data was not
> adequate to assess optimal dosing. After the sponsor addressed the
> above issues adequately, in August of 2003 the approval letter was
> issued to the company. At this time we approved 5 to 40 mg.
>
> \[Slide\]
>
> How could exposure response or PK/PD modeling guide optimal dosing for
> rosuvastatin?
>
> \[Slide\]
>
> This slide shows the LDL cholesterol percent change from baseline.
> This data is from two clinical trials. This slide clearly shows that
> lipid lowering is dose related from 1 mg to 80 mg even though the
> company proposed 10 mg to 80 mg.
>
> \[Slide\]
>
> This slide clearly shows lower than 10 mg and 1 mg to 5 mg, can have
> significant LDL lowering effect. For example, 1 mg has 33 percent LDL
> reduction; 5 mg has 43 percent LDL reduction. The titration from 40 mg
> to 80 mg does not provide any additional significant benefit. However,
> the 80 mg dose provides a mean of 2-4 percent of LDL reduction
> compared to 40 mg. However, the range of responses was very similar to
> that of 40 mg. So, at this moment I would like to draw your attention
> to the lower dose than 10 mg.
>
> \[Slide\]
>
> The Office of Clinical Pharmacology and Biopharmaceutics did PK/PD
> modeling. The first column is dose. The second and third column
> represent observed percent LDL reduction. The fourth column is the
> mean predicted percent in the reduction at week 6. The last column
> represents the minimum percent LDL reduction in 85 percent of the
> populations.
>
> Let\'s look at the fourth column. Our prediction shows that 1 mg has a
> mean of 38 percent of LDL reduction; 5 mg can provide 44 percent of
> LDL reduction; 10 mg can provide 50 percent of LDL reduction.
>
> Let\'s look at the last column, a 1 mg dose can provide a minimum 26
> percent of LDL reduction in 85 percent of the in patients; 5 mg can
> provide a minimum of 32 percent of LDL reduction in 85 percent of the
> population.
>
> \[Slide\]
>
> Since there are so many modeling people, I would like to satisfy you
> modeling experts. This is LDL percent changes from 1 mg up to 80 mg.
> The efficacy endpoint was after 6 weeks. This is our predictive
> simulated data and these are observed data from two clinical trials. A
> mean observed in clinical trial data overlaps with the predicted
> value. So, we can say our model was validated.
>
> \[Slide\]
>
> At this moment I would like to switch gears from efficacy to safety.
> This slide shows the incidence of CK elevations in myopathy seen in
> steady treatment. This summarizes the data from the clinical trial
> development from Baycol, rosuvastatin and all currently marketed
> statins. For rosuvastatin, a 40 mg dose lowers the incidence of CK
> elevation and myopathy within the range of all currently marketed
> approved statins. However, there is a clear break at 80 mg. The two
> highest does of Baycol, 0.4 mg and 0.8 mg and rosuvastatin 80 mg have
> similar frequency of CK elevations of 10-fold of the upper limit or
> normal and myopathy as you can compare these two values.
>
> \[Slide\]
>
> This slide shows the percent of patients with proteinuria. Patients
> include all controlled and uncontrolled clinical trials at any visit.
> The numbers in parentheses are total number of patients in each group.
> There is a clear percent of patients with proteinuria that is kind of
> dose related. There is a clear visible transition at 80 mg where the
> peak incidence of proteinuria was 17 percent. However, for all the
> marketed statins the frequency of proteinuria was less than 4 percent.
> It is very similar to the incidence of placebo. Actually, there is a
> typo; it is supposed to be dietary run-in.
>
> \[Slide\]
>
> This slide shows the steady state concentration of rosuvastatin. The
> rosuvastatin plasma concentration compared 20 mg, 40 mg and 80 mg, and
> these values were compared with patients who developed rhabdomyolisis
> or renal toxicity. There is no overlap in exposure among the patients
> who received 20 mg and patients with renal toxicities. There is a
> small overlap in exposure among patients taking 40 mg and patients who
> developed toxicities. However, one-third of the patients who took 80
> mg had steady state plasma concentrations of 15 ng/ml, which is the
> lowest concentration associated with toxicities. Therefore, this slide
> suggests that any drug-drug interactions or using special populations
> may result in steady state plasma concentration elevations similar to
> patients with these rhabdo. cases.
>
> \[Slide\]
>
> This slide shows the percent change in AUC and Cmax. Cyclosporine can
> increase exposure seven-fold. Gemfibrozil increases exposure two-fold.
> Japanese ancestry increases the exposure two-fold. Patients with
> severe renal insufficiency, creatinine clearance less than 30, had
> increased exposure about three-fold. These increases are considered
> clinically significant and require special consideration in dosing for
> patients.
>
> \[Slide\]
>
> Therefore, the highlighted statement was incorporated in the label
> under precautions: Pharmacokinetic studies show 2-fold elevation in
> median exposure in Japanese subjects residing in Japan and in Chinese
> subjects residing in Singapore compared with Caucasians residing in
> North American and Europe. These increases should be considered for
> dosing decisions for Japanese and Chinese ancestry.
>
> \[Slide\]
>
> Based on the finding of PK/PD modeling, the following dose and
> administration was incorporated in the label. For hypercholesterolemia
> and mixed dyslipidemia, baseline LDL lower than 190, the dose range is
> 5 mg to 40 mg once daily. Therapy should be individualized and the
> usual recommended starting dose is 10 mg. However, 5 mg should be
> considered for less aggressive LDL reduction or predisposing factors
> for myopathy.
>
> \[Slide\]
>
> In dosage and administration in the labeling there is a limit for the
> maximal doses as well. Patients who are taking cyclosporine should not
> exceed 5 mg. They should use only 5 mg. Patients who are taking
> gemfibrozil should not exceed a dose of 10 mg. Patients with severe
> renal impairment should not exceed 10 mg of rosuvastatin.
>
> \[Slide\]
>
> So, my conclusion is that although the sponsor has proposed doses of
> 10 mg, 20 mg, 40 mg and 80 mg, the exposure-response relationship
> clearly shows doses lower than 10 mg have a potential clinical
> utility. There is apparent relationship between adverse events and
> plasma concentration of the drug. Therefore, findings from
> exposure-response relationships were used in recommendations for
> dosing adjustments. That is my last slide. Thank you.
>
> DR. VENITZ: Thank you, Hae-Young. Any comments or questions by the
> committee? Let me make a comment, Hae-Young. If I look at your slide
> number nine that discusses the dose response of safety and the topic
> that we are discussing is end-of-phase-2A, here you are making the
> argument that the incidence of CK elevations goes up quite
> dramatically after a dose of 80 mg. I don\'t think that at a 2A stage
> you would have had that information. This is really looking at, I am
> assuming, a phase 2 and phase 3 large database in order for you to be
> able to assess 0.2 and 1.0 percent prevalence of adverse events. Is
> that true?
>
> DR. AHN: I agree with you because in all the phase 2A trials there is
> no way you can find CK elevation.
>
> DR. VENITZ: So, as far as the end-of-phase-2A meeting is concerned,
> the only contribution that exposure response would have been able to
> contribute is not based on safety because you wouldn\'t have that
> safety information at that stage.
>
> DR. AHN: But there is a possibility you can measure proteinuria in
> phase 2A.
>
> DR. VENITZ: Okay, and that is at a high incidence so you would have a
> better chance of seeing it in 2A. Any other comments? Go ahead.
>
> DR. SHEINER: Let me follow-up on that. You have to know the chemistry,
> the pharmacology and all that, but if you believe that these drugs are
> sufficiently similar both in mechanisms of efficacy and toxicity, then
> you could argue from the Baycol experience. So, the question is at
> what point what are there prudent plans for going beyond phase 2A. You
> could argue that maybe at that point in time\--I don\'t know where it
> occurred in the history of this whole story, but it could be argued
> that it might have been prudent at that point to have a plan to look
> very closely at the higher dose, both from the point of view of
> whether it added enough efficacy to be worth it and whether it was
> toxic. Again, you know, hindsight always gets you there, but you could
> say that even without toxicity data on the drug itself you might have
> been able to say something.
>
> DR. AHN: Actually, this is true because safety is one issue but
> efficacy is the other issue. When the company titrated from 40 to 80
> the LDL reduction was very small. So, that is one issue we can
> discuss.
>
> DR. VENITZ: Thank you again. Our last case study is going to be
> presented by Joga Gobburu.
>
> DR. GOBBURU: Dr. Venitz and Committee, I will be presenting a case
> study, from the same team you have heard so far, on the utility of an
> interaction between the agency and the sponsor early on. The drug I am
> going to present is a very simple, straightforward application of
> quantitative exposure-response analysis. So, the key point I would
> like to highlight here is not the methodology of quantitative analysis
> but, rather, the progressive thinking of the agency.
>
> \[Slide\]
>
> The drug I will be presenting is being developed for symptomatic
> benefit and is proposed to be given once a day. Clinically it is
> desired to have a sustained effect over the dosing interval, that is,
> 24 hours. However, the drug exhibits a short half-life of two hours.
> In this setting, typically we don\'t see large clinical trials. They
> are relatively smaller clinical trials. However, for this particular
> drug the sponsor elected a relatively large pivotal trial and the data
> from those trials were analyzed both using conventional and
> experimental analysis methods.
>
> \[Slide\]
>
> Let\'s briefly look at the development diary. As with any other
> compound, we had preclinical data and data from early drug
> development, including proof of concept and the PK/PD information in a
> small target population. So, there were data available in a target
> population for the intended effect. Then it was followed by the
> pivotal trials and regulatory review, which is about ten months.
>
> \[Slide\]
>
> Let\'s focus on the regulatory review box. The conventional analysis
> clearly showed that the treatment beat placebo. The endpoint was
> change in symptomatic benefit at trough versus baseline. So, by
> conventional means it met the primary analysis goal.
>
> As I said earlier, the drug is supposed to be a once a day drug.
> However, the magnitude of effect was small to modest, if at all. Then,
> given the fact that the terminal half-life is short, we don\'t need
> any modeling to come up with the question to ask whether this drug is
> really for once a day use.
>
> \[Slide\]
>
> But we do need the quantitative exposure-response analysis to answer
> the question in a very definitive manner by first answering several of
> these questions, such as is the effect in the first place, indeed,
> concentration-dependent at all? If so, is the concentration-response
> relationship, indeed, linear or nonlinear? Why that is important we
> will see in the next slide. If there is a delay between PK and PD,
> even though the drug is eliminated with a terminal half-life of two
> hours, the pharmacodynamic effect could persist for a long period of
> time. Is there tolerance that is being developed over the dosing
> interval? Importantly, is the toxicity concentration dependent? If we
> have answers for all of these, then we may have a proposal\--if it is
> not a once a day drug, what are the alternatives?
>
> \[Slide\]
>
> Let\'s get the toxicity out of the way. It was concentration dependent
> so there are limitations on how high you can push the concentrations
> beyond what was studied in the drug development. There was a clear
> concentration-effect relationship and no considerable delay that was
> estimable between the PK and PD. The relationship was nonlinear,
> meaning that having higher concentrations would prolong the duration
> of the effect but will not increase the magnitude of the effect.
> However, we have to keep in mind that the toxicity was also
> concentration dependent. So, we can\'t push the dose any higher.
>
> Now, all this analysis, for all practical purposes, was conducted by
> the agency and, unlike the conventional analysis which used the trough
> measurements only, the whole time course of the effect at several
> locations was used to utilize the data collected in these studies to
> the maximum.
>
> With respect to the time course of concentrations, the graph you see
> on the right-hand side has time on the X axis and concentrations on
> the Y axis, and there is a dotted line with the EC50 estimated using
> quantitative analysis. As you see, at about six hours, if we agree
> that EC50 is a reasonable target for the concentrations, the
> concentrations go below this level and then sustained effect is
> compromised. Clearly, modeling demonstrated by answering all the
> questions posed in the previous slide, the inadequacy of once a day
> dosing, at least for this formulation.
>
> \[Slide\]
>
> Quantitative analysis has offered us more, meaning what could be done
> to ascertain sustained effect over the 24 hours. So, you know, it is a
> very simple simulation. What if you give the same dose twice a day or
> thrice a day or, more practically, this graph shows that sustained
> release may be a reasonable alternative rather than this
> immediate-release formulation. So, as you see, with the more frequent
> administration the concentrations lie above the EC50 value and they
> assure that the effect is sustained over the dosing interval.
>
> \[Slide\]
>
> Regarding the drug development diary, we identified that the lack of
> sustained effect across 24 hours was a deficiency and that the sponsor
> needs to address that in the next round. We also encouraged them to
> consider more rational dosing strategies. What that has led to is an
> extension of the drug development program by probably three to five
> years. These are numbers that I have made up; I have no clue as to how
> long it usually takes to redevelop the formulation and recruit
> patients and conduct the pivotal trials. But the review will again be
> about six months.
>
> \[Slide\]
>
> To summarize the exposure-response analysis, first use of all the data
> collected in the trial, supportive evidence for effec in addition to
> the conventional analysis. It also aided in judging that once a day
> dosing is probably suboptimal and eliminated the need for testing
> higher doses but, rather, to focus on alternative dosing strategies
> because concentration-dependent toxicity was observed, as well as that
> the effectiveness was clearly plateau-ing at higher concentrations.
>
> \[Slide\]
>
> Now, if we rewind the development process and now introduce an
> end-of-phase-2A meeting somewhere before the total trials are
> undertaken, since we had the data from the proof of concept and target
> population earlier on, it would have been possible for us to first
> comment on the agency\'s view about the sustained effect over the
> dosing interval.
>
> So, early studies, as I said, were available. Of course, the
> availability of the data\--I mean, we have to make sure that they are
> properly analyzed before such a meeting takes place. It would have
> been very clearly communicated to the sponsor that the optimal dosing
> is expected not just a p value of 0.05. That would have led to a
> considerably smaller study because we don\'t need to power the study
> to get the significant p value and need a large trial. Ultimately,
> probably it would have led to improving the efficiency of drug
> development.
>
> \[Slide\]
>
> Finally, I would like to acknowledge our team, DPE-1, Division of
> Pharmaceutical Evaluation Pharmacometrics Team and the director and
> deputy director and their support. Thanks.
>
> DR. VENITZ: Thank you, Joga. Any questions for Dr. Gobburu?
>
> DR. SHEINER: I don\'t question that had they been able to look at what
> they were aiming for they could have designed a better phase 3 to get
> that, but I do question, and you admitted that you made up the
> numbers\--do you think the FDA would have demanded new pivotal studies
> at the end? I mean, wouldn\'t it have been enough to show that the new
> preparation sustained concentrations over that period of time? If you
> had a good concentration-response relationship, wouldn\'t that be
> enough to argue that that was adequate?
>
> DR. GOBBURU: Well, I am going to be very careful in answering this. I
> thought that somebody from the company would ask me this question. The
> very fact that there is a concentration-dependent effect and that we
> are testing new regimens, there is some uncertainty if you take the
> interdisciplinary team into account.
>
> I have two points to say about that. One is are we in that way
> supporting poor drug development, meaning it is okay to do a
> suboptimal study and then, since you have a model, we don\'t need to
> do anything else? The second point is that there is definitely a
> mixture of empiricists and modelers, Bayesian modelers here. So, there
> has to be empirical evidence. If I have to take a stand I would say
> that there has to be empirical evidence with the other dosing regimen.
>
> DR. SHEINER: I think we can discuss this more later but it certainly
> is true, for example, that drugs have been approved at doses that have
> never been tested.
>
> DR. GOBBURU: That is true.
>
> DR. SHEINER: Especially if you bracket it with one below and one above
> and it really looks like the one in the middle, which you didn\'t
> test, would really do a better job and you have nice dose response,
> toxicity and efficacy. So, it sort of sounds like you are giving and
> taking at the same time and it is really tough. I mean, if you are
> saying that science is going to be helpful here, then you want to, you
> know, sort of follow that through.
>
> I think the agency has to think about what its policy is and to what
> extent it will rely upon good empirical evidence that the drug works,
> good empirical evidence of what the concentration response is and,
> therefore, extrapolate or interpolate to a place that says, well, we
> know what is going to happen if we do this because we know what
> happens if you give more, if you give less, and so on. I mean, there
> has to be room for that. You can\'t just say that everything has to be
> empirically demonstrated.
>
> DR. GOBBURU: If you are increasing the frequency of dosing and we have
> never seen any safety information about increased dosing, it is just a
> black box. We have no clue as to what to expect. So, I would still
> stick with my stand that we need empirical evidence.
>
> DR. DERENDORF: We don\'t know what kind of a drug it is and what kind
> of an indication it is used for but conceptually you use the EC50 as
> your target. Now, EC50 is the concentration where you have 50 percent
> of the maximum effect. It doesn\'t tell you anything about where you
> stand in terms of therapeutic benefit. Actually, 30 percent
> concentrations below the EC50 may still have considerable therapeutic
> benefit. So, I am not sure if that is a given cut-off that you can
> use.
>
> I think the second part of the question is you said the dosing regimen
> is not optimal. Does that mean that if you have a suboptimal regimen
> that you propose that it would be acceptable from the beginning?
> Again, you could have a suboptimal regimen that is still of great
> therapeutic benefit.
>
> DR. GOBBURU: Okay, these questions are very hard to answer because you
> are asking me a question about what the target effect is. I think the
> meeting here is to really move from the conventional analysis to bring
> in more advanced technology in order to optimize the therapy. I do
> agree to that. But today we do not have\--for example, for this
> indication the target effect that is acceptable, nobody gives us that
> number. That is why when I presented the curve I said if EC50 is
> accepted as a reasonable target concentration. If you want to choose
> 70 percent or you want to choose 20 percent, that is fine but, still,
> you look at the effect curve over time and it is going back to
> baseline at about six hours. There is no question about that.
>
> DR. KEARNS: I think that is true but it is important to step back for
> just a minute. I mean, certainly the technology and the modeling\--and
> all of us can understand when it drops below some threshold number,
> but what if it was a drug and a disease where the relief of symptoms
> extended beyond the time when the concentration was below the EC50?
> Because in that instance it can be argued that the need to push a
> sponsor into another three to five years worth of study with a new
> formulation and more pivotal trials may not be wise. In fact, that
> would be contrary to the strategic plan of the agency now, which is to
> effectively collapse drug development.
>
> So, dragging this in early, Larry, as you mentioned with using the
> medical expertise in addition to the kinetic, dynamic modeling
> expertise I think is critical because at the end of the day you want
> to make the best decision for the life of the compound and its
> development, not necessarily say, well, we have created more
> questions; now we have to make answers to them.
>
> DR. GOBBURU: If you look at question number three, if there is a delay
> between PK and PD, if that is true, we would have found it and we
> systematically tested for that. So, I am not presenting this example
> saying that we didn\'t take the time course effect; we did.
>
> DR. VENITZ: Go ahead, Wolfgang.
>
> DR. SADEE: I think one of the critical questions is whether you really
> have enough information at the 2A step to decide here is your
> threshold; here is what you titrate for and that is how you go forward
> in designing the trial and you then come up with a relatively
> arbitrary sort of threshold, let\'s say the EC50 or something like
> that. Or, in the previous case with the statins you base your
> decisions on LDL cholesterol which is a very crude measure and, in
> addition, one that is not forward looking; it doesn\'t tell you
> possibly anything about the eventual outcome as to how this should be
> used. Personally, if I were to be put on this particular statin I may
> have started out with 2 mg, depending on what the case is, or 1 mg and
> that could have been just as effective.
>
> So, given the complexity I am just wondering\-- you said we want to
> bring in more technology or more science, that would mean more
> information. For instance, in the case of the statins I would say, all
> right, let\'s look at the different sizes of LDL and HDL and how that
> is affected by the different dosage levels and get a little bit more
> information on it. Then it may be worthwhile to come in early. So, I
> am just raising the question, after hearing the discussion, as to do
> we know what to recommend at that point?
>
> DR. VENITZ: Can I just make a statement? Let\'s just focus on the
> presentation and we may have a general discussion after the break. I
> think you raise a very important question but I would like that to be
> discussed after we have done with the individual cases. So, if you
> want to respond, feel free.
>
> DR. GOBBURU: Thank you. Dr. Lesko can comment more about this. I
> don\'t think the intention of these meetings is to pin-point exactly
> where to go. As long as we have a range of options the drug
> development could be tailored accordingly to answer those
> uncertainties. So, in this case, I agree that we didn\'t know what
> would have happened if you had given the doses repeatedly over the
> day. But we have identified the inadequacy of this once a day dosing
> so that has definitely opened up new avenues that need to be explored.
> So, I don\'t think we will ever have a precise answer at the end of
> phase 2A but at least we may have a more precise direction to go
> forward.
>
> DR. SHEK: Just a general question, I wonder whether this example is a
> good example. First, looking at the drug development diary, it looks
> like it took ten years to develop it, which maybe is on the high side.
> Then if the boxes are linear there in the diary, it looks like a long
> period of time, which I would assume is a phase 2 study. If you just
> think back, I mean some of those questions should have been answered.
> So, I think something was going on with this project and I just wonder
> whether that is a good or typical example.
>
> DR. GOBBURU: Well, as I said in my presentation, I have no clue about
> these numbers. I just made reference to the numbers so that we will
> have a time frame and a ratio of the period that\--extra time needed
> to redevelop the drug when compared to the original drug development
> time period. So, the ten years\--I have no clue how long it took the
> sponsor to develop it; it could have been five and a half but
> relatively there is a 20 percent to 30 percent increase in time, I
> would guess, because they had to go back and revisit the dosing issue.
> So, it is just a ratio you should be looking at.
>
> DR. LESKO: Yes, I think the three to five years was just a speculative
> estimate, you know, trying to make the point that whatever analysis
> occurred at the late stage led to a need to reformulate and some
> additional trials. Now, what those trials might have been is still
> open to question. As Dr. Sheiner pointed out, can you use the
> exposure-response relationship and treat this in essence as a
> therapeutic equivalence situation and look at comparable blood levels
> from a revised formulation, and if there were additional efficacy data
> needed, what would be the size of that study. So, I think it is an
> open question there.
>
> I think the point of it though is that this analysis occurred at the
> end of the game, a ten-year process when the NDA was submitted. It
> wasn\'t adequate and the data was available early on. So, I think it
> was trying to represent the type of information that could be used
> more optimally earlier in drug development. Yes, you can approve drugs
> based on doses that are effective and not necessarily optimal. I think
> one of the goals of this strategy is to try to move from just
> effective to something more optimal, taking into account the type of
> issues that we have seen in this case and the prior ones.
>
> DR. VENITZ: Any other questions or comments for Joga\'s presentation?
>
> \[No response\]
>
> Thank you, Joga. We are going to get an early break. It is now 10:25.
> We have a 20-minute break so let\'s get together at 10:45. So, the
> committee reconvenes at 10:45 for the discussions.
>
> \[Brief recess\]
>
> **Committee Discussion**
>
> DR. VENITZ: To get us started on our discussion I would like for Dr.
> Lesko to review the three specific questions that you have in your
> background material that he would like to get some feedback on.
>
> DR. LESKO: These are the questions that we wanted to bring before the
> committee. Just to summarize this morning\'s session, what we tried to
> present is a framework for thinking about improving drug development
> through a new initiative that would bring the agency and the company
> together to discuss, in specific terms, the dose response and the
> rationale for dose selection and dose-range selection as the drug
> development program moves forward.
>
> As a secondary objective, we also see this as an opportunity to review
> the overall clinical pharmacology development plan with respect to
> what the drug interactions are, special populations ara, and any
> formulation issues to try to come to some sort of agreement or
> dialogue on what is necessary in a particular case.
>
> So, what we presented today\--again, we recognize they weren\'t the
> technology underneath what was presented but each of those cases
> involved the usual technology of modeling, simulation, predictions and
> so on. More than the technology, what we really wanted to get some
> reaction to today was the general plan to move forward. As I mentioned
> in my introductory comments, this is really the first time we are
> discussing this publicly and the Center would like us to develop a
> guidance in this area and make it available to sponsors in the sense
> that it would lay out the goals and background information, and so on.
>
> So, what we are looking for today in these questions are your thoughts
> on the proposal that we have put before the committee, the rationale
> for it, any ideas you might have on how that could be improved, and
> any obstacles that you would anticipate from your own experience that
> would limit the success of this program.
>
> The second question\--we presented some examples of analysis and there
> were some comments with each case as it was presented. But, hopefully,
> it gave you a flavor for the types of things that might be discussed
> at this meeting, obviously dependent on a case-by-case basis.
>
> Then, the third point is that we have been asked by the Center to
> develop some measurements and metrics for measuring the success of
> this program in the sense of continuing it and adding more resources
> to it as we move forward.
>
> So, these are really the three broad areas and certainly any comments
> would be appreciated, or anything else that we haven\'t thought of in
> terms of these three questions.
>
> DR. SHEINER: First, let me say that I think it is a good idea but I am
> not exactly sure why and I think we need to think about that, or at
> least I do. So, let me just say that we even accept\--I mean, there
> are people who would argue with this but let\'s accept for the sake of
> argument that there is insufficient use of prior existing data in the
> planning of the later stages of drug development, to put it very
> broadly, and in particular with respect to dose or regimen that is
> going to be tested in later phases. That prior data consists of, you
> know, science which generally people agree is known; public domain
> type data, actual numbers and data that is out there that you could
> incorporate into your analyses; and then there is proprietary data,
> the stuff that the manufacturer has been developing in the course of
> phase 1 and whatever comes before this meeting.
>
> So, let\'s assume that they are not adequately taking advantage of
> that, as we see it, in planning what comes later. The question is what
> is the cause? Because you come up with a remedy in a sense. Without
> being a little facetious, if the remedy is a meeting in which you help
> them figure out how to use this data, it means they are not smart
> enough to do it themselves. That is what you have diagnosed as the
> cause and I don\'t think that is true. I think there are a lot of very
> smart people and obviously you do too.
>
> So, what is the reason that the smart people in the pharmaceutical
> industry who are perfectly capable of looking at the data when they
> change hats and go to work for you or change hats and go work in
> academics, or whatever, why those same people in industry are not
> doing that, and why could looking at these things, the kinds of
> examples we saw which are not, you know, rocket science, why is that
> useful and why does it look like it would have been useful to do that
> and why didn\'t they do it?
>
> I have thought about this a lot and a lot of people have thought about
> this a lot, and I am sure there are as many reasons in our minds as
> there are people in the room. So, the question really is will this
> particular action, which is offering help, aid, guidance\--will this
> help to get over whatever the reason is that they are not doing it
> themselves? Personally, I think calling attention to the whole issue
> and making a point of saying it is important, important to the
> regulatory agencies, will be a help because I think there are
> institutional reasons why it isn\'t happening which would, to some
> extent, be mitigated by doing that.
>
> Remember, I made a suggestion here the last time or the time before
> where I said, you know, maybe for a while the FDA could try saying you
> have to give us some reasonable decision analysis-based argument for
> why we should approve the dose that you are asking to be approved.
> Show us one efficacy endpoint, one toxicity endpoint and some utility
> function and a computation and data. Not that that is required for
> approval; we are not changing the rules but we just need one of those
> things before\--you know, that is part of the dossier.
>
> I was addressing the same issue. I said let\'s make people think about
> it and maybe if they have to think about it they will find that it is
> useful. Here you are not quite making them think about it. You are
> offering them the opportunity to think about it with you, and that is
> a little gentler and maybe it is a good idea. But I do think we should
> spend a little while thinking about whether this is the most efficient
> use of your time and effort to overcome that problem which doesn\'t
> look like it is because they are too stupid. That is not the issue.
> There is something else, some other reason why it is not happening.
>
> DR. LESKO: And it is an excellent question, and it is one we have
> asked during the sort of roll-out of this internally. We talked about
> the facts that I had on one of the slides about the failure rate of
> clinical trials. That number comes from the industry; it doesn\'t come
> from us. We don\'t know actually what the underlying reasons for those
> failures are. I don\'t think that has been studied in a systematic
> way.
>
> Some of the observations that we have are, for example, instances
> where a single dose is chosen for phase 3 trials. We have tried to
> encourage more dose-response data from phase 3 and continue to look at
> that, and that was the gist of the quote I had from Dr. Temple from
> his presentation at DIA. So, this might be a way to talk about that.
>
> You are right, you did make a point at one of our earlier meetings,
> and this does actually represent a time at which we might ask what is
> the rationale for this dose and discuss that collaboratively. I don\'t
> think it is an issue of people being too dumb to know what to do. I
> think it is an issue of a fair amount of uncertainty in the drug
> development process, for a variety of reasons, and can the agency
> offer some experience that it has from its NDA review. Most of our
> time goes to NDA review and, as you know, at that point in time
> everything is history. You are basically looking at a document and
> picking out deficiencies or looking at areas where missing data might
> occur.
>
> So, in terms of using resources efficiently, it seems like the
> efficient use would be to move the resources forward a bit and not
> sort of dwell upon\--although we have to but not necessarily dwell
> more than we need to dwell on the shortcomings of an actual submission
> but try to improve things early on. So, part of it is sharing
> perspectives on dose response, which is not predictable from a
> scientific standpoint. When a company comes in they don\'t exactly
> know how the agency is going to react to that assessment of dose
> response and risk-benefit. So, having the opportunity to talk about
> that earlier on I think allows one to be a little bit smarter about
> the way to move forward. But there is uncertainty here.
>
> The alternative ideas for looking at the problem, there aren\'t very
> specific suggestions that I can think of. So, we look at this as a
> pilot study; look at how it goes; and see where there are improvements
> to be made.
>
> DR. KEARNS: Larry, I think you just said it very much as a cart and a
> horse issue here. I mean, right now if your shop is brought in at the
> point of time of NDA review, with all the new technology it is easy to
> see the gaps. Then, as you go back and interact with the review
> division or the sponsor and begin to address ways so those gaps could
> be, or should be, or must be filled, then that has a definite impact
> on the process.
>
> I think there are a couple of key elements to doing it early and I
> support the integration of clinical pharmacology early in the process.
> Number one, when you go into that meeting with the sponsor not only
> does it have to be, quote, informal\--we know those interactions are
> never interpreted as informal by a sponsor, but the expectations that
> might be set out based on the information that is available have to be
> plastic because we all realize that in the subsequent process of drug
> development new information is going to come out that may cause us to
> go back and even make a mid-course correction or change. So, all the
> parties at the bar have to realize and agree with that and abide by
> it.
>
> The other thing is that what clinical pharmacology does and what the
> medical people in the review division do have to be congruent, and it
> has to be congruent at the beginning of the process not brought into
> some congruence at the end of the process. I know those are more
> political than practical\--well, maybe they are practical comments but
> I think it is workable if it is done right.
>
> DR. LESKO: When we discussed this internally with the different units
> of FDA that was an important principle, that this would be a
> collaborative meeting and there has to be congruence in order to make
> this work.
>
> We have had some experience with the informal meeting and I imagine
> this meeting would be similar to, say, meetings that we have had as
> informal meetings on the integration of genetics into drug
> development. This is an area of sort of evolving science as is, in
> some ways, the analysis of exposure response and modeling and
> simulation evolving. The meetings have been I think successful for
> everyone concerned, but it does have a little more of an
> acknowledgement that benefit-risk is a changing thing as you move
> through drug development. I think the informal meeting recognizes
> that. The atmosphere is different in those meetings, as I think it
> would be in this meeting as well.
>
> DR. SADEE: I want to reflect a little bit on what Lew said. The
> question is what is the purpose? If the purpose were to avoid error
> being made, that is easily picked up and that may not be the purpose
> because, as you said, there are lots of smart people out there who can
> look at this rather reasonably.
>
> But I think what you said that if an early stage a strategy is being
> devised to look at dose-response curves, and so on, and dose effect
> relationships, and that strategy could be viewed and kind of agreed
> upon\--but that may be dangerous too because it could lock the agency
> into something\--well, you agreed to this and this is the way we are
> going to go forward, and it turns out to be wrong. So, I think a way
> has to be found to say that the purpose of the meeting is to just give
> you this and, just like you said, to indicate that this strategy might
> be a good way to finding what the real relationships are and what one
> has to look at and do this in a quick way. That would make sense to
> me.
>
> DR. LESKO: One of the things that frequently characterizes the other
> type of meeting, a formal end-of-phase-2 meeting are specific
> discussions of study design, endpoints, statistics and so on, and I
> can imagine a meeting of the type we are talking about that would
> actually not necessarily be question based. It could be discussion
> based or exploratory based or informational based where people might
> discuss alternatives based on analysis of data, and there might be a
> sharing of experience between a sponsor and ourselves. It would be
> informal in that context. I think that would probably be
> characteristic of this meeting.
>
> DR. VENITZ: First of all, I am very much in favor of having this at
> least as an option and as something that we want to review on a
> regular basis to see whether it actually has an impact. But I look at
> this more as an evidentiary hearing, if you like, where you are not
> necessarily reviewing the evidence based on the merit but what are the
> rules of evidence.
>
> What do you think down the road in five, six years, would be evidence
> that is necessary to support an optimal dose? Are you going to at
> least be willing to consider biomarkers, something that I didn\'t see
> in your discussion? I think this, to me, is a key point in terms of
> assessing potentially biomarkers. Obviously, this should have been
> discussed pre-IND but at least at that stage you have some experience.
> You have some proof of concept possibly for biomarkers on efficacy.
> You may have some at least potential biomarkers of toxicity. All those
> are things that I think should be discussed not necessarily in terms
> of how they pick the right dose, but what kind of evidence would
> ultimately be needed for biomarkers from exposure-response modeling to
> support an optimal dose and to, hopefully, speed up the process of
> getting to approval.
>
> DR. LESKO: I agree with you. I mean, I think at this point in time
> there is usually a fair amount of biomarker data available, if not
> clinical endpoint data. One of the ideas of having this meeting is to
> look at things a little more mechanistically and integrate this
> information in a way that actually isn\'t being done very much at
> least by ourselves at the NDA stage where we tend to look at clinical
> endpoints.
>
> So, I think the idea is to look at this in a quantitative mechanistic
> way and integrate information perhaps in a way we haven\'t done before
> as part of the interactions with sponsors, and doing it in a sense of
> trying to improve things as opposed to being an obstacle, I suppose.
>
> DR. VENITZ: I think part of the discussion has to be what is the
> payoff. If certain things turn out the way you expect them at that
> stage, which is obviously affected by some degree of uncertainty, what
> is the payoff? What is the improvement on your side as well as on the
> sponsor side? Otherwise, while we are doing those studies, we still
> have to do a formal study to prove whatever needs to be proven. That
> is what I am concerned about.
>
> DR. FLOCKHART: I guess to put it bluntly, to me, it is a tradeoff
> between whether this would really make drug development better, as you
> point out, versus would it just be another piece of red tape, another
> hurdle that people would have to jump through.
>
> So, my question would be what are the alternatives. If you look at it
> historically, presumably in the old system we are saying, you know, we
> are very worried about this because the number of submissions is going
> down, and all the rest of it, but we had this system in place when
> they were going up as well before 1996.
>
> So, I guess an alternative might be to look at that from a distance.
> Okay, so why don\'t we just issue some good guidances, like you have
> done, in the interim period before the end-of-phase-2. These would
> include the kinds of things you have done on drug interactions, [in
> vitro]{.underline} and [in vivo]{.underline} and on PK/PD and a large
> number of other things. So, a way of thinking about this might be
> whether you consider those guidances to have been ineffective and
> whether they are not having the desired effect in terms of
> improving\--I mean improving, not speeding necessarily but improving
> drug development, and what effort\--this is kind of like an
> alternative resolution on the floor\--would effort put in the area of
> more consolidated or more effective guidances be as good as having a
> meeting like this?
>
> DR. LESKO: I don\'t know whether that was a question or not.
>
> DR. FLOCKHART: I am really speaking to the wisdom or lack of wisdom of
> having meetings like this. I think the question I am posing really is
> are there better alternatives and what do you think about them?
>
> DR. LESKO: Well, we think, and industry really can better speak to
> that\--we think the guidances have helped drug development and helped
> clarify regulatory thinking. We see a guidance as helpful in this
> initiative as well to lay out the goals and objectives. As I mentioned
> in my introductory remarks, this is a voluntary type of meeting, as
> are the other meetings, and we have sort of talked to companies about
> this as part of our interaction with them in the normal day-to-day
> business and the reaction has been positive in terms of the
> counterparts in industry to the clinical pharmacology group here, at
> FDA. Whether that positive feeling is pervasive through the regulatory
> affairs and clinical departments we don\'t know. But the initial
> reaction has been very positive.
>
> But I think the way forward is to put the guidance out as a draft
> guidance; get some experience with this type of meeting, and we think
> it will be at least two or three years out before we have enough
> examples of this to determine whether this has been helpful or not.
> But we need to get feedback from each individual company that would
> come in for a meeting like this and look at how that impacts the
> subsequent NDA that we had meetings on. I think we can look at this
> somewhat systematically and see what impact it might have.
>
> DR. SHEK: I agree with the guidance, that it is helpful, as well as
> the meeting. I look at that from the industry perspective. It is more
> setting up expectations as you go through. Guidances are fine but, you
> know, they are still open to interpretation and a specific case might
> be unique. It is also an opportunity for the FDA maybe to see some of
> the data that has been developed. So, I see benefits there.
>
> But, still, we have to look at the bigger picture and that was my
> question earlier, how many of those cases\--we are saying 50 percent
> of, let\'s say, programs in phase 3 are failing. I know from my own
> experience that the target is, you know, once you go into phase 3
> studies you want to be pretty sure that you know it will be a success.
> So, out of that 50 percent, what are the reasons for failing from a
> regulatory view? I would assume some of them are failing even by the
> company itself. Once they have the data, they say, well, we don\'t
> have the product here and they don\'t even submit an NDA. Or, the
> scope doesn\'t fit when they will try to position it into the market
> so it takes longer. But then if you take those out, how many of those
> are failing because the dose was the wrong dose and how many of those
> are failing for other reasons?
>
> So, I would assume the FDA is in the same position as the industry. If
> you have the resources and they are limited, where do you spend them
> and when do you spend them? So, I think here it would be interesting
> to go into that and maybe this two-year pilot will bring us some of
> the information.
>
> Saying that, basically I believe it picks up from the FDA strategic
> plan, whether this specific proposal will improve or to make
> innovative medical product development sooner and then, the other
> part, also developing safe and effective medical products. As I
> understand the proposal, it looks like let\'s tackle drugs that we
> know how they work and how they are effective. I wonder whether that
> is the target of drugs that you would like to look at or, rather, look
> at those maybe new breakthroughs where we really don\'t have a therapy
> this year. Maybe those should have more time spent looking at the
> system.
>
> DR. LEE: I just want to clarify that the guidance that Larry just
> mentioned is a procedural guidance, which is a guidance to industry
> regarding how the sponsor can request a meeting, not a guidance to
> discuss drug development.
>
> Secondly, to answer that question regarding the reason for failed
> NDAs, in the ten NDAs we looked at one of the most common reasons for
> failing is that the dose chosen was not optimal which led to lack of
> efficacy or safety problems. But I agree that it would be useful to
> look at not only the failed NDAs which have already been submitted,
> but also look at the failed phase 3 studies and see what the reasons
> are for the failed phase 3 studies.
>
> DR. HUANG: I was going to comment on guidance. I guess you said there
> are alternatives to communicate and we do have a lot of guidance
> documents. So, those may be helpful instead of additional ones. That
> is what I take from one of your comments. The guidance is a living
> document. For example, the Drug Interaction Guidance may not be
> updated and we have new information that we may have just learned from
> reviewing certain NDAs or company meetings where we know some other
> factors need to be considered.
>
> For example, Ameeta has shown an example where QT prolongation, if not
> evaluated properly, could be a cause for approvable instead of a first
> cycle approval. We did have quite a few examples. To communicate this
> information, this could happen when we have this type of information.
> I mean, some of the examples show that information comes in later and
> we might have communicated at end-of-phase-2 or pre-NDA. However, if
> you can do it earlier we probably can share the information early on
> with the sponsors with the current information or different
> interpretation based on the science which may not be covered in
> various documents already in place.
>
> Larry has mentioned about pharmacogenetics. With the information that
> we have right now, how do we learn about the information that industry
> has or how do they know what we will see as issues? This type of
> information, even if we have quite a few informal meetings, that is
> not exactly end-of-phase-2A but I think they have provided an
> opportunity for us to learn what are the issues that a company is
> facing. I think what we heard is valuable on what questions we would
> have when we see certain data that may not have been submitted early
> on.
>
> So, I think this offers an opportunity not only, hopefully, I think to
> be beneficial for the sponsor but also very helpful for us. Once we
> learn this information, we can also communicate it to the other
> sponsors.
>
> DR. MCCLEOD: I think it is a good idea but I am not sure why. I
> didn\'t find any of the three cases especially compelling. The reason
> why, as I thought about it, is you can\'t retrospectively reconstruct
> the data if you want to really answer whether this is a good thing to
> do or not. As you look back, there was great data that at the end you
> could have looked back and made a better choice, but not at the
> end-of-phase-2A. At the very end of the study you could have.
>
> I think maybe, if nothing else, going through this two-year pilot,
> whatever the time is, will at least allow you to construct the data
> and to come back and say that this is something worth doing or that
> this is really no more insightful than we have now. We really don\'t
> have enough data to say this is a good thing to do. It seems like a
> good thing to do. It should be a good thing to do but the examples
> that are out there don\'t say, yes, this is definitely something that
> is going to really improve the development of these drugs.
>
> DR. SHEINER: Again, putting the best possible light on it, let\'s
> imagine that, first of all, the basic hypothesis is true, that there
> is more information to be gathered from early drug development that is
> relevant to later drug development than is being fully exploited.
> Let\'s grant that and then let\'s also grant that the pharmaceutical
> industry in general and companies are trying to find a way to better
> exploit that data and that they might find this kind of a meeting
> useful. Even given those two things, you know, you sort of can\'t do
> any harm except for the cost in time and effort on the part of the FDA
> and that is a finite resource, and it is not holding anybody\'s feet
> to the fire and it is not making new rules, or anything like that,
> which is something that, you know, obviously would cause a much bigger
> shakeup.
>
> You know, I am just sort of trying to get to Larry\'s third question.
> I have no idea then, if that is the case, what you would use for a
> benchmark other than customer satisfaction. I can\'t think of how you
> would try to actually quantitatively measure the influence because, as
> I think you just pointed out, it is likely to show up in the quality
> of the data that is gotten after that meeting and it is very hard to
> say, well, it would have been otherwise or wouldn\'t have been
> otherwise. It is the same problem going forwards in a sense as going
> backwards and saying, you know, make believe I didn\'t know the end
> result now what would I have done back then if I had been faced with
> those data? It is just almost impossible to do.
>
> So, I don\'t think you can measure it. I do think that it can be seen
> as a positive endorsement of the idea of better exploiting all these
> data in a quantitative way that takes account of all uncertainties and
> tries to allow decisions to be made. I think in that sense it is a
> public service, but I don\'t know if you are going to be able to
> measure the impact.
>
> DR. MCCLEOD: You could do a randomized study of offering
> end-of-phase-2A consultation or not and see whether the doses are
> picked correctly.
>
> DR. JUSKO: I see this as a good idea from the viewpoint that it offers
> the companies a chance to interact with the FDA probably for problem
> situations. I kind of view 2A studies as proof of concept and none of
> the examples that we saw were really phase 2A situations with the
> great uncertainties that frequently exist.
>
> I was a little bit concerned by what Larry said early, that oftentimes
> at the end-of-phase-2 meetings the companies are already wedded to an
> array of plans for phase 3 studies and may have difficulties making
> adjustments in those plans. The examples that we saw were more of that
> ilk. So, this kind of proposal could offer opportunities to influence
> what would be happening in making plans for phase 3 studies earlier in
> the whole progression of things. So, in that context it seems like it
> could be very beneficial in certain situations.
>
> DR. LESKO: It has been interesting, in discussing this individually
> with companies, whether or not this is even an early enough meeting to
> discuss the issues we proposed to discuss in this meeting. Dosing
> strategies are set individually by different companies in many
> different ways but this seems to be a fair balance.
>
> The other thought we had on this, and we have begun to explore this,
> is the introduction of some discussion of disease progression models
> as part of this meeting, and determination of whether or not this
> might have some impact on the way exposure response is assessed and if
> that would have a positive impact on clinical trials in specific
> disease state areas.
>
> We are doing some ongoing research in certain diseases with disease
> progression models, and we have used it before in our analyses in
> selected cases but we think there is some potential to look at this
> more fully in the context of these meetings, again, with the
> collaboration and agreement of the company to do this.
>
> DR. VENITZ: Are there any more comments for question one because I
> think you got a lot of feedback from the committee? So, any more
> comments about the general objectives of this end-of-phase-2A program?
>
> \[No response\]
>
> Then let\'s see if we can focus on the second question. That is a more
> methodological question. What approaches can be used in order to
> maximize the efficacy, I guess, of those end-of-phase-2 meetings? Any
> comments by the committee to question number two?
>
> DR. SHEINER: Just to beat the same horse as before, obviously they are
> going to want to do the analyses in a sense. I mean, you are going to
> sort of help them out and make suggestions. But I do think that some
> attention to some kind of value function\--call it utility, whatever
> it is\--where you say, you know, there is something we are trying to
> learn here in particular; we have some measure of what we are trying
> to learn, rather than everything there is to know about concentration
> response and all possible responses. I am sure you would never say
> that but some formal attention, some agreement that one of the things
> you are going to talk about\--not formal because it is an informal
> meeting, but some agreement that one of the things you are going to
> talk about is how you are going to measure the value of what you are
> going to learn.
>
> DR. VENITZ: I would echo that. I think a lot of the things we have
> seen were retrospective data analysis and I think one of the
> objectives of this end-of-phase-2 meeting may be to decide or at least
> give guidance on which issues need to be studied in a prospective
> manner as part of a prospective study, be it a clinical or preclinical
> study. On the other hand, which other issues which may be playing for
> lower stakes can be dealt with retrospectively as part of some kind of
> a population PK approach.
>
> Again, just give guidance to the industry for what the stakes are for
> the different issues that are going to come up down the road, and what
> is the potential payoff if they improve on the way the analysis is
> being done.
>
> DR. SADEE: So, what you are saying is identifying the problem issues
> as far as they can become apparent so that there is already a
> foundation that would save maybe energy later for the FDA because the
> issue is already at hand. There may be new issues emerging, but I
> would imagine that at that point one would know what the key questions
> are. That would be very helpful.
>
> DR. VENITZ: And one component that didn\'t really get any discussion
> time today is to incorporate enough preclinical information, both [in
> vitro]{.underline} as well as animal pharmacology, safety and
> toxicology information that may be quite relevant at that early stage.
> How would that impact not only on endpoints that may need to be
> monitored but also in terms of dose selection, including using
> qualitative methods?
>
> Any more comments to question number two?
>
> \[No response\]
>
> Then let\'s look at question number three. We already heard Dr.
> Sheiner\'s recommendation that customer satisfaction might be the only
> measurable outcome. Any other recommendations or suggestions by the
> committee?
>
> DR. DERENDORF: Well, it is actually under strategic planning. It is
> steps to reduce the time, cost and uncertainty of developing new
> drugs. So, that is the goal and I think that can be measured. You said
> that in your examples there were a lot of components that were dropped
> because of the wrong dose. That number should come down.
>
> DR. LESKO: That is true, and there is another conceivable metric one
> might look at, and that is the dose changes post-approval. There is
> published literature on that recently by Jamie Cross and colleagues,
> looking at dose reductions post-approval in terms of the time
> following approval, what percent reductions were downwards, and so on.
> That also might be over time another metric that could be looked at I
> think.
>
> DR. SHEK: Yes, the only issue there is that in two years you wouldn\'t
> come out with the metrics I think. You would need a longer time than
> two years.
>
> DR. LESKO: Yes, I agree. I think we have said two or three years. It
> is hard to say, depending on the frequency of having these types of
> interactions.
>
> DR. FLOCKHART: I don\'t think it is actually very difficult. I think a
> simple catalog of decisions made by sponsors in itself would be very
> instructive. I mean, it goes everywhere from killing a drug\--I mean,
> how many drugs got killed and what kind of decisions sponsors made in
> response to those meetings. You could easily have an analysis to ask
> them, well, what did you do as a result of this that you wouldn\'t
> have done otherwise? Change your clinical trial design? Add a
> surrogate? Build in a toxicity monitor? Monitoring based on animal
> data or preclinical data that you hadn\'t done before? I mean, there
> are lots of potentially valuable things you could talk about that
> would be persuasive, simple broad statements.
>
> DR. HUANG: I was just going to say since initially the end-of-phase-2A
> meeting will be limited so we will only have a few cases\--this is
> like an open trial so we look at these cases and, like, a customer
> satisfaction survey including whether the sponsor changed a
> development plan based on the FDA input or based on this meeting. So,
> even though we don\'t have a randomized control, we do have the set of
> sponsors that went through the end-of-phase-2A meeting.
>
> DR. VENITZ: Can we maybe add a fourth question? I think you alluded to
> that, Larry, and that is, can we as a committee identify specific
> scenarios where the end-of-phase-2A may be most helpful? The new drug
> in class or first drug in this particular class or should it be a drug
> where we know a lot about the class? What does the committee think?
>
> DR. SHEINER: But the problem is that the answer to that depends very
> heavily on the first question we never answered, which is why is
> inadequate attention being paid to the information? But my guess is
> that the newer the drug in the class, the receptor and all that, the
> less advantage you can take of prior information because there isn\'t
> any. So, you are in a more empirical mode and we know that the
> pharmaceutical manufacturers do a reasonably good job of being
> empirical.
>
> So, my guess is that you might be most helpful in the case where there
> is a fair amount of knowledge and where the company maybe feels that,
> for some reason, it can\'t use that and they can be encouraged to do
> so for whatever is the problem that this is solving. It would seem to
> me it has to be most applicable in the case where there really are
> things that should be brought into the thought process that are not
> being brought in.
>
> DR. VENITZ: I would concur with that and add that I think it might be
> worthwhile particularly for drugs that treat symptomatic conditions.
> Again, the payoff might be earlier than for drugs to treat chronic
> conditions, depending on how much we know about the disease per se
> regardless of the pharmacology of the drug. So, actually acute
> indications might be the ones to focus on early on to see if it does
> any good.
>
> DR. KEARNS: Larry, I think one of the things is thinking about drugs
> that may be useful in children and other special populations. The
> end-of-phase-2A meeting could be a very important point for the agency
> to begin to discuss with the sponsor really what kind of studies need
> to be done; what do we need to think about; what are the endpoints
> that might be appropriate. As it goes now, those questions are often
> asked very, very late in the game when not a lot of synthetic thinking
> can be brought to the bar.
>
> DR. MCCLEOD: I was just going to ask, Peter, was there any central
> theme to the ten drugs where you could have predicted dose
> alterations? That failed because of incorrect dose? Were these all
> first time in class or were they all fourth time in class? Is there
> anything that could guide where you should be focusing this work?
>
> DR. LEE: I am not sure. I think at least they all have good
> exposure-response relationships, which means the endpoint is either a
> shortened endpoint or a surrogate endpoint that is easy to measure and
> connect to the exposure. But I think it was the clinical endpoint
> being used but it was a shortened clinical endpoint. Again, I think
> the central thing would be a good exposure-response relationship being
> established based on the early studies.
>
> DR. HUANG: If I remember correctly, the majority of them is not first
> in the class. Was that one of your questions?
>
> DR. MCCLEOD: Maybe what I am trying to get at is what drugs you should
> focus on to try to make this work or not work.
>
> DR. HUANG: Many of those are fast follow-ups but a lot of information
> developed later on. So, some of the information we may not have well
> elaborated or well recognized when they first come up. So, some of the
> examples you have seen, they are the fourth or the fifth on the
> market.
>
> DR. MCCLEOD: And certainly those are less interesting but might be a
> good place to start just because you might actually be able to
> intervene and see whether intervention improves things.
>
> DR. HUANG: Yes, I think it was in Larry\'s slide, either that we know
> a lot more now than when it was first introduced, or some of them may
> be novel so we want to help with the development. But in a lot of
> cases they are fourth or fifth in the class.
>
> DR. VENITZ: Any further comments to any of those questions? If not,
> Larry, I want to give you an opportunity to wrap things up before we
> take a break, if you choose to do so.
>
> DR. LESKO: I don\'t need to take much time but we presented this
> morning a concept for a new initiative and I think appropriately
> received some excellent input from this committee. We are going to
> continue to move this forward and maybe share with the committee at
> some point in time some experiences we have with this initiative.
>
> I believe our next step will be to develop a draft guidance for
> industry on this concept, taking into account what was said today, and
> put it out really for comments so people can raise issues, identify
> important aspects of it and continue to move forward.
>
> DR. VENITZ: Thank you. That brings us to our lunch break. We will have
> a break from 11:30 to 12:30. Just for everybody\'s information, we do
> not have any open public speakers so we will start with the official
> program at 12:30. So, I would hope that all presenters will be ready
> at 12:30 to present on the QTc prolongation modeling. Thank you.
>
> \[Whereupon, at 11:30 a.m., the proceedings were recessed for lunch,
> to reconvene at 12:30 p.m.\]
>
> \- - -
>
> [A F T E R N O O N P R O C E E D I N G S]{.underline}
>
> DR. VENITZ: Welcome back for the afternoon session. We are continuing
> with the general topic of exposure response, and our second topic for
> today is the use of PK/PD modeling in the context of QTc prolongation.
> I would like to ask Peter Lee to give us an introduction of the topic.
> Peter?
>
> **PK/PD (QT) Study Design: Points to Consider**
>
> DR. LEE: The next topic we are going to talk about is the PK-QT study
> design.
>
> \[Slide\]
>
> Specifically we will be talking about using the clinical trial
> simulation, which is a simulation methodology for designing a PK-QT
> study. I want to start by saying that there has been increasing
> regulatory interest regarding the QT prolongation. As a result, a
> number of drugs have been withdrawn from the market due to the QT
> prolongation property. Most recently we published a concept paper
> regarding the QT study design. I believe there is also an ICH E14
> guidance that is under preparation.
>
> \[Slide\]
>
> There can be several different objectives for a PK-QT study design.
> The first may be to use the study to determine if there is a drug
> effect on QT. Secondly, the objective could be to estimate the extent
> and the time course of the QT effect. Finally, to determine the PK-QT
> relationship so that a relationship can be used for dose adjustment if
> intrinsic or extrinsic factors may influence exposure of the drugs.
> So, the regulatory utility of a PK-QT study could be to evaluate the
> safety of the drugs; to determine the dose selection in the patient;
> or use information for dose adjustment.
>
> \[Slide\]
>
> Therefore, there are actually many different issues relating to the
> PK-QT study design. One of the most significant ones could be the
> large and unpredictable within- and between-subject variabilities,
> including inter-day variability as well as within sampling window
> variations which can cause a decrease of the study power to identify a
> small change of QT due to the drug effect.
>
> There is also a different way of selecting the baseline, sometimes one
> sample being selected pre-dose; sometimes 24 hours as a baseline. The
> sampling schedule is also an important factor that may influence the
> study power and other additional issues, such as the selection of
> meaningful and sensitive QT metrics and the variability associated
> with PK and PK/PD relationship.
>
> \[Slide\]
>
> Additional issues are dose-ranging studies. Whether a placebo control
> or active control is included as a comparison and different types,
> such as crossover or sequential designs.
>
> \[Slide\]
>
> So, when we see a study report where there is an X millisecond change
> in QT due to a drug effect, then we have to ask the question what is
> the correction method being used to correct the QT regarding the R
> interval? What is the QT parameter we are talking about? Is it the
> maximum QT effect, or the average QT effect, or just randomly selected
> drug dosing interval? We also have to ask what this QT change is from?
> Are we comparing to the placebo group? And, also ask the question at
> what doses has QT effect been observed? Once we have answered all
> these questions, the most important question we have to ask is how
> sure are we about this X millisecond change in QT.
>
> \[Slide\]
>
> I will just give you an example. This is just an informal survey of QT
> studies of terfenadine that have been published in the past. I have a
> list of ten different studies and their study designs. The dose
> regimen in those ten studies ranged from a single dose, 120 mg for
> most of them, to 60 mg BID.
>
> The general study design could be a sequential crossover, parallel,
> and the number of subjects could from 6 to over 60. The baseline is
> sometimes one sample; sometimes 12 hour. The sample of treatment is
> even more variable. It could be one sample, 6 hours, 12 hours or 24
> hours. The metric of QT is sometimes point-by-point comparison with
> the baseline, sometimes the maximal, sometimes one sample.
>
> \[Slide\]
>
> These are the study results from these ten literature studies. Seven
> out of the ten studies show no effect, no QT effect of terfenadine
> against either baseline or control depending on whether it is a
> sequential study design, crossover or parallel design. If we exclude
> the first two studies, the single dose studies, then five out of the
> eight studies actually show no effect against baseline or control.
>
> Although this survey is really informal and may not be conclusive, we
> really had to ask the question whether the inconsistent results are
> only by chance due to inter-study variability or is it a study design
> issue. I think we believe it is the latter because of the variety of
> study designs involving these ten different literature studies.
>
> \[Slide\]
>
> So, we proposed the use of clinical trial simulations for designing a
> PK-QT study to address the complexity of the study design issues
> because it was deemed that there is no one-size-fits-all PK-QT study
> design. Each study has to be designed for its own specific objective.
> You have to consider the variability of PK/PD. We can use clinical
> trial simulation to explore a variety of study designs and integrate
> the effects of all study design factors into the considerations. The
> trial simulation can be used to estimate the study power to achieve
> the specific study objective and it also can be used to address
> \"what-if\" scenarios under different possibilities.
>
> \[Slide\]
>
> So, today we will have two different presentations. The first
> presentation will be given by Dr. peter Bonate, from ILEX. He will be
> talking about the use of clinical trial simulation for PK/PD QT
> studies. The second presentation will be given by Dr. Leslie Kenna and
> she will be talking about the QT evaluation studies from some
> regulatory experience. With that, I will give it back to the chair.
>
> DR. VENITZ: Thank you, Peter. Are there any questions for Peter? If
> not, let\'s proceed to the first presentation. Dr. Peter Bonate is
> going to tell us about clinical trial simulation and QTc. Peter?
>
> **Use of Clinical Trial Simulation (CTS) for**
>
> **PK/PD QT Studies**
>
> DR. BONATE: I would like to thank you for inviting me to speak. I am
> very honored; a little intimidated.
>
> I am going to talk a little bit today about using simulation to
> address QT issues. I first got involved in this a couple of years ago,
> right at the time when Seldane\--you know, the QT issues about it were
> starting to come to light. So, I have been doing this now for a couple
> of years. I have had the opportunity, some might say misfortune, to
> work on about half a dozen of these compounds now, doing these
> analyses. They are very stressful. They are not like a regular
> exposure-response analysis. I think the stakes are a little big
> greater. The pressure on the kineticist are a little bit more because
> for a drug that has warts, this could kill it. So, it is a pretty
> stressful analysis.
>
> \[Slide\]
>
> What I am going to talk about today are some of my experiences with
> modeling and simulation of this type of data; how we have used
> simulation to address and interpret some of the results from these
> analyses.
>
> \[Slide\]
>
> Just to make sure everybody is on the same page, I am going to briefly
> address some of the issues regarding QTc so that we all have the same
> background, and I am going to talk about some placebo analyses that I
> did because in order to do clinical trial simulation you have to
> understand what the placebo response is before you can adequately
> model what your drug effect response is going to be. In doing the
> placebo analysis, some interesting results came to light and so I will
> talk a little bit about the pitfalls that might come from just naively
> modeling QTc data. Again, I am going to focus on using Monte Carlo
> simulation to help interpret our results.
>
> \[Slide\]
>
> There is a variety of different metrics to analyze this type of data.
> The guidance talks about different varieties of them. One is looking
> at mean QTc interval. This is probably the least sensitive metric
> because it basically dilutes the drug effect from ECGs that have no
> drug effect.
>
> Another one is maximal QTc interval. This one is relatively
> insensitive too because there is a lot of variability whenever you
> start talking about maximums.
>
> Another one is area under the QTc interval-time profile. This one is
> starting to gain more\--
>
> DR. SHEINER: Excuse me, Peter\--
>
> DR. BONATE: Yes?
>
> DR. SHEINER: Could you just say a word about the design? This is the
> mean of intervals, for example, across time beat-to-beat or is this
> moment-to-moment? Because not everybody here is exactly clear on what
> the design is.
>
> DR. BONATE: Well, let\'s say you collect ECGs at zero, 0.5, 1, 2, 3,
> 4, 6, 8 hours after dosing, the mean QTc interval is just the mean of
> all those measurements. I didn\'t want to talk about how do you
> actually measure QTc. That is more of a cardiology issue. But when I
> talk about mean QTc, it is just the mean across different time
> intervals. I am going to assume at this point that the QTc interval
> data that you have has been over-read by a cardiologist and that it is
> a real number.
>
> Another one that is just starting to appear, although it has been
> recommended for a number of years, is area under the curve. The
> problem with this approach is that the units are difficult to
> interpret. You get numbers like 10,000 millisecond times hour and
> nobody knows what that means. So, it is difficult to interpret.
>
> Then you have maximal change from baseline. When you are talking about
> baselines you are controlling a little bit for within-subject
> variability. These tend to be more sensitive metrics.
>
> Another one related to that is maximal QTc with baseline as a
> covariate. This is an ENCOVA approach. They tend to be more powerful
> than just simple ANOVA approaches which are what the other approaches
> use.
>
> Lastly, there is area under the QTc interval with baselines as a
> covariate. When I did some simulations a few years ago this was
> probably the most sensitive metric at detecting QT effects. But,
> again, you are confounded with difficult to interpret units and such.
> But these are basically the metrics that we have available to us and
> pretty much change from baseline and maximal QTc are the ones that
> people focus on.
>
> \[Slide\]
>
> I am sure everybody knows these, but the guidelines for what is
> \"prolonged\" are 450 msec in males; 470 msec in females, or 60 msec
> change from baseline. Then there is an absolute QTc greater than 500
> msec. These are all considered clinically significant QTc values.
>
> When looking at mean change from baseline, there really are no agreed
> upon guidelines for what constitutes prolonged. Generally we took 5-7
> msec as prolonged because, using terfenadine as the yardstick at the
> doses that were given clinically, that tended to produce a 6 msec
> increase in QTc and since that was pulled from the market for QT
> problems that is our yardstick that we have used. Hence, we now have
> the 5 msec change in QT as being a yardstick for what is prolonged.
> And, there are no guidelines on the AUC-based metrics at this point
> for what is significant.
>
> \[Slide\]
>
> I have found that companies tend to go through three stages when they
> are dealing with QT problems. One is\--remember the guy from
> [Mad]{.underline} magazine where he says, \"what? Me worry?\" There is
> the what QTc effect? It is the head in the sand approach\--we don\'t
> have a QT problem; we are not going to worry about it. That is a
> dangerous attitude to have.
>
> Then there is the, \"okay, yeah, we\'ve got a QT problem but we\'re
> not any worse than any other drugs on the market so we\'re going to
> take this approach and since they\'re approved, we\'re going to get
> approved.\" Then there is the, \"yeah, we\'ve got a QTc effect. We\'re
> going to characterize it and, hopefully, we\'ll be okay at the end of
> the day.\"
>
> I think more companies are coming around to this third approach of we
> are going to characterize it and we are going to understand what are
> the intrinsic and extrinsic variables that affect it so that we can
> make some rational decisions for whether this drug is safe or not.
>
> \[Slide\]
>
> So, I would like to move back to a study we did actually back in 1998
> and 1999. Seldane has just got pulled off the market. We just had
> Allegra approved. At the time we were extremely sensitive to QT issues
> and so we had a new drug that was in development and we were concerned
> about QT issues, obviously. We felt that because we were Hoechst
> Marion Rousel, we would be looked at for QT problems a little more
> closely than maybe other companies at the time.
>
> So, we went and we did what was probably a cutting-edge study at the
> time; it seems fairly straightforward now. We wanted to characterize
> the QTc response relationship for our drug. This was a single-center,
> randomized, double-blind, placebo-controlled, 4-way crossover where we
> took 20 males and we took 20 females, with standard phase 1 exclusion
> criteria.
>
> \[Slide\]
>
> We gave them three doses, 20 mg, 30 mg and 60 mg once a day for seven
> days, the fourth arm being a placebo arm. Within each period we also
> had a placebo day on day minus-one. There was a week washout between
> periods. And, we gave meals one hour post-dose in the morning, lunch,
> dinner and snack. Interestingly, at the time we felt that our case
> report forms were getting too big so we were looking for ways to cut
> down on how we could make them a little bit smaller and one of the
> things we thought at the time was let\'s get rid of the mealtimes. We
> don\'t really need that. You know, it is a phase 1 study. The food
> effect for QT wasn\'t known at the time so in hindsight we kind of
> wish we had kept that data. It would have made interpreting some of
> the food effects a little better. All ECGs were taken prior to meals
> if they were scheduled at the same time. So, in hindsight, this seems
> like a pretty straightforward design but it was probably one of the
> first of its kind.
>
> The results of this analysis were published last year in a book by
> Kimko and Duffull and I am going to talk just very briefly about it.
>
> \[Slide\]
>
> We did ECG analyses on 0, 1.5, 3, 5, 9, 12 and 24 hours on day 1, day
> minus-1 and day 8. So, we did it after the first dose of active drug
> and then at steady state, and also on the placebo lead-in day. We also
> did it at trough on days 4, 5, 6 and 7. All the ECGs were over-read by
> cardiologists blinded to treatment, dose and period. They calculated
> Bazett\'s QTc for each chest lead and the largest one was taken as the
> QTc at that time interval.
>
> \[Slide\]
>
> We had a number of issues arising from this data set. First of all,
> what is the baseline? Is it the pre-dose at time zero on the day of
> dosing? At the time, much of what I am going to be talking about we
> really didn\'t know at the time. For instance, the circadian rhythm,
> we didn\'t really know that that was really such a big issue. I am not
> really sure that it is a circadian rhythm; I think it is more food
> effect that gives it a circadian nature. We also took only one ECG at
> each time point. I wish, you know in hindsight, we had collected
> multiple ECGs to lower inter-subject variability.
>
> We could have used the mean of the placebo date, day minus-one. It is
> more robust. It is going to be based on many measurements. But it too
> fails to correct for any circadian food effects that happen on the day
> of dosing. If were to take this forward into phase 3, you know, such a
> design couldn\'t be useful for phase 2 or phase 3. Lastly, there is
> point to point with placebo administration. For instance, we could
> take the 1.5 hour on day 1 with the 1.5 hour on day minus-1 and that
> would be the baseline. But then the question becomes, well, should the
> baseline be day minus-one or should the baseline be the placebo
> period?
>
> So, there are a lot of different ways to analyze this data. The
> proposed guidance talks a lot about these things and I think one of
> the things that it could do a little bit better is to more fully
> delineate what should be the preferred baseline when doing these
> analyses.
>
> \[Slide\]
>
> We decided to build a placebo model because you need the placebo model
> to really understand what is going on with drug. We had a number of
> covariates available. We had period, day and time. We had chest lead;
> time of the last meal. We didn\'t know exactly what the last meal was
> but we could guess probably within five or ten minutes what it was.
> The sex; the race; what was their baseline calcium and potassium at
> the beginning of each period; body surface area; and stress. When I
> say stress, the way they do these studies is that on days one, seven
> and day eight there are a lot of ECGs being taken so it is a pretty
> hectic day around the clinic. Everybody is running around so stress
> tends to be a little bit higher. So, we thought that might be an
> interesting covariate to look at.
>
> \[Slide\]
>
> We did the modeling using NONMEM. I will show you a little bit later
> why I used NONMEM instead of mixed, but all models were developed
> using LRT, standard model building techniques. The factors were
> entered into the model linearly and random effects were treated as
> normally distributed, which seems reasonable for QT data.
>
> \[Slide\]
>
> Just for the placebo period we had 769 ECGs from 40 subjects. That was
> a 449 msec^2^ variance. So there was 5 percent variability across all
> the ECGs that were collected.
>
> Interestingly, the placebo data showed a trend over time, over day of
> administration and the QTc intervals tended to go up from day
> minus-one to day eight. The way I interpret that is that these phase 1
> studies\--we call them healthy normal volunteers but they are not
> exactly healthy normal volunteers; they are marginally healthy normal
> volunteers. Some of these guys go out bringing a couple of days before
> they enter the clinic. They get sobered up and they come in and they
> dry out enough to pass the screens and then they are in the clinic.
> What they are doing is while they are in the clinic they are getting
> healthy. They are getting three square meals a day. They are
> showering. You know, they are starting to get healthy. So, that is
> kind of how I interpret this trend effect over time. You know, they
> are getting better is what is going on.
>
> We also found that chest lead was important. Lead IV tended to be
> about a 9 msec greater than other chest leads. Now, if you look at
> other papers in the literature, chest lead II tends to pop out more
> often but chest lead is an important covariate that needs to be
> controlled for.
>
> This was probably the first time where we actually quantified the food
> effect. We found that breakfasts increased QTc and that lunch
> increased QTc and dinner increased QTc, and each one of these
> increased them a little bit more. You know, each one of these meals
> tends to be a little more fatty than the one before it and fat tends
> to prolong the QTc interval, which raises an interesting question.
> Because of the food effect, it is going to make analyzing QTc data a
> little more problematic and I will show you that in a minute.
>
> There was a stress effect. On the days that there were a lot of ECGs
> being taken the QTc intervals tended to be a little bit higher, and
> females were greater than males. You know, I did this about four years
> ago and now it seems really straightforward but back then this was
> cool stuff.
>
> \[Slide\]
>
> You don\'t have to worry about it but if anyone is interested, here
> are the quantifiable numbers for the model. The reason that NONMEM was
> used to do this analysis is that to model the food effect what I did
> was I just assumed that the QT effect declines exponentially since the
> last meal. I could have done this using a linear model and treated
> meal as just a fixed effect but, because I included the exponential
> term in there, I had to use a nonlinear mixed effect model. In doing
> so, I probably could have increased the time it took to do this by
> about 100-fold.
>
> \[Slide\]
>
> Here is a fit for what the day 1 data looked like. If you look at
> where breakfast, lunch and dinner is you can see that after every meal
> QT intervals tend to be a little bit higher than the interval before
> it. The spike out at 16 hours were there is no time point, that is
> where they got their snack just before bedtime.
>
> \[Slide\]
>
> Here are the results over eight days of treatment. I won\'t show you
> all the goodness of fit plots but the results fit pretty well so we
> were pretty confident in the model that we had.
>
> \[Slide\]
>
> It raised some interesting observations. One was that there was a
> relatively large variability and when you broke it down to
> within-subject and between-subject variability we found that
> within-subject variability was more than between-subject variability,
> which is not something you see every day. Within-subject variability
> was about four percent but between-subject variability was only about
> three percent. So, it is kind of an unusual finding.
>
> Keep in mind that within-subject variability also includes measurement
> error and model misspecification. So, that may be the reason why we
> have such large within-subject variability and had we done replicate
> ECGs at each time point, we could have been able to separate the
> variance components maybe into a measurement error and into something
> else. At the time I was trying to convince people to include dummy
> ECGs to the cardiologist so that we could get a better ideal for what
> his reliability was but that was a can of worms that nobody wanted to
> open. Every time I proposed that, that is a very difficult sell.
>
> Interestingly, when inter-occasion variability was added to the model,
> it accounted for very little of the variability, less than 10 msec^2^
> so it was not included in the model. I have seen other papers where
> they have looked at this and they have pretty much come to the same
> conclusion, that if you look at individual corrected QT intervals over
> different days that tends to remain fairly constant across days, which
> is kind of surprising.
>
> \[Slide\]
>
> I am just going to take a step aside and do my sell for the AUC
> corrected QTc. I think more effort should be spend in identifying this
> as a variable measurer instead of change from baseline or maximal QTc.
> AUC is an integrated measurement over the drug effect and it tends to
> be more sensitive than any of the other metrics that we are looking
> at. When you look at maximal change from baseline you are only looking
> at one time point and you are ignoring all your other observations,
> which is a loss of information. So, when you look at AUC, it tends to
> be more sensitive. As I said before, if you use just raw AUC the
> numbers are like 10,000 so it is difficult to interpret.
>
> But if you divide by the interval in which the AUC was measured, now
> you get a weighted average QTc which is interpretable with the weights
> proportional to the time difference between measurements and the
> numbers are right in accord with what you would expect. So, when I did
> the placebo model for the AUC many of the covariates that were
> important before no longer become important.
>
> Here is my methodology In this case I just did linear mixed effect
> models. You can see my covariates. But in this case none of the
> covariates were statistically significant. The day effect was gone.
> So, it is something that we need to consider. More people need to do
> research on this so that we can get a better feel for how it performs
> as a metric.
>
> This time the between-subject variability is greater than the
> within-subject variability, which is what you would like to see.
> Interestingly, the sex effect that you normally see with QTc was not
> observed with the AUC metric. I don\'t know whether this was a power
> issue or what.
>
> \[Slide\]
>
> Now that you have a model\--you know, just having a model isn\'t of
> any value unless you do something with it and that is where simulation
> plays a role because simulation is really just applied modeling. It is
> a tool that can help you understand the behavior of your system. It
> can help you assist in discovery and formulating new hypotheses; where
> you need to go next. Of course, it can be used for prediction. That is
> probably what it is most often used for. Sometimes you can use it to
> substitute for humans, like with expert systems. You can use it for
> training and, of course, you can use it for entertainment, not just
> for the modelers but for the people that use it.
>
> \[Slide\]
>
> If you want to simulate QTc trials, what is it that you need to know?
> Well, you need to define your metrics. What is going to be your
> primary metric? What is your goal at the end and what is the metric
> that you are going to use? Once you know your metric you need to know
> the variability of that metric, both within a patient, across
> patients, measurement error, that kind of thing, and how it is
> distributed. Is it normal distribution? Is it log normally
> distributed? QTc intervals tend to be normally distributed. I have yet
> to see a log normal QTc distribution. If you have an estimate of
> variability, does that estimate of variability pertain to the
> population that you are interested in studying?
>
> What I showed you was done in healthy normal volunteers. The question
> then becomes are those variance components applicable to the
> population of interest? Probably not because patients tend to be more
> heterogeneous than healthy normal volunteers. So, the question then
> becomes, well, how useful are the results of your simulation if your
> variance components might not be valid?
>
> Of course, you need a PK/PD model. You need to know what the
> variability is in those estimates. Then, what is the experimental
> design? How are you going to actually dose the drug?
>
> \[Slide\]
>
> One of the things that came out of the placebo analysis, as I said,
> was the food effect. Well, surprisingly, if you just do a QTc analysis
> you can get food effects that mask drug effects, that act like drug
> effects. Think about this, on days when we were doing intensive
> sampling we had patients fast for 14 hours. Then they get their meals
> and then they go on to the next day. Well, QT is prolonged after a
> meal. So, right away we are increasing QTc from baseline, regardless
> of whether the drug has any effect or not, simply because of the
> timing at which the samples were taken.
>
> So, I did an experiment. I simulated 100 subjects after oral
> administration of the drug\--the same time points as in the last
> study. Concentration and QTc were totally independent. There was no
> drug effect in the simulation. Then I analyzed the data using pop
> mixed and used a random effects model. I treated concentration as a
> covariate in the model.
>
> \[Slide\]
>
> Here is the simulated QTc data. There is nothing unusual about it. It
> looks exactly like what you would expect when you look at population
> QTc data.
>
> \[Slide\]
>
> Here is the PK data. It is actually pretty tight. There is nothing big
> there.
>
> \[Slide\]
>
> Then, when you look at the concentration QTc effect relationship, it
> doesn\'t look like much but it is statistically significant. The p was
> less than 0.0001. What it said was when you look at the solution to
> those fixed effects is that for every 100 ng/ml increase in
> concentration QTc is going to go up 2.2 msec. If you look at where
> Cmax is on the previous curve, 400 ng/ml, QTc in this study is going
> to go up 8 msec. That is not a drug effect. That is a total artifact.
> So, you have to be careful.
>
> So, I said, okay, what if I control for baseline? As my baseline I am
> going to use my pre-dose sample. This is a real common way of
> analyzing retrospective phase 1 QTc data because these studies are
> often done where the patients come into the clinic; they get their
> ECG; and then they are dosed with the drug and then they get an ECG
> maybe at Cmax and then again off-study. The question then becomes, you
> know, is there a QTc effect? Well, the only baseline you got is the
> one at time zero. So, when you do that you get the same results. I
> mean, you are just subtracting out a constant. You get exactly the
> same effect.
>
> So, this is the pitfall of using a time zero baseline and doing your
> QT analysis. You can get a total artifact and be totally fooled by it.
> The only way to avoid this is to do a point-by-point baseline
> correction.
>
> \[Slide\]
>
> Here is another simulation that I did. It is a very simple one. What
> is the false-positive rate of these metrics that we are using, that
> the EMEA put forth in their guideline? This was done a couple of years
> ago as well.
>
> A percent of subjects will have a QT more than 470 msec in females.
> This is after placebo administration. What percent will have a change
> from baseline of 30 msec to 60 msec of greater than 60 msec?
>
> So, I sampled 5,000 subjects and I serially sampled the ECG values and
> calculated the percentages for each of these. What it shows is that
> these metrics do have a false-positive rate. For instance, for a 450
> msec change in males the baseline false error rate is 1.5 percent. So,
> under these metrics you are going to have a QT effect in your
> analyses. The question is, is it real and is it important?
>
> So, by using simulation in your study you can help interpret the
> results from your analysis so you can show, well, if concentration is
> independent from QT, then this would be my false-error rate. This is
> what we showed with the drug. So, now we can interpret the relevance
> of these percentages.
>
> \[Slide\]
>
> This goes back to a different drug. We did a pop PK analysis on it. We
> did a QTc analysis of it. We saw that there was a QT effect with this
> drug. We were convinced it was real. We found out that body surface
> area was an important covariate. The idea was that we would do the
> PK/PD analysis for identifying the important covariates and then use
> simulation to determine the impact of those covariates on the QT and
> with or not we needed to do any studies in special populations, like
> maybe obese versus anoretic patients.
>
> It turned out that once we did the pop PK analysis we only found one
> covariate, which was BSA. It was on intercompartmental clearance
> which, if you think about it, is probably not going to lead to
> anything but we continued the exercise anyway and I will just go
> through the motions for you because it is an informative exercise.
>
> \[Slide\]
>
> The question was is BSA and important covariate? This was our change
> from baseline model. We showed that there was a 2.94 msec increase for
> every 10 ng/ml with the drug. This kind of plot\--and I show it to
> clinicians who are unfamiliar with population data or with ECG data,
> they look at this and they go, how in the world? I mean, this is all
> over the place. You can\'t fit a model to this. So, you had better
> have a good answer for that question when it becomes time.
>
> \[Slide\]
>
> What I did, I simulated the placebo lead-in day and then
> concentration-time profile for 150 subjects at steady state. We took
> the worst-case scenario. We dosed from 10 mg to 60 mg once daily and
> we varied the body surface area from 1.2 m^2^ to 2.2 m^2^. We
> simulated the placebo data and then we added on the drug effect. From
> that we calculated the standard metrics for assessing QT prolongation
> and we computed the means by dose and weight, and we fitted a response
> surface to this. Now, there was more to this analysis. We looked at
> the percent of subjects having values more than 45, etc., etc. but I
> will just show you the mean profiles.
>
> \[Slide\]
>
> When we got through at the end of the day, we saw that there was a
> linear relationship with dose. That is the axis, over towards the
> right. But BSA, as you might expect, had no effect on QT interval so
> we felt there was no need to do any further studies with weight as a
> special population. We saw that the 5 msec point was at the 60 mg
> dose. Clinically, we were planning on going to phase 3 studies with 10
> mg and 20 mg. So, we felt we were at a pretty good place on the
> concentration-effect curve.
>
> \[Slide\]
>
> Here are the males. It is the same thing, just a little shifted. So,
> at this point we felt that there was no further need to do any special
> population studies with weight as a covariate.
>
> \[Slide\]
>
> The last application I want to show you is using simulation to test
> the power of a phase 2 study where now you are given a study design
> and you want to know what is the probability of detecting a true QTc
> effect-response relationship in that population.
>
> This is what the project manager gave me. He said, look, we are going
> to do 10 mg, 20 mg, and 40 mg in a three-arm study. They are going to
> get dosed every day for 8 weeks. I want to collect ECGs on screening,
> week 4, week 8, at zero and 8 hours post-dose. We will collect 4 hours
> post-dose because we know that is around where Tmax is. We are not
> sure of the sample size; we are flexible on that. You can help us on
> that, but 30 to 120, that is kind of what we are leaning towards.
>
> So, a varied the sample in 30 to 120 by 10, and I just analyzed the
> results using mixed effect models, using sex, day, time within day,
> concentration at baseline as the fixed effects and intercept and
> concentration as random effects between subjects. I repeated the
> simulation 250 times.
>
> There are two ways you can analyze this data. You can treat
> concentration as a continuous random variable. you can treat dose as a
> continuous random variable or you can treat dose as a categorical
> variable. I think in the last meeting that we had here there was a
> discussion on categorizing continuous variables and its effect on
> power.
>
> \[Slide\]
>
> Here is an example of what could happen. The solid circle is when
> concentration is used in the model. The squares are when dose is
> either continuous or dose is categorical. You can see that when you
> categorize dose the power becomes a little bit smaller, but by far the
> most powerful metric was concentration. But even with 120 subjects we
> only had a 60 percent chance of detecting a true QTc effect. So, I
> told them if you really want to power the study to find something, you
> are going to have to go back and either increase the sample size or
> come up with a better design.
>
> \[Slide\]
>
> But there are a lot of unresolved issues in this. There are a number
> of issues that the guidance does not address and I just want to raise
> those. One is the choice of the covariance matrix. A lot of studies
> have shown, particularly in the linear mixed effect model literature,
> that the choice of the covariance matrix can have a profound effect on
> whether you detect fixed effects. So, how you go about choosing that
> covariance matrix, which one to use, has not been addressed yet.
> Should it be simple? Should you treat the intercept and concentration
> as independent? Should you allow them to be unstructured? You know,
> how should you do this?
>
> And, what about within-subject variability? These observations are
> probably correlated. Every analysis that I have seen so far has
> treated the within-subject variability as independent, which is
> probably incorrect.
>
> \[Slide\]
>
> When I did the lagged residuals on an analysis from a couple of years
> ago, this plot is a lag 1 correlation plot. So, this is the residual
> against the observation next to it. Here is lag 2 which is the
> correlation between two observations later. You can see that the
> correlation tends to dissipate as time goes on. So, treating
> within-subject variability as a simple covariance matrix is probably
> not entirely appropriate. It may be an AR1 or Toeplitz is probably
> more appropriate for this kind of data.
>
> \[Slide\]
>
> The other issue is whether we should use maximum likelihood or REML
> estimation. This applies if you are going to use a linear mixed effect
> approach. You have two options, particularly within SAS, REML being
> the default. But in order to these simulations you need to know what
> the variance components are, and whether you use maximum likelihood or
> REML you are going to get different variance components.
>
> I think it was shown about 20 years ago that the within-subject
> variability is more than between-subject variability but you probably
> want to use maximum likelihood, whereas most people would probably
> just use REML and be done with it. So, you know, which estimation
> method is best hasn\'t really been examined.
>
> The other is what is the best model selection criteria? Everybody uses
> likelihood ratio test, particularly when using NONMEM, but when you
> use SAS you get AIC, you get BIC, corrected AIC, and which of these
> metrics is most relevant to model selection I don\'t know.
>
> \[Slide\]
>
> In summary, I think there are a couple of points I want to point out.
> One is that using a time zero baseline just pre-dose is probably the
> worst baseline you can use. It leads to a lot of artifacts in the
> data, the food effect in particular, and you just want to avoid it as
> much as possible.
>
> Whatever metric you are going to use, there is going to be a
> false-positive error rate and the question is what can we live with.
> You know, if placebo data has a three percent false-positive rate, is
> it five percent that you should be concerned with? Is it six percent?
> You know, if you get ten percent of your subject meeting the criteria?
> When it is important and what are we willing to live with?
>
> Simulation can be a powerful tool to help answer some of these
> questions, not only with the agency but internally it can help you
> make decisions on where to proceed next.
>
> \[Slide\]
>
> Lastly, this is my opinion and I am probably going to take a little
> bit of heat for this but I think we are spending a lot of time on QT
> and I am not quite sure exactly, totally why. I mean, QT is really no
> different than any other laboratory parameter. We need to decide how
> to measure it. We need to decide what if important, what is clinically
> significant. I have a theory. This is my snowball theory. We started
> to get a little sensitized to QT because of a couple of drugs that
> might have shown it. Not everybody that has a prolonged QT develops
> Torsade. We need to more fully understand what are the issues relating
> QT to Torsade and sudden death before we start throwing the baby out
> with the bath water. If the NIH needs to get involved, so be it.
> Let\'s have a prospective study to really examine is this an issue
> because all of these analyses are retrospective and whenever you do a
> retrospective analysis you have the benefit of hindsight. So, we may
> be missing something here. We may be making a lot out of nothing.
>
> I think that a couple of years ago when this first started being an
> issue a couple of conferences were held and maybe a QT topic was held
> within those things. Then somebody else said we need to have a whole
> meeting on QTc and the next thing you know, we are at the FDA. Let\'s
> put some perspective on QT and let\'s do this right. Let\'s not just
> say that a drug that has prolonged QT is the death knell for the drug.
> Let\'s be reasonable about it. Let\'s understand what is the science
> behind this and how it relates to patient safety.
>
> I want to thank you for letting me speak here today. I would like to
> thank Tania Russell and Quintiles and Danny Howard at Adventis for
> helping me bounce some of these ideas around. Thank you.
>
> DR. VENITZ: Thank you, Peter. Any questions for Dr. Bonate?
>
> DR. SHEINER: I will start with questions and do comments in another
> round. I had a question but I think you answered it, which is that
> this artifact that you think will happen is with the meal so if you
> did, in fact, prevent people from eating then maybe the zero time
> baseline correction might be okay. Is that what you were saying?
>
> DR. BONATE: You know, I think a more appropriate study design would be
> one where patients get low fat meals at every meal and maybe just
> small meals throughout the day. I don\'t think you can reasonably
> prevent them from eating throughout the day.
>
> DR. SHEINER: No, but it is the confounding of the time effect which
> you believe is due to a meal\--
>
> DR. BONATE: Correct.
>
> DR. SHEINER: \--with the drug effect that is the problem. So, however
> you might get rid of that time effect, whether it is changing the type
> of meal, not getting a meal or whatever, that was the issue, that
> confounding.
>
> DR. BONATE: Yes.
>
> DR. SHEINER: Because you didn\'t have the placebo, so to speak, curve
> over time to compare to.
>
> DR. BONATE: Yes.
>
> DR. SHEINER: That is the usual design. The other question I had was I
> didn\'t understand what your point was about the false positives. You
> said 1.5. Was it that 1.5 percent of males, for example, would show a
> QT prolongation greater\--
>
> DR. BONATE: Yes.
>
> DR. SHEINER: Okay, but that doesn\'t mean your study would show a QT
> effect.
>
> DR. BONATE: No.
>
> DR. SHEINER: No.
>
> DR. BONATE: That is just the placebo baseline.
>
> DR. SHEINER: Yes, but that is individuals. What you are saying is that
> you have a threshold that says it is abnormal to be above the
> following thing. Typically in laboratory tests when there is no
> biology to tell you, you take five percent. So, actually, that is
> pretty good, 1.5 percent\--
>
> DR. BONATE: Yes.
>
> DR. SHEINER: \--false positives is actually a pretty specific
> laboratory test.
>
> DR. BONATE: Yes, but in some of the metrics, like the 30 msec to 60
> msec, the number was 50 percent.
>
> DR. SHEINER: Oh, I agree. That is very non-specific. I just didn\'t
> understand. You weren\'t talking about studies at that point.
>
> DR. BONATE: No, I was not.
>
> DR. DERENDORF: The QT intervals are a classic biomarker. We are not
> interested in them as such but we are interested in them to maybe make
> them surrogates for other events, as you mentioned. You said that
> right now the cut-off is sort of a 5 msec change where people get
> worried. If I look at the effect that you get from your dinner, that
> is 10 msec. So, there is something that I don\'t understand. If that
> biomarker is effective for something as trivial as a dinner, then that
> is not a biomarker.
>
> DR. BONATE: Well, the 5 msec is based on a mean. So, it is based on
> the average across all the observations within the day. It is
> completely taking out the time course of it. When you talk about the
> food effect at dinner, that is a particular point in time. So, they
> are kind of apples and oranges comparisons.
>
> DR. DERENDORF: The question that comes up then is what is the
> mechanism of these changes? What does the food do that causes the
> prolongation and what does the drug do? Are they the same mechanism?
> Are they additive or are they two completely different events that are
> manifested in the same change?
>
> DR. BONATE: I imagine that would be drug dependent. I mean, not all
> drugs prolong QT by the same mechanism and why food does I don\'t
> know.
>
> DR. DERENDORF Coming back to the original goal of this whole thing, it
> is that we want to measure something that tells us something,
> something else that we are really interested in. That should be as
> specific as possible and that doesn\'t seem to be the case.
>
> DR. BONATE: No, I don\'t think it is.
>
> DR. VENITZ: Peter?
>
> DR. LEE: I was just wondering how conclusive we can be regarding the
> food effect. Would it be just some sort of variation during the day
> that just happened to coincide with the food? Would a study comparing
> different foods on QT be more conclusive, say, giving low fat food
> compared to high fat food? If, indeed, there is a food effect, would
> including a placebo arm in the study take care of the food effect,
> which means that if you see a food effect in the placebo arm you can
> subtract that from your drug effect?
>
> DR. BONATE: Going to your first question about quantifying the food
> effect\--I know I skipped through the slide very quickly, but I did
> quantify the food effect in this analysis and for breakfast it was
> 10.6 msec; lunch, 12.5 msec; and dinner was 14.7 msec. I don\'t know
> if it is a volume effect or if it is a fat effect.
>
> DR. FLOCKHART: But is that an average of an area or single time point?
> What is that number?
>
> DR. BONATE: It is a fixed effect. It is more of a shift from the
> baseline. So, the baseline is 389. So, if you had breakfast it would
> be 399. Do you see what I am saying?
>
> DR. FLOCKHART: Yes.
>
> DR. BONATE: If you think of it like an analysis of variance, that is
> kind of what it is. So, if you included the placebo\--I think if you
> did the point-to-point correction you would control the food effect,
> provided the same meal was given on both days.
>
> DR. VENITZ: Let me give you a possible mechanism for the food effect.
>
> DR. BONATE: Sure, please.
>
> DR. VENITZ: Did you look at your heart rates at all? Because you are
> looking at Bazett-corrected QT intervals.
>
> DR. BONATE: Oh yes, I didn\'t even want to go there. Right.
>
> DR. VENITZ: But my point is you might well look at secondary effects
> to the heart rate because every time you eat your heart rate will go
> up, as most of us who have just had lunch can experience. So, it might
> be an artifact in your correction. It may well be that you have
> sympathetic activation that somehow affects repolarization as well.
> So, I think it is not unexplainable that you see food effects on
> something as esoteric as the QTc interval.
>
> DR. BONATE: No, you are absolutely right. I left this on my slide but
> I wasn\'t going to talk about it, but I will now, and I want to say
> our \"Slavic\" devotion to Bazett\'s\--I mean, why can\'t we dump this
> dog and go to something that is a little less sensitive to heart rate?
> I have heard this argument that with Bazett\'s we have historical data
> to compare it to. Well, if your historical data is wrong what is the
> point of making the comparison? Let\'s just say in the guidance no
> Bazett\'s. Why can\'t we say that? I don\'t know. Let\'s go to
> Fridericia\'s or something.
>
> DR. SHEINER: Fridericia\'s doesn\'t work any better either.
>
> DR. BONATE: Well, it is better than Bazett\'s.
>
> DR. SHEINER: Maybe, but not much. It is an interesting point. First of
> all, I have to correct your English there. There is nothing about the
> Slavs that\--
>
> \[Laughter\]
>
> \--it is \"slavish.\" You know, I think it is interesting. It is an
> artifact that I think is very similar to sketcher plots and stuff like
> that. There was a time when you could only make a scattergram so if
> you had two factors that were affecting what you were interested in,
> heart rate and, let\'s say, drug or something else, you had to get rid
> of one of them. So, what you did was divide it by its square root,
> cube root or whatever it is, and then it just sort of persists like
> body surface area, and we know that formula is not the formula for
> body surface area. In 1919 it was\--well, I won\'t go off on that.
>
> In any event, what you want to do is heart rate as a covariate. You
> may find that you can find some kind of parametric formula and you may
> find that you can\'t. It doesn\'t much matter, but you can correct for
> it and I think that some of this sort of stuff, you know, may go away.
> So, I think the general principle is we have measurements, like
> interval, ECG and heart rate, and keep them separate because now we
> don\'t have the problem that we can only look at one variable at a
> time.
>
> DR. BONATE: Well, I think an ideal situation\--I mean, I think there
> is a lot of value to individual corrections, which I think is where
> you are going with that. The problem with that is that you need a lot
> of data for an individual to be able to make that correction. If you
> have one ECG on a person it is difficult to say what is the correction
> that you use for that subject.
>
> DR. SHEINER: I am not saying that. I am saying we could analyze lots
> of data and find what the heart rate correction in general was. It
> might not be any particular simple formula that allows us to then take
> that \"corrected\" thing and plot it against something else. It might
> be more complicated. The point is we have plenty of data.
>
> DR. BONATE: Yes.
>
> DR. HUANG: A quick question. You mentioned that the area under the QT
> time curve has potential but is not really investigated. I wonder,
> with the several applications that you listed, have you tried to use
> that? For example, in the food effect you said if you do a
> point-by-point in the placebo phase you might be able to correct it if
> they are taking the same food, but we know that is probably not
> reality. So, if it is the other measure would it provide a method to
> decrease the sensitivity of this circadian or food effect? You have
> shown that using AUC a lot of other measures become insensitive\--the
> differences that you would ordinarily see that you don\'t see anymore.
>
> DR. BONATE: Well, I think it depends on what your baseline is. If you
> use a time zero baseline the AUC metric will exacerbate the food
> effect.
>
> DR. HUANG: I am talking about if you do have a placebo. The concept
> paper recommends using a placebo.
>
> DR. BONATE: Yes, if you have a time-time, then AUC I think would still
> be more sensitive and you wouldn\'t have to worry about the food
> effect.
>
> DR. HUANG: More sensitive or less sensitive?
>
> DR. BONATE: It should be more sensitive. I think you have to have the
> point-to-point correction to really do this.
>
> DR. HUANG: That is what is recommended.
>
> DR. BONATE: Yes.
>
> DR. HUANG: By the way, I think Bazett\'s being mentioned partly
> because a lot of devices right now are calibrated with Bazett\'s.
>
> DR. BONATE: You know, in 1920 they could probably only do the square
> root on a slide rule. I don\'t know; that is all I was thinking.
>
> DR. VENITZ: Wolfgang?
>
> DR. SADEE: Just a comment on the food effect. If you test chemicals,
> drugs maybe ten percent have a chance of causing QT prolongation. With
> a meal you take in about 10,000 compounds. So, I think it is a
> chemical effect.
>
> DR. BONATE: Maybe.
>
> DR. VENITZ: Any further comments or questions?
>
> \[No response\]
>
> Thank you, Peter.
>
> DR. BONATE: Thank you.
>
> DR. SHEINER; Let me just say one thing. It is a biomarker and the
> problem is that it is probably the heterogeneity of repolarization
> that is the problem in Torsade so the average goes up if it is a real
> food effect. My guess is it is also a heart rate effect. But if it
> were a real effect, it might be that it is a general effect with,
> let\'s say, a vagal effect and sympathetic effect and it is going to
> happen everywhere. It is not increasing the heterogeneity.
> Unfortunately, we haven\'t got a measure of the heterogeneity or
> repolarization so we take the average as a poor measure of it. So, for
> drug it is one thing; for food it is another thing. That is entirely
> reasonable, you know, to have two different causes of the same
> biomarker and one of them you consider dangerous and one you don\'t.
>
> DR. DERENDORF: Oh, I completely agree. It just becomes a design issue.
> I fully agree with your approach that the point-to-point comparison
> would be the way to go. But looking at your curve here, you need a lot
> of data points to get that sensitivity to detect the difference there.
> That is going to be the issue.
>
> DR. BONATE: Especially if you were comparing, say, day 8 because then
> you would need a day 8 point-to-point to really make a proper
> comparison. Yes.
>
> DR. SADEE: I have one more quick comment. You mentioned 30-50 subjects
> or so. Their polymorphisms in the candidate genes are associated
> possibly causatively, in a causative way, that have a frequency maybe
> much less than that. Since the real danger is 1/1,000 it is not quite
> clear to me whether 30 or 50 subjects would do. So, if you have
> polymorphism as one percent that sensitizes a particular individual to
> a particular chemical, you will not detect it.
>
> DR. BONATE: You are talking about the link between the biomarker and
> the outcome. I think, you know, 30-50 subjects is more than adequate
> to determine the change in biomarker. Making the next step, you are
> absolutely right.
>
> DR. VENITZ: Thank you again. Our next speaker is Dr. Leslie Kenna. She
> is going to give us the second part of this case study on QTc.
>
> **Case Studies**
>
> DR. KENNA: It is a great privilege to be able to present to this
> committee. I have to say though that if Peter, with his years of
> experience felt intimidated, I am going to try not to act like a deer
> in headlights up here. This is a very wonderful opportunity.
>
> \[Slide\]
>
> My presentation has four parts. First, I will present the question of
> interest. Then, I will present data from the trenches to illustrate
> some of the challenges we face. Next, I will present the clinical
> trial simulation methodology under consideration to address those
> issues. Finally, I will present some very preliminary results. As you
> listen keep in mind that this is a work in progress. We are assembling
> a QT database and developing tools to analyze those data. We are
> soliciting your advice today on an effective approach.
>
> \[Slide\]
>
> In the interest of safety, we would like to know the effect of drug on
> QT interval in the worst-case scenario. That is, to know what response
> might occur in the case of increased drug exposure due to, say,
> drug-drug interactions.
>
> \[Slide\]
>
> As Peter said, a major challenge is that there is tremendous variation
> in observed QT response, greater than the response of interest.
>
> \[Slide\]
>
> There is wide variability in measured QT interval in a given subject
> at a given time in a given day.
>
> \[Slide\]
>
> Just to give you a sense of that, this is a plot of
> Fridericia-corrected QT data collected in one subject on one
> particular day before any drug was dosed. So, that is baseline,
> before\--you can\'t see that? At each point ten measures were taken at
> one-minute intervals. Just by looking at the data, you can see, for
> example, that at that nine-hour time point measures taken one minute
> apart had a range of 15 msec. Maybe you can\'t see it but this cloud
> of points is shifting over the course of a day.
>
> \[Slide\]
>
> So, not only is this response shifting over the course of a day but a
> given subject may have different QT response patterns at baseline, one
> observed on different days and now we actually have a black line
> connecting basically the average between the ten points on a given day
> in a subject. You can see that the lines don\'t overlap from one day
> to another.
>
> \[Slide\]
>
> We just looked at data from one subject but if you compare subjects
> you can see that different subjects have different QT response
> patterns over time.
>
> \[Slide\]
>
> This slide provides a side-by-side comparison of the QT measurements
> taken over four baseline days in two different subjects. We looked at
> subject I but now subject K\'s data exhibits the same overall
> characteristics but the pattern of change appears out of sync with
> subject I. You see all the points going down when the other subject\'s
> points are going up.
>
> Given that we may want to detect a change in QT interval of about 5-10
> msec, if there can be about a 15 msec change in response over
> measurements taken one minute apart before any drug is even given, in
> some ways we are trying to find a needle in a haystack. That response
> is not impossible to find but it becomes very important to design QT
> evaluation studies effectively.
>
> \[Slide\]
>
> For this reason, we set out to review the study designs used in
> several recent submissions. A review of several recent submissions to
> the FDA revealed that different study designs have been used, for
> example, in terms of the duration time.
>
> \[Slide\]
>
> To illustrate this point consider the definition of baseline in six
> recent submissions. Here you see that baseline was defined as anything
> from a single measure taken 14 days before the start of a QT
> evaluation study to over 100 EKGs taken during two pre-dosing days.
>
> \[Slide\]
>
> Another observation is that in different studies a different response
> has been observed to the same drug at the same dose. 400 mg of
> moxifloxacin is recommended to be tested in subjects to evaluate
> whether a trial is sensitive enough to detect a change in QT interval.
> The moxifloxacin label says that it cases a 6 msec increase in QT
> interval at that dose. In one study we reviewed, however, 400 mg of
> moxifloxacin was associated with an 8 msec change in Fridericia
> corrected QT interval. In another it was associated with a 13 msec
> change.
>
> \[Slide\]
>
> Just to show you some key features of those two studies, you can see
> from these confidence intervals that case one yielded a much more
> precise estimate of drug effect than case two. There were some subtle
> differences in terms of the number of baseline measures and the number
> of replicate EKGs.
>
> So given that study design is something we can control if it becomes
> important to identify how much of this difference between effects
> estimated depends on the study design, especially if you consider or
> if you imagine that moxifloxacin was actually your drug of interest
> because, depending on the indication and effect of 8 msec, might have
> been considered clinically insignificant while an effect of 13 msec
> might have raised concern.
>
> \[Slide\]
>
> Just getting back to observed trends, we have also been presented with
> incidences where the observed response was sensitive to the data
> analysis method.
>
> \[Slide\]
>
> For example, consider the following difference with regard to mean
> versus outlier analysis, drug X was associated with a 4 msec increase
> in Fridericia corrected QT interval at Tmax. The positive control in
> that study was associated with a 9 msec change. This suggested that
> the drug had less of a QT liability than the positive control.
>
> \[Slide\]
>
> The outlier analysis, however, suggested that the drug and positive
> control yielded a similar effect on QT interval and that this effect
> was greater than that on placebo. So, this raised the question of what
> data analysis method we should trust.
>
> \[Slide\]
>
> Then consider the following example of how the estimated risk depended
> on the definition of baseline. In one analysis of a particular data
> set baseline was defined as measures taken during a treatment-free
> period plus measures taken on placebo.
>
> \[Slide\]
>
> In that case a five-fold increase in exposure was associated with a
> two-fold increase in the number of outlying QT measurements. The
> appearance of a shallow dose-response relationship suggested that
> increased drug exposure would have little effect on QT interval or
> that the drug was relatively safe.
>
> \[Slide\]
>
> However, when the same data set was analyzed having baseline defined
> as measures taken during the treatment-free period only, it appeared
> that a five-fold increase in exposure was associated with a four-fold
> increase in the number of outliers. This suggested that the response
> was proportional to dose and could potentially increase with greater
> exposure.
>
> \[Slide\]
>
> Given these challenges, our goal is to learn from available data to
> aid in the prospective design of QT studies.
>
> \[Slide\]
>
> The specific aims are to assemble a QT database from data in
> submissions, then resample from those data and use clinical trial
> simulation to evaluate the clinical trial designs and data analysis
> methods.
>
> \[Slide\]
>
> I will now shift and give you an overview of our proposed approach and
> then go into greater detail illustrating each step.
>
> \[Slide\]
>
> To evaluate the success of a study design we need to know the true
> underlying effect of the drug. So, the first step is to simulate your
> data. The proposal is to use baseline QT data that we have, much like
> the data I presented earlier, so we don\'t have to assume a shape of
> the distribution. We will choose a study design and models for the
> drug\'s PK and PD profile. We will then add baseline response to the
> simulated response to treatment.
>
> In any real study one only gets to sample the QT responses according
> to the study design. The next step then is to sample from the true
> data according to the chosen study design. Then response will be
> estimated by the methods of analysis of interest. We can explore those
> proposed in the concept paper and those used in recent submissions. In
> order to get a sense of how a particular study design performs it has
> to be repeated many times. Finally, performance will be quantified
> after all the repetitions are carried out. One possible way to do this
> is by computing power.
>
> \[Slide\]
>
> Now just to show you our plan in greater detail, we start by randomly
> drawing baseline data for each subject in the trial from the database.
> In the data I showed earlier we had four baseline days of
> measurements. If we only need baseline observations from one day, then
> a particular day will be selected at random from these data. Here you
> see ten observations for time as collected on a given day.
>
> \[Slide\]
>
> Next, depending on the study design under investigation, N
> measurements will be sampled at random at each time point in a given
> individual from the day of baseline measures selected. Here you can
> see that three measures were randomly selected at each time point from
> the original data set.
>
> \[Slide\]
>
> Given a study design where we evaluate two doses\--two doses because
> one recommend in the concept paper is that you would use a therapeutic
> dose and a super-therapeutic dose that covers drug-drug interactions
> or whatever that worst-case scenario is for your drug\--two doses of
> drug, and using both placebo and active controls we would like to
> investigate the impact of the following parameters, whether you have a
> crossover or parallel design; single dose versus steady state design;
> the number of subjects; timing number and duration of EKG measures;
> the PK/PD model for the drug, for example, whether maximal response
> occurs at the time of maximum drug concentration or whether there is a
> delayed effect and, along those lines, one mechanism for effect delay
> that we can simulate is if the drug and the metabolite both affect QT
> interval. Then, the PK model for the drug would also be varied. For
> example, we could explore the effect of the clearance of the parent
> and, say, an active metabolite.
>
> \[Slide\]
>
> After we have randomly chosen a baseline profile for a subject before
> and while receiving drug and before and while receiving placebo\--so
> here is baseline before drug; baseline before receiving placebo\--we
> are going to add the baseline to the simulated true response to a
> given treatment. For drug the treatment effect over time might be as
> follows, QTc might increase with time and decrease just due to the
> fact that it is driven by drug concentration which is also rising and
> falling. Then, for placebo there might be a slight increase in QT that
> has no dependency on time.
>
> \[Slide\]
>
> Then one adds the sample baseline to the true underlying treatment
> effect to get treatment resistant pathogen observed in a subject. The
> responses that are shown here are just what you get when you add each
> of the baseline points to the true drug or placebo effect at that
> time. Here, for placebo you see a trend that just simply reflects the
> baseline variability in QT.
>
> \[Slide\]
>
> In the previous slide I showed you how to simulate true underlying
> response, as shown here, but in clinical trials, as you know, you only
> get to observe the response according to the study design. From that
> true response, if one chooses to sample one QTc value at a given time,
> then you might see this response to drug and this response to placebo.
> Likewise, for baseline.
>
> \[Slide\]
>
> If you sample three QTc values, for instance, as baseline just before
> starting treatment, then your sample baseline might look something
> like this.
>
> \[Slide\]
>
> Then to estimate response we performed some operation on the collected
> data to evaluate the difference in response to the treatment after
> baseline effect is accounted for. That is just symbolized here as a
> minus sign. One example of an approach that you might use to do this
> is, for example, you might take the mean sampled response on treatment
> minus the mean response on baseline. Some others are listed here and
> this is certainly not an exhaustive list.
>
> \[Slide\]
>
> These are not supposed to be question marks. They are supposed to be
> arrows. This process of randomly sampling baseline data, simulating
> response to treatment and then estimating response will be repeated
> many times because, due to all the sources of variability including
> baseline QT variability, although we have fixed the drug effect within
> a given simulation study, different trials will enroll different
> subjects causing the estimated effect to vary, as I just show here.
>
> Since we set the drug effect parameters when we design the simulation
> study we know the true underlying response that we are trying to
> detect, so we can just compare the estimates across all those
> replications to compute performance.
>
> \[Slide\]
>
> One way to evaluate how study designs and data analysis methods
> perform is to compute power. That is, given a particular study design,
> we can tally up what fraction of simulations allow you to detect the
> drug effect on QT interval when there really is such an effect.
>
> \[Slide\]
>
> I will now show you some very preliminary results of our
> investigations.
>
> \[Slide\]
>
> As I pointed out earlier, we need baseline data to conduct our
> simulation studies. The source of the baseline data presented here are
> 72-hour baseline profiles in 45 subjects. The simulation conditions
> were as follows, the trial was a randomized, parallel design with two
> arms, treatment and placebo. There was a 24-hour placebo run-in and 24
> hours on treatment. QT sampling was hourly from 1-24 hours post-dose.
> We varied the number of subjects.
>
> Treatments were administered orally at a dose of 100 mg. The drug
> exhibited one compartment PK. PK/PD was a linear effect added to the
> baseline variation, and there was no effect delay.
>
> Analysis methods included taking the difference in maximum QTc on
> treatment and maximum QTc at baseline, taking the difference in the
> mean QTc on treatment and mean QTc at baseline. These are things that
> may have either been seen in submissions or in the concept paper.
>
> \[Slide\]
>
> This slide illustrates how PK/PD data in 40 subjects looked for a
> trial under the parameters just presented. As you can see, we presumed
> that response was directly related to concentration so both of them
> peaked at the same time, and that maximum response was about 16 msec.
>
> \[Slide\]
>
> This slide shows the power of the data analysis methods to find that
> the drug caused a significant change in QT interval relative to
> placebo as a function of the number of subjects in the study. Each
> line represents a different way of analyzing the data. Power ranges
> from zero to 100 percent where 100 percent means the method correctly
> identified a significant difference every time it was used. Recall
> that the difference really was significant; it was about 16 msec.
>
> \[Slide\]
>
> As you would expect, all methods have more power as the number of
> subjects is increased. For a given study size you see that the methods
> of analysis influence how often you can expect to correctly identify
> drug response. For example, when we subtracted the man QT value at
> baseline from the mean response after taking drug, which is the black
> square at the highest point on the plot, 85 percent of the time we
> were able to identify that the drug prolonged QT interval if 80
> subjects were in that trial.
>
> In that same trial if you, instead, subtracted the maximum QT value at
> baseline from the maximum QT value on drug, the correct response was
> instead identified 55 percent of the time. Keep in mind that the data
> didn\'t change, just the way they were analyzed.
>
> \[Slide\]
>
> So, we slightly altered the study design so that instead of collecting
> several measures at baseline only one sample was collected at baseline
> which, as Peter has already pointed out, is a horrible way to design
> your study.
>
> We examined the result in the top panel on the previous slide where
> baseline included measures taken hourly over 24 hours. The bottom
> panel shows the results under the same conditions except that, as I
> said, one baseline measure was taken. You can see that power is
> greatly reduced. If you estimate response by subtracting the single
> baseline value from the mean response on drug you only identify
> significant difference between drug and placebo seven percent of the
> time if the study has 75 subjects. You also see that the metrics
> actually flip around in terms of which was more powerful and now
> taking the maximum is a little more powerful than taking the mean.
>
> \[Slide\]
>
> As you can tell, this is definitely a work in progress and we would
> greatly appreciate the committee\'s feedback on the following
> questions. These questions could just guide the discussion but we are
> certainly eager to hear what you have to say. Thank you.
>
> DR. VENITZ: Thank you, Leslie. Before we get into the specific
> questions, are there any comments or questions about Leslie\'s
> presentation?
>
> DR. SHEINER: Leslie, did you sample the QTc in you baseline, your 72
> hours? Was that the QTc or the QT?
>
> DR. KENNA: That was the QTc.
>
> DR. SHEINER: So, apropos of the last discussion, it might be
> interesting to sample both the QT and the heart rate since they are
> both available, and then see, making this particular correction you
> are using, whether it is Bazett\'s, Fridericia\'s or whatever you are
> using, whether there is a better way to do it with respect to that as
> well. You have the potential to do it. You are investing a lot of
> effort and that would be a small addition that might have a payoff in
> showing what the price is of using this standard correction, which we
> all know isn\'t very good.
>
> DR. FLOCKHART: What surprised me about Leslie\'s data was that one of
> the things that has been a kind of unquestioned assumption is that
> when we do circadian rhythm once in a person, that will be the same if
> we did it ten times, but it is not. I think that is a really important
> message in what you are saying.
>
> I think the thing I am most worried about in this approaches, and this
> comes somewhat from history, if you like, the history of quinidine to
> terfenadine to, in our case, pimozide. The thing with quinidine
> was\--we did this in the same study where we gave people intravenous
> quinidine\--we wouldn\'t be allowed to do it now\--to see if there was
> a gender between men and women, and if you had analyzed that study
> using an averaging effect, if you had done a circadian rhythm before
> on one day and then you had done an averaging effect after, you would
> have missed a humongous change because we were sampling for two days.
> If you had actually done an average, the average would have diluted
> it. Point-to-point comparisons would have done the same thing, you
> would have missed this thing that lasted no longer than about an hour,
> even though you are giving a drug that prolongs the QT 30 msec, 40
> msec, 50 msec, because of the very short time interval.
>
> I actually don\'t know a drug\--and I would be interested if there are
> other members of the committee who do\--where you don\'t see this
> cardiac reaction to the prolongation of QT. In three of the drugs that
> I have studied, pimozide, haloperidol and ziprasidone, you see an
> actual reverse, a negative QT interval change. It is like the heart
> knows somehow that it is being prolonged and it protects itself in a
> kind of rebound way. Again, that can dilute the effect that you see.
> So, timing here is important because, again, if you are doing averages
> or you are doing point-to-point comparison with circadian rhythms you
> miss that effect completely.
>
> The other thing, you build it into your model but I think you did the
> absolute best thing to do, you built in a model where the time effect
> was immediate. In other words, you see it right away. Obviously, you
> can\'t do that always. It is hard for a sponsor in advance to know
> what that thing is going to be, whether it is going to be four hours.
> Imagine you have a situation where you have a drug whose concentration
> Cmax is at two hours, the Tmax is at four hours and then it is gone,
> and you are looking for that within\--you know, you have a relatively
> short period of time in which the thing is prolonged.
>
> Now having said all of that, if you look at quinidine itself which is
> a drug, you know, known to cause Torsade. The Torsade seem to occur in
> the early phases of when the drug is given, shortly after change in
> dose or shortly after a rapid infusion. It is debatable whether a
> decrease might do that as well. But it is very possible that averages
> are not the biological parameter we care about anyway; that a high
> number in general simply reflects the fact that at some time points
> you are much higher than that, or you are changing quickly.
>
> So, I think the models you need to put in, in terms of delay\--I think
> the metabolites are a totally appropriate model and it could actually
> be that a delay in a metabolite would simulate that perfectly well, I
> think. The models that you need to build in need sometimes to be
> models that can that can pick up something that happens over a
> relatively short period of time during the dosing interval.
>
> DR. SHEINER: So, what you are saying, and I think it is a good idea,
> is that you consider other models for the drug effect. You add that
> one that was perfectly proportional to concentration. I am fascinated
> by the adding one that goes up and then has a rebound and then comes
> back to baseline because that, you know, with the averaging, would
> really create havoc for anybody to detect it. You can do all this
> stuff with simulation. I think it is a nice opportunity.
>
> DR. VENITZ: I would also suggest, as Lew already said, not only to
> look at heart rate as a covariate to explain your QT, but look at
> drugs that change heart rate and QT at the same time. We are going to
> hear about sotalol in a minute which does exactly that.
>
> DR. KENNA: Okay.
>
> DR. VENITZ: So, can you differentiate the primary effect of heart rate
> on QT versus the intrinsic effect that the drug has on prolonged
> repolarization? That might be a significant issue.
>
> DR. SHEINER: This is a quick question. What do you have, 48 patients
> that you are resampling from?
>
> DR. KENNA: When we resampled there were 45 I believe.
>
> DR. SHEINER: Is there any thought on whether\--it is a funny thing, it
> is 5,000 simulations but 48 distributions. You kind of wonder how you
> should trade those things off.
>
> DR. DAVIDIAN: Yes, I was wondering that myself. I am not sure; I am
> not sure exactly what I think. That is what you have available, right?
>
> DR. KENNA: Yes. Well, we have other data so we are up to about 100
> subjects having four baseline days. Peter had an approach to address
> that issue, and it was if you assume that there is no diurnal
> variation he would pick different points on the time axis and shift it
> that way so that you were getting a difference. Peter?
>
> DR. LEE: Yes, if you have a continuous measurement and you don\'t
> assume that there is a circadian variation that doesn\'t repeat
> itself, later if, for example, you want to simulate to baseline you
> could pick, say, a 12-hour baseline here and then pick another 12-hour
> baseline even over the original 12 hours. With that approach you could
> literally get hundreds, thousands of simulated baselines with 50
> subjects or even 100 subjects.
>
> DR. DAVIDIAN: I just have a question. Did you simulate a case where
> there was no treatment effect and see what the power is?
>
> DR. KENNA: This is Peter\'s call.
>
> DR. LEE: Yes, there is a placebo arm and there is a treatment arm. So,
> there is comparison between placebo and treatment.
>
> DR. DAVIDIAN: So, when there is no treatment effect at all\--you had
> that hump, right?
>
> DR. KENNA: Yes.
>
> DR. DAVIDIAN: So, what if you just had the same?
>
> DR. KENNA: Yes, there is a placebo arm without any effect.
>
> DR. DAVIDIAN: Suppose there really were no treatment effect, you are
> doing it at 95 percent\--
>
> DR. KENNA: Yes, I guess we are revealing our regulatory spin, which is
> looking for the false negative\--
>
> DR. DAVIDIAN: Sure. I was just wondering because some of these powers
> that are higher than others might be the fact that at no treatment
> effect it is, you know, not consistent there. So, that could possibly
> carry over to where there was a treatment effect.
>
> DR. SHEINER: Let me ask you about that because they are doing pretty
> standard statistical tests. I mean, once they have their statistics
> they are doing a pretty standard test on it. So, do you really think
> it isn\'t operating at the right\--
>
> DR. DAVIDIAN: I would expect it were but just for completeness I would
> do it, just to be sure, just in case there was something strange going
> on, you know, working with these maximums, or whatever. I don\'t know.
> I would think it would be fine, but just to be sure.
>
> DR. KEARNS: Leslie, I am going to ask you a question that is
> theoretical and probably a little unfair but it is after lunch, so. I
> am sitting here, listening to all this and looking at your excellent
> presentation and thinking, well, the approach is evolving on how to
> examine QT data. So, sometime we are going to come up with something
> that is going to be predicated from a lot of adult studies, and I am
> thinking about the pediatric world where\--and I should publish
> this\--we observed in a study of cisapride what I have called the
> pacifier effect on QT. If I have a baby and I am doing an ECG, getting
> a reasonable QT and the baby is crying, and I measure it and I put the
> pacifier in the mouth of the baby it changes. It changes very quickly,
> which has nothing to do with diurnal anything. So, how do we take this
> and apply factors in another population that may drive this whole
> thing in a much different way?
>
> DR. KENNA: Then, the other thing to consider is that both of us have
> looked at baseline variability, and Peter looked at placebo
> variability, I don\'t know if the drug effect on top of that is
> somehow an interacting component or if that is just additive on top of
> that. So, that is another thing to consider.
>
> DR. JUSKO: I have a question that kind of relates to the underlying
> mechanism. Dr. Lee pointed out that most of the studies that he found
> most believable with terfenadine were multiple dose studies. Dr.
> Bonate did simulations based on the multiple dose regimen. Most of
> what you presented, although you proposed doing steady state
> experiments, is based on a single dose exposure. Is it known with
> these drugs whether the duration of exposure is a factor in changing
> QTc intervals?
>
> DR. FLOCKHART: That is partly what I was trying to get at. I think it
> goes beyond that. I think the actual risk you are incurring might be
> different for different drugs. So, in the case of Seldane, you know,
> the studies that Peter Honig did were steady state studies in which he
> did see a real increase. That is where the 6 msec comes from. He could
> see a real increase when he measured the QT before the dose in that
> kind of trial design.
>
> Lots of other people did sampling in other ways and missed that
> effect. But if you look at the real time effect in Peter\'s studies
> there was absolutely no debate that in a short period of time\--we did
> a similar thing with pimozide. There was a short period of time when
> it was unquestionably prolonged and then it goes away. The problem is,
> and the thing I am trying to figure out how to do in terms of
> statistics, if you have the possibility\--if you have a data set there
> and it is possible that out of a 24-hour time interval you have 3
> hours during which it is prolonged, and you don\'t know when that is.
> It might be immediate; it might be 8 hours later. How do you do a
> statistical test that allows all the multiple comparison testing, and
> all the other things you guys do, to pick that up? Does that really
> hurt your power or can you design it in such a way that you are able
> to simulate it well enough to pick it up?
>
> DR. SHEINER: That is a little bit like what the maximum does. I don\'t
> like the maximum as a statistic. You just pick the longest QT you saw
> all day long. In a way, it is saying let\'s find the worst point, and
> you can do statistics on anything. So, the nice thing about this kind
> of simulation thing is you could add in an effect which was
> essentially a spike at six hours, even though the dose was given at
> time zero and the concentration didn\'t spike then, and analyze that.
> What is the kind of design, what is the kind of analysis that, under
> the constraint that it have the proper operating characteristics under
> the null, gives you the greatest power? The greatest theoretician
> could tell us but otherwise you could just grind away and find a
> reasonable one.
>
> DR. LESKO: I don\'t know if you had mentioned this or not, but in the
> six studies on that one slide\--six drugs, I should say, which
> represent six studies, what was the range of subject numbers across
> those studies? What was the sort of range between subject variability
> given the different baseline methodologies? It was slide number 12.
> What was the range of subjects in those cases?
>
> DR. KENNA: In terms of the numbers?
>
> DR. LESKO: Number of subjects, yes.
>
> DR. KENNA: They were fairly similar. I would say anywhere from about
> 40 to about 60 subjects seems to be what we are seeing.
>
> DR. LESKO: And how about the variability within each case given the
> way the baselines were varied? For example, which one had the highest
> and lowest variability?
>
> DR. KENNA: Between confidence intervals? I would have to go back and
> take a look at that.
>
> DR. LESKO: I was wondering did the studies control for diet or food
> effects at all? How much attention is paid to that in the study
> design?
>
> DR. KENNA: Well, I know they pay a lot of attention to when they are
> going to sample blood. They definitely lay out that they don\'t want
> to poke somebody and then do a QT interval. I haven\'t seen so much in
> the way of food till more recently.
>
> DR. LESKO: Yes. Is it controlled, do you know, from placebo to drug?
>
> DR. KENNA: I think the meals were the same for all arms of the
> studies, but in only two of these six I believe were meals really paid
> attention to.
>
> DR. VENITZ: Any additional comments or questions for Leslie? Yes, go
> ahead.
>
> DR. MCCLEOD: One thing you may want to start thinking about including
> in your model in the future is going from the QT interval to Torsade
> de pointes because that is what is cared about. You can now model in
> either allele frequency for the high risk genotypes or preclinical
> data on sensitivity of HERG, whatever other channel to the drug. I
> know it is premature to include it now because you are generating the
> front end, but that way you get to a point where it might get to what
> Peter talked about at the end of his talk where you can stop using to
> kill drugs and start using it to better select drugs in an earlier
> setting.
>
> DR. KENNA: That is a great idea. Thanks you. Thank you very much.
>
> **Committee Discussion**
>
> DR. VENITZ: Thank you, Leslie. If you don\'t mind, can you post the
> questions so we can kind of go through them one at a time? I think the
> first one is asking for the committee\'s input on additional study
> design points for the analysis. Any additional comments on study
> design?
>
> \[No response\]
>
> Then what about question number two?
>
> DR. FLOCKHART: Lew and I were talking over here. I think the thing
> about the maximum\--it is so easy to critique but often it actually
> represents the most important thing you are going after and it is
> what, in my experience, is very often the most valuable thing. The
> problem is that to determine whether the maximum that you actually
> determine is not just a random fluctuation.
>
> So, in study designs it would be possible to figure out how many
> patients you needed to study to figure out where the maximum is
> basically in a pre-study and then, subsequently, to intensely sample
> around that. That would get around the issue of what we are really
> doing all the time; we are testing for some long period of time in the
> hope that during that period of time you are going to pick something
> up. It is not really a time-directed thing. So, the right way to do it
> or a reasonable way to do it, if you are not dealing with something
> that stays up for days, weeks and months and then comes down but
> usually you are dealing with something that does this, is to determine
> where the time is first and then intensely sample right there, and
> Leslie\'s model would be great to test that in. You could basically
> figure out how many patients you needed to get power to do that for a
> given change.
>
> DR. SADEE: It is not quite clear to me, since this is such a major
> issue for the industry and can cost extraordinary amounts of money one
> would like to ask what would be the best way of studying this. The way
> I would go about it, and there is a lot of literature, if we agree
> that polymorphisms do play a role in whether or not a person responds
> more or less, a company would go ahead and sample, let\'s say, a 1,000
> patients and genotype those 1,000 patients to get a fair
> representation\--or let\'s say 2,000 and select 50 patients that are
> representative of the major phenotypes, in which case one would have
> much greater assurance of seeing unusual reactions that one would have
> to then treat very carefully, maybe with lower doses, because one is
> probing exactly where one should be probing.
>
> So, I am not sure. That wouldn\'t be such a big expense to actually
> find these people because apparently it is done with every single new
> drug. So, that would be my suggestion.
>
> DR. FLOCKHART: Are you saying, Wolfgang, to simply collect the DNA and
> keep it? I mean, I would totally endorse that, but actually finding it
> right now would be\--I mean you would have to take a trip to Stockholm
> to be able to do that right now.
>
> DR. SADEE: Well, there are a lot of polymorphisms known and the five
> candidate genes so you and you just then would sample a population for
> these 15 main polymorphisms and select your study population of 50
> people.
>
> DR. FLOCKHART: Well, I think there are a number of issues there. One
> is I think we have registered that the five candidate genes only
> explain only about at third or, at most, a half of the total deal. So,
> we would be missing a half to two-thirds by doing that. I would never
> argue against collecting the DNA; I wouldn\'t do that. I think right
> now though it would be incredibly hard to do. You have so many
> variants and so many genes. I mean, there are more than 500 you would
> actually have to put in the pattern. You might mathematically be able
> to do that but at the moment it would be extremely challenging I
> think.
>
> DR. SADEE: It would be challenging but considering the amount of money
> that goes into studying this and the failures, and if you really would
> catch half of the problem I think it would be worthwhile.
>
> DR. SHEINER: You are not talking about simulation now. You are talking
> about an enrichment design where you have a bunch of people and you
> keep on having them come back every time you have a new drug and say
> you are a panel. I think that is a kind of futuristic vision and I
> think it is a good idea, although the safety issue would be something
> that people\--but I guess you would watch them very carefully and I
> suppose you could do it.
>
> DR. VENITZ: Just a more general comment along the same lines, I am not
> sure how much longer it will be ethically justifiable to actually
> expose individuals, without having genotyped them, to positive
> controls. You would obviously emphasize the need or at least the
> possible need for positive controls to rule out baseline changes. What
> that means is that you know a healthy volunteer, who is not going to
> benefit other than the stipend that you pay him, is going to be
> exposed to a risk.
>
> DR. FLOCKHART: But we are doing that. We are doing moxifloxacin in
> positive controls all over the place.
>
> DR. VENITZ: And I am saying wait until the IRBs get full understanding
> of what we are testing for and it may not be permissible any longer.
> That is what I am basically telling you.
>
> DR. HUANG: Jut to clarify, you are suggesting that maybe certain
> subjects with certain genotypes, that we actually recruit them to the
> study. A lot of times our study protocol will pre-specify subjects
> with certain prolonged QTs are not qualified. So, in a way, you are
> saying we want to modify the protocols purposely to include subjects
> with baselines that are higher than normal, than the usual limit that
> we have set up.
>
> DR. SHEINER: I think it kind of goes against\--how can I say
> this?\--the current philosophy which would say let\'s find the
> biomarker like the QT, bad as it is, that regular people can
> demonstrate without danger, which we believe is an indicator that the
> people who have a high propensity will get into trouble, and that will
> occasionally knock out drugs that weren\'t going to bet anybody into
> trouble and it will occasionally miss things. But I think that is more
> sort of in the philosophy. What you are suggesting is a very empirical
> approach, which is let\'s get the people who are in trouble and try it
> on them, under conditions we can control, so we will know for sure. I
> think the whole philosophy, if you will, of clinical trial simulation
> is that you are doing all this kind of stuff with the data to see how
> we ought to best test this is more in the direction of trying to see
> what we can do without actually exposing people who could get hurt.
>
> DR. VENITZ: Any other comments about question number two? Other
> methods? We talked about genotyping, preslecting.
>
> DR. SHEINER: I just wanted to add I think it is a very powerful tool
> and I love the idea of sampling from real data. I mean, that at least
> gets you away from having to make a bunch of assumptions that you
> can\'t justify about distributions, and if you have lots of
> data\--that is one of the things I have always thought, that the FDA
> is in a wonderful position. They have all this data that is handed to
> them in a more or less machine-readable form and they can do these
> kinds of simulations. They are limited only then by the kinds of
> subject matter imagination, like the sort of thing David was
> suggesting, that those models for drug effect be varied across a much
> wider range than just proportional to concentration. I think you may
> well find that there are some designs that are, you know, much better
> than others and that is at least a place to start.
>
> DR. SADEE: If there are limits as to what the QT interval would be and
> those individuals who are truly at risk would be excluded, then I do
> see a problem with it. So, maybe one should rethink that because you
> could then say, well, these individuals should be exposed to maybe
> one-tenth of the dose so that the risk is reduced because eventually,
> if you don\'t test these individuals, you will hit them with any new
> drug coming on the market and it will cause fatalities. So, there must
> be something about how can we prevent this type of risk by tests that
> are more forward looking and more realistic, and at the same time not
> put people at risk.
>
> Alteratively, I don\'t know whether one can study cardiomyocytes
> directly electrophysiology but I suggest that to companies that deal
> with stem cells. They could turn them into cardiomyocytes and genotype
> them and have a panel and that would be another methodology to look
> into [in vitro]{.underline}.
>
> DR. VENITZ: Let\'s move to the last question, question number three,
> clinical design elements to identify meaningful change in QT.
>
> DR. KEARNS: One of the comments that Leslie made at the beginning of
> her talk was about the attitude perhaps of the agency for looking at
> this with some kind of idea of wanting worst-case, especially for
> drug-drug interactions. I think something that is critical in an
> interaction study is understanding the potential of both drugs to have
> an effect on QT, which has not been done uniformly. There are a lot of
> assumptions in the 3A4 interaction arena that if you give an inhibitor
> and you increase the AUC of the drug that can alter QT that you will
> automatically increase the risk, only to find out that the inhibitor
> also has an effect. That wasn\'t in all cases assessed independently.
> So, I think it is critical to think about that before making
> generalizations because the implications of a pharmacodynamic
> interaction here may be far greater than a pharmacokinetic
> interaction.
>
> DR. VENITZ: I don\'t have a comment but I have a question. What is a
> meaningful change in QT that you are trying to identify? Obviously
> that drives your own measurement mechanisms. So, what is considered to
> be meaningful so that you have a decent target that you can shoot for,
> because I don\'t know what it is?
>
> DR. FLOCKHART: It is Seldane right now; it is terfenadine right now.
> That is what it is. If it is like terfenadine it is meaningful.
>
> DR. VENITZ: I guess I am trying to point out that, as much as I
> understand what you are trying to accomplish in terms of trying to
> find very small differences and correcting for as many of the unknown
> variances as possible, that doesn\'t give you a meaningful change.
> That just gives you a change that you are able to detect with lots of
> sophisticated methods. I am personally not convinced that a 6 msec
> change in whatever the mean QTc is a meaningful change.
>
> DR. FLOCKHART: Well, let me just expand a little bit. Obviously the 6
> msec only looks at one side of the equation. It is a risk/benefit
> analysis. Seldane is kind of easy to beat on because the efficacy of
> treating a bit of a stuffy nose is not considered sufficient benefit
> for a lot of women to die. But in many, many, many situations we are
> not talking about that; we are talking about drugs that add real
> benefit for people. So, it is 6 msec weighed against something that we
> really have to deal with most of the time. So, I think 6 msec for
> Seldane is really the outside end of it. It is the most extreme
> situation where you have relatively little benefit and a very
> significant harm relative to that.
>
> We haven\'t talked about how we are weighing, but I think the answer
> to that question, what is clinically significant, actually varies a
> lot depending on what benefit. It is not like drugs are bad or drugs
> are good. I mean, these are parameters, unfortunately, of benefit
> versus risk.
>
> DR. LEE: I also have a question. That 6 msec or 10 msec change, are we
> talking about change from pre-dose or change from the average over 24
> hours?
>
> DR. FLOCKHART: The way it was used with Seldane; the way it was used
> with terfenadine, which is the change I believe from the average of
> one day versus the average of a steady state treatment day.
>
> DR. BONATE: I have a comment. We talk about terfenadine as the gold
> standard but let\'s not forget how many millions of people took
> terfenadine when it was the number one selling antihistamine on the
> market for years, and years, and years, and how many cases of Torsade
> were reported. Is there any reasonable expectation that in a phase 3
> study we are going to be able to detect a QT change of significance
> for Torsade or are we fooling ourselves? I mean, is this a
> postmarketing thing that we should be considering?
>
> DR. FLOCKHART: Well, no one would suggest that we actually want to
> power it to detect Torsade, I hope.
>
> DR. BONATE: I think it is just a matter of perspective.
>
> DR. HUANG: And I would add that knowing terfenadine and its metabolic
> pathway, with our current recommendation we really want to push the
> exposure up. I mean, the terfenadine itself may not really pose a
> significant problem, it is when it is used with an enzyme inhibitor
> which greatly increases exposure where you can actually see plasma
> levels with the contemporary detection method. It is really the
> maximum exposure that would have QT effect. If this drug is not
> metabolized, has no interactions, it is not really a big concern and
> it would not be a gold standard.
>
> DR. VENITZ: Any further comments or questions?
>
> \[No response\]
>
> Thank you. Then, we are going to move to our next topic for today, and
> that is a pediatric topic. Here we are going to review the pediatric
> decision tree that we heard about in both of the previous meetings.
> Again, I am going to ask Dr. Lesko to give us an introduction to the
> topic.
>
> **Pediatric Bridging: Pediatric Decision Tree**
>
> **Introduction**
>
> DR. LESKO: We are going to switch gears on you again and cover, as Dr.
> Venitz said, further discussions with the pediatric bridging area and
> the pediatric decision tree. I will be up here relatively briefly to
> introduce the topic before I turn it over to some of the others.
>
> \[Slide\]
>
> This is the pediatric decision tree that was posted as an addendum to
> our Exposure-Response Guidance, and it is really a general framework
> that we have been dealing with in assessing pediatric approvals and
> extrapolations of efficacy from adult databases.
>
> In the decision tree I have highlighted with underlines a few things,
> as you can see\--similar disease progression; similar response to
> intervention; and similar concentration-response relationships; and
> then down below, on the right-hand side, similar levels to adults. So,
> similarity comes into play in practical applications of this decision
> tree and part of what we want to look at today is what does that
> exactly mean, what does that similarity mean both conceptually and
> what does it mean quantitatively.
>
> \[Slide\]
>
> The background in pediatric bridging refers to the extrapolation of
> efficacy. It doesn\'t refer to the extrapolation of safety. Safety and
> dosing must both be determined in the pediatric population. We also
> have some conclusions that we have to make from that pediatric
> decision tree, similar disease progression, similar responsive to
> therapy and also similar exposure-response relationships.
>
> Many factors come into play in applying this decision tree in a
> regulatory decision framework. Some of those factors include the
> bullets on this slide\--prior experience with the classic drug,
> whether it is first in class or one from a well-known class; what data
> might be available from older children; age-defined subgroup
> differences and efficacy that we might be aware of; the prevalence of
> the disease in various age groups and we are talking about a host
> disease or a disease that involves a host and either microbes or
> viruses. So, all of these factors come into play on a case-by-case
> basis to interpret the decision tree.
>
> \[Slide\]
>
> There are some clinical pharmacology issues in here. PK and safety may
> provide enough data to extrapolate the adult efficacy and define the
> pediatric dose, but that really leads to two questions. When may the
> concentration-response relationship differ between adults and
> pediatrics? What is it we know about that? Secondly, how should the
> similarity or differences between exposure-response relationships be
> determined? So, these are pivotal questions that we are going to focus
> on today.
>
> \[Slide\]
>
> The way we are going to do that is to look at two case studies. These
> are examples of different approaches to the pediatric extrapolation
> and dosing. They illustrate different principles. Then the case
> studies will lead to a general approach that will look at comparing PK
> to relationships between two populations. Finally, we will close out
> this session with some input from research experience with Dr. Kearns
> in the use of the pediatric decision tree in conducting trials, and
> the regulatory experience from Dr. Bill Rodriguez in terms of applying
> the pediatric decision tree in regulatory decision-making.
>
> Now, the questions for this session, which we will get back to at the
> end but just to lead into them, would be basically to provide a case
> study perspective; provide some feedback on the current use of the
> pediatric decision tree in the framework of the case studies that will
> be presented. We are looking for some input on the methodology that
> will be presented to determine similarity of exposure-response
> relationships and then, finally, maybe some discussion around the
> assumptions that are inherent in terms of adjusting dose and exposure,
> and under what circumstances the assumption of similar exposure
> response might deviate what we think it to be.
>
> So, with that in mind, I will transition to the first presentation.
>
> DR. VENITZ: Our first speaker is Dr. Peter Hinderling. He is with the
> Office of Clinical Pharmacology and Biopharmaceutics. Peter?
>
> **Case Studies**
>
> DR. HINDERLING: Thank you.
>
> \[Slide\]
>
> It is a particularly interesting situation I find myself in because I
> will discuss with you the data, now as a regulator, that I previously
> obtained together with my colleagues in the pharmaceutical industry.
> Also, I would like to point out that the data that were obtained were
> obtained in 1999, which is four years ago.
>
> \[Slide\]
>
> So, sotalol pediatric decision tree and exposure-response
> relationship: First of all, I would like to talk about the indication
> of sotalol in adults and briefly summarize the important
> pharmacokinetic and pharmacodynamic characteristics of sotalol.
> Sotalol in adults is indicated for life-threatening ventricular
> tachycardia and ventricle fibrillation, and a little bit later also an
> indication for maintenance of sinus rhythm in symptomatic atrial
> fibrillation and flutter.
>
> The PK of sotalol in adults is linear. There is high bioavailability.
> The drug is largely excreted unchanged and the half-life is about 12
> hours. The PK/PD is linear with respect to Class III antiarrhythmic
> activity as well as for beta-blocking activity.
>
> I also would like to point out that the pharmacokinetics of sotalol
> are non stereo-specific, however, the pharmacodynamics are in that the
> beta-blocking activity is basically due to the L-sotalol moiety,
> whereas the Class III antiarrhythmic activity is shared by both the DL
> and Tl form.
>
> \[Slide\]
>
> What was the knowledge of sotalol PK and PD-wise in pediatrics when we
> started the studies? There were a few published however uncontrolled
> studies in children that used the adult doses which were adjusted for
> body surface area or body weight and used the dosage interval which is
> used in adults, namely 12 hours. However, looking more carefully at
> those studies, it became apparent that at the end of the dosing
> interval of 12 hours there were some breakthrough arrhythmias.
>
> \[Slide\]
>
> Study demonstration of efficacy and safety of an antiarrhythmic in the
> pediatric population is a particular challenge. If you think about
> suppression of the arrhythmias as well as demonstration, for instance,
> of Torsade de pointes in children, this is clearly a challenge which
> cannot be surmounted.
>
> Basically, Lipicky\--and I would like to cite his paradigm\--proposed
> the following: Do what is feasible in children, see what can be
> extracted and use it. In the case of antiarrhythmics where the
> demonstration of efficacy even in adults is shaky, it is not
> reasonable to ask for efficacy in children.
>
> \[Slide\]
>
> Basically, we had to determine biomarkers instead of real clinical
> endpoints. The biomarkers that one can use are the Class III probes
> for activity, antiarrhythmic activity, as well as safety, the QTc
> interval, and then the resting RR interval to check out, again,
> efficacy and safety of the Class II activity of the compound.
>
> \[Slide\]
>
> Here is the pediatric decision tree which you just saw before. In the
> case of sotalol, based on some of the published data, it was
> reasonable to assume that there was a similar disease progression as
> well as a similar response so we could say here to both yes.
>
> The next question, is it reasonable to assume a similar
> concentration-response in pediatrics and adults? The answer here is we
> don\'t really know. So, we say no.
>
> Is there a PD measurement that can be used to predict efficacy? Yes,
> as we just saw. Therefore, conduct PK/PD studies to get the
> concentration response for the PD measurement. Conduct a PK study to
> achieve target concentration based on concentration-response
> relationship and conduct safety trials.
>
> \[Slide\]
>
> The written request that we obtained stipulated the following studies?
> First of all, a PK study, an open-label, single-dose study, one dose
> level with extensive sampling, at least six neonates, at least ten
> infants, and least ten preschool children and at least ten school
> children.
>
> A second study, a PK/PD study, similarly open-label but a multiple
> ascending dose study using three dose levels, with sparse sampling.
> This study should be done in at least either eight neonates or eight
> infants.
>
> \[Slide\]
>
> The study protocols\--the PK study used a single dose of 30 mg/m^2^.
> This label extrapolates from adult data. The PK samples, 12, were
> taken over a period of 36 hours after administration. The PK/PD study
> was executed at three dose levels, 10 mg/m^2^, 30 mg/m^2^, and 70
> mg/m^2^. The 10 mg was not effective, we knew that; 30 was and 70 was
> the uppermost dose that could be tolerated that was considered safe.
> We used, as you can see here, an 8-hour interval because of the
> breakthrough arrhythmias that were demonstrated in the published but
> uncontrolled studies. The sampling mechanism for both PK and PD was
> sparse sampling. We added for PK about 4-5 samples. Similarly we took
> about 4-5 samples for PD. We took very careful measurements over the
> entire dose interval at the same time of the day during baseline.
>
> \[Slide\]
>
> A brief summary of the methodology that was used\--the formulation was
> a syrup and extemporaneous compounding procedure was used. A very
> sensitive assay, LC/MS/MS that required 0.4 ml of blood. The ECG, the
> same type of machine was used in all sites. Baseline values ruing the
> 8-hour dosing interval were taken. There was a blinded cardiologist.
> Measurement was manually using a digitizing pad. The QT heart rate
> correction was according to Fridericia or Bazett. Data analysis used
> the traditional and population approaches. PK used a linear
> two-compartment model. There was also a non-compartment model method
> used, and the PK/PD used a non-compartment model dependent methodology
> using either linear and/or Emax models.
>
> \[Slide\]
>
> We enrolled 24 sites for the PK study and 21 sites for the PK/PD
> study. Totally, there were 59 patients enrolled and the database
> included 58 patients with analyzable PK data and 22 patients with
> analyzable PD data.
>
> \[Slide\]
>
> Here are the results. We looked first at semi-log plots in four
> representative individuals in all four age categories. Patient 1 was a
> neonate; patient 6 was an infant; patient 11 was a preschool child;
> and patient 21 was a school child. You see that the half-life is very
> similar in all four age categories. That tells us basically that the
> volume of distribution and clearance relationship ought to be constant
> and independent of age, weight or body surface area.
>
> \[Slide\]
>
> Here we see plots of the apparent total clearance against the body
> surface area. On the right-hand side you see that these data can be
> fitted by linear curves with small intercepts.
>
> \[Slide\]
>
> On the next plot we see all data of the entire population, 58
> pediatric patients, and added to them 40 adults. You see on the Y axis
> area under the curve normalized for dose and body surface area against
> the body surface area. What becomes quite clear from this plot is that
> basically down to about 0.3 m^2^, children that had body surfaces
> larger than that particular critical value behaved like adults. They
> are basically on one line. Below 0.3 m^2^, which corresponds to an age
> of about two years, just about the end of the infant stage, you see
> that there is decidedly larger exposure.
>
> \[Slide\]
>
> Here is the dose-response relationship. In red you see the beta
> blocking effect; in blue, the effect on QTc. On the left-hand side you
> see the observed Emax. Again, these are point-to-point baseline
> corrected values. On the right-hand side you see the average value
> basically, represented by the area under the curve at steady state of
> the effect. You can see that increasing dose both affect increase, but
> it is clear that the beta-blocking effect, like in adults, is greater
> than the QTc effect.
>
> \[Slide\]
>
> On this slide we see the impact of body surface area on the PK. Red
> now means basically the young children, the infants and the neonates,
> and the blue represents the older children. You can clearly see, with
> respect to Cmax and AUC at steady state, that the young children, the
> infants and neonates, have a larger exposure than the older children.
>
> \[Slide\]
>
> This has an impact on the PD. Basically, the increased effects in the
> PD in the neonates compared to the older children are simply a
> consequence of the increased exposure in terms of the concentrations
> that we observed in the previous slide.
>
> \[Slide\]
>
> Here are some representative plots of the QTc intervals against the
> predicted sotalol concentrations in four individuals representative of
> the four age groups. You see that QTc was linearly correlated with the
> concentrations. There is some variability, as you clearly can see.
>
> \[Slide\]
>
> The same thing can be said for the plots of RR against the plasma
> concentrations. There seems to be a linear relationship, quite a bit
> of variability.
>
> \[Slide\]
>
> In summary, we can say that the pharmacokinetics are basically linear
> and dose proportionate in children. The half-life, like in adults, is
> about 10 hours and is independent of body surface area. The clearance
> and the volume of the central compartment are linearly dependent on
> the BSA, and BSA clearly is the most important covariate. It is also
> clear that the smallest children, infants and neonates, have greater
> exposure and, therefore, need an additional dose adjustment.
>
> \[Slide\]
>
> You see that in this plot on the Y axis you have the age factor and on
> the X axis the age in months. So, we are talking about a person that
> has an age of two years and the factor will be 1. So, up to this point
> we would just normalize based on body surface area. However, if we go
> to smaller children this age factor would decrease to 0.5, 0.3 and we
> would have to multiply that factor into the dose equation.
>
> \[Slide\]
>
> With respect to PK/PD, the doses were tolerated well. The responses,
> as you have seen, increased dose dependently. Pharmacological
> important effects were obtained for Class III at the highest dose only
> for beta-blocking at the 30 mg/m^2^ and 70 mg/m^2^ dose. There was a
> trend for greater effects in smaller children entirely due to
> pharmacokinetics, and the effects were linearly correlated with the
> concentration. Interestingly, it was also noticeable that the
> beta-blocking effect increased with body surface area. Not only are
> the heart rates, of course, a function of age but also the
> beta-blocking effect has an age dependency to it. Thank you.
>
> DR. VENITZ: Thank you. Any questions or comments?
>
> DR. JUSKO: I have two questions for clarification. You were
> administering the racemic form and probably analyzing for both the DNL
> and combination.
>
> DR. HINDERLING: No.
>
> DR. JUSKO: What form of the drug did you administer?
>
> DR. HINDERLING: We administered the racemic drug.
>
> DR. JUSKO: And you analyzed for both forms?
>
> DR. HINDERLING: We didn\'t analyze for both forms. Preliminary data
> showed that there was no stereo specificity in terms of the kinetics,
> as in adults.
>
> DR. JUSKO: And you are sure of that in young children also?
>
> DR. HINDERLING: Yes.
>
> DR. JUSKO: Secondly, when you measured the beta-blocking effects, I
> don\'t imagine you gave a stress test to the different\--
>
> DR. HINDERLING: No, it was the resting heart rate.
>
> DR. JUSKO: No, just the resting heart rate?
>
> DR. HINDERLING: You know, when you deal with neonates and infants\--
>
> DR. JUSKO: That is why I was wondering.
>
> DR. HINDERLING: \--there are some limitations. But, of course, all the
> kids were pacified.
>
> \[Laughter\]
>
> DR. LESKO: Peter, just one clarifying question on the dose-response
> relationship that compared the beta-blocking effect on RR, the one
> that compared the percent delta Emax and percent delta area under
> effect as a function of dose at 10, 30 and 70\--yes, that one. These
> are both relationships in children. Right?
>
> DR. HINDERLING: Yes.
>
> DR. LESKO: Did you have relationships of this sort in adults?
>
> DR. HINDERLING: Yes.
>
> DR. LESKO: And how were they when you compared them side-by-side? What
> was the shape?
>
> DR. HINDERLING: It was basically very similar. The order of magnitude
> in adults was similar to that of the children. Therefore, one could
> really deduce that the concentration-effect relationship is really the
> same. The only difference is really due to the fact that the exposure
> in the youngest children is larger which can be, and has to be
> compensated by the appropriate dose adjustment.
>
> DR. DERENDORF: Could you explain this AUE steady state?
>
> DR. HINDERLING: AUE is basically the area under the effect curve taken
> over the entire zero to eight-hour interval.
>
> DR. DERENDORF: So, how many points?
>
> DR. HINDERLING: Five.
>
> DR. KEARNS: I think it was very fortunate for you in your previous
> life and your company that Dr. Lipicky said what he said.
>
> DR. HINDERLING: Yes.
>
> DR. KEARNS: And the bar for you to do these studies and to ultimately
> get approval and exclusivity was not raised but it was lowered a bit
> because I can tell you that if this were an antihistamine drug and
> there were patients that had more than a 500 msec QTc, it would have
> died a horrible, swift death. The trials would have been stopped and
> there would have been much worry. But here we have a pediatric study,
> a small number of patients and, of course, a drug that we expect to
> have some cardiac effects and the end result is quite different. So,
> that is not so much a question as a bit of commentary.
>
> DR. HINDERING: I agree.
>
> DR. VENITZ: Any other questions or commentaries?
>
> \[No response\]
>
> Thank you again, Peter. Our next case study will be presented by
> Albert Chen and he is with OCPB as well. Albert?
>
> DR. CHEN: Good afternoon.
>
> \[Slide\]
>
> This case study is from Merck\'s montelukast tablet. The brand name is
> Singulair.
>
> \[Slide\]
>
> Montelukast is a leukotriene receptor antagonist. It is indicated for
> prophylaxis and chronic treatment of asthma. Two original NDAs were
> approved simultaneously in 1998. One is for a 10 mg film-coated tablet
> for adults and adolescents greater than 15 years old. The other one is
> for a 5 mg chewable tablet for children 6-14 years old. The dosing
> regimen is one tablet QD given in the evening. Unlike the previous
> case study for sotalol, the 5 mg chewable tablet wasn\'t approved
> until the original request based on the previously approved NDA.
> Therefore, this case study is to show you the sponsor\'s rationale and
> thinking during the clinical development for the pediatric program
> prior to the NDA approval.
>
> \[Slide\]
>
> This is the decision tree. I am going to use this to explain this
> company\'s thinking and rationale and I will use the same decision
> tree to summarize at the end.
>
> \[Slide\]
>
> I will go over adult PK dose-ranging studies; adult clinical efficacy
> and safety trials and then move to pediatrics in sequence. Adult PK
> was obtained in healthy volunteers. The basic PK information is shown
> here. A mean absolute bioavailability was about 70 percent. It was
> about 65 percent from the film-coated tablet and for the chewable
> tablet it was a little bit higher, 73 percent. It is extensively
> metabolized, greater than 86 percent of an oral dose of about 100 mg
> C14, the montelukast was excreted in the bile and through the feces.
> Only less than 0.2 percent was found in the urine after five days. The
> parent drug is predominant in the systemic circulation. We are
> presenting about 98 percent of the total radioactivity over the
> initial ten hours post-dosing. The T half-life is about 4-5 hours.
>
> \[Slide\]
>
> The first PK study is a dose comparison study. This is the pivotal
> study because it provided the head-to-head comparison between the 10
> mg film-coated tablet and the 10 mg chewable tablet. It also provided
> the dose proportionality information regarding the chewable tablet.
>
> The objective of this study was two-fold It allows for conversion of
> the AUC from the 10 mg film-coated tablet to a 10 mg chewable tablet,
> after taking into consideration the difference in the absolute
> bioavailability, 73 percent versus 65 percent. It also allowed for
> scaling down the AUC of a 10 mg chewable tablet to a smaller pediatric
> chewable tablet dose in order to obtain similar AUC as adults
> receiving the 10 mg film-coated tablet.
>
> \[Slide\]
>
> The adult dose-ranging information was obtained from the subgroups of
> earlier phase 2 trials. the dose range studied from 10 mg QD up to 200
> mg QD plus placebo. In the parentheses are the patients who
> participated.
>
> The results of the study showed that the active treatments were all
> significantly different from the placebo, and no differences were
> found among the active treatments.
>
> \[Slide\]
>
> So, based on the above observations, the proposed dose selection for
> adult patients was one 10 mg dose QD given in the evening.
>
> \[Slide\]
>
> Two adult clinical efficacy and safety trials were conducted.
> Similarly, they were 12-week studies in patients with mild to moderate
> persistent asthma at baseline. The primary endpoint was changes in
> FEV1, forced expiratory volume in one second, and the daytime asthma
> symptom score.
>
> \[Slide\]
>
> These are the results obtained from clinical trial 01 during the four
> visits every three months regarding the mean percent change in FEV1
> from baseline. The montelukast was significantly different from
> placebo at each visit. The overall mean of the four visits was 12.8
> percent for montelukast and 4.1 percent for placebo. Regarding the
> mean percent change in the daytime asthma symptom score from baseline,
> montelukast was also significantly different from placebo.
>
> \[Slide\]
>
> Results from clinical trial 02\--the same results were obtained.
>
> \[Slide\]
>
> Also safety profiles between active treatments and placebo were found
> to be similar. So, the proposed dosing regimen was confirmed by adult
> clinical efficacy and safety studies.
>
> \[Slide\]
>
> Now we move to pediatric studies. Since montelukast is a new molecular
> entity and a new class of drug without previous pediatric data, the
> sponsor\'s answer to the above two questions is no and this is for the
> case of 6-14 years old. So, the sponsor conducted PK studies and also
> safety and efficacy trials.
>
> \[Slide\]
>
> Pediatric PK was obtained in pediatric patients only. Study 02 is a
> single-dose PK in early pubertal adolescents 9-14 years old. Two dose
> levels were tested, 6 and 10, using the film-coated tablet. Study 03
> was a single-dose montelukast PK in pediatric patients 6-8 years old
> using the 5 mg chewable tablet.
>
> \[Slide\]
>
> Table 1 shows the mean PK data obtained from the pediatric PK study 02
> and also compares with the adult historical data. Pediatric patients
> not greater than 45 kg received the 6 mg dose and pediatrics greater
> than 45 kg received the 10 mg dose. This is the adult historical data
> using the 10 mg dose. For this age group the systemic exposure in
> terms of AUC is about 2,900. It is very close to the adults receiving
> 10 mg film-coated tablets, about 2,700. Actually, this value is within
> the mean adult AUC plus/minus two standard deviations. For this age
> group the AUC is too high.
>
> \[Slide\]
>
> Table 2 shows the mean PK data obtained from another pediatric study.
> For this age group the 5 mg chewable tablet dose was given. As you can
> see, the AUC is about 2,900, very close to the adult AUC 10 mg
> film-coated tablet. So, based on the dose normalization in AUC, it was
> concluded from table 1 after converting a 6 mg film-coated tablet, a 5
> mg chewable tablet given QD to children 9-14 years old is expected to
> provide similar systemic exposure as adults receiving the 10 mg
> film-coated tablet. From table 2, similar AUC in 6-8 year old patients
> was obtained.
>
> \[Slide\]
>
> So, the 5 mg chewable tablet was chosen for the pediatric efficacy and
> safety trials. Since montelukast was a new class of drug, this study
> was conducted to confirm the dose selection and also to prove some
> concept and assumption which I will explain later. I put a note here
> that since the adolescents, 15 years and older, had similar plasma
> profiles compared with adults, they were included in the adult phase 3
> trials.
>
> \[Slide\]
>
> So, for this age group of 6-14 years old no pediatric dose-ranging
> trials were conducted. What are the assumptions? Similar disease
> progression in asthma between pediatric and adult patients and
> comparable efficacy is associated with similar systemic exposure in
> terms of AUC.
>
> \[Slide\]
>
> So, this pediatric clinical efficacy and safety trial was an 8-week
> treatment study in more than 300 pediatric patients. The mean percent
> change in FEV1 from baseline was 8.7 percent for montelukast and 4.2
> percent for placebo, and the difference is statistically significant.
> So, the original NDA for the 5 mg chewable tablet was approved for
> 6-14 years old.
>
> \[Slide\]
>
> Now we move to younger pediatric patients, 2-5 years old. Based on the
> previous successful experience in dose selection, the same principle
> with similar mean AUC, a smaller 4 mg chewable dose was selected. This
> dose was tested in a PK study employing sparse sampling technique
> using a pop PK approach. The mean AUC estimated was about 2,700, again
> very close to adult AUC for the 10 mg film-coated tablet.
>
> \[Slide\]
>
> Since efficacy has been demonstrated in children 6-14 years old, and
> the assessment of FEV1 in the children smaller than 6 years old will
> be problematic, it is decided that only a safety trial is needed. So,
> the sponsor conducted a 12-week clinical safety trial in greater than
> 600 patients. There was no dose-ranging study conducted, nor formal
> clinical efficacy trial conducted. This study actually supported the
> safety of the 4 mg chewable tablet in this age group and also
> confirmed the efficacy in this age group. So, the 4 mg chewable tablet
> was approved later for the children 2-5 years old. It is under
> internal request based on the approved NDA.
>
> \[Slide\]
>
> After the sponsor learned more and more from the previous case, 6-14
> years old, and they are willing to answer yes to the above two
> questions, and to assume a similar concentration response in pediatric
> patients, and this is the case for 2-5 years old, the sponsor only
> conducted PK studies and safety. The safety trial actually included a
> secondary efficacy assessment, and they proved that efficacy is okay
> in this age group.
>
> \[Slide\]
>
> I would like to thank my previous medical colleague Dr. Bob Meyer,
> Peter Honig, Anne Trontell and also my supervisor, Dr. Larry Lesko and
> Shiew-Mei Huang.
>
> DR. VENITZ: Thank you, Albert. Any questions?
>
> DR. DERENDORF: Yes, in the decision tree it says that it is reasonable
> to assume similar exposure response in pediatrics and adults. If you
> look at the data that you have in adults, first of all, you really
> don\'t have a good exposure-response relationship. You have a placebo
> and then you have a range of doses that all do the same thing.
>
> DR. CHEN: Well, that is the phase 2 trial. Because the safety profiles
> looked very clean the company actually precluded the dose-response
> study. But with the development of the guidance, we will probably ask
> the company to conduct it but at that time they did not conduct a
> dose-response study.
>
> DR. DERENDORF: Right, but what you did, conceptually, you took one of
> these doses and you reproduced the same exposure in terms of AUC\--
>
> DR. CHEN: Right.
>
> DR. DERENDORF: \--in children and they also were different from
> placebo, but that is different than having the same exposure-response
> relationship.
>
> DR. CHEN: That is true but this is a special case and they selected
> the smallest dose.
>
> DR. DERENDORF: We don\'t know if it is the smallest.
>
> DR. CHEN: The company reported the effective dose could be as low as 2
> mg but they submitted the report for review.
>
> DR. LESKO: Just to follow-up and make sure I understand the point that
> Hartmut was making, the early decision was that there was no
> information basically to assume that disease progression response to
> therapy would be the same. So, there was a PK study. It was sort of a
> hypothesis in the first age group that exposure response was similar.
> Once it was demonstrated for an older age group, you sort of went back
> to that top box and said now I have some data that sort of underpins
> the notion that I can answer yes to both of those, and then subsequent
> age groups went down a different path.
>
> But I think the efficacy in the pediatric older children, 9-14 or
> whatever it was, had a similar change in clinical endpoints as the
> adults had for similar exposure. So, that was pretty confirmatory at
> that point that the answer would be yes to the first two. I think the
> percent change in FEV1 was 9 versus 12, or something very close, so
> that exposure response was similar.
>
> That gets to your point because if that is the case, then what you
> said wasn\'t clear to me, the point you were trying to make.
>
> DR. DERENDORF: The point I was trying to make is that if you don\'t
> have any data on the lower end of the children, which I don\'t think
> you have or at least it is not in here, it would be possible that
> there is a different concentration or exposure-response relationship
> that you just don\'t pick up. In children maybe a lower dose would do
> the job.
>
> DR. LESKO: Okay, so targeting the same exposure\--
>
> DR. DERENDORF: Oh, it wouldn\'t be the same exposure. If the exposure
> response would be different, you wouldn\'t know.
>
> DR. LESKO: Yes, we don\'t know the shape of that relationship
> basically.
>
> DR. SHEINER: Similarity at one point doesn\'t necessarily mean
> similarity elsewhere.
>
> DR. VENITZ: Any other comments for Albert?
>
> DR. SHEINER: Let me pursue that point because it is interesting.
> Remember, we are in a pediatric situation and we are trying to do
> something reasonable. So, if you had good safety and you had similar
> response which is acceptable at one point of the dose-response curve,
> wouldn\'t that, in the pediatric case, be enough to say, well, okay,
> go ahead and do that? Even if it is possible conceptually that you
> could have exactly the same response in children, nonetheless, it is
> giving you good response, similar to adults; it has adequate safety
> and, you know, maybe it is okay.
>
> DR. LESKO: Yes, it is almost like the dose selection was based on PK
> but the real trump card, if you will, was the evidence of efficacy and
> safety in that clinical trial. Yes, the open question is could those
> results have been achieved at a lower dose maybe? But the dose that
> was achieved, it wasn\'t bad.
>
> DR. VENITZ: Thank you again, Albert. Our next presenter is Dr. Stella
> Machado, and she is going to introduce a method to compare
> exposure-response relationships and see if they are similar or not.
>
> **Methods for Determining Similarity of Exposure**
>
> **Response Between Pediatric and Adult Populations**
>
> DR. MACHADO: This is a great privilege, to be here, speaking with you
> this afternoon.
>
> \[Slide\]
>
> I will be talking about methods for determining similarity of exposure
> response between pediatric and adult populations. I am with the Office
> of Biostatistics in CDER, and we are working together with the team
> from OCPB in a real situation, pediatric bridging situation.
>
> \[Slide\]
>
> I would like to acknowledge substantial contributions from my
> colleague, Meiyu Shen, who is also in statistics. We gleaned ideas
> from many colleagues, both from within the agency and outside, and
> also even from the Internet.
>
> \[Slide\]
>
> This is not complicated statistics. It is more of a way of looking at
> things. I am just going to talk really in generality about a method
> for comparing two response curves with the pediatric population and
> adult population. This could be equally well applied to, for instance,
> comparing between ethnic regions or comparing response curves for
> gender and so on. I am presuming that the exposure metric could be
> dose, it could be area under the curve, it could be Cmin, whatever.
> The response metric could be a biomarker or could be a clinical
> endpoint.
>
> \[Slide\]
>
> The goal in bridging is to evaluate the similarity in PK/PD
> relationship between adults and pediatrics where we have plenty of the
> adult data, the original population, and the pediatric population is
> the new one. The conclusions we can come out with could be that we
> conclude similarity. Or, we could conclude similarity of shape of the
> dose-response curves but with some dose regimen modification needed.
> Or, we also could conclude at the end of this a lack of similarity.
>
> When we started working on this there really was an absence of precise
> guidance as to how we should proceed. What I am going to recommend is
> that really we are in an exploratory activity at the minute, not
> confirmatory hard and fast statistical testing situation.
>
> \[Slide\]
>
> Now, we did work with a real drug situation but for the purposes of
> this talk we invented drug X and heavily disguised it so that you
> can\'t guess what it was, the real situation. For drug X there were
> about 240 patients in the adults and 120 in pediatrics. Those are
> numbers close to the original. About 40 percent of each of the groups
> took placebo.
>
> \[Slide\]
>
> Here is our plot. Here is drug X. The triangles are the new
> population, the pediatrics; the squares are the original, the adults.
> How do we compare? How do we say this is similar or not? It is just,
> gosh, what a mess!
>
> \[Slide\]
>
> A little bit of notations but I am not going to go heavily into the
> statistics, we have a different number of adult patients, generally a
> smaller number of pediatric patients. Y is our response measure and C
> is the concentration metric. I will call it concentration but, as I
> said, it could have been area under the curve or Cmin. Generally, the
> concentration measurements are all different unless you got data from
> a concentration-control trial. For drug X, you saw that the
> concentrations were all over the place.
>
> \[Slide\]
>
> To establish similarity we need to compare the average shapes of the
> response curves, taking into account variability of the measurements.
> The response curve depends on the exposure measure and some various
> unknown parameters. The adults and the children may have similar
> response curves but they may have different parameters.
>
> \[Slide\]
>
> As a first step, looking a little bit further at the data, these are
> lowest fits, local regression lines plotted onto the data and here we
> see for the first time that there seems to be a bit of a separation
> between those two curves. The upper curve is for the pediatric
> patients and, with increasing concentration, does seem to drift up
> away from the adults. So, the suggestion is that there is some
> difference here but the big question is how much of a difference.
>
> \[Slide\]
>
> In terms of thinking about it, what we should be doing is assessing
> similarity between the responses at all the concentrations that are
> likely to be encountered. So, we are not interested in postulating
> response curves out into the very, very high doses. That is not
> realistic. We are interested in the distance between the curves, like
> the average behavior for the population and accounting for the
> variability of the response. We suggest an equivalence type approach
> rather than hypothesis tests, trying to test that the response is not
> significantly different.
>
> \[Slide\]
>
> So, where do we start? Well, the hypothetical situation is to focus on
> what we would do at a single exposure measure? One single
> concentration, what would we do? Well, this would reduce to the usual
> equivalence-type analysis and there are various ways to analyze this,
> different response metrics. We could look at comparing the average
> response between pediatrics and adults at every exposure or a
> combination of average and variance metrics, for instance a population
> bioequivalence approach or Kullback-Liebler distance metric, or we
> could actually compare the whole statistical distribution,
> Kolmogorov-Smirnov type generalization. But we chose to look at the
> simplest of these, which is comparing the average response.
>
> \[Slide\]
>
> Again continuing, we are only talking about one concentration. We
> defined similarity to be the requirement that the average responses in
> the two populations, for the same concentration, are closely similar.
> We choose goalposts, for instance, the 80 percent or 125 percent which
> are familiar, and calculate a 95 percent confidence interval for the
> ratio of the average responses.
>
> \[Slide\]
>
> If the 95 percent confidence interval at this ratio falls entirely
> within our goalposts, then we say that the null hypothesis of lack of
> equivalence is rejected, therefore, we are accepting the fact that we
> have similarity here. This is the usual simultaneous two one-sided
> test procedure. So, our proposal is to use confidence intervals to
> measure similarity, to quantify similarity, quantifying what was
> actually determined from the data we have in the two populations.
>
> \[Slide\]
>
> Just a note on getting the confidence intervals for this ratio, there
> is a bit of work required. There are some methods in the literature
> based on normal distributions. If you are not willing to make that
> assumption you could use the bootstrap method or computer simulation.
> My opinion is that it is easier to use the actual data. Then we end up
> with useful statements. For instance, we are able to say that the
> average response at this concentration, level C, among pediatrics is
> 93 percent of that in the original population, and we are 95 percent
> sure that the ratio of these averages lies between 83 percent and 105
> percent. That is possibly a summary statement that we can deal with
> and make decisions from.
>
> \[Slide\]
>
> Moving away from one single concentration to the real situation where
> we have response curves over a whole range, the easiest thing to do is
> to categorize the concentration axis into intervals\--we chose five or
> six here\--and for each interval estimate the 95 percent confidence
> interval for the ratio and interpret. A useful way to interpret is to
> use graphs.
>
> \[Slide\]
>
> Here is our drug X. That is the range of concentrations. There are
> quite a number of patients receiving zero dose of this drug. It is
> sort of interesting that the placebo dose actually falls below the 0.8
> lower bound with no drug. I am not sure what that is about. But then
> there is a tendency for the confidence intervals to drift upwards,
> outside of the 80 percent to 125 percent, and definitely for the
> highest concentration range, 80 and above, and that is where we have
> the least amount of data so the confidence intervals are quite wide
> out there.
>
> \[Slide\]
>
> I summarized that. The ratios trend upwards and the upper limits
> exceed 1.25 for all of the exposures, all the positive exposures.
>
> \[Slide\]
>
> A second way of doing it is to actually fit a model to the data and
> estimate the unknown parameters; use the fitted model to simulate the
> ratios for each different concentration and estimate the 95 percent
> confidence intervals, which we went ahead and did.
>
> \[Slide\]
>
> For fitting the models we actually found that the square root of the
> response stabilized the variance. The linear models were fitted
> separately. In the simulation we used 5,000 pairs of studies to
> estimate different estimates of the ratio and percentiles.
>
> \[Slide\]
>
> Here we have a smoothed plot of the confidence intervals for the ratio
> of the two means, again showing a drift upwards. I should say that
> these particular concentrations I chose for the graph were the
> mid-points of the intervals that I chose for the categorized
> concentrations. Because of the model fitting, this picture is quite
> smooth but we do see a great tendency for the ratios to climb, much
> bigger than 1, and we really see that for these higher concentrations
> this new population, the pediatric population, is substantially
> different from the adults.
>
> \[Slide\]
>
> Here is the graph of the two methods compared. The first is the pairs
> from the simple, straightforward method of categorizing the
> concentrations, and the second is the model fit. They are kind of
> similar as we would expect; it is the same database.
>
> \[Slide\]
>
> In comparing the two approaches, I really feel that both are useful,
> the rough and ready one, but then the model-based method\--well, you
> have to make some assumptions like actually fitting the model and what
> is the best shape for it but it is less influenced by outliers and
> generally has greater precision, not a huge amount, I must say, from
> this example. But I would say that both of the methods are useful. So,
> it is not particularly complicated but it will show you whether there
> are trends in the differences in the two population responses.
>
> \[Slide\]
>
> In terms of designing a study among the pediatric population, or
> another situation we looked at, if you are going from one country to
> another and you want to do a bridging study in the new country, the
> design should be based on parameter estimates from the data you
> already have in the original population, the adult population, and any
> prior information that you have from the pediatric population.
>
> Make sure to include doses that are likely to produce these
> concentration metrics in the whole range of interest. Then, perform
> simulations to determine the required number of patients needed in the
> new population. You can assess robustness to the model assumptions,
> and so on, your variance estimates, to see what would happen
>
> \[Slide\]
>
> I apologize for the spelling mistake here. This general approach can
> work for response curves for efficacy and for safety. What we are
> doing is proposing a method to quantify the similarity between the
> adult and the pediatric populations over the whole range of
> concentrations. Rather than trying to test that adults and children
> are different, we are trying to test how close they are and where they
> are close. This can be applied easily to data from trials with
> different designs. Then, as a final thought, I put up the usual
> goalposts such as 0.8 to 1.25, but that may well not be meaningful for
> this particular drug, depending on therapeutic range, or the disease
> of interest. So, interpretation of how much similarity is acceptable,
> of course, requires medical input. Thank you.
>
> DR. VENITZ: Thank you, Stella. Any questions or comments for her?
> Greg?
>
> DR. KEARNS: I am glad to see your last point because I was troubled
> until you put this slide up. I think most of us would agree that the
> demonstration of statistical difference and clinical difference is not
> always the same. I mean, not knowing what drug X is, one could argue
> that that difference, in terms of a clinical context of drug effect,
> would be not meaningful despite its significance.
>
> My question to you and really to anybody from FDA is what are the
> implications of finding a difference, especially when you are looking
> in a retrospective way? I mean, the data that you shared with us
> ostensibly would come out of the review of an NDA when all the
> pediatric stuff had been done, the adult stuff had been done and the
> company has performed now the pediatric studies with consultation from
> the agency, perhaps it is being done under the Best Pharmaceuticals
> Act so there is some hope of exclusivity; maybe some hope of labeling.
> Then it goes to your Office and, voila, there is a difference. So,
> what are the implications for the agency to go back to the sponsor and
> say, well, it was a good try, boys and girls, but no exclusivity for
> you today because there is a difference between adults and children
> that we can\'t resolve from your data?
>
> DR. MACHADO: Thank you, that is a very insightful question. I don\'t
> have a nice selection of slides of the pediatric decision tree, but
> there is one element on the pediatric decision tree that asks the
> question can we consider that the response curves for pediatrics and
> adults are similar enough. So, what I am addressing is part of the
> whole pie that goes into deciding whether to approve a drug for
> pediatric use. Larry, would you like to comment on that?
>
> DR. LESKO: I guess it goes back to a case-by-case interpretation of
> the differences that you would observer in that case. Then, I think
> you would have to draw in some of the clinical efficacy data that were
> available and try to interpret that. I think the soft spot in this
> approach is what those boundary conditions are going to be. When you
> get to the end the 80 to 125 is a default that we have borrowed from
> some other areas, but the problem with that is we have tried to apply
> it in other similar situations, like drug interactions or renal
> disease versus normals, and the number of subjects needed to meet that
> boundary condition, given the variability, is unrealistic.
>
> So, the next question then is what are those boundary conditions that
> we be appropriate to declare similarity and it seems you go down two
> paths. One would be what do I know about the exposure-response
> relationship, and what are the boundaries I might draw from the shape
> of that relationship in adults, with the assumption that PK/PD is
> similar?
>
> I guess the other question would be kind of a joint medical-artistic
> sort of approach, well, what difference would be clinically important
> if you were to think about it in an empirical way? But you have to
> somehow set some boundaries I think.
>
> DR. VENITZ: The boundaries that we are talking about here are not
> boundaries on concentrations. We are talking about boundaries in the
> response\--
>
> DR. LESKO: They would have to be wider. Obviously, the variability is
> going to be more than concentrations.
>
> DR. LEE: I think my other question to the committee is should we also
> not only look at the mean value or the difference between the two mean
> curves, but also looking at the whole distribution of the PK/PD
> relationship because what we are really concerned about is not the
> typical patient but the patient who may be exposed to a very high
> concentration or very low concentration? So, do we really want to make
> sure that the distribution of the response is similar between adult
> and pediatric populations?
>
> DR. SHEINER: You are going in a little different direction but we
> started talking about something that I think is pretty clear, that is
> to say, two different issues: How do you measure a difference between
> these two curves, let\'s say, and then what do you use as regulatory
> guidelines with respect to that measurement? So, the measurement has
> to be adequate to the task of ultimately making a decision. That
> decision issue is always going to be trickier than the measurement one
> I think. So, I would like to focus a little bit on the measurement
> one.
>
> I just wanted to say that I noticed in one of your slides, Stella,
> that you had the statement\--you know, we can make statements like we
> are 95 percent sure that the range is something or other. That kind of
> almost smacks of a Bayesian statement so I am going to take that as
> permission because you opened the door\--it seems to me what we are
> really talking about is the posterior distribution, estimating the
> posterior distribution on some feature of these doser-response curves
> that talk about a difference. So, if it is in the log world it is a
> ratio. So, that might be what we are interested in or, as Peter just
> sort said, we might be interested in some other aspect of the curves
> than the difference in the means. We might be interested in the
> difference in the fraction lying outside of a certain range, or
> something like that.
>
> So, we have to decide, it seems to me, what those things are and they
> are just qualitative issues of value, not quantitative which is the
> tough one. The tough question is the second question, where is the
> cut-off? But the qualitative issues of value, what kinds of things are
> we interested in, what are things that are relevant, I think we can
> probably agree on those.
>
> I would say that, you know, personally I would just like to see us
> talk about posterior distribution of a difference of some kind between
> the two. Then I would make the point about that that when you get to
> regulating\--even though I don\'t know how to resolve that\--you do
> really have to be quite careful about saying that because there is a
> significant amount of the probability mass that lies outside of some
> acceptable boundary, though there isn\'t very much evidence that it is
> there. It just means you don\'t know very much. It is the same kind of
> story as, you know, accepting the null hypothesis in the opposite
> situation. So, I the hard questions are the questions about what
> regulations you make and how you regulate it.
>
> I think the thing you finally drew there with those confidence
> intervals, they are not too different than a posterior distribution on
> the ratio, and you can computationally get it more or less the same
> way and I do think that is the right way to look at it, but I would
> say for those of us who tend to sort of enjoy being kind of the
> technical heads here, let\'s stop at making the picture that shows the
> differences and then let the regulators worry about where to cut off
> the lines.
>
> DR. MACHADO: Thank you.
>
> DR. VENITZ: Any further comments or questions? If not, thank you
> again, Stella. I suggest we take our break. We will take a 15-minute
> break and reconvene at 3:45.
>
> \[Brief recess\]
>
> DR. VENITZ: We are still continuing on our topic on pediatrics,
> pediatric decision tree, and our next presenter is our very own Dr.
> Greg Kearns. He is going to give us an academic perspective in using
> the pediatric decision tree. Greg?
>
> **Research Experience in the Use of**
>
> **Pediatric Decision Tree**
>
> DR. KEARNS: Thank you very much.
>
> Larry gave me kind of a complex task here today. He said I want you to
> talk about the decision tree but I also want you to review some of the
> basic stuff on pediatrics and why are children different. So, if this
> is a little bit of a hodge-podge, forgive me; I am just executing my
> orders.
>
> \[Slide\]
>
> This is one of my favorite all-time quotes from the man who is
> considered to be the father of American pediatrics. I like it because
> in 1889 Dr. Jacobi recognized that the issue of dose being different
> was of paramount importance.
>
> \[Slide\]
>
> One of the differences from what we have heard today about empaneling
> a group of professional subjects who go out for a bender, clean up and
> come in, is that few of our children that we have in clinical trials
> do that, maybe some of the adolescents but certainly not the younger
> ones, and there are many, many differences between adults and children
> and we tend to think of pediatrics as a continuum.
>
> \[Slide\]
>
> Certainly there is a physiological continuum. There is a behavioral
> continuum, all of which must be considered in the context of a
> clinical trial. We know that children are different. They have
> different body composition, as illustrated by these data. This impacts
> the pharmacokinetics, especially with respect to drug distribution.
>
> \[Slide\]
>
> If you look at their renal function as a function of age for pre-term
> and term babies over the first two weeks of life, there are dramatic
> increases which, if you look at the kinetics of a drug like
> famotidine, translate directly into changes in the behavior, changes
> in the concentration-response relationship which are predictable when
> one simply looks at the pattern of development and its impact on GFR
> in this case.
>
> \[Slide\]
>
> As summarized by Alcorn and McNamara in a recent paper in [Clinical
> Pharmacokinetics]{.underline}, if we look at many of the drug
> metabolizing enzymes and we express their activity relative to the
> activity in adults, look at them over age, in this case about 160
> days, we see some patterns. It is the patterns that are so important
> for those of you involved in the modeling business because a pattern,
> to me, means prediction. Prediction is, as we have heard time and time
> again today, critical for understanding the behavior of something
> being studied or what might we expect in the context of clinical use.
>
> \[Slide\]
>
> In the case of something like cisapride\--since we are talking about
> QTc I couldn\'t help but include one of my favorite drugs in here\--we
> are not going to talk about QTc but just the kinetics of this CYP 3A4
> substrate very nicely go along with the delay in maturation for the
> enzyme.
>
> \[Slide\]
>
> If you take a group of very small babies that are not very mature and,
> in fact, have low surface areas because they are tiny, the clearance
> of this drug is markedly impaired, which is something you would expect
> to see. It is not only the enzymes in the liver, as we are finding
> out\--Trevor Johnson and his colleagues, in 2001, looked at 3A
> activity in the gut and the same type of maturation pattern is
> evident. This, of course, has implications for bioavailability of
> drugs that are given to kids that are 3A substrates.
>
> \[Slide\]
>
> Phase 2 enzymes as well show a developmental pattern. These are some
> data from Martin Behm, one of our fellows. They were presented at the
> CPNT meetings in 2003. This is a plot of glucuronide to sulfate ratio
> of acetaminophen in urine, done in a group of healthy children and
> looked at, in this case, over nine months of time. Sulfotransferase
> activity comes on very quick, as most of you know. UGT activity has a
> delay. So, if you look over time you see this ratio increase until
> about six to nine months when it seems to level off\--again, another
> developmental pattern.
>
> I would be remiss to not put the bars on here that indicate that there
> are outliers. Even at every developmental stage the inter-individual
> variability in the activity of drug metabolizing enzymes is very, very
> large. That is important because as we look at some of these pediatric
> studies with six neonates and the conclusions that are being drawn, it
> is\--at least for me, anyway\--a little statistically worrisome at
> times.
>
> \[Slide\]
>
> Then there are drugs like linezolid\--and we were privileged to do
> this work several years ago\--that are not metabolized by cytochrome
> P45; not substrates for UGTs. If you look at the impact of age on
> clearance, you see dramatic increases that suggest that something
> important, something interesting for this compound goes on in the
> first week of life but, again, a predictable pattern.
>
> \[Slide\]
>
> So, clinical pharmacology facts\--kids are not small adults. They have
> different PK for sure. In some cases the PD is different. Despite our
> advances, we are still in an age where about 80 percent of all drugs
> on the market are not labeled for kids. With rare exception, pediatric
> patients are still thought about late in the game of drug development,
> something we need to fix. The biggest issue far and away is what is
> the dose. What is the proper dose that will make the exposure that has
> the greatest chance of being effective and safe?
>
> \[Slide\]
>
> Previously, historically there were some challenges to pediatric drug
> development and most of these have been taken care of in 2003.
> Analytical issues, we heard so sotalol a method that required 0.4 ml
> of blood. PK/PD approaches abound. Some of the other scientific
> issues, the incorporation of pharmacogenetics; logistical issues, we
> have come up with ways to study children; designs; we have even dealt
> with the lawyers in some measure. Lawyers who used to say it is very
> risky to do studies in children; it was dangerous; it was expensive,
> therefore, we shouldn\'t do them; have now changed their tune after
> the course of a few lawsuits. Ethical considerations have been largely
> taken out of the equation. Programmatic things, we have networks in
> our country now to study drugs in children. Even the FDA has gotten
> pretty sharp about this and have included children in their plans,
> hence the decision tree.
>
> \[Slide\]
>
> There are some remaining challenges, for sure. I think these are
> important, and these are things that have not yet been lit, to use a
> Missouri word. First, relevant extrapolation of adult data and animal
> data. There are times to do it and there are times not to do it. But,
> certainly, the adult data can still be critical.
>
> Study designs\--much of what we have talked about today, study designs
> that are optimal; scientifically robust so they don\'t make sacrifices
> beyond belief; study designs that are synergized by adding relevant
> science; and capable in as many cases as we can of truly addressing
> drug effect.
>
> Then we need dosing approaches that control the exposure; that we can
> verify; and that, most importantly, are age appropriate. This even
> gets into the arena of formulation just a bit.
>
> \[Slide\]
>
> Here is the decision tree, and you have seen this a lot today. I am
> going to talk about this not in the context of examples\--we have
> heard some excellent examples, but in the context of where it might be
> working and where it might be tweaking.
>
> \[Slide\]
>
> I want to do it by a general example. I am not going to call this drug
> X but let\'s call it an acid-modifying drug. The goal that we had to
> study this drug was to look at it in children 1-12 months of age. The
> question is how would you do it or how would most people do it? Well,
> we would look at what is available and then we would make a stab at
> several things.
>
> First we might select otherwise healthy infants who are being treated
> with acid-modifying drugs, children who are not severely handicapped,
> who don\'t have renal failure or hepatic compromise but kids who are
> getting these medicines anyway. We would use known PK and PD
> properties of the drug plus evidence that demonstrates the impact of
> ontogeny on the clearance pathways or drug metabolizing enzymes and in
> some cases even the effect, much as we heard for the montelukast
> story. There was a pretty good relationship in the adults between the
> improvement in FEV1 and the exposure. We would use robust, minimal
> sampling techniques when appropriate. We would assess the
> pharmacologic effect of the drug if possible; design effect studies
> with a target exposure-response approach to drive the selection of
> dose as we looked at effect; and then assess the effect of the drug as
> a molecule as well a treatment effect and tolerability in an age
> appropriate manner.
>
> To get back to the montelukast story for just a minute, I think it is
> incredible that approval and labeling for that drug was done based
> upon changes in FEV1 that many of us would sneeze at as being
> important. But the fact is when it is given to children with asthma
> and you look at its anti-inflammatory effect and you look at long-term
> outcome, it is a medicine that works. In that case we made a good leap
> of faith and it is possible to do that.
>
> \[Slide\]
>
> Those of you at the agency, please don\'t take this personally. I am
> going to share some of the things that were recommended for study our
> acid-modifying drug from the agency, and we all know that the FDA is a
> big, big organization and certainly none of the people associated with
> Dr. Lesko would ever recommend what I am going to show you today.
>
> I put a little asterisk here because I have to give the disclaimer,
> and rightfully so, that the recommendations that are coming out from
> the FDA about how to do these studies are an evolving work in
> progress. But let\'s look at a few things that were recommended.
>
> First, the primary disease endpoints. To assess the efficacy of this
> drug in infants, we were told to look at its effect on obstructive
> apnea. Some of you have a somewhat confused look on your face. I still
> have one on mine.
>
> Secondary endpoints, to look at pH of the stomach. That makes sense
> for an acid-modifying drug, but then to assess its effect on
> esophageal motility. We were asked to do single and multiple dose
> kinetics standard sampling through 24 hours with a drug that has a
> half-life of one hour.
>
> We were asked to study two to three different fixed doses of the drug.
> We were asked to look at the kinetics and safety of the drug in
> neonatal mice and p53 knockout mice and then, in the infant studies to
> follow the children up through adolescence.
>
> These are all things that at some point or another came out in the
> recommendations. Fortunately, these didn\'t stick\--these didn\'t
> stick. We are finally getting our way to do this correctly. But why do
> I show you this horror story? It is not to make light of the agency,
> but when these recommendations came out I can tell you, from working
> with the sponsors, it was almost as if their head was put in a vice
> and they began to think how in the world could we do these studies;
> should we do these studies? Are they even in some cases ethically
> defensible to do\--esophageal impedance in an otherwise health
> two-month old child? What parent would agree to have that done? So,
> there were a lot of issues.
>
> \[Slide\]
>
> Sometimes it is good to look at mistakes that might be made because is
> lets us improve what we might do. In this case, I have to admit it
> really is not the usual scenario. We know that from what we have heard
> today. I am picking at off-the-wall examples to make a point.
>
> The approach, if we look at this example, the approach now becomes not
> a solution but an impediment to pediatric drug development because of
> slippage in the regulations and their interpretation. How is that so?
>
> If we look at the exclusivity provisions under the Best
> Pharmaceuticals Act which still brings a lot of marketed products to
> study in pediatrics, they enable labeling only if the disease process
> is substantially similar, the disease process. Now, every company that
> studies the drug, I can guarantee they are interested in labeling.
> There is a belief by some that dosing and safety information is not
> wholly sufficient for exclusivity or pediatric labeling but in every
> instance in pediatric a pivotal phase 3 study is necessary. That is
> not what the regulations say but there is enough slippage in the
> regulations to allow this interpretation to be propagated in the
> course of discourse between the sponsor and the agency.
>
> Granting of exclusivity is increasingly viewed as a privilege and
> there is a control on it. About 25 percent of issued written requests
> for pediatric studies have resulted in exclusivity. We are not
> breaking the bank with it. There is differential interpretation of the
> regulations by what I have termed the \"Tower of Review Divisions.\" I
> can tell you that the review divisions that looked at montelukast took
> a very different approach than the review division that looked at
> sotalol and the review division that looked at the acid-modifying
> drug. So, there is not uniformity of interpretation across the board.
>
> Problems and in some instances failures with regard to integration of
> both the Pediatric Division at FDA and Clinical Pharmacology with what
> the review divisions do. Much of the discussion this morning at the
> end-of-phase-2A, to me, goes toward solving some of this problem.
> Then, the entire pediatric initiative clearly largely remains an
> unfunded mandate. So, there are some problems that exist that turn
> into decision-making.
>
> \[Slide\]
>
> Let\'s go back to the decision tree for just a minute. You have seen
> it and I am going to modify it just slightly by getting rid of the
> first two things in the top box. Let me explain why I am trashing the
> top box.
>
> \[Slide\]
>
> If you look in pediatrics, from what I have been able to learn in the
> few years of dealing with it, is that in most instances the disease
> process is rarely substantially similar to adults. It is rarely
> similar with respect to onset, progression, expression of symptoms,
> and the disease environment-treatment interface. There are many, many
> differences. So, it becomes an interpretation issue to say is it
> similar or is it not, and I think we heard that with the last
> presentation. When you get down to the end of the day with numbers and
> you say is this a meaningful difference between these two populations,
> we ask the medical officers is it really different.
>
> Now, what many people have shown is similar is the relationship
> between the concentration of the drug and the effect of the drug. It
> is often similar between adults and children. That is not to say that
> develop doesn\'t influence receptor expression certainly in the first
> few months of life but beyond that it is pretty much the same.
>
> \[Slide\]
>
> Ergo, here is what the decision tree might look like in my mind. In
> the top box we have similar drug effect or mechanism of action. Is
> there similar concentration effect or is there similar effector
> response? This moves it away from disease and squarely puts it into
> issues regarding the clinical pharmacology of the drug. Once you
> satisfy a couple of those you march down, and march down in such a way
> as to determine tolerability and what is the right dose.
>
> \[Slide\]
>
> So, the \"holy grail\" of extrapolation, as I see it, is forget about
> the disease being substantially similar because in many cases it
> won\'t be. Focus on the drug response being similar. That is what
> clinical pharmacology does best. Again, in many cases this notion of a
> morbid-mortal outcome for studies because that is just not the way it
> is done. But base the assessment on drug efficacy and tolerability
> associated with similar\--I didn\'t say equivalent but similar
> exposure. Then, mandate the use of a decision tree that is driven by
> the Exposure-Response Guidance, something that really lets us look to
> see if similarity exists. When that is done and it is woven together,
> like this picture of an Indian blanket, it becomes not only a thing of
> great beauty but something of great function and potential
> significance.
>
> \[Slide\]
>
> But to do it we have to improve what we do in development, and it is
> real simple because if you think about it like Einstein did, which is
> to think out of the box and much of our discussion today has been
> about thinking out of the box, the problems and the challenges of
> pediatrics, many of which are insurmountable, we are always going to
> have small numbers, we are always going to be dealing with what you
> can do and what you can\'t do, what you shouldn\'t do, but if we apply
> the best that technology has to offer we can make effective solutions,
> and I think that is my last slide.
>
> DR. VENITZ: Thank you, Greg. Any questions for Dr. Kearns? Larry?
>
> DR. LESKO: Just a terminology question, Greg, what do you mean by
> tolerability in one of those boxes that you modified?
>
> DR. KEARNS: That is my way, Larry, of saying that we never truly get
> safety data from any of the pediatric things that we do. For most of
> them that have less than 100 subjects, it is only tolerance data.
>
> DR. LESKO: Then, just to understand your point in the first box where
> you are suggesting to drive it by exposure response primarily, is that
> by demonstration with data that one would get during the drug
> development process?
>
> DR. KEARNS: Yes. That was actually done in the pediatric labeling of
> famotidine by Merck where in a limited number of children and infants
> we were able to measure intragastric pH, calculate EC50, Emax, the
> pharmacodynamic parameters, compare those to the parameters in adults
> and we found that there was no difference. Then the approach that was
> used for the labeling of famotidine was one driven by exposure
> response and kinetics.
>
> DR. LESKO: So, the assumption kind of is that we need to have response
> correlates. In other words, there is going to be a subset that do and
> a whole bunch of drugs that don\'t.
>
> DR. KEARNS: But it is even possible I think to\--one of the early
> pediatric studies, one of the early drugs that had some labeling was
> Tegretol, carbamazepine. Those studies on response were done using [in
> vitro]{.underline} systems to show that the concentration-effect
> response of Tegretol on the gating I think of sodium was similar to
> what it was in adults. But we have moved far afield of that now in
> terms of our thinking about pediatrics and I am saying if there are
> relevant approaches that come from animals or [in vitro]{.underline}
> that deal with effect, that should be something to look at.
>
> DR. FLOCKHART: Greg, I guess this is the pediatric internal medicine
> conversations. So, first of all, I totally agree with you that we to
> think a lot more carefully about the differences in disease
> progression and so on, but I would like to explore with you what some
> of those might be, just to flesh out some good examples.
>
> Now, the first thing that strikes me is that the diseases aren\'t
> actually the same. You know, adults get high blood pressure and kids
> don\'t much. On the other extreme, you know, asthma would seem to be,
> to a very naive internist, not terribly different. The kinds of drugs
> we use in kids tend to be similar and that we be representative of a
> group of diseases where we have been somewhat successful in
> transferring adult methodologies\--well, not methodologies but PK/PD
> relationships to kids.
>
> This begs the question of the vast untouched swath of disease where it
> is not similar. So, could you talk a little bit about what that might
> be. What would be diseases where there are very substantial
> differences that we might expect?
>
> DR. KEARNS: Well, let me use asthma as an example. Yes, it is similar
> from the standpoint of what the symptoms are; that anti-inflammatory
> medicine is something good for all asthmatics. But if you look at the
> impact of development on remodeling of the airways, it is much
> different in a young infant than it is in an adult. If that has
> something to do with the long-term outcome of treatment in terms of
> morbidity and mortality, there could be very, very important things.
>
> The other side of the coin is the acid-modifying drugs. Again, I go
> back to the example. For adults, probably 30 percent of adults in the
> room here today have some proton pump inhibitors in their kit.
> Certainly I d. They work; they work. They are given to infants not
> because infants have gastroesophageal reflux disease, not because
> there are many infants running around with Barrett\'s esophagus. They
> are given to infants who throw up and are unhappy when that occurs
> because of the acidic gastric content that is thrust into their
> esophagus. So, if you can make that better, the baby still spits up
> but the kid is a lot happier and that is why the drugs are used.
>
> Now, that may seem like a lame reason if you are a regulator, but it
> is the context of use. So, at the end of the day acid-modifying drugs,
> if you look at the proton pump and all the studies, or you look at H2
> antagonists, they seem to work with the same concentration-effect
> relationship in babies that are a month old as they do in adults who
> are 40 years old. A lot of the disease stuff from a scientific
> perspective has not been well explored.
>
> DR. VENITZ: Any other questions?
>
> \[No response\]
>
> Thank you, Greg. Our next presentation is by Dr. Rodriguez. He is
> going to talk about the regulatory experience with the very same
> decision tree that we just talked about.
>
> **Regulatory Experience in Using the**
>
> **Pediatric Decision Tree**
>
> DR. RODRIGUEZ: I am a pediatrician; I am not a pharmacologist so
> obviously what you are going to hear is from the perspective of a
> pediatrician who is, however, as interested as we all are in the
> appropriate, number one, use of the drugs and the observation of
> effectiveness and the safety or tolerability depending where we end
> today or in the future.
>
> \[Slide\]
>
> This is one of the reasons why I am doing some of this stuff. We are
> starting here a few years ago with some of my grandchildren. The
> reason I do that is because my children used to complain all the time
> that I didn\'t pay much attention to them; I was too much at work or
> in the hospital, whatever, so now I spend more time with them and,
> therefore, I have them there as a reminder. But specifically they are
> the ones who are going to get the drugs that are studied appropriately
> and that is why I put them at the beginning and I put them at the end
> too.
>
> \[Slide\]
>
> It is interesting because the issue of pediatric labeling has been
> around for quite a number of years and, of course, Greg mentioned
> Jacobi\'s commentaries and, in fact, in 1979 there was a statement
> which I will read to you: statements on pediatric use of a drug for an
> indication approved for adults must be based on substantial evidence
> derived from adequate and well-controlled studies unless a requirement
> is waived. So, that is a little thing on the side. That was in 1979.
>
> From there we progressed to 1994 where we had probably the first
> almost legalization of the extrapolation. Essentially, we were
> allowing people to infer or estimate by projecting or extending known
> information in the field of pediatric drug therapy.
>
> \[Slide\]
>
> This \'94 rule required the sponsors of marketed products to review
> existing data and submit appropriate labeling supplements. Do you know
> how many came in? Very few. Anyway, it applied to drugs and biologics
> and pediatric applications could be based or may be based on adequate
> and well-controlled trials in adults with other information supporting
> the pediatric use. Here we are talking about PK and safety data.
> However, there was no requirement to perform new studies in pediatrics
> and, in fact, some drugs have actually been labeled from information
> that is out in the literature essentially, and that could be one way
> to look at it if the studies were well done.
>
> \[Slide\]
>
> The efficacy could be extrapolated in the \'94 rule if the course of
> the disease and effects of the drugs, beneficial and adverse, are
> sufficiently similar in pediatric and adult population and, therefore,
> it would be permissible to extrapolate the adult efficacy data to the
> pediatric patient. So, sufficiently similar is a little bit more open
> than substantially similar. It is what the \'79 rule was talking
> about.
>
> \[Slide\]
>
> Other supporting information included information which would be
> appropriate for the pediatric rule which supports use in that age
> group and minimum PK and safety data must be obtained. I am not
> wording this; I am actually getting it out of the regulation. However,
> if the PK parameters are not well correlated with activity in adults,
> a clinical study would more likely be requested.
>
> \[Slide\]
>
> So, an approach based only on PK is likely to be insufficient when
> blood levels are known or expected not to correspond with efficacy or,
> for example, when there is concern that the concentration-response
> relationship varies with age, and we have heard about that today, and
> in such situations there is need for studies of clinical or
> pharmacologic effects. If the comparability of the disease and outcome
> of therapy are similar but appropriate blood levels are not clear, a
> combined measurement PK/PD approach may be possible.
>
> \[Slide\]
>
> So, today what I would like to do, among other things is, first of
> all, share something that we did within the agency where we actually
> got people together from various divisions and looked at drugs that
> were actually being studied or have been studied in response to
> written requests. I want to share that information with you because it
> might actually help us identify areas where there are problems and
> areas where we are likely to fail.
>
> Where may extrapolation not be the right approach? For example, adult
> efficacy cannot be extrapolated or the response of drug may differ
> because of receptor differences or the disease manifestations may be
> different.
>
> Difficulties may be posed also by the child\'s inability to cooperate.
> You have heard about some of the pulmonary drugs today. Essentially,
> if you are trying to measure the effect of something used in a spacer,
> the four or five-year old kid may not be able to help you or may not
> be willing to cooperate in the carrying out of an FEV1 evaluation,
> although people have gotten strong enough to say if you take some of
> these young kids and you squeeze their chest real hard you will be
> able to find out some of the response, and it has been done, by the
> way, in the younger population but we are not pushing for that.
>
> \[Slide\]
>
> The extrapolation may not be the approach if the disease is different
> in etiology, pathophysiology and/or manifestations. There are some
> pretty good examples particularly in the area of psychopharm., such as
> neonatal seizures, infantile spasms and febrile seizures. Therefore,
> in those situations you would expect that there would be nothing to
> extrapolate from or that the therapy might be different. Antiepileptic
> drugs effective in adults may actually be ineffective proconvulsants
> in children, such as phenytoin and carbamazepine which may exacerbate
> certain pediatric types; or vigabatrin, which is not approved in the
> U.S.A., and may exacerbate myoclonic seizures; or we may find drugs
> that are ineffective in adults but therapeutic in children, like ACTH
> and steroids in infantile spasms.
>
> So, we have another way and that is important to keep in mind because
> if we sit around waiting for extrapolation we may actually not study
> drugs that could actually be useful in the pediatric population.
>
> The pathophysiology may be comparable but the response to therapy may
> not be predictable in adults and children. This happens with many of
> the psychotropic agents. In fact, CDER had a program last week in the
> area of the use of extrapolation and the various divisions came that
> we invited. Essentially, some of the areas from pulmonary, etc. were
> actually discussed. And interesting one was drugs for allergic
> rhinitis where in the physiologic area the pathophysiology was
> understood and, therefore, the drug was approved for use in the
> pediatric population, whereas neuropharm. felt very uncomfortable in
> extending that type of process in some of their products.
>
> \[Slide\]
>
> The favorable scenarios where it may be okay to extrapolate are, for
> example, if the drug has been effective in adults and in children down
> to six years of age. You have heard about one exercise in which they
> went under that age group. In order to extend the labeling down to one
> month you must establish that the disease is similar; response to
> treatment is similar; plasma levels of drug dosing is in the
> therapeutic range; and the safety profile is acceptable\--essentially
> what you have been talking about today.
>
> There are some areas in which extrapolation has generally been very
> appropriate. That happens to be one of my areas of expertise,
> essentially antimicrobial and antiviral. I am an infectious diseases
> pediatric specialist. You heard about bronchodilators. In fact, in
> AIDS it is fascinating because there, even though the disease may
> actually differ in terms of the progress, the markers, for example,
> are looking at something as the viral effect of the drug and also
> looking at some of the markers like CD4 were actually used to approve
> drugs for use in the pediatric age. So, essentially, in some areas of
> the agency some of the stuff we are talking about today has been used
> rather readily.
>
> \[Slide\]
>
> What I have in this slide is actually what this multi-disciplinary
> group actually said how about if we were to consider extrapolation in
> children to support the efficacy data. What would we actually be
> looking at? We looked at the nature of the evidence, such as empirical
> comparison; knowledge of mechanisms; known adult physiologic and
> clinical properties of the analogous drugs; known sensitivity of
> children to specific toxicities.
>
> And, how do we get there? Let me give you a little bit of background.
> These were actually 35 drugs that had been turned into the institution
> in response to written requests. They are drugs that have been granted
> exclusivity, etc. The reason I am telling you this is because I want
> you to see that in order to get exclusivity you may not have to show
> that your study showed efficacy. However, you have to follow what the
> agency actually asks you and I will show you an example about that.
>
> So, how do we get there? Well, non-clinical studies\--I was very glad
> to hear that people might take a look at cell lines for example; they
> might take a look at animal studies; they might take a look at patient
> samples. In fact, somebody was talking the other day about use of
> tissues from a brain that had undergone surgery for whatever reason,
> and looking to see how the drug acted in there. Looking at the
> pathophysiology, in other words, similar clinical and symptom markers
> in adults and children or the involved cell types; similar natural
> history in an affected population. Essentially, the continuity across
> age spans may be helpful, and similarity of response to therapy such
> as improvement in the same clinical signs and symptoms for example.
>
> I have not been exhaustive there. There are quite a number of other
> factors that we have in there. But we felt that an evaluation of some
> degree of safety is essential. Granted, when we thought about safety
> in adult studies we have thought sometimes of 300-plus patients in a
> study essentially to pick up a signal that may actually be at a
> relatively high level, let alone the ones that are at a very low
> level. But if you take a look at the process of drug approval, you see
> the word safety used in phase 1, phase 2 and phase 3. Again, this has
> to be supported with pharmacokinetic and exposure response.
>
> \[Slide\]
>
> I actually went to the regulation of \'94 and said let me take a look
> and see how this really fits into the decision tree. Essentially, we
> can see that the first column would probably not fit into the decision
> tree and essentially there we have to include in pediatric use or
> limitations or pediatric indications, for example, the difference
> between pediatric and adult responses for the drug and other
> information related to the safe and effective pediatric use of the
> drug. We could be using the same example of ACTH and steroids in the
> issue of infantile spasms.
>
> We move down the line and we look at pediatric use for the indications
> also approved for adults and the simple product that came to my mind
> was actually the use of drugs for inflammatory response in the eye or
> infection in the eye. We could conceivably say that in those
> situations we don\'t need to really get PK/PD. We are actually
> specifically looking at the response and could use the data from
> adults to specifically say that we would not need two well-controlled
> studies and we might be able to get away with one.
>
> Of course, in the third row we have essentially the closest thing to
> the decision tree, which is indications based on raw data and that is
> where we are talking about use of the well-controlled information
> supporting pediatric use. In that situation, again, we still have to
> note that the course of the disease and effect of drug, both
> beneficial and adverse, are sufficiently similar in adult and
> pediatric populations to permit extrapolation. Again, we have to spell
> out the indications for that.
>
> Essentially, I am not going to spend much time with this, I know that
> in April of this year Dr. Rosemary Roberts spent quite a bit of time
> going into the various drugs that fit into this tree and what I
> decided to do was to essentially show you\--
>
> \[Slide\]
>
> I am sorry, before I go there, for all these drugs that we want to
> study we ask the following questions: What is the public health
> benefit for using the product in children? What is it? For what ages?
> What information is needed? What other products are available or
> approved for this indication? And, what type of studies are being done
> or should be conducted?
>
> \[Slide\]
>
> Essentially, what I am going to show you over here is information
> which is as up to date as of September 3 and we essentially looked at
> the studies that were requested for written request in response first
> to FDAMA and then BPCA. You can see that 284 written requests were
> issued. Now, 93 written reports have come back to the agency as of
> September, by the way. Of those, 60 have already been labeled, which
> is quite a bit of progress. And, 85 have been granted exclusivity,
> which means that only 9 studies did not get exclusivity, and they
> didn\'t get exclusivity because they weren\'t providing or they
> haven\'t provided the information that they had agreed to provide in
> the report.
>
> I think Dr. Lesko showed you something earlier, showing the percentage
> for efficacy and safety, PK and safety, and you can see it has changed
> very little over the period. You could argue, well, we haven\'t
> changed anything or we are getting the information that we need to go
> forward. So, there are two ways to interpret that.
>
> \[Slide\]
>
> Now I would like to share with you some experiences and these
> experiences came from this group that was put together to look at
> drugs that have been granted exclusivity, have been labeled and have
> provided some type of information.
>
> \[Slide\]
>
> The first one that we have here is the psychotropics. I have selected
> the psychotropics because that is where we had the biggest problem in
> thinking about the way that the decision tree would help us.
>
> Essentially, for this drug, over here, there was absence of prior
> data, according to the division, that would allow extrapolation. So,
> they actually went ahead. Our group went ahead and said, okay, what
> factors could be used for extrapolation? Essentially, we felt that
> there was similarity of symptoms in children at least over six years
> of age. We felt that the response to therapy would probably be similar
> and so would the natural history. Essentially, the division asked for
> multicenter, randomized, double-blind, placebo-controlled studies to
> evaluate efficacy and safety, and PK open-labeled escalation.
>
> Let me tell you that there were well over 500 patients, almost 600
> patients enrolled in these. What did we come out with? Safety and
> effectiveness was not established in patients 6-17 years at doses
> recommended for use in adults. PK parameters, area under the curve and
> Cmax of drug was found to be equal to or higher in children and
> adolescents than in adults. Maybe in the future something like this
> may actually benefit from some of the stuff that we are talking about
> today but essentially that is what came. Let me tell you that this
> company did get exclusivity. Why? Because they did everything that was
> in the written request. So, essentially, that is the criteria for
> granting exclusivity.
>
> \[Slide\]
>
> Another example is the psychotropic fluvoxamine. Let me tell you first
> of all that exclusivity came to the agency on 1/3/00. Remember that
> these are in response to the FDAMA in 1997-98. So, within a couple of
> years we had this area on our hands. This was for obsessive-compulsive
> disorder. Essentially, again the group said similarity of symptoms and
> response to therapy would be areas where extrapolation could be done.
> There was a multicenter, open-label PK study and long-term open-label
> safety study.
>
> The result was that, number one, we already had an efficacy study of
> this drug at the time this drug came to us. It was actually in the
> label but there were questions about why aren\'t we having some effect
> in the adolescents? Why do we seem to be having more effect in the
> girls or in the children 8-11 years of age with the doses that were
> recommended in the label?
>
> To make a long story short, nonlinear pharmacokinetics was a part of
> the answer to this, and this was corrected and essentially girls 8-11
> years of age may require a lower dose while the adolescent may require
> doses to be adjusted to actually be increased over what they were
> constantly getting.
>
> \[Slide\]
>
> Essentially, we are learning and we could learn more. This is
> gabapentin, an antiepileptic. Actually, that came to the agency on
> 2/2/00 and, again, it was labeled by October of that year. The
> concerns with respect to this drug were that safety and efficacy could
> not be extrapolated. Remember, this is in the psychopharm. group again
> where they have had some of the bigger problems for extrapolation.
>
> But our group said that they could extrapolate on the basis of
> similarity of symptoms and response to therapy. Essentially, they
> actually did a double-blind, placebo-controlled, parallel group
> efficacy and safety study as add-on therapy; population PK; open-label
> extension study and single-dose PK. There were quite a few patients
> that were studied there, almost 1,000 patients.
>
> \[Slide\]
>
> The results were there was safety and effectiveness down to 3 years,
> however, we identified some neuropsychiatric disorders in 3-12 years
> old such as emotional lability with attention problems in school and
> hyperkinesis. The product clearance, normalized by body weight,
> increased in children less than 5 years of age. So, between 3-5 higher
> doses were required in that population.
>
> \[Slide\]
>
> The next two drugs were in the cardiovascular group. Again, there were
> some problems in the area of extrapolation. Essentially we have here
> hypertension. The thought was there was similarity in symptoms and
> that the natural history was similar. We have to remember that
> hypertension in kids may actually be the result of structural
> abnormalities for example which may differ from the adult population.
>
> There was an open-label PK study, double-blind dose-response study.
> The result was that the drug was labeled for one month to 16 years of
> age, and there was information on dose efficacy and pharmacokinetics
> and, more beautiful, there was information on preparation of a
> suspension. So, essentially, we had good information that actually
> made it into the label.
>
> Let me just add here that we had at least two situations where there
> has been information on a suspension and five situations of the first
> 34 drugs that were approved where we had new formulations made for use
> in the pediatric population.
>
> \[Slide\]
>
> Here we have the last one that I want to share with you, which is
> fosinopril. Essentially, that drug came in on 1/27/03. The indication
> was hypertension. Essentially, areas that could actually be used for
> extrapolation were similarity in symptoms and the natural history.
> Essentially, there were open-label studies, multicenter, single-dose
> PK studies were requested in one month to 16 years of age;
> multicenter, randomized, double-blind dose ranging and
> placebo-controlled studies in 6-16 years of age.
>
> The results are as follows: New recommendation for dose in children
> weighing more than 50 kg; new information on PK parameters and
> appropriate dose strength is not available for children weighing less
> than 50 kg. The company did not come in with a formulation or with a
> preparation for suspension and even though data is available, that was
> not included in the label at this moment. Essentially, you can see
> that this is a two-way street.
>
> \[Slide\]
>
> So, what have we learned from the point of view of pharmacokinetics
> and pharmacodynamics? Some populations may need to start therapy at
> the lower end of dosing to avoid adverse events. That was for
> midazolam hydrochloride in patients with congenital heart disease and
> pulmonary hypertension.
>
> Elimination half-life may be shorter in pediatric patients than in
> adults. That was in atovaquone/proguanil. Essentially what we saw is
> that atovaquone clearance in children was 1-2 days\--I am sorry, the
> half-life, not the clearance. The volume of distribution and half-life
> may differ in a fashion which necessitates doses higher in younger
> children than adults. That happened with etodolac.
>
> \[Slide\]
>
> Higher oral clearance by body weight in patients less than five years
> of age necessitated higher dose concerning gabapentin. You have
> already gone extensively over sotalol hydrochloride. Buspirone
> hydrochloride from kinetic parameters, area under the curve and
> maximum concentration of the drug may be equal to or higher in
> children and adolescents than in adults, and no demonstrated efficacy.
> As I mentioned earlier, in fluvoxamine there were nonlinear
> pharmacokinetics.
>
> \[Slide\]
>
> So, what are the gaps in information? There are many but I have
> selected three. Many populations such as infants and neonates, both
> term and pre-term, remain to be studied. There is still a lot to be
> learned in terms of clear exposure-response relationship across the
> various special populations. Very importantly, it is very hard to meet
> these criteria in some of the drugs and essentially try to find
> appropriate pediatric formulations. But if somebody comes home with a
> correct formulation the agency is ready to look at it favorably.
>
> \[Slide\]
>
> This is the end of my comments and I am open to questions and if I
> don\'t know, I will communicate with you later.
>
> DR. VENITZ: Any questions?
>
> DR. FLOCKHART: Well, I would like to thank you too. I think this was
> really tremendously valuable to me in terms of my thinking about this
> from many respects.
>
> I would like to ask you about two kinds of studies you presented. The
> first is the hypertension ones. I am an internist. Hypertension in
> children or adolescents, to me, is different in that it is rarely what
> I would call essential hypertension. As you indicated, it is much more
> neurofibromatosis induced or one of those things. So, are the studies
> that you are talking about ruling those out because they would be
> separately treated? And, you are essentially dealing with essential
> hypertension in children which would be a very, very narrow group of
> patients.
>
> DR. RODRIGUEZ: These studies, in response to written requests on which
> a protocol was developed, would specify clearly the diagnostic
> criteria by which the patients would be enrolled in the study. In
> other words, it was not all hypertension. It was stenosis for example.
>
> DR. FLOCKHART: Right. The second question, you mentioned specific
> liabilities that children might have to side effects. What about
> actually testing side effects? I am interested particularly in the
> situation with HIV drugs\--side effects that might occur more in
> adults, something like lipodystrophy, and less in children? Has that
> been the case also?
>
> DR. RODRIGUEZ: To the best of my knowledge, no, but I am not sure. So,
> if you want I will give you my e-mail and we can communicate.
>
> DR. FLOCKHART: Sure.
>
> DR. KEARNS: Bill, that was a great talk, as usual. My question is
> based on the examples that you showed of the drugs recently studied,
> almost all of them had some type of efficacy study associated with
> them. You showed the earlier regulations and went back to 29 CFR, dot,
> dot, dot. The third point that you made is that if pediatric use was
> based on adult data, then it could be the case were appropriate
> dose-finding safety studies could be done, which is very much part of
> the pediatric decision tree but, yet, your examples all deal with an
> efficacy study and in some cases with some of the psychoactive drugs
> it has been debated that those efficacy studies were probably
> under-powered to really assess an effect because the things measured
> in children are sometimes very difficult. So, if most or all of these
> are going to involve efficacy studies do we need to redo the decision
> tree that has the first box immediately going to an efficacy study?
>
> DR. RODRIGUEZ: I thought I had said that but I will repeat it, one of
> the reasons I selected these drugs is because these were the drugs
> that we actually had some problems with, and these are two divisions,
> for example, that have had some problems\--not problems, I should say
> maybe different mechanisms, I mean the psychopharm. drugs for example.
> So, essentially what I did was I selected the ones where the problems
> were because I figured there were enough people here that might come
> up with some suggestions on how we can deal with that.
>
> You raise a point. It might be the power. But when you hear about
> 500-plus kids, that is a pretty good sized study. In fact, one of the
> things I said was maybe those kids needed higher doses and that was my
> naive way to look at it. Anyway, I selected the problems on purpose.
> But if you look at the breakdown of the various requests, a lot of the
> drugs did not necessarily require efficacy. They had the PK/PD and, of
> course, they had safety.
>
> DR. LESKO: To follow on the question that Greg raised, Bill, in the
> type of study, that is the study breakdown on the issue of written
> request, there are 284 or 660 studies, it looks like, and there is a
> percentage. In the written requests only 35 percent\--getting back to
> what Greg asked\--are efficacy studies, although for the ones you
> showed in the area of the antihypertensives and the psychotherapeutic
> agents it was 100 percent efficacy.
>
> There are two questions. Of the 93 that you said came in, and you said
> 60 have been labeled, does the percentage in terms of the type of
> study remain the same as it is for the written requests?
>
> DR. RODRIGUEZ: I have that tabulation on the first 33 drugs that were
> labeled. That is over 50 percent of the drugs that have been labeled.
> We published this in JAMA.
>
> DR. LESKO: Okay.
>
> DR. RODRIGUEZ: There we have around 43 percent efficacy and safety; 34
> percent PK/PD; and 12 percent were combination where the topics were
> actually safety.
>
> DR. LESKO: So, it sounds like it is kind of similar in terms of what
> actually is done in studies as opposed to what is put in a written
> request.
>
> DR. RODRIGUEZ: But if you take a look at that, we have almost 56
> percent that were PK, safety; PK/PD and safety and 43 percent that
> were efficacy, safety.
>
> DR. LESKO: Just continuing with that, can you think of several
> therapeutic classes\--we know where efficacy studies predominate, for
> example, in the antihypertensive and psychotherapeutic agents, were,
> on the other hand, approvals based not on efficacy studies but on
> other information, the PK, safety or the PK/PD\--
>
> DR. RODRIGUEZ: Well, you heard about the pulmonary allergy type
> reactions. That has been one where there has been a mix of drugs where
> some biomarker or some other finding has been used for that.
>
> DR. FLOCKHART: HIV with a CD4 count.
>
> DR. RODRIGUEZ: HIV with CD4, that is right. You see, the area where it
> is relatively easier is in the infectious diseases because if you draw
> a triangle and you put the human over here, you put the drug over here
> and you put the virus or the bacteria over there, you can do\--I mean,
> we do a lot of things [in vitro]{.underline} which adds validity. In
> fact, even there, there is a problem because, you see, when you
> approve drugs for viruses you approve drugs for viruses. When we
> approve drugs for bacteria we are sometimes approving them for otitis
> media or sinusitis or pneumonia even though, for example, in H. flu it
> would be H. flu or strep. pneumo., strep. pneumo., strep. pneumo. but
> we are applying it for the various clinical indications. But in the
> virology field it is easier because for some reason that rationale has
> actually prevailed. I wouldn\'t be surprised if we progressed toward
> that direction. I am speaking off the top of my head right now.
>
> DR. VENITZ: Any other questions? If not, thank you.
>
> DR. RODRIGUEZ: You are welcome.
>
> **Committee Discussion**
>
> DR. VENITZ: Larry, I would ask you to put your last slide up so we can
> go through the three questions that you want us to give you some
> feedback on.
>
> DR. LESKO: I actually don\'t have one. I don\'t have a slide on the
> questions but they are in the background package and maybe we can
> refer to that because there are only really two questions. One of the
> questions refers to the methods of analysis that Dr. Machado showed us
> in terms of determining similarity and exposure response between
> adults and pediatrics, and we did have some discussion of that
> already.
>
> However, the second question really revolved around providing some
> feedback on the current way the pediatric decision tree is being used
> in the context of the numerous examples that were presented today. In
> other words, does this seem like it is on the right track?
>
> Furthermore, some suggestions were made that maybe there is room for
> other approaches than what we have in the pediatric decision tree
> based on what Dr. Kearns presented. Are there comments on potential
> alternative ways of thinking about, in particular, that first box? I
> think if we can sort of go in that area for discussion it would be
> helpful.
>
> Maybe rephrasing the question, if we think of the current pediatric
> decision tree as the current situation, in essence a one-size-fits-all
> because that is the decision tree, are there any situations where a
> different approach might work, similar to what Greg had suggested, to
> approach it and drive it from an exposure-response mechanism of action
> point of view? For example, could that be an approach that would work
> well in areas of drugs that are well understood in terms of their
> mechanism of action, drugs which might be a third in class for
> example, a drug with a wide therapeutic index where pharmacodynamic
> endpoints are reasonably measured and are thought to correlate not as
> surrogate endpoints but with clinical endpoints? And, given certain
> criteria, could an alternative approach be used to go down that
> decision tree? So, that is kind of an area that I would like to maybe
> hear about as well from the committee.
>
> DR. KEARNS: Larry, I think one thing I would like to add to this, and
> Bill\'s talk alluded to it, is that the pharmacodynamic endpoints that
> are measured have to be appropriate so things can be done in children,
> and they must relate to the effect of the medicine. That is easier
> said than done. I mean, psychometric testing in young children is not
> an easy thing.
>
> What happens sometimes is that in the course of pediatric drug
> development and trying to satisfy the questions we are faced with,
> almost being forced out of necessity or in some cases desire\--and
> that is my impression, to develop endpoints in the context of the
> trial, none of which are validated and in some cases the endpoints
> have nothing to do with effect. Again, case in point, an
> acid-modifying drug doesn\'t influence esophageal motility. So, as
> long as we are basing what we do on the clinical pharmacology of the
> drug and doing the best we can, I think we get the best approach and
> at the end of the day the best answer.
>
> DR. SHEINER: The example you used, the acid-modifying drug, that is a
> tough one. What you are saying is, look, it is getting rid of the acid
> and when the kid spits up it makes him happier and there is no
> equivalent adult disease per se. So, you are saying that here is an
> indication that doesn\'t exist in the adults, treated by the same
> mechanism as something that does.
>
> If you find that the physiology is the same, the acid is turned off at
> the same concentrations, lasts as long, and everything like that,
> first of all I have a question, doesn\'t the indication have to be
> approved? Maybe your drug has some safety consideration that would
> make it approvable for something that was life-threatening but not
> something that as symptomatic, etc. I mean, I just don\'t see how you
> are going to be able to automatically find that because the physiology
> is the same after the drug, that because the indication is different
> you get approval in pediatrics. You wouldn\'t get it in adults. If it
> turned out that there was a new condition that was treatable\--I mean
> off-label use is fine because the drug is approved but for approval
> you would have to show that it is efficacious in that condition.
>
> DR. KEARNS: A good question. Again, my impression and I am not
> speaking here for the agency, but I referred to some of the slippage
> in interpretation. Children per se, young infants especially, do not
> characteristically have gastroesophageal reflux disease.
> Histologically many of them are normal or they may have a little bit
> of hyperemia but it is not the same thing in adults. Well, if we
> interpret that as saying, oh, well, that is a different indication,
> then as you interpret the regulations you could certainly go down and
> say, okay, we have to do efficacy studies of these drugs. So, you
> interpret the regulation. But if you went back to 29 CFR dot, dot,
> dot, and you read if pediatric use is based on adult data, and proton
> pump inhibitor use in pediatrics is based on adult data, and the data
> it is based on is the ability of the drug to modify the pH of the
> gastric content, not anything else.
>
> So, there is a tremendous amount of interpretation that has to go on
> and that is why I said earlier it is imperative that the Office of
> Clinical Pharmacology and Biopharmaceutics be involved early and,
> hence the decision tree. Be involved early and try to work
> cooperatively and collaboratively with the review divisions to make
> sure that the studies that we think we need in kids are done and that
> they are done right because some things in children you just can\'t
> do. Parents will not volunteer for repeat endoscopies in young infants
> and, arguably, they shouldn\'t be done because of the risks associated
> with anesthesia and stuff like that. So, we can\'t use the old adult
> ways to do the pediatric studies. But it is hard. There is room for
> slippage.
>
> DR. SHEINER: But I think there are two issues there. You know, all my
> sympathies are with you. My guess is that you are saying is that
> modifying the acid production is going to help condition X whether it
> is adults or children, and what I have is approval of things that
> modify the acid production for condition Y. So why not? And there will
> be plenty of off-label usage of that and it may never-ever come to the
> FDA because they can sell it for that. We know lots of drugs where a
> given action turns out to be good for something else and people use it
> for that.
>
> But if you want, you know, the \"Westinghouse seal of approval,\" you
> have to show it for that indication. That is the rule. I am not saying
> it is right. Therefore, this is not a pediatric problem; this is a
> general problem of discovering that a given action of a drug is useful
> for another indication and whether or not you can get the FDA to say,
> well okay, if you think so\--it just doesn\'t do that, I don\'t think.
>
> DR. KEARNS: Well, one of the worries has been the concern that if you
> put information in the label, if you put PK or PD information in the
> label absent information that proved efficacy in a condition, the
> label would then foster additional off-label use of the drug in
> children. You know, I think that is a little bit laughable because
> historically pediatricians have not been inhibited at all from using
> drugs off-label. They won\'t be compelled by that issue in the future,
> but what is helpful for many people is to know that if they gave a
> dose of X it would make exposure Y which was similar to that in
> adults. Then at the end of the day the medical practitioner has to
> make the decision whether he or she will utilize a medicine.
>
> I don\'t have any trouble with labeling saying that this drug has not
> been evaluated in children and its efficacy is not known. I think that
> is okay because I am willing to use other information to make the
> decision. But in an environment that is indication driven where the
> indications in adults and kids can be very different, it could set us
> back a little bit and the decision tree, if done right, can fix a lot
> of that.
>
> DR. SHEINER: I won\'t get the last word in because I know you but\--
>
> \[Laughter\]
>
> \--one more time, the thing is that what you would have to say is that
> this has not been shown empirically to be safe and effective for this
> indication. That doesn\'t mean it isn\'t, it just hasn\'t been shown.
> The mismatch between what is approved for children and what is used in
> children\--I think the attempt of the flow chart is to get close to
> that. But I think what you are saying is that in the end it is only
> going to get us part of the way there, and how should we deal with the
> rest of the way because it would be nice for the public to be
> reassured at some level that what the pediatricians are doing has been
> inspected to some degree. But I am not sure that we want to mix that
> with the issue here.
>
> They have bitten off an easier part, the same indication, and now can
> we establish that the concentration response is the same for the same
> indication, and then we can just approve with the PK, or something
> like that. That is an easier problem. Let\'s get that one all straight
> and then let\'s move on. As I say, I am totally sympathetic.
>
> DR. KEARNS: And I appreciate that more than you know. The same
> indication and the same use is oftentimes different and that is the
> problem. If you look at the labeled indication for many of the
> acid-modifying drugs, it is to treat nocturnal heartburn associated
> with symptomatic GIRD in adults. That is nutty. You know, that is
> really nutty. But we use drugs in pediatrics for the same reasons.
> Whether it is hypertension, asthma the same target, the same
> therapeutic target is there so I appreciate your words and I will stop
> talking now.
>
> DR. VENITZ: Larry, maybe just one comment, you are looking for
> scenarios where it is likely to use the currently modified decision
> tree, acute indications, symptomatic indications. You may be more
> likely to use pharmacology-driven approval/labeling rather than
> chronic indications.
>
> DR. LESKO: It would seem like that would have to be the case in the
> sense that it is the effect that you would measure early on in this
> decision tree. Thinking of the alternative or the pharmacological
> effect in an acute condition, I would expect that would be fairly
> close to the clinical endpoint in the sort of chain of events. As in
> Greg\'s example, you have a modifying of the acid secretion in the
> gastric pH and then there is an immediate benefit from that in the
> short term and the change in the environment of the stomach would be
> close to what you want to achieve at the clinical endpoint. It gets a
> little more complicated in terms of picking on the effect when you
> move into some of the therapeutic areas that Bill mentioned in the CNS
> area and the seizure area where you don\'t have the convenience of the
> same type of biomarker, if you will.
>
> So, that was why one way I was thinking about this, you know, rather
> than one-size-fits-all, would be are there alternative decision trees
> that could be thought about in terms of what we have now and an
> alternative for those indications where use and indication are
> somewhat different but there is a close relationship between drug
> mechanism, marker and endpoint where you could do something that could
> rely on less than efficacy studies basically. But that is the open
> question.
>
> DR. VENITZ: But it might be those drugs as well that allow you to
> incorporate some of the preclinical information that he was talking
> about.
>
> DR. LESKO: Of course. I don\'t know the extent to which that has been
> done. It makes sense and Bill had a slide on that where he had prior
> information. It was animal data. I don\'t know how much of that is
> relied on in the current situation. I don\'t have any first-hand
> experience with that so maybe Bill can answer.
>
> DR. RODRIGUEZ: Without mentioning the drug, there is one drug that has
> been used off-label in the pediatric population and there have been
> concerns about some studies that were done in the rodent model.
> Essentially, the agency right now is actually conducting studies in
> primates, newborn, juvenile primates. We have already collected the
> animals, and everything, and the studies are about to start and,
> hopefully, we will answer the question once and for all. Not only have
> the animal studies been done but you wonder how applicable they are so
> you have to be careful about that. So, we are trying to get as close
> as we can to the human primate with a non-human primate so we can then
> actually say, fine, let\'s forget about it; go forward and label this
> drug; it is okay.
>
> So, we have to be careful about it but, on the other hand, Phil
> Sheridan was talking the other day about the tissues that were
> actually obtained from surgical interventions in patients with
> seizures and how those tissues were actually [in vitro]{.underline}
> exposed to medications and the effect of the medication was actually
> being studied there. Of course, we cannot do brain biopsies on
> everybody so that is the problem there. But, essentially, there could
> be, again, primate models that could be used. It is expensive but
> actually in the long-run may be less expensive than the 800 million
> dollars that were mentioned over here.
>
> DR. VENITZ: Any more comments to question number two?
>
> \[No response\]
>
> Then let\'s try to tackle the last question for today.
>
> DR. KEARNS: To answer number three, first get a crystal ball.
>
> \[Laughter\]
>
> I don\'t think that we can ever know for sure that adjusting dose and
> exposure will give us what we want. I think that extrapolation is
> predicated upon assumptions that are reasonable from the scientific
> and clinical perspective; that are predicated upon approaches that are
> well proven and tested and show that they work, and when done by men
> and women who understand the scenario in which they are to be applied
> generally do produce good results. At the end of the day as
> perfection, I don\'t think we will ever achieve that but we have come
> a long way. I think the stuff Bill presented is evidence that we have
> come a long way with the pediatric initiative. I think we can improve
> it. It is a work in progress. Then we should be expected to deal with
> the deviations.
>
> Tomorrow we are going to talk about pharmacogenetics and I am looking
> forward to that, and I can tell you that in doing phase 1 and phase 2
> PK work, having pharmacogenetic data in children is very, very
> important to understand how much of that variability is really
> associated with age as opposed to a certain polymorphism and an
> enzyme. But I don\'t think we will ever reach perfection.
>
> DR. VENITZ: Let me maybe add something more specific to that. I think
> in general when we are adjusting doses based on exposure we are
> talking about exposures to the parent drug. So, I am always worried
> when I look at drugs that are highly metabolized. Phase one
> metabolites may be active or have safety issues related with them. So,
> as a general rule I would be more skeptical about dose adjustments for
> highly metabolized drugs that form potentially active metabolites,
> again, just as a way of stratifying risk. So, drugs that are readily
> eliminated via metabolism, I think adjusting the dose to achieve the
> same exposure with the intent to achieve the same response makes
> sense. But if you have a drug that has ten metabolites and three or
> four of them are known to be active and you don\'t really know how
> active relative to the parent, then adjusting the dose just based on
> parent exposure may not be reasonable.
>
> Any final comments? It looks as if we are all metabolized for today.
> Everybody is ready to take a break. So, let me conclude our first
> day\'s meeting. Let me thank all the speakers and committee members
> for their valuable input. We will reconvene tomorrow morning,
> bright-eyed, bushy-tailed, at 8:30, same place. See you tomorrow.
>
> \[Whereupon, at 5:10 p.m., the proceedings were recessed to resume
> Tuesday, November 18, 2003 at 8:30 a.m.\]
>
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