1

                            DEPARTMENT OF HEALTH AND HUMAN SERVICES

                                  FOOD AND DRUG ADMINISTRATION

                             INFECTIOUS DISEASES SOCIETY OF AMERICA

                            INTERNATIONAL SOCIETY OF ANTI-INFECTIVE

                           PHARMACOLOGY FOOD AND DRUG ADMINISTRATION

                            ANTIMICROBIAL DRUG DEVELOPMENT WORKSHOP

                                     Friday, April 16, 2004

                                           9:05 a.m.

                         Advisors and Consultants Staff Conference Room
                                       5630 Fishers Lane
                                      Rockville, Maryland

                                                                             2

                                                                             3

                                          PARTICIPANTS

                  John E. Edwards, Jr., M.D., Moderator

                  INDUSTRY

                            Lisa Benincosa, Ph.D.
                            Mike N. Dudley, Pharm.D.
                            Barry Eisenstein, M.D.
                            Dennis M. Grasela, Pharm.D., M.D.
                            Timothy J. Henkel, M.D., Ph.D.
                            John H. Rex, M.D.
                            Frank Tally, M.D.
                            Gregory A. Winchell, Ph.D.

                  ACADEMIA

                            David Andes, M.D.
                            William A. Craig, M.D.
                            Hartmut Derendorf, Ph.D.
                            George L. Drusano, M.D.
                            Jerome J. Schentag, Pharm.D.
                            George Talbot, M.D.
                            Paul M. Tulkens, M.D.

                  FDA

                            Renata Albrecht, M.D.
                            Chuck R. Bonapace, Pharm.D.
                            Phil Colangelo, Pharm.D., Ph.D.
                            Edward Cox, M.D., MPH
                            John Lazor, Pharm.D.
                            J. Robert Powell, Pharm.D.
                            John Powers, M.D.
                            David Ross, M.D., MPH
                            Janice Soreth, M.D.
                            Donald Stanski, M.D.
                            Jenny J. Zheng, Ph.D.

                  MISCELLANEOUS

                            John S. Bradley, M.D.
                            Dennis M. Dixon, M.D.
                            J. Todd Weber, M.D.

                                                                             4

                                        C O N T E N T S

                                                                          Page

                        Dose Selection in Antimicrobial Drug Development
                             Incorporation of Pharmacokinetics and
                                        Pharmacodynamics

                  Opening Remarks:  John E. Edwards, Jr.                     4
                  Introduction, FDA:  John Lazor, Pharm.D.                   9

                          I. Overview of Use of PK/PD in Streamlining
                                        Drug Development

                  Academic Perspective:  William A. Craig, M.D.             17
                  Industry Perspective:  Mike N. Dudley, Pharm.D.           29
                  FDA Perspective:  John Powers, M.D.                       42
                  Discussion                                                62

                             II. In Vitro/Animal Models to Support
                                         Dose Selection

                  Academic Perspective:  Dave Andes, M.D.                   75
                  Industry Perspective:  Lisa Benincosa, Ph.D.              90
                  FDA Perspective:  Chuck Bonapace, Pharm.D.               103
                  Discussion                                               119

                                Current Status of Dose Selection
                           in Antimicrobial Drug Development Programs

                  Academic Perspective:  George L. Drusano, M.D.           165
                  Industry :  Dennis Grasela, Pharm.D.,Ph.D.               185
                  FDA Perspective:  Frank Pelsor, Pharm.D.                 202

                               IV. Improvement in Dose Selection
                             through Clinical Applications of PK/PD
                           in Antimicrobial Drug Development Programs

                  Academic Perspective:  Hartmut Derendorf, Ph.D.          214
                  Industry Perspective:  Gregory A. Winchell, M.D.         228
                  FDA Perspective:  Jenny J. Zheng, Ph.D.                  252
                  Discussion                                               266

                  Concluding Remarks:  John E. Edwards, Jr., M.D.          295

                                                                             5

              1                      P R O C E E D I N G S

              2         Dose Selection in Antimicrobial Drug Development

              3              Incorporation of Pharmacokinetics and

              4                         Pharmacodynamics

              5                         Opening Remarks

              6             DR. EDWARDS:  Good morning and welcome

              7   back.

              8             I presume that all of you were given the

              9   revised schedule when you came in this morning, and

             10   we have made some changes, which I don't think will

             11   compromise the quality of the meeting at all, but

             12   as you can tell, we are scheduled now to end at

             13   least an hour and a half earlier than the original

             14   schedule you received yesterday.

             15             I am anticipating there are not going to

             16   be a lot of major objections to this change.  If

             17   there are, I will be happy to entertain them at the

             18   break.

             19             For those of you who were not here

             20   yesterday, my name is Jack Edwards and I am from

             21   Harbor-UCLA Medical Center, and I am also a member

             22   of the Antimicrobial Availability Task Force of the

                                                                             6

              1   IDSA.

              2             I am going to begin by taking the

              3   chairman's prerogative and taking just a few

              4   moments to give a very brief summary of yesterday.

              5   We will be preparing an extensive executive summary

              6   of this meeting, which will be on the IDSA web

              7   site, and I think it may also be on the FDA web

              8   site, but at least for certain, it will be on the

              9   IDSA web site.

             10             Last night, the members of the

             11   Antimicrobial Availability Task Force had a meeting

             12   that lasted quite long.  It was a very stimulating

             13   and animated meeting, and I must say we enjoyed

             14   very much the discussions yesterday, and I just

             15   want to take an opportunity to once more thank the

             16   individuals involved with organizing this meeting

             17   and especially the FDA for hosting this meeting

             18   here as we have found the conversations and the

             19   discussions extremely valuable and extremely

             20   stimulating.  So, let me thank you again.

             21             By way of brief summary, I am going to

             22   make just basically four points.  The progress that

                                                                             7

              1   has occurred since the meeting of November 2002, as

              2   described as both the IDSA and the FDA

              3   presentations, is very substantive and very

              4   encouraging.  To summarize in just a concept for

              5   each area, the IDSA is in final stages of

              6   preparation of the white paper and actively

              7   entertaining, beginning to engage the resources

              8   necessary to create legislative recommendations

              9   that will begin going to the Hill.

             10             FDA gave us a very beautiful summary of a

             11   large number of meetings and forums which they have

             12   created, this being one of them in a sense, and

             13   used to continue to develop many of the concepts

             14   that came from the November 2002 meeting and

             15   meetings both prior and after that.

             16             We also heard a very strong commitment to

             17   focus on the guidance document finalizations.

             18             The three specific points that I wanted to

             19   make that came from the discussions from the IDSA

             20   Task Force last night are as follows:

             21             The Task Force felt that it would be

             22   highly useful to continue to have the guidance

                                                                             8

              1   documents developed with the most desirable

              2   timeline being their getting to a form where they

              3   could be posted on the web site for external review

              4   and then finalized by the end of the year, end of

              5   2004.

              6             We listened to the pros and cons of the

              7   guidances and also the response of industry to

              8   them, but we have felt that from the IDSA

              9   perspective as we have gone to industry repeatedly,

             10   we consistently, over and over again, from all

             11   types of industry hear the importance of the

             12   guidance documents surface as a number one

             13   priority, if you will.

             14             We fully understand the complexities of

             15   producing those guidances, and we also understand

             16   the desirability for additional resources for the

             17   guidances to be done and are very sensitive to

             18   those issues, but nevertheless, the guidance

             19   documents conceptually seem to be of extremely high

             20   priority on the part of industry.

             21             As we moved through the day yesterday, and

             22   addressed the issues of the surrogate markers in

                                                                             9

              1   the primary indication for staphylococcal

              2   bacteremia, again, the group felt that it would be

              3   very useful to try to focus at this point on the

              4   prosthetic joint infection issue in form of

              5   development of a plan to study that would

              6   incorporate the usefulness of a surrogate marker

              7   which would be culture negativity.

              8             So, focus on that particular entity was

              9   thought to be very desirable, and focus to the

             10   point of perhaps future discussions in the

             11   Anti-Infective Advisory Committee meeting on the

             12   issue of the staphylococcal bacteremia as a primary

             13   indication was highlighted as a second area for

             14   overall focus.

             15             So, I have tried to be just be very

             16   concrete, and in the interest of time, I am not

             17   going to go through each of the individual

             18   discussions although there will be basically a

             19   summary of everything in our final summary

             20   document, and I will leave the summary for the

             21   moment at that now.

             22             Thank you for your attention to that

                                                                            10

              1   issue, and now we are going to get directly into

              2   our program, and I will ask John Lazor from the FDA

              3   to begin with some introductory comments.

              4             John.

              5                           Introduction

              6             DR. LAZOR:  Good morning.  I am John Lazor

              7   with the Office of Clinical Pharmacology and

              8   Biopharmaceutics.

              9             First of all, I would like to welcome the

             10   members of the panel and the speakers for today.  I

             11   would also like to thank IDSA and ISAP for

             12   cosponsorship of this workshop.

             13             [Slide.]

             14             You may ask why we decided to select dose

             15   as a topic for the second day of the workshop.

             16   There is many articles published in the literature

             17   that have described PK/PD relationships for

             18   antimicrobial drug products.

             19             In addition, there seems to be a

             20   systematic approach for dose selection based on

             21   PK/PD that has surfaced.  Because of this abundance

             22   of literature, we put together an internal working

                                                                            11

              1   group to try to assess how this information is

              2   being utilized in the antimicrobial drug

              3   development programs.

              4             We did this by trying to look at how these

              5   approaches were being used for dose selection.

              6   During this process, we did discover that there was

              7   a wide spectrum of approaches, and I should say a

              8   wide spectrum of rationale for the selection of

              9   dose.l

             10             It ranged from what I would perceive as

             11   empiric or at least not very transparent to us, to

             12   what I would refer to as a science-based approach.

             13             So, because of these observed

             14   inconsistencies and because of questions that were

             15   raised by the working group with respect to the

             16   methodologies, the assumptions, and the

             17   extrapolations, it was decided that a workshop

             18   would be beneficial.

             19             In addition, getting the dose right is

             20   embedded in many FDA and CDER initiatives.

             21             [Slide.]

             22             Three out of the five elements of the FDA

                                                                            12

              1   strategic plan stresses the importance of dose.

              2   For instance, the plan has identified that one of

              3   the reasons for the decline in the number of new

              4   applications is the number of multi-cycle reviews.

              5   Many of these multi-cycle reviews have been related

              6   to safety and efficacy issues.

              7             One can only question what the role of

              8   dose was in these efficacy and safety issues.

              9   Getting the dose right is important for improving

             10   patient safety and for drug development for

             11   counterterrorism measures.

             12             [Slide.]

             13             Yesterday, you heard about the FDA's

             14   Critical Path Initiative which was launched last

             15   month.  I won't go into detail, but I just want to

             16   reiterate that an objective of the program is to

             17   use science and technology to create new tools to

             18   improve product development process.

             19             Some examples could be the development of

             20   animal or computer models to predict outcomes,

             21   development of biomarkers, and the development of

             22   new clinical evaluation techniques.

                                                                            13

              1             [Slide.]

              2             The critical path has been defined as

              3   starting at the time when a compound has been

              4   determined to move forward into product

              5   development, and it is to end at the product

              6   launch.  This slide represents again that research

              7   is a major component of the critical path.

              8             [Slide.]

              9             The FDA has published guidance documents

             10   that promote science-based dose selection.  The

             11   exposure-response guidance, which was published

             12   last year, is an example.  This guidance promotes

             13   the use of exposure-response relationships to guide

             14   the design of Phase III trials and to support dose

             15   and dose range selection.

             16             [Slide.]

             17             The end of Phase II meetings is a new

             18   program designed to create opportunities for

             19   sponsors to have informative discussions with the

             20   FDA.  One objective of the meetings is to discuss

             21   the use of quantitative methodologies in the drug

             22   development program.

                                                                            14

              1             These meetings could address the use of

              2   models to forecast clinical outcomes and the use of

              3   exposure-response for better informed

              4   decisionmaking.  Another goal of these meetings is

              5   to discuss dosing strategies for Phase III.  Other

              6   issues, as appropriate, could be topics for these

              7   meetings, as well.

              8             [Slide.]

              9             At the local level, the Office of Clinical

             10   Pharmacology and Biopharmaceutics, through good

             11   review practices, emphasizes the importance of

             12   knowing exposure-response and whether dose regimen

             13   and dose adjustments for subpopulations are

             14   rational based on these relationships.

             15             [Slide.]

             16             So, there is a general theme across many

             17   programs, and that is getting the dose right.  The

             18   goal is to optimize efficacy and minimize risk.

             19             [Slide.]

             20             A paradigm that has surfaced for

             21   antimicrobial dose selection is to determine the

             22   PK/PD in in vitro and animal models.  Human PK is

                                                                            15

              1   added, so that a dose is identified to give a high

              2   probability of success.  This dose is evaluated in

              3   Phase II and then it moves on to Phase III.

              4             In Phase III, we have clinical outcomes,

              5   we may have microbiological outcomes, and sometimes

              6   we may have measures of drug exposure--I should say

              7   measures of drug plasma or serum concentrations as

              8   a measure of exposure.

              9             What seems to be absent is an integration

             10   of the outcome, the micro, and the exposure.  It is

             11   not known how the results from the Phase III study

             12   relate to the initial predictive PK/PD promise.  It

             13   is important to have this understanding, so that

             14   knowledge gained can be applied to the specific

             15   product, so that it can be used for the advancement

             16   of PK/PD in antimicrobial drug development.

             17             [Slide.]

             18             Today's workshop will have four sessions.

             19   We will begin with an overview of PK/PD in

             20   antimicrobial drug development, and then we will

             21   move into a discussion on the use of in vitro and

             22   animal models.

                                                                            16

              1             After lunch, we will talk about the

              2   current status of dose regimen selection, and then

              3   we will end with a session on what can we do

              4   better.

              5             [Slide.]

              6             In today's discussion, factors important

              7   for the selection of dose and dose interval will be

              8   discussed, however, we cannot forget that duration

              9   of therapy is an important part of an optimal dose

             10   regimen.

             11             [Slide.]

             12             Based on the say the agenda has been

             13   constructed, much of today's discussion will be

             14   focused on dose regimen with an emphasis on

             15   efficacy.  We all recognize that it is extremely

             16   important to minimize risk, so efficacy needs to be

             17   balanced with safety.

             18             Resistance is a third dimension that needs

             19   to be considered in antimicrobial dose

             20   optimization.

             21             [Slide.]

             22             There are some PK/PD terms used in the

                                                                            17

              1   antimicrobial area that are not used in other

              2   therapeutic areas.  For example, PK/PD index or,

              3   which is the Pk-PD parameter, is a measure of drug

              4   exposure.  It is not only a measure of the

              5   exposure, but it is linked to a measure of potency

              6   relative to the pathogen.

              7             You may hear reference to the terminology

              8   PK/PD target.  This is the magnitude of value of

              9   the PK/PD index associated with either a

             10   microbiological effect or an endpoint.

             11             [Slide.]

             12             What do we expect the outcome of today's

             13   meeting to be?  Well, through the presentations and

             14   discussions, we hope to learn what works, we would

             15   like to know what doesn't work, and we would like

             16   to know the assumptions and limitations in the

             17   approaches used to getting the right dose.

             18             It is expected that we will learn ways to

             19   better utilize the tools that we have and hear

             20   proposals for improving the current approaches.

             21             In fear of being I guess pinned up against

             22   the wall, I also will say that one of our goals is

                                                                            18

              1   to evaluate the need to update the guidance

              2   document developing antimicrobial drugs general

              3   considerations for clinical trials, and this is

              4   with respect to the Clinical Pharmacology Section.

              5             [Slide.]

              6             The outcome that we want to avoid is being

              7   in a situation of not knowing what the dose should

              8   be at the end of the product development program.

              9             I look forward to the presentations, the

             10   discussions, and a productive workshop.

             11             Thank you.

             12             DR. EDWARDS:  Thank you very much for that

             13   very nice introduction, John.  That last slide is

             14   priceless.

             15             I am now going to call on Bill Craig from

             16   the University of Wisconsin to begin the

             17   discussion.

             18           I. Overview of Use of PK/PD in Streamlining

             19                         Drug Development

             20                       Academic Perspective

             21             DR. CRAIG:  Thank you, Jack.

             22             [Slide.]

                                                                            19

              1             Why all the interest in pharmacodynamics?

              2   Well, if you look over the years, it has always

              3   come up of interest whenever there is a narrow

              4   difference between the drug exposure and the MIC of

              5   the organism, and this even goes back to the early

              6   days of penicillin.  You can find a lot of PK/PD

              7   studies in the old literature.  That was because

              8   the penicillin doses that were used back then were

              9   very low, but as we started to find that the drug

             10   was non-toxic and we could give much higher doses,

             11   PK/PD sort of disappeared and there was not much

             12   interest until Pseudomonas started to become a

             13   significant pathogen in the 1960s and '70s, and

             14   again we started to see more and more papers

             15   occurring looking at PK/PD, and then the latest

             16   explosion has really been with the emergence of

             17   resistance to Strep pneumo, MRSA, a whole variety

             18   of different organisms, and I think, though, that

             19   PK/PD now is going to stay because it has other

             20   applications as was mentioned for deciding on dose

             21   development and dose selection for clinical trials.

             22             [Slide.]

                                                                            20

              1             Now, for clinicians, PK/PD has had a

              2   variety of different applications.  It has been

              3   used to help establish more optimal dosage

              4   regimens, for example, once daily aminoglycoside

              5   use is very common throughout the United States

              6   even though none of the package inserts from the

              7   FDA talk about this dosage regimen.

              8             Prolonged or continuous infusion of

              9   beta-lactams is also used at various institutions,

             10   and again this is not always information that one

             11   can find in the FDA package.

             12             It has also been used to help establish

             13   more reliable susceptibility breakpoints.  For

             14   example, the NCCLS has used PK/PD to establish new

             15   breakpoints for the oral cephalosporins and oral

             16   penicillins, and then also it has been used for the

             17   parenteral cefotaxime and ceftriaxone for newer

             18   breakpoints for pneumococci.

             19             [Slide.]

             20             It has also been used for preventing the

             21   emergence of resistance, and I show here one of

             22   Jerry Schentag's group's study in which they look

                                                                            21

              1   at fluoroquinolones and found that if a value had

              2   an AUC to MIC ratio less than 100, resistance was

              3   very common when you look at the gram-negative

              4   organisms.  If that value was 100, it was

              5   significantly less, but I should point out on this

              6   slide that is you used a combination of drugs, it

              7   was even less, and I think that is what happens to

              8   most of us in clinical practice now with

              9   Pseudomonas is we actually drug combinations.  Very

             10   rarely do we use a single drug.

             11             [Slide.]

             12             It has also been useful for guideline

             13   development. For example, the CDC guidelines for

             14   pneumonia and otitis media clearly used PK/PD in

             15   coming up with those guidelines, and the Sinus and

             16   Allergy Health Partnership Guidelines for sinusitis

             17   also have a heavy input from PK/PD.

             18             With all the Pharm D.'s at hospitals now,

             19   and many of them with infectious disease training,

             20   what we are also finding, that PK/PD is being used

             21   for formulary decisions as to which drugs actually

             22   get on the formulary based a lot on their PK/PD.

                                                                            22

              1             [Slide.]

              2             But what we are here today to talk about

              3   is the application of PK/PD for new drug

              4   development, and clearly, where it has been

              5   applied, as was mentioned, is for dose selection

              6   for Phase II and III studies.

              7             What was also sort of given is the usual

              8   way to do that is from in vitro or animal studies

              9   to identify the PK/PD target for efficacy, and then

             10   to use your Phase I pharmacokinetic studies to

             11   determine which doses reach the target with a high

             12   probability.

             13             Now, this has been applied mostly to

             14   antibacterials, but we are starting to also see it

             15   now with antifungals.

             16             Now, in terms of the susceptibility

             17   breakpoint selection, clearly, this is required by

             18   NCCLS now by their M23 document.  It is clearly one

             19   of the four issues that the committee looks at for

             20   breakpoint selection, the others being the

             21   population distributions, the mechanism of

             22   resistance in the organism, and then, of course,

                                                                            23

              1   clinical results.

              2             As far as the FDA is concerned, sometimes

              3   it is used and sometimes it is not, so it seems

              4   that it is more variable at least from what I have

              5   been able to see as far as the FDA.

              6             [Slide.]

              7             What companies would like to do is to

              8   start doing more studies actually in the Phase II

              9   and the Phase III clinical trials.  The techniques

             10   are clearly there.  There is optimal sampling

             11   techniques, so we can reduce the number of blood

             12   samples that have to be done.

             13             There is interest in getting more frequent

             14   data, so time to events.  There is also statistical

             15   strategies to model both clinical and microbiologic

             16   outcomes.  Just in talking about those two types of

             17   outcomes, I said there the bacteriologic cure is

             18   harder to obtain.  What I meant to say is it is

             19   more conservative, it usually requires more drug to

             20   get a good microbiologic cure than it does to get a

             21   clinical cure.

             22             Again, even when you look at some of the

                                                                            24

              1   old data, as you see here, from Dr. Schentag's

              2   group and Alan Forest, you always find that

              3   clinical cure is higher than what one finds for

              4   microbiologic cure.

              5             In the studies that Dr. Drusano did with

              6   levofloxacin, the area under the curve to the MIC

              7   was about half of what it was for microbiologic

              8   cure to develop a clinical cure.  So, looking at

              9   microbiology is actually a more conservative

             10   approach.

             11             [Slide.]

             12             The problem with this, though, is doing

             13   these kind of trials increases the complexity of

             14   the trial, it also increases the cost, and at least

             15   as right now, there is no established benefit with

             16   regulatory agencies for doing it, so as people

             17   talked about yesterday, doing a better job upfront,

             18   with a small number of patients you would like to

             19   help, but that would enable you to reduce the

             20   number that you would like to have to use later on,

             21   and maybe something like that can happen in the

             22   future.

                                                                            25

              1             Clearly, PK/PD was used with the

              2   fluoroquinolone to reduce the number of cases for

              3   inclusion of penicillin-resistant pneumococci in

              4   the label.

              5             [Slide.]

              6             Now, what do we need and what kind of

              7   questions should we be asking when we are looking

              8   at PK/PD?  I think, first of all, you want to know

              9   what indices best determines the efficacy of the

             10   drug, and it really does require animal or in vitro

             11   studies, because you really do need to use a whole

             12   variety of different dosage regimens to reduce the

             13   interdependence between the parameters, because if

             14   you just use one dosage regimen, you will come up

             15   with, for example, Dr. Schentag's first paper on

             16   fluoroquinolones talked about time above MIC.

             17             His second paper on fluoroquinolones

             18   talked about area under the curve MIC.  George

             19   Drusano's paper on the same topic talked about peak

             20   to MIC, so you can pick any one of the parameters,

             21   they are all going to be correlated and you don't

             22   really know which is the correct one, so it really

                                                                            26

              1   does require in vitro or animal studies to select

              2   that out.

              3             What is the magnitude of the indices

              4   required for efficacy?  Again, as Dr. Andes will

              5   show to you later, free drug is really the

              6   important thing, protein binding does have its

              7   importance and does need to be considered, and the

              8   other thing I think we try to do, at least at our

              9   institution, in our work, is to try and link it

             10   also with survival.

             11             While we may be talk about a certain

             12   number of organisms that we want to kill or reduce,

             13   we try and also link that to some clinical outcome,

             14   which is going to be survival.

             15             You also want to know, since most of the

             16   time neutropenic animals have to be used in order

             17   to get the organism to grow, what effect does white

             18   cells have on the parameters because most patients

             19   that we treat are not going to be neutropenic?

             20             How does the magnitude vary with different

             21   organisms and especially this is the time of

             22   bringing in resistant strains to see if the

                                                                            27

              1   magnitude varies there.

              2             Does the magnitude vary with different

              3   sites of infection?  I think clearly, there is an

              4   area where we clearly can see some differences.

              5             [Slide.]

              6             For example, if we just measuring serum,

              7   there is probably a very good correlation with

              8   interstitial fluids and with fluid collection, such

              9   as sinusitis, an acute otitis media, but as we keep

             10   moving down the line, it will start getting to

             11   poorer and poorer correlation.

             12             Clearly with ELF, I think we have good

             13   data now even from human trials that there are

             14   higher values for macrolides and epithelial lining

             15   fluid, and decreased values for vancomycin and

             16   daptomycin, and I think just recently it has been

             17   found that daptomycin also binds to surfactant,

             18   which is another factor that would reduce the

             19   amount of drug and cause some problems for treating

             20   pneumonia.

             21             So, one needs to know this and just to

             22   point out one potential problem, I do not know

                                                                            28

              1   which animal model best has ELF levels that are

              2   similar to what we see in humans, and without that

              3   knowledge, we are sort of advising most of the

              4   companies that do those studies in humans to get

              5   those values until we can eventually find an animal

              6   model.

              7             I will tell you right now I do not think

              8   that it's the mouse because we find for macrolides,

              9   the same amount of drug works in the lung as work

             10   in the thigh model, so we don't see this markedly

             11   elevated level that are 10 to 15 times higher in

             12   human ELF fluids.

             13             [Slide.]

             14             Lastly, the last two questions again is

             15   does the magnitude of the PK/PD is required to

             16   prevent the emergence of resistance.  This is

             17   obviously becoming a more important question all

             18   the time.  Unfortunately, for some of the drugs,

             19   the parameters or the magnitudes that are required

             20   to prevent the emergence of resistance are so high

             21   that they are never going to be reached with the

             22   current doses that are being used, so combination

                                                                            29

              1   therapy is really what is going to be required.

              2             [Slide.]

              3             Lastly, one wants to know about the

              4   kinetics of the drug, can, with non-toxic doses of

              5   the drug in humans, reach the magnitude of the

              6   PK/PD index that is required for efficacy, and also

              7   for prevention of resistance with a high

              8   probability.

              9             It is becoming a challenge all the time

             10   because marketing has gotten into making some of

             11   the decisions on drugs as to how frequently they

             12   can be administered, and because of that, that is

             13   starting to put the challenge on PK/PD in being

             14   able to come up with an adequate dose.

             15             I will just give the old example of the

             16   old penicillins.  If you gave them four times a

             17   day, you wouldn't need as much drug.  On the other

             18   hand, nowadays, where we are trying to give the

             19   drugs once a day, at most twice a day, we have to

             20   increase the doses significantly in order to be

             21   able to reach the parameter that is important for

             22   efficacy.

                                                                            30

              1             So, with that, I will stop and turn it

              2   over to the next presentation.

              3             DR. EDWARDS:  Thank you very much, Bill.

              4             Next, we will call upon Mike Dudley from

              5   Diversa.

              6             Mike.

              7                       Industry Perspective

              8             DR. DUDLEY:  Thank you.  Good morning.  I

              9   would like to also thank John and John for the

             10   invitation to speak to you this morning about an

             11   industry perspective on this very important area.

             12             [Slide.]

             13             In the beginning, I want to pick up on

             14   some ideas that were raised yesterday before I

             15   really focus on PK/PD, and that is, that it was

             16   discussed yesterday about where are the new drugs

             17   going to come from and, in fact, a question was

             18   asked about what is the rate of submissions of

             19   INDs.

             20             What I wanted to show here, although this

             21   slide summarizes what happens in the discovery

             22   phases for a variety of targets, I think the

                                                                            31

              1   experience of certainly myself and other colleagues

              2   in the area would say that this is also the case

              3   for anti-infectives, as well.

              4             This is a slide drawn from a recent

              5   survey, so it is very unscientific, but I think it

              6   depicts for you what the problem is with respect to

              7   drug discovery overall and particularly I think it

              8   also describes a lot of experience of small

              9   companies and large companies alike in trying to

             10   find novel agents, as well.

             11             What you see here is that when one look

             12   then at the very early stages of discovery where

             13   one is trying to find novel targets, and then

             14   progress, though, then hits from high throughput

             15   screens or other methods, then, of those hits into

             16   a preclinical candidate stage, and then finally,

             17   from preclinical to IND, you can see that the

             18   highest dropout rate here, which range in some

             19   companies between 10 to 80 percent, and again that

             20   being target dependent or therapeutic area

             21   dependent, is around 57 percent of that attrition

             22   from taking the hit to preclinical candidate.

                                                                            32

              1             Overall, of course, it is 75 percent then

              2   taking things from target to IND.  The point is, is

              3   where the problem exists is trying to find good

              4   leads that are going to be drug-like, that can be

              5   brought forward.

              6             This is what I think is the real

              7   difficulty, then, for small companies and large

              8   companies alike to try to find good leads.  It is

              9   important and it is also risky business, and I

             10   think was mentioned yesterday, is that small pharma

             11   can't take this on by itself because of all the

             12   risks being up here in terms of getting a

             13   preclinical candidate, we need a partner to share

             14   the risk for that.

             15             So, because it is so difficult, then, to

             16   try to find drugs in the early setting, good leads

             17   to take forward into that, we really rely very

             18   heavily on the notion of being able to find good

             19   drugs and using PK/PD very early on as a means of

             20   trying to find dose selection.

             21             One can think about the idea as that one

             22   is trying to find the zip line that is going to get

                                                                            33

              1   you, then, from the early stages of drug discovery

              2   and into the clinic, and we believe that PK/PD is

              3   one of those tools that can allow this to occur.

              4             [Slide.]

              5             So, it really starts in the beginning of

              6   drug discovery.  It is an integral part now in many

              7   companies and the part of candidate selection in

              8   the drug discovery process, and certainly

              9   progresses through to the idea of selecting

             10   compounds for preclinical development.

             11             It enables programs to move forward and it

             12   rightly oftentimes kills the drug leads, as well.

             13             A critical step oftentimes in the

             14   discovery process is the in vivo proof of concept.

             15   As was being mentioned yesterday, if you are trying

             16   to raise money or you are trying to interest then a

             17   large pharma partner, everyone talks about a proof

             18   of concept study, but no one every really defines

             19   what the proof of concept study is.

             20             If you have a PK/PD as a tool, it is that

             21   tool that links the effects the one sees in vitro

             22   at a given concentration of the drug, on the bug,

                                                                            34

              1   on the large organism itself, to the in vivo

              2   exposure and the effect that happens in vivo at the

              3   same concentration of drug.

              4             So, a PK/PD proof of concept says that an

              5   effect that is associated with a drug in vitro can

              6   be translated if one gets the same concentration in

              7   vivo to an effect on the microorganism in vivo.

              8             Thus, as I think it was mentioned in

              9   John's opening comments, it is a translational

             10   science.  It takes us from very early stages, then,

             11   of drug discovery in the preclinical setting, and

             12   then, of course, through all the phases of drug

             13   research, but I think also particularly the

             14   opportunity in Phase IIA where one can identify

             15   these relationships for effects and validate and

             16   refine it all the way through the clinical

             17   development process.

             18             [Slide.]

             19             Now, one example then of how you can do

             20   this, and as Bill mentioned to you, is the use of

             21   in vitro models of infection where one can, in

             22   fact, start to study these issues before we even

                                                                            35

              1   know what the pharmacokinetic properties may

              2   actually be in an in vivo system, where one can

              3   then expose growing cultures of an organism, either

              4   a bacteria or a virus, to changing concentrations

              5   of drugs.

              6

              7             [Slide.]

              8             This is an example for a novel agent, in

              9   fact, before it has actually gone into man, where

             10   one can look at the effects here against MRSA,

             11   using a predictive pharmacokinetic profile based

             12   upon preclinical animal species and then one can

             13   begin to get insights in terms of both dose and

             14   dose frequency that is required then to get an

             15   antibacterial effect against target organisms.

             16             [Slide.]

             17             So what does drug industry then view as an

             18   important use of PK/PD in streamlining drug

             19   development? Well, it goes without saying for

             20   dosage regimens for clinical development, and the

             21   subsequent speakers are going to focus upon that,

             22   as well, Dr. Craig has spoken about in vitro

                                                                            36

              1   susceptibility in resistance breakpoints.

              2             This is, in fact, a very transparent

              3   process where one can then use scientific data and

              4   common criteria for then an effect that helps us

              5   then to determine how clinicians can use these

              6   drugs.  Breakpoints are used in the clinic to help

              7   define, then, what drugs are going to be used in an

              8   individual patient.

              9             One thing that I think that is needed is

             10   what about what I will call the care and feeding of

             11   these breakpoints, what happens then as resistance

             12   changes or as new data become available for old

             13   drugs about the existing breakpoints that are in

             14   the labeling?

             15             Presently, right now that task is taken up

             16   only by the NCCLS, which strives to harmonize what

             17   is happening in the regulatory environment, as well

             18   as within the clinic.

             19             Labeling for resistant organisms, which is

             20   what Dr. Craig talked about, as well, and we will

             21   hear more about that today, about PK/PD exposures

             22   that may be relevant from animal models, and we can

                                                                            37

              1   link that to human pharmacokinetics, and then, of

              2   course, I think, which was brought up in one of the

              3   other workshops before, is the idea of being able

              4   to provide PK/PD parameters or indices for

              5   organisms, such as the AUC to MIC, even though a

              6   full indication or a full, well-controlled clinical

              7   trial has not been made available with the proviso,

              8   of course, that these observations may not have

              9   been validated in clinical studies in patients.

             10             [Slide.]

             11             What I think is clear, though, is that

             12   these can be used in very, very useful

             13   relationships, and I have drawn from one example

             14   from Dr. Ambrose and colleagues where he went and

             15   pulled out information for pneumococci across

             16   several clinical trials for fluoroquinolones, and

             17   what you see is a very, very consistent picture in

             18   terms of free drug AUC-to-MIC ratio and the

             19   probability of eradication here in patients with

             20   lower respiratory tract infections involving

             21   Streptococcus pneumoniae.

             22             So, these relationships do work within the

                                                                            38

              1   clinic, and they can be used then to guide the

              2   development process, as well.

              3             [Slide.]

              4             What about pharmacokinetics?  I think as

              5   Dr. Lazor and Dr. Craig mentioned already, as well,

              6   is that we now have techniques for getting

              7   pharmacokinetics in clinical trials.  This should

              8   not be an excuse for not doing the proper

              9   experiments, so as one Bush's once said, "Read my

             10   lips," that we should be able to be able to do this

             11   through the techniques of sparse sampling,

             12   population pharmacokinetics and Monte Carlo

             13   simulation, and that can be done, not only taking

             14   into account, then, the concentrations in the

             15   dosing or serum compartments, but it also can be

             16   taken into account in specialized tissues as has

             17   been recently shown by the Albany Group, such as

             18   modeling the prostate.

             19             [Slide.]

             20             What about PK/PD in the response or the

             21   endpoint? First, I think it is very important

             22   especially in light of the discussion yesterday to

                                                                            39

              1   remember that these are definitely not surrogate

              2   markers.  Although this has oftentimes been used

              3   erroneously in the literature, PK/PD parameters or

              4   indices are not surrogate markers.  They are at

              5   least maybe two steps removed from a surrogate

              6   marker based on the discussion yesterday.

              7             But the analyses that are generated from

              8   this can help us to understand how to get to those

              9   endpoints, as well.  We need consensus on those

             10   relevant clinical endpoints and those markers to be

             11   able to really move the science forward.

             12             One issue may be, in fact, using validated

             13   composite endpoints in the clinic.  We know, for

             14   example, now that there are these endpoints that

             15   are used for making treatment and hospitalization

             16   decisions within patients, so what about using,

             17   then, composite endpoints for these patients?

             18             [Slide.]

             19             Finally, what about, then, getting more

             20   information from smaller trials or more focused

             21   studies, which I think were some of the themes that

             22   have been brought up already here, and this is just

                                                                            40

              1   one example, which is really the same as the

              2   oseltamivir studies that were described yesterday

              3   where it may be, in fact, the speed of response

              4   that one can see by taking serial measurements that

              5   may distinguish, then, both the dose, as well as

              6   the type of therapy that are used within individual

              7   patients.

              8             So, by getting information earlier and

              9   sequentially within individual patients, we may be

             10   able to define differences that are real and

             11   important between both dosage regimens, as well as

             12   agents in there, as well.

             13             That can have an enormous impact, then,

             14   upon the number of patients that may be required

             15   for us to be able to detect differences in a

             16   clinical trial.  From a paper that will be

             17   published short in Clinical Infectious Disease, one

             18   can see that, in fact, that for the sample sizes

             19   that may be required for looking for a

             20   time-to-event analysis, here being negative sinus

             21   cultures in patients who are then having serial

             22   measurement for recovery of bacteria in sinus

                                                                            41

              1   aspirates, one can certainly see that one can,

              2   using hours or time-to-event, one can have

              3   meaningful data in as few as 26 to 50 patients.

              4             [Slide.]

              5             Finally, what are, then, some of the

              6   provisions then that we can use for early market

              7   entry of new drugs or for infections due to

              8   priority-resistant organisms, which I think was

              9   certainly the context of the discussion yesterday,

             10   and I think that one thing that we would like to

             11   see is what can we build on these CFR fast-track

             12   provisions, can we use that now against the target

             13   pathogens on the priority lists that were discussed

             14   last year where one has full delineation of these

             15   relationships in animal and in vitro models of

             16   infection, then, using accepted endpoints or

             17   surrogate markers from well-designed and executed

             18   Phase II studies, then, to demonstrate then that we

             19   have efficacy at these target exposures, we may

             20   need to include comparators in there to ensure that

             21   we have got sensitivity, as well as the comparator

             22   regimens are optimized, and then to make agents

                                                                            42

              1   then available on a limited basis, much as what was

              2   done in the nineties with the HIV agents, and then

              3   build in the post-marketing phase, then, trials

              4   that really continue to build on this PK/PD zip

              5   line, if you will, and then also expanded then to

              6   include both susceptible organisms and resistance.

              7             I think it is important, of course, that

              8   safety does need to be demonstrated, and it does

              9   need to be demonstrated in comparative trials that

             10   are going to need to be taking place, but it all

             11   comes down to risk management. It all comes down to

             12   whether or not the risk you are willing to take

             13   with respect to the resistance that's at hand.

             14             [Slide.]

             15             So, to summarize, much is known about

             16   PK/PD of drugs very, very early on and prior to

             17   entry of man.  It isn't that we go into man and

             18   then try to figure this out, but it is oftentimes

             19   bred into the drugs that are moving forward.

             20             Streamlined evaluation, I think, of

             21   efficacy, as you will see, can be obtained from

             22   data-rich PK/PD Phase II trials, and then safety,

                                                                            43

              1   of course, is important and will ultimately,

              2   though, need to be determined in the comparative

              3   trials.

              4             Thank you.

              5             DR. EDWARDS:  Thank you very much, Mike.

              6             I will now call on John Powers for the FDA

              7   Perspective on PK/PD issues.

              8                         FDA Perspective

              9             DR. POWERS:  Thanks, Jack.

             10             [Slide.]

             11             What I would like to do is to give a

             12   little background on this information today, and

             13   one of the main messages that I want to get across

             14   is that we do feel that this information is useful.

             15             I know in talking with Dr. Craig a couple

             16   of times before, he has told me about uncertainty

             17   about does the Agency find this information useful,

             18   and we definitely want to get across that we do,

             19   but then to discuss some of the potential strengths

             20   and limitations of PK/PD and the overall drug

             21   development program, which I think the previous two

             22   speakers have already touched upon, and then talk

                                                                            44

              1   about some of these applications in clinical

              2   trials.

              3             What is PK/PD actually going to do for us

              4   in shrinking the overall size of the clinical

              5   development program or in being able to shrink the

              6   overall size of an individual trial, and then talk

              7   about some of the applications for prescription

              8   drug labeling or potential applications.

              9             [Slide.]

             10             So, we had previous discussions at this

             11   meeting in November of 2002, and also at various

             12   advisory committees about what is the role of PK/PD

             13   in clinical development programs, and then the IDSA

             14   sent a letter to Commissioner McClellan in November

             15   of 2003, and one of the suggestions on that letter

             16   was for FDA to find ways to incorporate PK/PD to

             17   shrink the size of clinical trials.

             18             So, again, we do find that this can be

             19   useful and, in fact, this is an integral part of

             20   the FDA's Critical Path Initiative is using PK/PD

             21   as one of the development tools.

             22             So, what are some of the things that PK/PD

                                                                            45

              1   can do for you?  Well, let's look at it from the

              2   other point of view.  If you don't use it, and you

              3   select the wrong dose, you can end up actually

              4   having a bigger clinical trials' database because

              5   the cure rate comes out lower, or even worse, your

              6   drug comes out ineffective in the clinical trials.

              7             That gets to the issue that John Lazor

              8   brought up, if your drug doesn't work, then, you

              9   have got to go back to square one, and that results

             10   in a multi-cycle review and that takes you a longer

             11   time to get your drug approved.

             12             I think one of the things, when we talk

             13   about clinical approval times that gets lost in

             14   that discussion, is two things can happen that can

             15   result in a multi-cycle review--well, three things.

             16             One, we have seen things that are just

             17   sort of technical problems in that the submission

             18   that comes in, we can't evaluate because it doesn't

             19   work in the computer or something.

             20             The second one is that the drug has

             21   efficacy issues, and we need to go back and study

             22   it more thoroughly, or the third one is that a

                                                                            46

              1   safety signal pops up that requires further

              2   exploration.

              3             The other thing is selection or the

              4   inappropriate dose, and I think we can't forget

              5   about this one.  It may impact your development

              6   program, but it is going to impact patients, too.

              7   We don't want to be selecting the wrong dose and

              8   have more people be failing from those diseases.

              9             So, again, failure to show efficacy may

             10   require further trials, but even if your drug does

             11   look better than placebo, which is the regulatory

             12   hurdle for approval, coming out with a lower

             13   success rate than your competitor doesn't help you

             14   in the marketplace either, so picking the proper

             15   dose to get the highest success rate will actually

             16   help your drug overall.

             17             Then, of course, picking the proper dose

             18   may limit dose-related adverse effects, as well,

             19   and it may give some clues--and we talked about

             20   this yesterday, I kind of tacked this on at the end

             21   this morning when we were talking about the

             22   endocarditis discussion--it may give you some clues

                                                                            47

              1   as to which indications you should study and which

              2   indications you should avoid.

              3             If you do some preclinical work and it

              4   looks like your drug isn't so good for Pseudomonas,

              5   hospital-acquired pneumonia is probably not

              6   something you should go after.  On the other hand,

              7   if it looks like you are good against something

              8   like E. coli, urinary tract infections, et cetera,

              9   it may be where you want to go.

             10             [Slide.]

             11             So, can PK/PD shrink the size of

             12   individual trials?  Well, if PK/PD, optimizing the

             13   dose results in a higher success rate for your drug

             14   in the trial, the answer to this question is yes.

             15             [Slide.]

             16             And I showed this slide yesterday again.

             17   So, if you can just increase the success rate by 10

             18   percent in your clinical trials, you can shrink the

             19   clinical trials' database from 252 patients per arm

             20   with a 10 percent non-inferiority margin to 142

             21   patients per arm.

             22             So, yes, it can help as long as you can

                                                                            48

              1   have some reliable impact on the success rate,

              2   clinical success rate in that trial.

              3             [Slide.]

              4             But the question then comes up is PK/PD

              5   sufficiently accurate to predict relatively small

              6   differences in success rates between drugs, is

              7   PK/PD best at predicting which drugs will be

              8   effective and which drugs will be ineffective

              9   rather than selecting differences between effective

             10   drugs?

             11             I remember somebody from a pharmaceutical

             12   company was sitting next to me once and said if

             13   drug X is so bad, how come we can't beat it.  So,

             14   their drug being better on the PK/PD parameters,

             15   yet, when you do the clinical trials, the drugs

             16   come out equivalent to each other.

             17             Now, is this just because you need to do a

             18   10,000 patient trial to show those small

             19   differences?  Then, you have got to ask yourself

             20   the question, if you have got to do a trial that

             21   big, are those differences clinically relevant at

             22   that point.

                                                                            49

              1             So, the reasons why may be do hosts and

              2   other effects predominate in affecting the clinical

              3   outcomes and those differences in the microbiologic

              4   effects get lost in the wash there.

              5             [Slide.]

              6             So, what are some of the other potential

              7   uses?  The previous speakers have touched upon

              8   this.  They can come up with preliminary

              9   information to come up with hypotheses for

             10   potential susceptibility breakpoints.  So, I used

             11   the word "preliminary" and the word "hypothesis" in

             12   the same sentence.  I have to correct Dr. Craig.

             13   We have used this. Al Sheldon, who is sitting out

             14   here, worked tremendously on this--well, we do look

             15   at them.

             16             Mike Dudley and I talked about this at

             17   ICAAC last year.  It gives us a hint that the drug

             18   ought to work, might work, and should work, but the

             19   level that we need to actually put this in a drug

             20   label is proven safe and effective.

             21             So, Mike and talked about ceftriaxone

             22   should work at an MIC of 16 for Strep pneumo, but

                                                                            50

              1   when we go and look at the databases, we don't see

              2   any organisms with an MIC that high, and then the

              3   question is--and John Bradley has brought this

              4   up--what do we use these breakpoints for?  I think

              5   there is a big distinction between what the

              6   Europeans use them for and what the Americans have

              7   used them for in the past.

              8             Are we just trying to separate out two

              9   populations, or are we trying to describe for

             10   clinicians which drug may be effective in what

             11   situation?  I would argue as a clinician it is the

             12   second one, and we know that clinicians use these

             13   drugs to say, well, if I got the big R on that lab

             14   sheet coming back, I am not going to use it.  So,

             15   it really has clinical implications for people.

             16             Now, after having said all that, we don't

             17   want to talk about that today, because that is

             18   going to require a big discussion.  We do think

             19   this is really important, but there is a lot of

             20   stakeholders in this--and I never realized this

             21   before either--not only is there the NCCLS, there

             22   is the device manufacturers who put together the

                                                                            51

              1   plates that actually get tested, and we need to get

              2   all those stakeholders together and talk about that

              3   at some time in the future.

              4             The next issue is the potential to prevent

              5   the development of resistance, but again we have to

              6   ask the question of what is the clinical effect of

              7   preventing development of resistance.

              8             There was a study back in the late

              9   eighties, I think, of ciprofloxacin versus imipenem

             10   in hospital-acquired pneumonia, where they showed

             11   that ciprofloxacin selective for fewer resistant

             12   pseudomonas in the sputum, but only one person got

             13   sick.  But that does mean that that is not an

             14   impact?

             15             Well, there is the question does it impact

             16   that person?  Does it impact other patients?  What

             17   we would need as an agency is that clinical data to

             18   show what does the prevention of resistance

             19   actually translate to in the clinical setting.

             20             Again, this is something we touched upon

             21   yesterday, and Dr. Ross brought this up, drug

             22   labeling for antimicrobials is either you are

                                                                            52

              1   treating a disease or preventing a disease, and we

              2   need that clinical information to show that.

              3             The other issue when you are talking about

              4   resistance is what do we care about here, so you

              5   can prevent resistance in pseudomonas when the

              6   person has a pseudomonal infection, but what

              7   happens to what I refer to as collateral organisms.

              8   I think about the governor of California's movie

              9   "Collateral Damage."

             10             So, what happens if I prevent the

             11   resistance to pseudomonas, do I then get resistance

             12   in some other organism as well because my drug

             13   isn't as good against the gram-positives, and now I

             14   select out resistance to Streptococcus pneumoniae

             15   or other commensal flora.

             16             So, when we talk about developing

             17   resistance, we need to look at that information.

             18   The idea of combination therapy certainly has a lot

             19   of play in the antifungal world at this point in

             20   time, and some of the discussions that have come up

             21   there is, well, if you use combination therapy at

             22   that point to try to increase efficacy to prevent

                                                                            53

              1   resistance, what happens on the toxicity side, are

              2   you going to cause more toxicity, and that balance,

              3   we actually need the clinical data to actually

              4   show.

              5             [Slide.]

              6             What are some of other limitations here?

              7   Well, typically, PK/PD and anti-infective drug

              8   development has focused on the effects on

              9   microbiological outcomes, and Mike Dudley already

             10   mentioned this, that in terms of a surrogate

             11   marker, we are a couple of steps removed from that

             12   even.

             13             So, in many situations, as we discussed

             14   yesterday, the validity of that microbiologic

             15   outcome as a surrogate for clinical outcomes

             16   remains unclear.  I like Mike's example of Dr.

             17   Ambrose's study in sinusitis.

             18             We think that is really important and

             19   using that in a Phase II proof of principle is

             20   great, but when we talked about this, is that going

             21   to garner you an approval all by itself, and the

             22   answer is no, because at this point, we don't what

                                                                            54

              1   eradicating the organism means in terms of the

              2   clinical outcomes in acute bacterial sinusitis.

              3             So, the other issue I think that is really

              4   important, that folks who don't work at the Agency

              5   don't realize, is a lot of times in these diseases,

              6   the microbiological outcomes are imputed from the

              7   clinical outcomes, so they look the same because

              8   they are the same.

              9             So, you have a person who has

             10   Streptococcus pneumoniae in their sputum at

             11   baseline in a pneumonia trial.  They come back

             12   after 10 days of treatment, on day 14, and they are

             13   clinically well, they are not coughing up any

             14   sputum, and they feel fine.

             15             The person checks off the box they are

             16   cured, and that goes down as "Presumed

             17   microbiological eradication" when we don't have

             18   that information.  Now, we can't get it obviously,

             19   the person is not making any sputum, but when

             20   people then come to us and say, well, there is this

             21   great correlation between micro and clinical

             22   outcomes, it is because you didn't have any micro

                                                                            55

              1   data at the end anyway.

              2             So, where we would really like to see that

              3   is places where we can get that information.  Some

              4   of the information I showed yesterday says you

              5   can't do this in some diseases like otitis media,

              6   where over 60 percent of the kids with these

              7   presumed eradicated, when actually the double tap

              8   studies are done, the bug is still there, so it

              9   turns out that "presumed" probably is incorrect in

             10   some situations.

             11             Obviously, we need to know if the effect

             12   on the organism translates into clinical outcomes.

             13             [Slide.]

             14             So, again, we have this question of can

             15   PK/PD differentiate.  It looks like it can

             16   differentiate ineffective drugs or doses, but can

             17   it differentiate between effective drugs or doses,

             18   and Dr. Craig brought this up, this issue of

             19   getting on formulary.

             20             So, how does a person on a P & T Committee

             21   look at this information and say, well, drug X has

             22   an 85 percent cure rate in community-acquired

                                                                            56

              1   pneumonia and drug Y has 84 percent cure rate?

              2   They look pretty much the same to me, but this

              3   guy's PK/PD looks better than that guy's.  What

              4   does that mean to me when I am going to decide

              5   about putting this drug on formulary?  Again, are

              6   there other factors that are more important?

              7             For instance, the mortality in severe

              8   community-acquired pneumonia remains at 30 percent

              9   despite the introduction of more active drugs in

             10   vitro.

             11             [Slide.]

             12             So, again, some of the other issues that

             13   may come up here that may dissociate the

             14   microbiological and clinical outcomes are things

             15   like pH at the site of infection, and one of the

             16   issues I hope we really touch upon today is what

             17   endpoint do we want to use in some of these PK/PD

             18   studies, is it static growth, is it 1 log decrease,

             19   is it 2 log decrease, what should we be using as

             20   that target.

             21             We talked a lot yesterday about direct

             22   immunologic effects of the drug on the host, and

                                                                            57

              1   immunologic effects on the host by the organisms.

              2             The other issue is does PK/PD give us

              3   enough information on non-dose related adverse

              4   effects, and all of this is just a prelude to

              5   saying that we still need clinical trials to

              6   determine the effects of the drug on clinical

              7   outcomes in a given disease entity.

              8             [Slide.]

              9             And why am I bringing this up?  Because

             10   several pharmaceutical companies have come to us

             11   with the suggestion that they should receive what

             12   they have termed "follow-on indications" based on

             13   PK/PD data alone.  So, what we presume they mean by

             14   this is we go out and we do a community-acquired

             15   pneumonia trial and then the FDA should grant us

             16   indications for sinusitis, otitis, and AECB based

             17   on our PK/PD information.

             18             Again, as Mike has pointed out, we can't

             19   even use these as surrogates yet at this point, so

             20   the point we are trying to make is we do find PK/PD

             21   useful, but we actually still need that clinical

             22   information in those trials, and we still need

                                                                            58

              1   clinical information from patients infected with

              2   resistant bacteria to be able to make that

              3   decision.

              4             The other thing is we have clearly seen

              5   differences across the safety and efficacy of drugs

              6   in various diseases.  Now, is this related to the

              7   population?  Maybe.  For instance, it is

              8   fascinating to look at the indication of acute

              9   bacterial sinusitis.

             10             Acute bacterial sinusitis, when you just

             11   look at the spread, is usually younger, healthier

             12   women, and we have seen a number of adverse events

             13   with various drugs pop up in that patient

             14   population more commonly.  Is that related to the

             15   disease or is it related to the host?

             16             Be that as it may, we definitely see

             17   different side effect profiles across different

             18   drug indications.

             19             [Slide.]

             20             So, the issue here is we still need

             21   clinical data from each indication, and at the

             22   March 2003 Anti-Infective Drugs Advisory Committee,

                                                                            59

              1   we did talk about this issue that Dr. Cox brought

              2   up yesterday, of clinical data from one indication

              3   supporting another, and Dr. Talbot brought up this

              4   idea of what does supportive actually mean.

              5             Well, supportive presumes there is still

              6   at least one trial in each indication since you

              7   have to have something there to support, and this

              8   also goes for the idea of resistant pathogens.  We

              9   have had several cases recently where folks came to

             10   us and said, well, here is my MRSAs in

             11   hospital-acquired pneumonia, and you should just

             12   give us an indication for MRSA community-acquired

             13   pneumonia, and then the question we ask is we still

             14   need to see that, first of all, that organism is

             15   relevant in that disease, so we want to see some

             16   cases, and we have asked for as little as 10 cases

             17   there.

             18             Then, the other issue is we do need to see

             19   there are differences across those diseases and

             20   across those hosts, how the drugs actually work

             21   there.

             22             [Slide.]

                                                                            60

              1             So, what about the issue of prescription

              2   drug labeling?  Dr. David Gilbert, who couldn't be

              3   with us this time, brought this up at the last

              4   November 2002 meeting, that the FDA should put

              5   PK/PD information in labeling.

              6             So, we went back and we thought about that

              7   some more, and the first question that came up was

              8   what information should we put in labeling, and

              9   then the second came up, why should we put it in

             10   labeling.

             11             So, first of all, clinicians don't have

             12   the information needed to make these PK/PD

             13   assessments in a lot of cases.  Cultures aren't

             14   commonly done in some diseases like uncomplicated

             15   UTI or even when clinicians make their best efforts

             16   like community-acquired pneumonia, we can't find

             17   the organism in about 50 percent of cases.

             18             Also, drug concentrations are rarely

             19   available for the clinician to make these kinds of

             20   decisions although we can make some guesses based

             21   on modeling, but which concentration is relevant,

             22   is it the concentration in the blood or the

                                                                            61

              1   concentration at the site of infection, which the

              2   clinician will almost never have in making that

              3   decision.

              4             Then, one of the other issues we have

              5   really hoped to get at today is, is there this "one

              6   size fits all" PK/PD parameter.  So, if I hit an

              7   AUC MIC over 100, does that just fit every

              8   gram-negative organism for every disease

              9   indication, or are there some differences across

             10   there?

             11             I can tell you that is what clinicians

             12   think. George brought up when I was the University

             13   of Maryland, and I remember standing there with a

             14   fellow attending who went to prescribe a quinolone

             15   at 2,000 milligrams for a person with Strep pneumo

             16   bacteremia, and said, oh, but it works better

             17   because it is concentration-dependent, so we will

             18   just jack up the dose.

             19             [Slide.]

             20             So, that gets us to a really important

             21   piece for us, as the FDA, is would PK/PD

             22   information in labeling imply a superiority claim

                                                                            62

              1   for one drug over another that has not been

              2   demonstrated in the clinical trial, and would PK/PD

              3   information spur clinicians to use a higher

              4   unstudied dose that may not be as safe in hopes of

              5   improved efficacy based on looking at that

              6   information in a label.

              7             The final thing for us, that question that

              8   came to our minds is how does this information

              9   actually help practicing clinicians to prescribe

             10   the drug appropriately in their patients, which is

             11   what our goal is when we put something into the

             12   label in the first place.

             13             [Slide.]

             14             So, our discussions today would focus on

             15   dose selection in clinical trials because we all

             16   agree that that is the place where we can really

             17   use this to streamline the development process, but

             18   this requires a discussion amongst all the parties

             19   here today, is what constitutes an adequate PK/PD

             20   database for a drug development program.

             21             We still do need to have discussions in

             22   the future about what are some of these issues

                                                                            63

              1   related to selecting breakpoints, and we do want to

              2   do that in the future, but that is going to require

              3   getting all the parties together and we will

              4   discuss that at a point in the future.

              5             So, I will stop there.  Thanks very much.

              6             DR. EDWARDS:  Thank you very much, John.

              7             I am just going to make one request to the

              8   future speakers, and that is, if we could just

              9   please not refer to the governor of California.

             10   This is a very sensitive issue, and I will allow

             11   you, John, but that's it for today.

             12             We actually have very few minutes for

             13   discussion of this topic at this particular

             14   interval.

             15             Would someone like to begin?  George.

             16                            Discussion

             17             DR. DRUSANO:  The single most important

             18   thing is the idea needs to be understood that PK/PD

             19   targets are fully stochastic, so that when somebody

             20   says it's an AUC to MIC of, or time above MIC of,

             21   fill in the blank, that that is a point estimate

             22   with a 95 percent confidence interval about it, and

                                                                            64

              1   one size does not fit all.

              2             One need only look at pneumococcus

              3   relative to gram-negative organisms, and not all

              4   gram-negative organisms are the same.  Now, we can

              5   make judgments, we can make conservative judgments

              6   because if you pick the one that is the highest,

              7   then, you will pick up all the ones that are the

              8   lower, that are lower than that value.

              9             But I think if we have a further

             10   discussion on this area today or on some other day,

             11   one of the real critical pieces about the use of

             12   PK/PD is to prevent its misuse, which is prevalent

             13   even now in terms of how this is getting used.

             14             The other issue that you really have to

             15   get to is what do the physicians do with it.  Most

             16   physicians really could care less about PK or PD

             17   except--I think all physicians are the same in one

             18   respect--they want their patients to get the best

             19   available therapy.

             20             So, one of the ways to do that is to, in

             21   the labeling practice, push it back a notch.  We

             22   have a tool, the Monte Carlo simulation and then

                                                                            65

              1   expectation over MIC distributions.  You can use

              2   that tool to back the PK/PD target information back

              3   into the labeled doses.  At this labeled dose, you

              4   can expect hitting this particular target a certain

              5   fraction of the time.

              6             You can have warnings that this does not

              7   necessarily impute that you are going to have a

              8   good clinical outcome.  It does impute that you are

              9   going to have this kind of microbiological effect

             10   and that there is uncertainty about it.

             11             Those are the kinds of things I think that

             12   you can do to roll it back a notch into a language

             13   that a good clinician is familiar with and can

             14   apply to his or her patient without the danger of

             15   saying I'm going to give 2 grams.

             16             DR. CRAIG:  I feel very strongly the same

             17   thing.  We use PK/PD at our hospital as a guideline

             18   for when combination therapy should be used.

             19   Again, we have already done the Monte Carlo and

             20   have that data, so we tell our clinicians if they

             21   are using a fluoroquinolone and the MIC is below a

             22   certain level, monotherapy would be okay, but if

                                                                            66

              1   the MIC gets high enough, you are not going to get

              2   an adequate PK/PD and drug combination should be

              3   used.

              4             So, there is information that you can

              5   glean from PK/PD that can be used clinically by

              6   clinicians.

              7             DR. EDWARDS:  John.

              8             DR. LAZOR:  George, I really appreciate

              9   your comment with respect to the PK/PD target, and

             10   it not being a single number.  Unfortunately, that

             11   is all we hear.  We always hear the single point

             12   determination, and we never know what the

             13   variability is around that target.

             14             Also, on your point with respect to

             15   labeling, even though we have put that information

             16   in the label, for instance, you know, you have X

             17   probability of achieving a target, I still don't

             18   understand what a physician would do with that.

             19             I can imagine that company A would have X

             20   probability, company B would have Y probability,

             21   and then somebody is going to interpret that as Y

             22   being better than X, of superiority.  That is one

                                                                            67

              1   of the things that needs discussion, because that

              2   is one of the things that you don't want to happen.

              3             DR. DRUSANO:  I have got 84, and he only

              4   has 82.  I mean that shouldn't happen.  That is not

              5   what it was supposed to do, and the problem comes

              6   is that, you know, we need to do the math a little

              7   better, so that we can actually get, not only a

              8   point estimate of what that target should be for a

              9   specific endpoint, but also its 95 percent

             10   confidence.

             11             Oftentimes we do this by classification

             12   and regression tree analysis, and because it is a

             13   recursive partitioning algorithm, it just chops

             14   things up, and you don't know whether it's here to

             15   here, or right in the middle, or anywhere in

             16   between.  All you know it is around here, and that

             17   is a message that we have been terribly remiss at

             18   getting out.

             19             DR. EDWARDS:  Dr. Rex.

             20             DR. REX:  I have a very small comment for

             21   Bill Craig, a different direction.  You put on

             22   several slides, the blanket statement that it is

                                                                            68

              1   always free drug.  That may not always be true.

              2             DR. CRAIG:  I would agree there are some

              3   situations where a drug may still have activity

              4   when bound to albumin, and those are going to be

              5   primarily sites where the drug does not have to get

              6   inside the cell, but I would say if the drug has to

              7   get inside the cell in order to reach its target,

              8   that free drug would be the important determinant

              9   of efficacy.

             10             DR. REX:  Well, I think that you need to

             11   entertain the possibility that it is not always

             12   albumin that things are bound to.  Things do pop

             13   off and on of proteins, and it may be that the

             14   available drug at the site of action is differently

             15   measured.

             16             The point I want to make is that you need

             17   to do a test in which you decide, you know, most

             18   crudely, is the MIC affected by proteins.

             19             DR. CRAIG:  That is what we do and, for

             20   example, I can tell you for a membrane drug like

             21   daptomycin, for a membrane drug like amphotericin

             22   B, protein binding is not as effective in reducing

                                                                            69

              1   activity, but, as I said, those drugs do not have

              2   to get inside the cell for their activity.

              3             For drugs that do have to get inside the

              4   cell for their activity, protein binding is

              5   important.

              6             DR. DRUSANO:  If I could just amplify on

              7   that just a little bit, the other issue where it

              8   isn't quite mathematical in that way, is when you

              9   have receptor that the drug has to bind to, that

             10   has about the same KD for the drug as it does for

             11   its binding site.

             12             I don't care whether it's alpha-1

             13   glycoprotein or albumin, or whatever it is bound

             14   to, the closer the KDs get, the more it is a crap

             15   shoot as to whether the drug is going to go that

             16   way or that way.

             17             Having said so, I think it is not always

             18   free drug, but if you look at 95 to 99 percent of

             19   the instances, it is free drug, and we define the

             20   likely effect by virtue of free drug.  There are

             21   exceptions, but you just have to be cognizant of

             22   where those exceptions are, but the general

                                                                            70

              1   principle remains true.

              2             DR. EDWARDS:  Jerry Schentag.

              3             DR. SCHENTAG:  Thank you.  I have to, of

              4   course, say something about those target things

              5   since everybody looks at me every time every says

              6   100, and I, first of all, want to say that I

              7   believe also that we should avoid abuse of this.

              8             I don't mean by that what some of you may

              9   have thought that that means George shouldn't use

             10   this technology.  What I mean is we need to--and

             11   John's challenge is appropriate to all of us--we

             12   need to link the PK/PD marker to both clinical and

             13   micro outcome, and not issue a value unless we have

             14   done that, and we need to do that in patients.

             15             I think it is really where we get into the

             16   most trouble in those of us that do it in the lab

             17   or in animals, we don't always have that clinical

             18   outcome linked to it.  I come from a field where

             19   either you kill the bug or the bug kills you, so I

             20   always thought that there was a 1 to 1 link between

             21   killing the organism and your target.

             22             I think that just because naively, I go

                                                                            71

              1   along trying to deal with nosocomial pneumonia on a

              2   regular basis, or bacteremia, and finding out that

              3   it works, and it is pretty much always the same

              4   number when I do that.

              5             That is probably wrong of me to have made

              6   that statement without more effective validation,

              7   and I stand in front of all of you and say that I

              8   think a commitment that we all ought to try do, and

              9   I am going to try to do that very soon, is to try

             10   to create a validation model for the PK/PD target

             11   that we use, and bring you that data.

             12             I would like to argue that that is

             13   something ISAP and IDSA could very well do together

             14   and involve the FDA in that process, too.  There is

             15   data that we could work together on to do that, and

             16   I would be pleased to help with that.

             17             DR. EDWARDS:  Thank you for those comments

             18   and it leads me to do something I am going to have

             19   to do here.  We are really at the end of the time

             20   we have available for discussing the details of

             21   this, and there are many people who have comments

             22   they would like to make, and I apologize right now

                                                                            72

              1   for having to move on.

              2             But I just wanted to call on George Talbot

              3   to ask a question he and I both have regarding the

              4   big picture within this area.

              5             George.

              6             DR. TALBOT:  Thanks, Jack.  I think that

              7   my comment is a natural follow-on from what Jerry

              8   was mentioning.  I was sitting here listening, with

              9   great interest actually, to the discussions about

             10   protein binding, and so forth, but my eye fell on

             11   the title of the session, which was Overview of Use

             12   of PK/PD in Streamlining Drug Development, and I

             13   guess the question or challenge I would pose is

             14   given that we are looking for ways to move forward

             15   to address this question, could we come up maybe

             16   with written comments to the Agency with two or

             17   three consensus action items, such as perhaps Jerry

             18   mentioned, for moving forward to determine how we

             19   could, in fact, streamline drug development using

             20   this other than just some of the points that John

             21   mentioned.

             22             What I am asking for is a focus on

                                                                            73

              1   answering the question and coming up with

              2   constructive ideas.  Perhaps FDA could suggest a

              3   format that would be useful to them.

              4             DR. POWERS:  I think one of the reasons we

              5   are doing the rest of this today is to try to get

              6   that information to come up with that.  I think,

              7   George, we keep that in mind while we are doing the

              8   rest of today's discussions, that that would be

              9   very useful.

             10             DR. TALBOT:  I guess what I see is,

             11   though, dose selection, dose selection, dose

             12   selection, and yes, I had hoped we could think a

             13   little bit outside the box, understanding very well

             14   your point that you need clinical efficacy and

             15   safety data to approve and label a drug, but the

             16   questions might come up, for example, as to

             17   whether--and I think you alluded to this--the

             18   number of clinical trials could be reduced.  You

             19   still would need clinical trials.

             20             So, again, rather than starting the day

             21   with a conclusion that things can't happen, I would

             22   just urge the group to focus on how could we make

                                                                            74

              1   something happen here. That is the philosophical

              2   point I had.

              3             DR. DRUSANO:  Could I ask John a quick

              4   question? This will be very quick.

              5             John, if you did a Phase II PK/PD bridging

              6   study in which you had two or three indications,

              7   and then you picked your right dose and you did

              8   your large traditional Phase III in each of those

              9   areas, could you use the PK/PD Phase II bridging

             10   trial as supportive information to hang up the

             11   tent, if you will, for one adequate and

             12   well-controlled trial per indication?

             13             DR. POWERS:  We have done that before and

             14   that is the suggestion we are trying to get to.

             15   Not only that, if you are studying various

             16   indications, those Phase III trials support each

             17   other, as well, so the whole thing hangs together.

             18   But I think one of the issues that we would like to

             19   get to today was the end of yesterday's discussion.

             20             If you are company that wants to go study

             21   endocarditis, what kind of Phase II trial can you

             22   do that would make people feel comfortable to go

                                                                            75

              1   forward there.  One of the things, that maybe Dr.

              2   Stanski can comment on that, he has been

              3   instrumental in helping develop this critical path,

              4   is the idea that what we see is a lot of companies

              5   skipping over Phase II, going from Phase I, and

              6   just going right to the Phase III.

              7             Then, when it doesn't work, what do you

              8   do?  You are stuck, and you come back and say

              9   doggone it, the FDA is taking so long to approve

             10   our drug, because you had to go back and do the

             11   trials all over again.

             12             So, I think that is what we are trying to

             13   do, we look at this--I think Dr. Craig sort of

             14   mentioned it--it is almost like an investment

             15   upfront, to do the right thing, so that when you

             16   get to Phase III, that you don't have to go back to

             17   square one and start over again.

             18             DR. DRUSANO:  And if you choose the right

             19   dose, and you have the highest possible clinical

             20   response rate, as you showed, the numbers of

             21   patients get smaller.

             22             DR. EDWARDS:  We will move on then and we

                                                                            76

              1   are now going to enter the area of the discussions

              2   on animal models to support dose selection.

              3             I will start with David Andes from the

              4   University of Wisconsin, who will begin the

              5   discussion from the academic perspective.

              6             David.

              7            II. In Vitro/Animal Models to Support Dose

              8                 Selection - Academic Perspective

              9             DR. ANDES:  I would like to first start by

             10   thanking the organizers for the invitation to speak

             11   today.

             12             [Slide.]

             13             What I will discuss this morning is how

             14   one can begin to use, looking at the relationship

             15   between a measure of drug exposure in animals,

             16   pharmacokinetics in animals, looking at the

             17   relationship between that, a measure of potency in

             18   vivo, in vitro, the MIC, and a variety of outcomes

             19   to aid in dose selection for clinical trials.

             20             [Slide.]

             21             As Bill Craig mentioned, there really are

             22   four primary questions that animal models can help

                                                                            77

              1   to address, that can help in dose selection.

              2             First, what is the pharmacokinetic

              3   parameter that drives efficacy, what PK

              4   characteristic do I need to optimize?

              5             From the standpoint, then, of dose

              6   selection, what PK/PD target or what magnitude of

              7   this parameter drives efficacy, how much drug do I

              8   need?

              9             What I will spend most of this morning on

             10   is discussing the variables that one might consider

             11   that may impact the amount of drug or the magnitude

             12   of the pharmacodynamic parameter that drives

             13   efficacy or leads one to some outcome.

             14             Then, most importantly, does any of this

             15   matter, are the predictions from animal

             16   pharmacodynamic studies predictive of what one can

             17   see in clinical trials?

             18             [Slide.]

             19             Although I will not spend any time at all

             20   talking about how one addresses the first question,

             21   certainly, animal models have been critical because

             22   of their ability to look at a wide variety of dose

                                                                            78

              1   levels and dose fractionation schedules in

              2   determining which pharmacodynamic parameter best

              3   drives efficacy.

              4             Here is one example looking at therapy

              5   with the beta-lactam ceftazidime in a Pseudomonas

              6   pharmacodynamic model, here the thigh-infection

              7   model, and one can clearly see that when looking a

              8   wide variety of dose levels and dose

              9   fractionations, that here, as we all know, time

             10   above MIC is the pharmacodynamic parameter that

             11   drives efficacy with the beta-lactams.

             12             [Slide.]

             13             But what I will spend most of this morning

             14   talking about is again what magnitude of that

             15   parameter or what pharmacodynamic target is one

             16   looking to achieve.

             17             [Slide.]

             18             Most importantly, what variable should be

             19   considered in looking or defining the magnitude of

             20   pharmacodynamic parameter that leads to efficacy,

             21   and these can include, although this list is not

             22   all-inclusive, do drugs within the same class

                                                                            79

              1   require the same pharmacodynamic target?

              2             Does the dosing regimen that one uses

              3   impact the amount of drug you need?  As we have

              4   already begun to discuss, does protein binding

              5   matter, should we consider free drug levels or

              6   total drug levels, does that impact the amount of

              7   drug you need?

              8             Does the site of infection or the animal

              9   model you use give you a different answer when you

             10   are looking at pharmacodynamic targets?

             11             Is the pharmacodynamic the target for all

             12   organisms, or does it vary from species to species,

             13   and within a species, does it vary when you are

             14   looking at different resistance mechanisms?

             15             Is the immune system important?  As Dr.

             16   Craig mentioned, we commonly look at neutropenic

             17   animals, but also look at normal animals, and what

             18   impact does that have on the amount of drug that

             19   one requires or is necessary for efficacy?

             20             Lastly, as John Powers mentioned, what

             21   treatment endpoint is important?  We often look at

             22   a variety of microbiologic outcomes, does this have

                                                                            80

              1   any relationship when we look clinically?

              2             [Slide.]

              3             Here is a dataset that begins to show how

              4   one can look at a variety of these factors.

              5   Firstoff, if you look on the lefthand side here, of

              6   this graph, we are looking here a microbiologic

              7   outcome in two animal infection animals.

              8             First, the traditional pharmacodynamic

              9   model, the thigh infection model, and the lung

             10   infection model here, a therapeutic model, and you

             11   can see here that despite the fact that we are

             12   looking at, in this case, two infection sites,

             13   outcomes seem to be the same.

             14             Here, we are also looking at a wide

             15   variety of drugs, all within the beta-lactam class,

             16   with carbapenems in red, penicillins in aqua, and

             17   cephalosporins in yellow, and you can see that

             18   despite the fact that we are looking at a variety

             19   of drugs, within these drug classes, the

             20   relationship is very strong.  The amount of drug or

             21   the PK/PD target in this case, the time above MIC,

             22   if one were to look for maximal efficacy, if one

                                                                            81

              1   looks at the red circles here, the carbapenems, you

              2   see maximal efficacy with times above MIC of 20 to

              3   40 percent among all of the drugs within the

              4   carbapenem class.

              5             [Slide.]

              6             Here is an example of a dataset that looks

              7   at the impact of protein binding, and as George

              8   Drusano mentioned, certainly, what I will

              9   demonstrate here is what we found for certainly, I

             10   would argue more than 95 percent of the case, here,

             11   we are looking at the impact of protein binding

             12   among 7 fluoroquinolones, and what we are looking

             13   at here is that microbiologic outcome in a

             14   pharmacodynamic model, the thigh infection model,

             15   and the endpoint we are looking at here is

             16   microbiologic endpoint, the amount of drug, or in

             17   this case, the 24-hour, AUC to MIC ratio, that was

             18   needed in this case to produce a static effect or a

             19   static dose.

             20             You can see here, looking across, the

             21   amount of drug necessary for each of these

             22   fluoroquinolones, looking at total drug levels,

                                                                            82

              1   they all look to require about the same amount of

              2   drug until you run into two of the drugs that have

              3   higher degrees of protein binding, in which case it

              4   would look as if you would need much more

              5   gemifloxacin or garenoxicin when considering just

              6   total drug levels.

              7             However, when you correct for protein

              8   binding, the same amount of drug is needed to

              9   achieve efficacy, in this case, the static dose.

             10             Again, I could also show you examples of

             11   where this doesn't fit, but those examples are few

             12   and far between.

             13             [Slide.]

             14             Here is an example of a dataset that

             15   addresses two additional variables.  First, it

             16   addresses the impact of the infecting species on

             17   the pharmacodynamic target.  You can see here data

             18   with the cephalosporins, penicillins, and

             19   carbapenems, in treatment again in a thigh

             20   infection model, the pharmacodynamic model, looking

             21   at the impact of treatment of gram-negative

             22   bacilli, pneumococci, and staphylococci.

                                                                            83

              1             You can see here that there are slight

              2   differences, so the infected species does matter

              3   although sometimes only very slightly.

              4             Here, also, you can see the impact of

              5   looking at different treatment endpoints.  On the

              6   left here, you can see the amount of drug or, in

              7   this case, the time above MIC needed to achieve a

              8   static effect, a net static effect versus the

              9   amount of drug or the time above MIC needed for

             10   maximal microbiologic efficacy, and you can see

             11   certainly there is a step-up when you look at these

             12   two different endpoints.

             13             [Slide.]

             14             Here is a set of data that looks at the

             15   impact or the variable of resistance within the

             16   organism.  Certainly, the target within these

             17   organisms is changing over time.  We are seeing a

             18   creep in MICs.

             19             Here is a dataset looking at therapy with

             20   two beta-lactams in the thigh infection model

             21   against pneumococcus, and organisms in this case

             22   had MICs varying roughly 100-fold, and the endpoint

                                                                            84

              1   here we are looking at, here again is microbiologic

              2   endpoint, in this case, the net static effect.

              3             You can see here that the amount of drug

              4   or the time above MIC that was necessary was not

              5   impacted by resistance in the organism in this

              6   case, with amoxicillin and cefpodoxime.

              7             [Slide.]

              8             Here is an example of looking more closely

              9   at the impact of infection site on the magnitude of

             10   the parameter needed for efficacy, here again with

             11   the beta-lactam amoxicillin looking at two

             12   infection models.

             13             Here again, our primary pharmacodynamic

             14   model, the thigh infection model, as well as the

             15   therapeutic model, the pneumonia model.  You can

             16   see here again, looking at microbiologic efficacy

             17   again, that the relationships are very similar at

             18   these two infection sites.

             19             Bill Craig mentioned that there are

             20   exceptions to this, and macrolides are one good

             21   exception where again if I were to show you this

             22   data for macrolides, they would also look very

                                                                            85

              1   similar, but we know very well that ELF levels in

              2   the mouse are not the same as ELF levels in

              3   patients, so one certainly needs to be careful and

              4   look at pharmacokinetics also at sites of infection

              5   in situations where the infecting pathogen is not

              6   just in the interstitial space.

              7             [Slide.]

              8             Now, what I have shown you primarily so

              9   far is data in the animal models using

             10   microbiologic endpoint.  We also look at a

             11   therapeutic endpoint looking at mortality. Here is

             12   data both from Bill Craig's laboratory, as well as

             13   data from the literature, looking at the impact of

             14   the target and mortality with the beta-lactams

             15   penicillins and cephalosporins, and this is data

             16   from three animal species and four sites of

             17   infection.

             18             One can see that the pharmacodynamic

             19   target, in this case, the time above MIC, that is

             20   needed for this therapeutic endpoint, in this case,

             21   survival in the animals is very similar to what one

             22   sees when one looks at microbiologic efficacy in

                                                                            86

              1   these animal models with times above MIC of 30, 40,

              2   50 percent needed for maximal survival with the

              3   beta lactams.

              4             [Slide.]

              5             Here is a closer look at the relationship

              6   between microbiologic outcomes in these animal

              7   infection models and a therapeutic outcome or

              8   mortality in these infection models, and I will

              9   direct your attention, first, to the graph on the

             10   right, where you can see here, looking at the

             11   relationship between the microbiologic endpoint,

             12   the static dose, and the therapeutic dose, in this

             13   case, 50 percent survival in the animals, and one

             14   can see a very strong relationship, and one can

             15   sort of dissect this relationship across a variety

             16   of microbiologic endpoints to the point where one

             17   can say I would expect maximal survival or 100

             18   percent survival with this microbiologic endpoint

             19   if you look on the left.

             20             We have looked at this with a variety of

             21   drug classes, a variety of infection sites, and a

             22   variety or organisms, and this has really sort of

                                                                            87

              1   held true.  What this data also suggests and looks

              2   at is the impact of treatment duration.  With the

              3   majority of our microbiologic studies, we look at

              4   24 to 48 hour endpoints.  With the survival data,

              5   we are looking at anywhere from 5 to 7 days.

              6             What we find is that for the majority of

              7   the situations that we have looked at, the

              8   treatment duration from 24 hours to 7 days, we find

              9   the same pharmacodynamic target.

             10             [Slide.]

             11             One thing you will find in the literature

             12   when you look at therapeutic endpoints, however, is

             13   variability in when one looks at mortality or

             14   survival in relationship to when therapy ends.

             15             We have traditionally looked at survival

             16   in these animals at the end of therapy, as

             17   represented by the yellow circles in this case.

             18   You will find in the literature again quite a bit

             19   of variation.

             20             Some people will look, at antibacterial

             21   therapy, 7 days or even 2 weeks after therapy has

             22   ended, and when you add that into the equation,

                                                                            88

              1   what you get then is a few organisms left behind

              2   regrowing in these animals and subsequently killing

              3   the animals, really limiting one's ability to

              4   derive a strong relationship.

              5             [Slide.]

              6             The last variable that I will discuss this

              7   morning is the impact of the immune system in the

              8   animals and the impact of the immune system on that

              9   pharmacodynamic target. The example I will use in

             10   this case is with the fluoroquinolones.

             11             Looking at the left, as one might expect,

             12   in healthier animals, in this case, animals that

             13   aren't neutropenic, one requires less drug than in

             14   animals that are neutropenic.

             15             I will tell you that the degree of this

             16   impact varies for drug classes, and it varies for

             17   organisms, as one can see in the righthand graph.

             18   Here again is looking at treatment with a variety

             19   of fluoroquinolones in both normal and neutropenic

             20   animals.

             21             On the lefthand side, you see treatment of

             22   pneumococcal infections.  Here, you see quite a

                                                                            89

              1   marked effect in the amount of drug necessary for

              2   the endpoint, in this case, the static dose in

              3   normal animals is roughly 10-fold less than the

              4   target needed in the treatment of neutropenic

              5   animals.

              6             However, if one moves go gram-negative

              7   infections, the impact of the immune system, in

              8   this case, neutropenia, is much less marked, 1- to

              9   2-fold.

             10             So, certainly, I think the immune system

             11   is important to consider because you can see

             12   differences that are organism and drug class

             13   related.

             14             [Slide.]

             15             Lastly, and most importantly, does any of

             16   this matter?  Bill Craig has shown the slide on the

             17   right, and many of you have seen this before, and

             18   this is one of about 10 datasets that are now out

             19   there, clinical datasets where one has either

             20   clinical or clinical and microbiologic endpoints

             21   that can be related to pharmacokinetics in the

             22   patients.

                                                                            90

              1             For each of these 10 clinical datasets

              2   that have been available, what we have seen in the

              3   animals has ended up being supportive or predictive

              4   of what one has seen in the clinical datasets,

              5   here, looking at the left, mortality in the

              6   animals, which we found really best correlates with

              7   what one sees in these clinical datasets, in this

              8   case with fluoroquinolones, looking at treatment of

              9   gram-negative bacilli and fluoroquinolones, and

             10   survival, where one sees, as Bill Craig pointed

             11   out, maximal survival in these animals when the

             12   fluoroquinolone 24-hour AUC to MIC reaches a

             13   magnitude of roughly 100.

             14             Again, as pointed out in the clinical

             15   dataset, one sees a similar relationship here, both

             16   clinically and microbiologically, and this is just

             17   one example, and one example of hopefully, many to

             18   come in clinical trials.

             19             I will end there and thank you for your

             20   attention.

             21             DR. EDWARDS:  Thank you very much, David,

             22   very nice summary.

                                                                            91

              1             I am now going to call on Lisa Benincosa

              2   from Pfizer to continue with the discussion.

              3                       Industry Perspective

              4             DR. BENINCOSA:  Good morning.

              5             [Slide.]

              6             I would like to begin with just a brief

              7   summary of the objectives of early drug

              8   development.  During our early drug development, we

              9   have two primary objectives.  We are interested to

             10   identify critical risk factors prior to investment

             11   in full clinical development, and in answering this

             12   question, we hope to select the most promising

             13   compounds to move forward.

             14             Some of the critical risk factors that we

             15   might identify at this point are the therapeutic

             16   index or anticipated therapeutic index, the dosing

             17   regiment is inadequate or the early identification

             18   of the need for controlled release development.

             19             In addition, we hope to provide critical

             20   data to identify safe and effective dose and dose

             21   regimens, and this, as we have discussed this

             22   morning, can lead to more efficient development. 

                                                                            92

              1   We hope to create optimal study designs, as well as

              2   efficient development strategies.

              3             If we think about the tool kit, as we have

              4   been discussing, we have animal models of human

              5   disease, we have development of biomarkers, we have

              6   pharmacodynamic modeling and clinical trial

              7   simulation.  So, all of this has advanced our

              8   sophistication in being able to deliver on these

              9   objectives.

             10             [Slide.]

             11             So, when we think about pharmacokinetic/

             12   pharmacodynamic modeling in drug development, it

             13   really is a continuum.  In 1993, Dr. Gerhard Levy

             14   published an article, The Case for Preclinical

             15   Pharmacodynamics, and really the premise is that

             16   the effective concentration or the concentration on

             17   the biophase will translate from animals to human,

             18   and that is a 1 to 1 translation.

             19             If you look at dose, for example, in this

             20   publication, the effective dose range varied

             21   10,000-fold, but in this survey, the effective

             22   concentration across animals and humans was at most

                                                                            93

              1   about a 2-fold variation.  So, that is the premise

              2   for preclinical pharmacodynamic research.

              3             [Slide.]

              4             So, we begin in experimental models of

              5   human disease, and we understand the

              6   exposure-response relationship.  We then move into

              7   healthy subjects, and we look to refine that model,

              8   that understanding, later moving into patient

              9   studies, looking at proof of concept.

             10             Now, the endpoint may change from

             11   biomarker to a clinical endpoint, and we validate

             12   the translation of that relationship between

             13   biomarker and our clinical endpoint, and later

             14   confirming the safety and efficacy in our pivotal

             15   studies.

             16             The red arrows to my left show the

             17   validation of this relationship, so as those data

             18   emerge, and we refine our understanding, the

             19   usefulness of these biomarkers in these models, the

             20   types of decisions we can make and how we can

             21   leverage that information will increase for

             22   compounds, may be the second in the class once that

                                                                            94

              1   validation with clinical data exists.  So, we are

              2   going to make decisions with much more confidence

              3   and certainty following the validation.

              4             Another point that I would like to make

              5   here is that when we think--Dr. Lazor mentioned

              6   this morning about safety, and truly another place

              7   for us to impact is on the dose selection and

              8   making sure that the dose is not too high.

              9             So, particularly in antibacterial

             10   research, our contribution in pharmacokinetic

             11   dynamic modeling will also be in understanding

             12   exposure response for safety and optimizing that

             13   dose and dose regimen, and thinking about the

             14   therapeutic index.

             15             [Slide.]

             16             Preclinical models.  Listed on the top

             17   here are the animal models that are used at Pfizer.

             18   You see various murine animal models, as well as

             19   gerbil otitis media model.

             20             The advantages of the preclinical

             21   pharmacodynamic research using these models is that

             22   we can explore in vivo exposure-reponse

                                                                            95

              1   relationship.  We can explore hypotheses, which may

              2   be very difficult to do in the clinical situation.

              3   We can assess pharmacodynamics a suboptimum doses

              4   and supra-therapeutic, allowing us to explore the

              5   full dose range.  Also, the assessment of tissue

              6   distribution.

              7             The challenges to use are to validate

              8   these animal models for the extrapolation of the

              9   results into the clinical setting.

             10             [Slide.]

             11             As Dr. Craig discussed this morning,

             12   around the global indices, these

             13   pharmacokinetic/pharmacodynamic indices, there are

             14   some limitations, so we have been exploring new

             15   approaches in addition to the global indices where

             16   we incorporate the time course using

             17   mechanism-based pharmacokinetic/pharmacodynamic

             18   models.

             19             When I say "mechanism-based," the models

             20   are consistent with the pharmacology of the drug

             21   and also incorporate both what would be a placebo

             22   response or disease progression, which really

                                                                            96

              1   characterizes the system, so you have your

              2   drug-specific parameters and the parameters of the

              3   modeling that characterize the system.

              4             [Slide.]

              5             The example that I would like to use to

              6   illustrate here, thinking about the time course,

              7   really the true pharmacodynamics, is to illustrate

              8   this through a study of azithromycin.

              9             [Slide.]

             10             This was published by Dr. Girard showing

             11   the results in a gerbil otitis media model,

             12   following azithromycin administered, the same total

             13   daily dose, a single dose versus the dose split in

             14   a 2-day or 3-day regimen.

             15             You can see the curve in blue, that the

             16   most rapid eradication occurred following one

             17   single dose rather than splitting this dose into

             18   different regimens.  So, the total daily dose

             19   stayed the same, area under the curve would be the

             20   same, but clearly, a difference in the performance

             21   of the single-dose regimen.

             22             [Slide.]

                                                                            97

              1             So, based on these results, the hypothesis

              2   was that front-loading, or giving one dose, appears

              3   to be more effective although the 1- and 3-day

              4   regimens were also effective.

              5             Now, the original study design, the data

              6   were not captured to allow for

              7   pharmacokinetic/pharmacodynamic modeling, so an

              8   additional series of experiments were designed to

              9   address this.  The goal was to develop a

             10   pharmacokinetic dynamic model for azithromycin to

             11   quantitate the effect of front-loading and to

             12   differentiate from 3-day and 5-day regimens.

             13             [Slide.]

             14             We used the gerbil otitis media model and

             15   studies were done to identify--and this is an

             16   important point--threshold doses, and I will

             17   discuss this again in just a minute, that would be

             18   around the ED50, which would allow us to make these

             19   comparisons and being in the most sensitive dose

             20   range.

             21             We had to what we are going to call

             22   humanize the pharmacokinetic profiles, basically to

                                                                            98

              1   take account with the differences in

              2   pharmacokinetics to manipulate the dose, so that

              3   the shape of the exposure over time would mimic

              4   what we would see in humans.

              5             Two strains were used and plasma

              6   concentrations were determined over 72 hours, so

              7   you see that we did capture the full time course,

              8   and a group of controls were included, as well.

              9             [Slide.]

             10             Just to stop a minute on the rationale for

             11   the dose selection.  The most informative region

             12   for us to work in is going to be between the ED20

             13   and ED50 in the steep portion of the dose-response

             14   curve.

             15             If the experiments are done, which

             16   typically we see, where all doses result in very

             17   good response, you can't identify the true

             18   differences if all doses were administered and

             19   maximal response was achieved, and the same for the

             20   lower end of the curve.

             21             [Slide.]

             22             This illustrates if we were to look at a

                                                                            99

              1   global pharmacodynamic measure, and shown here, if

              2   you just follow the blue circles, those are all

              3   from administering azithromycin as one single dose.

              4   You see that there appears to be a relationship for

              5   this regimen, but we don't have a universal

              6   description of the data because you see the 3- and

              7   5-day at a particular dose.  If you just select one

              8   dose, there certainly was suboptimal performance of

              9   the 3- and 5-day regimen.

             10             Now, in pharmacodynamics, we hope to

             11   understand, our drug should be characterized with

             12   an EC50 and an Emax. That is a drug-specific

             13   parameter and it is independent of dose regimen.

             14   So, you can see in looking at the data this way,

             15   you really could not explain all of the data

             16   simultaneous and really to understand the

             17   characteristics of these results.

             18             [Slide.]

             19             Now, I am just going to move briefly into

             20   the pharmacokinetic and pharmacodynamic results.

             21   This is just to illustrate to you, and you can see

             22   the little pulses on the dashed lines in gerbils,

                                                                           100

              1   that the way we emulated the shape of the

              2   concentration time course was to give--you know,

              3   azithromycin has a very, very long half-life, so

              4   you can see the pulse doses at 24, 48 hours to

              5   mimic that disposition.

              6             [Slide.]

              7             This is the pharmacodynamic model.  It is

              8   a very generalizable model, and it is shown here in

              9   its most generalizable form, but basically, it is

             10   to describe the time course of bacterial growth.

             11             This can be done using a mixture model of

             12   different subpopulations, if that is necessary.

             13   The model incorporates bacterial replication as a

             14   capacity-limited function.  The important point

             15   here, the first order constant for death.  For

             16   azithromycin in this case, the drug acted to

             17   stimulate that first order rate constant.

             18             Shown below is the equation, but I would

             19   just like to say that in thinking about this, what

             20   you can test and evaluate is whether your drug has

             21   effects on replication, enhancing the cell death.

             22             So, in this case, we were able to

                                                                           101

              1   characterize that drug-specific effect through what

              2   is shown here is the equivalent of an EC50 and an

              3   Emax.  This becomes important in the interpretation

              4   and guiding your dose regimen.

              5             [Slide.]

              6             This shows the pharmacokinetic results.

              7   You can see the green symbols of the observed data

              8   and the simulated profiles, which I showed

              9   previously.

             10             [Slide.]

             11             This shows the eradication for both the

             12   1-day and 3-day, and you see again, consistent with

             13   the results that Dr. Girard had presented, very

             14   rapid eradication following one large single dose.

             15             When we administered that over 3 days, you

             16   see there was eradication for a period of time,

             17   some regrowth, again eradication and regrowth.  So,

             18   very striking differences between the 1- and 3-day

             19   regimens.

             20             [Slide.]

             21             The real interest was in looking at the

             22   time course to understand the appropriate dose

                                                                           102

              1   regimen.  Shown on my left is just a repeat of the

              2   prior slide, and what I would like to focus on is

              3   on my righthand panel.

              4             Remember that I told you that we

              5   characterized the effects of azithromycin on this

              6   rate constant, so what you are looking at here, the

              7   red shows the eradication, but you are looking at

              8   the percent of baseline for this rate constant, so

              9   what we are able to understand here and delineate,

             10   these two different dose regimens, that

             11   azithromycin concentrations rose, and they were

             12   sufficient to shift this rate constant to its

             13   maximal value.

             14             It was sustained for a period of time.  If

             15   we just look right here, this is the very rapid

             16   eradication for 1 day, the 3-day, there is some

             17   eradication, and eradication again.  This line is

             18   that rate constant, so you see there was

             19   stimulation of the rate constant for a long enough

             20   duration of time to allow for complete eradication.

             21             Then, as the azithromycin concentrations

             22   declined in plasma, that effect on that rate

                                                                           103

              1   constant disappeared.

              2             [Slide.]

              3             So, in summary, the preclinical modeling

              4   using this animal model provided us a means to

              5   quantitate this effect of front-loading the dose of

              6   azithromycin.  There was more rapid and complete

              7   kill, and there was a concentration-related

              8   amplification of the bacterial death.

              9             This research allowed us to understand

             10   that having the highest AUC at the time of greatest

             11   bacterial counts would result in greatest kill

             12   possible, and optimizes the likelihood of positive

             13   clinical outcome.

             14             So, overall, the utility of preclinical

             15   pharmacokinetic dynamic modeling enables us to

             16   support the dose selection, and what is most

             17   important here is dose regimen really enabling us

             18   to optimize our dose regimen.  I don't want to

             19   underestimate our interest in understanding the

             20   time course of pharmacodynamics.

             21             We would propose that the best surrogate

             22   of efficacy should be identified in utilizing the

                                                                           104

              1   mechanism-based pharmacodynamic models, and that in

              2   some cases, in the question that you are asking,

              3   the global indices may not be optimal.

              4             [Slide.]

              5             In conclusion, preclinical PK/PD models

              6   can be used to support the overall clinical benefit

              7   of the proposed clinical dosing regimen.

              8             DR. EDWARDS:  Thank you very much, Lisa,

              9   for bringing out those very interesting and

             10   important points in this area.

             11             I am now going to call on the last speaker

             12   in this section, Chuck Bonapace from the FDA.

             13             Chuck.

             14                         FDA Perspective

             15             DR. BONAPACE:  Good morning.

             16             [Slide.]

             17             My objectives for this morning are to

             18   discuss the role of in vitro and animal models in

             19   drug development, to discuss the characteristics in

             20   animal models that should be stated in study

             21   reports submitted to the Agency, to discuss the

             22   endpoints of in vitro and animal models in relation

                                                                           105

              1   to Phase II and III studies, and finally, to

              2   discuss the limitations of therapeutic animal

              3   models.  I am going to discuss these terms a little

              4   bit later.

              5             [Slide.]

              6             I just want to begin with the overall use

              7   of in vitro and animal models in drug development.

              8   As we heard this morning, the primary purpose of

              9   this is to determine the principal PK/PD index.  By

             10   the "principal index," this is the index which is

             11   the best predictor of the outcome.

             12             The reason why this is common to do in in

             13   vitro and animal models is that we can easily

             14   assess a wide range of doses and dosing intervals.

             15   This is not always possible in clinical studies due

             16   to the limitations of dosing, we usually see a

             17   single dose, and the ethical considerations, the

             18   ramifications of a subtherapeutic dose.

             19             We have heard already this morning about

             20   the common PK/PD indices, and these consist of the

             21   24-hour AUC to MIC ratio, Cmax to the MIC ratio,

             22   and the time above MIC.

                                                                           106

              1             The definitions of these indices are not

              2   standardized, and we would encourage other indices

              3   to be considered with future submissions.

              4             Consideration of the principal PK/PD index

              5   and the magnitude of that index should be used when

              6   determining the appropriate regimen to be evaluated

              7   in Phase II and III studies.

              8             We haven't heard this morning of any data

              9   from in vitro models.  We have been told that

             10   sponsors perform these models early in drug

             11   development, but the data is not submitted.  We

             12   would encourage submission of in vitro model data

             13   in addition to animal model data with submissions.

             14             This would be used to support the regimens

             15   for Phase II and III studies.  The information

             16   obtained from in vitro and animal models has the

             17   potential to increase the probability of a

             18   successful outcome in clinical studies.

             19             One of the things we want to avoid is

             20   unsuccessful outcomes.  It is a probability, it

             21   does not guarantee that using the dose that is

             22   likely to increase the probability of a successful

                                                                           107

              1   outcome will have a successful outcome.

              2             The next few slides discuss the

              3   considerations of using in vitro and animal models.

              4   We have plenty of this, this morning, with protein

              5   binding.  I just want to state that it is not

              6   always taken into consideration.

              7             It is common reported as total

              8   concentrations, sometimes it is not even stated

              9   whether they are total of unbound.  Our advice is

             10   to report total and unbound and clearly state which

             11   one is which.

             12             The considerations of initial inocula,

             13   depending on the initial inocula used in in vitro

             14   and animal models can determine the outcome with

             15   similar exposures from drugs. We would ask that

             16   sponsors clearly state the initial inocula used and

             17   justification for why they chose that initial

             18   inocula.

             19             The pre-treatment interval, this is the

             20   interval between the inoculation and the initiation

             21   of drug therapy. It ranged from an immediate, which

             22   is sort of a prophylaxis, to several hours.  Again,

                                                                           108

              1   it should be clearly stated and any justification

              2   of why one was chosen and may be relation to an

              3   established infection in human be stated in study

              4   reports.

              5             [Slide.]

              6             Duration of experiment.  It is a variable.

              7   It is usually about 24 hours.  Longer experiments

              8   have been shown to reveal different outcomes.

              9   Again, we would ask that this is clearly stated in

             10   submissions and justification provided for the

             11   rationale of the determination of the duration of

             12   the experiment.  It may be limited to the

             13   practicalities of the experiment.

             14             Surface area-volume ratio is something

             15   that is only used with in vitro models,

             16   hollow-fiber models are very common.  There are

             17   other in vitro models that are used.  We would ask

             18   that the sponsors state the surface area-volume

             19   ratio and any justification because the surface

             20   area-volume ratio can impact the concentration of

             21   the time profile in the peripheral compartment if

             22   it is a two-compartment model.

                                                                           109

              1             One benefit of in vitro models over animal

              2   models is that the half-life is easily altered, so

              3   it is relatively easy to mimic human or animal

              4   pharmacokinetics or half-life, and again, this is

              5   something that we would ask to see in study

              6   reports.

              7             [Slide.]

              8             The next few slides go over the

              9   determination of the principal index.  This usually

             10   involves a large number of doses and dosing

             11   intervals known as dose fractionation. The degree

             12   of fractionation is not consistent between studies.

             13   We have seen studies that only study long intervals

             14   in a sense.

             15             Other designs that have the same rationale

             16   are acceptable.  We just saw one where we are

             17   looking at the most relevant portion of a curve

             18   from the ED20 to an 80, and then usually, those

             19   doses are fractionated, so you are not studying

             20   unnecessary doses in a sense.

             21             Consideration should be given to the

             22   dosing frequency with concentration-dependent

                                                                           110

              1   drugs.  We know that overextending the interval can

              2   lead to different results especially with

              3   concentration-dependent drugs.

              4             We have seen submissions where there is a

              5   possibility that this is what happened, but again,

              6   we don't know early on.

              7             [Slide.]

              8             Many of these models will normally assess

              9   an ATCC strain for select gram-positive and

             10   gram-negative organisms. It is not to say that this

             11   is not representative of a clinical isolate, but it

             12   may not represent the organisms likely to be

             13   encountered in clinical infections.

             14             What I mean by that is we have seen

             15   submissions in which an animal model was performed

             16   with an organism, such as Streptococcus pneumoniae,

             17   and this is used to predict outcome in complicated

             18   skin and skin structure.

             19             So, the organisms that are studied should

             20   be relevant to the indication that it is likely to

             21   be supporting.

             22             Because a single isolate is used to

                                                                           111

              1   represent a genus and species, we commonly do not

              2   see a range in MIC values, and because of this, the

              3   range and the effect from an in vitro and animal

              4   model is based on altering the dose and the dosing

              5   interval, and not on the MIC, and thus, we don't

              6   get information for the impact of the MIC on the

              7   principal PK/PD index, so this is usually not

              8   assessed.

              9             Sometimes we do see clinical isolates

             10   studied in addition to an ATCC strain, many times

             11   we don't.

             12             [Slide.]

             13             The determination of the PK/PD index is

             14   usually based on the coefficient of determination

             15   of a sigmoid Emax analysis.  It sounds simple.

             16   This is not always presented, and many times the

             17   method that was used to determine the principal

             18   index is not stated, so we can't confirm what was

             19   the basis of that.

             20             When results from in vitro and animal

             21   models are submitted, we would like to see the

             22   results in agreement, so that they are supporting

                                                                           112

              1   that in vitro and animal models are given the same

              2   conclusion, the same principal index.

              3             Ideally, and I emphasize the word

              4   "ideally," results should be confirmed for more

              5   than one preclinical study.  An example of this may

              6   be submission of results from an in vitro and an

              7   animal model, animal model studies performed by the

              8   same investigator, so we confirm one study with

              9   another, or animal model studies from more than one

             10   investigator, so again we have some confidence

             11   that, in fact, the principal index has been

             12   confirmed in more than one study.

             13             [Slide.]

             14             My next several slides talk about the

             15   magnitude of the principal index.  The magnitude of

             16   the principal PK/PD index is dependent upon the

             17   endpoint chosen.  It will depend whether it is a

             18   static of 1 log, a 2 log, or Emax effect.

             19             The endpoint essentially should depend on

             20   the endpoint that is associated with the clinical

             21   outcome.  Now, this is not always known early on,

             22   especially if this is the first drug in a new

                                                                           113

              1   class, but for drugs and which it is a new class,

              2   and there are other drugs on the market, there

              3   should be some information to suggest what outcome,

              4   what endpoint is associated with clinical outcome.

              5             Also, the impact of immune function is not

              6   always addressed in animal models.  We don't always

              7   see neutropenic and immunocompetent animals.  We

              8   would like to see neutropenic and immunocompetent

              9   animals, also, the effect on the immune system in

             10   animals, and how does the neutropenic and

             11   immunocompetent animals relate to clinical

             12   infections.

             13             For instance, hospital-acquired infection

             14   versus community-acquired infections, where

             15   patients with hospital-acquired infections may be

             16   more immunocompromised than those who are

             17   relatively healthy patients in the general

             18   population in the community.

             19             [Slide.]

             20             Again, the degree of protein binding.  I

             21   know I have said this already, but this has a big

             22   impact on the magnitude of the principal index. 

                                                                           114

              1   So, again, they should clearly state whether it is

              2   based on total and/or unbound, and we would

              3   certainly like to see submissions using both, if

              4   not unbound only.

              5             [Slide.]

              6             How is this information used to determine

              7   dosage regimens in Phase II and III studies?  Well,

              8   consideration of the target population should be

              9   used, and whether this is again going to be a

             10   community-acquired infection drug or nosocomial

             11   infection drug.

             12             Again, it brings into the immune status of

             13   the patients.  We are encouraging dose-ranging

             14   studies at least in Phase II.  We have heard some

             15   other speakers mention this, this morning.  These

             16   are examples of ways doses could be determined in

             17   Phase II studies, but it depends on the

             18   indications, the patient population, and so forth,

             19   so isn't something that I am saying to be used in

             20   every one.

             21             One example would be to use an endpoint of

             22   like a 2-log kill for the highest dose studied, and

                                                                           115

              1   then an endpoint, such as 1-log or a static

              2   endpoint for a lower dose.  So, essentially, you

              3   are going to have a dose-ranging based upon what

              4   might work.

              5             Another way to do this is to base it on

              6   the percentage of the population that is achieving

              7   an endpoint. So, if an endpoint has been shown that

              8   it correlates or is associated with clinical

              9   outcome, the high dose would be a dose that 100

             10   percent of the patients who will receive that drug

             11   will achieve that target, and than a lower

             12   percentage.

             13             In this case, I have 80 percent, but it

             14   could be any number would achieve that target would

             15   be a lower dose.

             16             [Slide.]

             17             I just want to talk a few minutes about

             18   animal therapeutic models.  These are a little bit

             19   different than a PK/PD model in the sense that they

             20   may, if it's certainly not a valid model, but a

             21   model that may describe a little better what is

             22   going in clinical infections, may give some insight

                                                                           116

              1   into infections in humans, and there are various

              2   models.

              3             Three of them, which I have up here, is

              4   like an endocarditis, pneumonia, and a meningitis

              5   model.  A well-designed animal therapeutic model

              6   can provide information for clinical trials.  It

              7   may give a little insight on the potential efficacy

              8   based on drug concentrations at the site of

              9   infection assuming the concentrations in the animal

             10   and human are similar, and provide insight for the

             11   dosage regimens to be evaluated in clinical trials.

             12             Dose-ranging PK/PD studies performed in an

             13   animal therapeutic model may provide additional

             14   information to support clinical efficacy.  We

             15   commonly do not see this. Many of the animal

             16   therapeutic models that are submitted contain one

             17   or two doses.  We do not see dose-ranging studies

             18   and many of them do not even have the

             19   pharmacokinetics in the model, so we can't do a

             20   PK/PD analysis.

             21             The utility of this will depend on the

             22   applicability to a clinical setting.  The

                                                                           117

              1   difference between treatment of an established

              2   infection in humans versus prophylaxis, so if the

              3   drug is administered almost immediately after the

              4   infection is initiated, difference in

              5   pharmacokinetics and tissue penetration between

              6   animals and humans, and also the outcome may be

              7   dependent upon the animal species.  We may see

              8   outcomes in different animals.

              9             [Slide.]

             10             Some limitations with animal therapeutic

             11   models, the efficacy may be dependent upon various

             12   factors, the virulence of the bacteria, the growth

             13   phase that the bacteria is in, the initial

             14   inoculum, the time to initiation of treatment, host

             15   immune reponse, animal pharmacokinetics, protein

             16   binding difference between humans and animals.

             17             Drug concentrations at the site of

             18   infection again may differ between animals and

             19   humans.  So, there are a lot of differences between

             20   human infections and animal therapeutic models.

             21             The predictability of outcome in animals,

             22   which is usually based on a microbiologic endpoint

                                                                           118

              1   or survival endpoint, to humans, which is generally

              2   a clinical endpoint, is not always known.

              3             One thing I haven't talked on is the

              4   toxicity of the drug, and this is important because

              5   the toxicity of the drug may differ between humans

              6   and animals, or between different species of

              7   animals.

              8             So, it may be important when you are doing

              9   a dose-ranging study, and the dose is toxic, and

             10   the efficacy you see may be--for instance, if

             11   outcome was survival--it may be related to the

             12   drug, and not the lack of efficacy.

             13             [Slide.]

             14             In conclusion, in vitro, animal models can

             15   serve as the foundation upon which anti-infective

             16   drug development should be based, and we emphasize

             17   this.  This is where we really see this information

             18   being used, as the foundation of drug development.

             19             In vitro, animal models represent an

             20   important tool for determining the principal index.

             21   They may be used to identify dosage regimens for

             22   evaluation in Phase II and III studies, and

                                                                           119

              1   ideally, the results from clinical studies should

              2   be used to confirm the in vitro and animal model

              3   endpoint associated with efficacy.

              4             It sounds pretty simple, but this is

              5   something that we usually do not see.

              6             [Slide.]

              7             Closing the loop is a phrase that we have

              8   coined for this.  Essentially, what this means is

              9   using the information determined by in vitro and

             10   animal models, combine with pharmacokinetics from

             11   Phase I to predict regimens, which are studied in

             12   Phase II, and then the clinical efficacy, which is

             13   determined in Phase III, that information should be

             14   fed back to sort of confirm the original

             15   hypothesis.

             16             Although this sounds simple, we rarely see

             17   this. So, one of the things we need to advance the

             18   whole field or area is sort of a confirmation of

             19   the results from Phase II and Phase III and how

             20   this relates in PK/PD to the original hypothesis.

             21             One of the problems with doing this is

             22   that PK/PD is usually not done in Phase II and III,

                                                                           120

              1   and actually is fairly common to see plasma

              2   concentrations even obtained in Phase II and Phase

              3   III.  So, many times we do not have the tools or

              4   answers we need to confirm the original hypothesis.

              5             Thank you for your time.

              6             DR. EDWARDS:  Thank you very much, Chuck.

              7             At this point, we are going to take a

              8   15-minute break and then we are going to have a

              9   relatively large block of time for discussion

             10   before lunch.  We will come back in 15 minutes.

             11             Thank you.

             12             [Break.]

             13                            Discussion

             14             DR. EDWARDS:  We have 30 minutes now

             15   approximately for the discussion of this very

             16   complicated, complex area.  I might just make a

             17   couple of comments before we start.

             18             I think what we would like to do with this

             19   discussion is to make it in some ways more general

             20   rather than highly specific in terms of small

             21   points that have to do with PK/PD indices and

             22   interpretation.

                                                                           121

              1             I think what we have heard is one thing,

              2   one concept, is that the PK/PD issues in some ways

              3   have been laid down over many years.  The

              4   fundamental principles are there, but the execution

              5   is an issue that Chuck clearly and explicitly laid

              6   out for us.

              7             So, in many ways, a lot of the tools are

              8   available, but the utilization is not at the

              9   optimum level at the present time.  I think that is

             10   a fairly fair summary of what Chuck was telling us.

             11             I think one of the issues that would be

             12   highly desirable to perhaps focus much of this

             13   discussion on is getting back to the central

             14   question of how can the PK/PD be used to shorten

             15   trial time, and I would like to start this part of

             16   the discussion refocusing on the issue that we

             17   spoke about for quite a long time yesterday and

             18   have touched on very briefly today, and that is the

             19   use of the PK/PD for approval for endocarditis.

             20             John, let me ask you to do two things.

             21   One is to tell us what you would like to get from

             22   this discussion, where you would like to see the

                                                                           122

              1   precious few minutes we have for this huge area,

              2   which, you know, has been the whole focus point of

              3   a task force in the past, and tell us that and let

              4   us get focused in the direction you would like us

              5   to go, understanding that we are interested in the

              6   primary issue of how can we devise perhaps, not

              7   only specific issues, but also a process to get

              8   closer to the goal of being able to use PK/PD to

              9   shorten trial time.

             10             DR. POWERS:  I think what we are going to

             11   do is this afternoon's sessions, the two of them

             12   are what are we doing now and then the second

             13   session this afternoon will be what can we do

             14   better, so I think we will have more opportunity to

             15   discuss that this afternoon.

             16             I guess the issues that come up here, if

             17   we can use sort of the endocarditis template as an

             18   example, and that is where we ended yesterday and

             19   George Talbot brought up I would feel uncomfortable

             20   in giving this drug to somebody without it having

             21   been used clinically, and George Drusano brought up

             22   wait a minute, is there a way we can look at this

                                                                           123

              1   in Phase II that would give us some comfort.

              2             I would ask the people in industry that

              3   are here of what their view on all of this is,

              4   because as I said earlier, what we see is some

              5   people skipping from Phase I directly to Phase III.

              6   We implied that what that means, and we don't know

              7   this for sure, is that industry is seeing Phase II

              8   as slowing them down and that since it is important

              9   to get their drug to market as quickly as possible,

             10   that perhaps they are not seeing any benefits of

             11   this Phase II.

             12             So, I would ask both people around the

             13   table and in the audience and industry to say do

             14   you find this useful, is this something your

             15   management is going to say, look, forget it, this

             16   Phase II stuff is taking too long, and is it help

             17   for, as an agency, to try to do this and say, look,

             18   this is how we can find it to be helpful in those

             19   terms.

             20             Again, let's go back to the endocarditis

             21   example, is there some in vitro, hollow-fiber, and

             22   animal model that would give people some comfort

                                                                           124

              1   that they could roll in an endocarditis study into

              2   their Phase III development program rather than

              3   have to do all your other, you know, complicated

              4   skin, hospital-acquired pneumonia first and then go

              5   back and do an endocarditis study, which may not be

              6   as palatable to some people in terms of prolonging

              7   their development process.

              8             DR. EDWARDS:  Bill.

              9             DR. CRAIG:  We have gone back and looked

             10   at all the data in the literature on animal models

             11   for endocarditis to see if we could model anything

             12   of such a paper is published.

             13             The problem that you have is these

             14   one-dosage regimens, so whatever parameter you want

             15   to pick up will show correlation because of their

             16   interrelationship, but at least for the

             17   fluoroquinolones, it appears, in terms of

             18   magnitude, to be somewhat similar to what we see

             19   for gram-negative organisms in the thigh.

             20             The problem again is that there is

             21   different sampling times.  Some people look after

             22   day one, some look at day three, some look at day

                                                                           125

              1   seven.  There is a whole variety of different

              2   sampling times, so it is not consistent and it

              3   makes it much harder to look at, but it is an area

              4   in which if there was better design, one could look

              5   at, because oftentimes what is done is one dose is

              6   looked at, and that is all you get, not a

              7   dose-response or anything.

              8             These models are expensive and ethically,

              9   you know, you can only use so many animals, so you

             10   can't oftentimes get as much as you want from

             11   endocarditis models as you can get from a thigh

             12   model.

             13             DR. EDWARDS:  George.

             14             DR. TALBOT:  Thank you, John.  I think it

             15   would be helpful to go in that direction, to focus

             16   some questions.  I view this really as related to

             17   the FDA's wonderful document on the critical path,

             18   which makes a point that innovation is stagnating,

             19   the costs and difficulties are going up, and what

             20   is needed is really translational research or

             21   applied research to move forward from the

             22   technology base that exists.

                                                                           126

              1             So, just to expand a little bit, the ideas

              2   you mentioned, it seemed to me that some of the

              3   specific concrete questions that could be asked

              4   could be the following.  Should, in fact, Phase II

              5   studies be more robust as opposed to less, would

              6   that, in the end, save a lot of time?

              7             If you collected more dose-response and

              8   exposure-response data in Phase II, and I would

              9   add, not just efficacy, but also safety, realizing

             10   that sample size and numbers of adverse events in

             11   small trials may limit the power of your analyses,

             12   but shouldn't safety be more a part of this whole

             13   PK/PD question both in Phase II and Phase III.

             14             Another concrete question we could ask is

             15   what degree and precision of characterization of

             16   both efficacy and safety PK/PD relationships in

             17   Phase II, based on the animal data that Bill and

             18   others create, what degree of characterization

             19   would allow, for example, a single trial for

             20   indication, and how would that apply, if was a

             21   within-class, that is, another fluoroquinolone

             22   versus a new class. Chuck sort of mentioned that

                                                                           127

              1   point.

              2             And if you could do a single trial based

              3   on a robust Phase II program and in a well-defined

              4   animal in vitro database, what type of trial would

              5   that have to be? You know, would it be a trial of a

              6   standard size, such as you would do when you are

              7   doing two, well-controlled, statistically adequate

              8   trials per indication, or would it have to be more

              9   robust, allowing for more sensitivity analyses?

             10             I would hope that some points like that

             11   could be discussed, because that would take us back

             12   to where we started yesterday, which is how to make

             13   this easier, how to reinvigorate the process, how

             14   to keep those people who are in, in, and move

             15   forward.

             16             DR. EDWARDS:  Yes.

             17             DR. EISENSTEIN:  John, you asked from an

             18   industrial point of view, and given that Cubist is

             19   in the midst of an endocarditis study, I thought I

             20   could make a few comments.

             21             Maybe as an overarching point, it seems

             22   that what we are all talking about here is a

                                                                           128

              1   balance between human health, which is obviously

              2   the primary point, that could actually then be

              3   subdivided into several issues.  One of them is the

              4   ethics for the individual patient.  The second is

              5   the larger public health issues, and clearly, the

              6   Agency should take a lead role in thinking in that

              7   regard.

              8             There is the science that we are thinking

              9   about that allows us to come up with predictive

             10   models.  Then, as a point that the companies have

             11   to deal with, as well as all of those others,

             12   particularly as you have well pointed out in

             13   dealing with upper management or the business

             14   aspects.

             15             It seems to me that what we are really

             16   trying to describe today is a way to manage risk,

             17   and risk management can be looked at in the various

             18   concepts of the individual patient.  We want to be

             19   as ethical as possible.  We also want to manage

             20   risk at the public health level, so that we are

             21   capturing as much value as we possibly can.

             22             From the business standpoint, one is

                                                                           129

              1   dealing with a variety of various issues. It all in

              2   a way boils down to return on investment, which

              3   means cost, time, likelihood of success, and

              4   likelihood of success is another way of saying

              5   managing risk, so risk can be viewed in that

              6   regard, as well.

              7             Now, insofar as our own indication and

              8   experience with that indication with endocarditis

              9   and daptomycin development, we have significant

             10   background information that we think has enabled us

             11   to move appropriately into the study of this very

             12   difficult-to-treat disease.

             13             In terms of Phase III studies, clearly, we

             14   already have an indication with two pivotal complex

             15   skin, complicated skin and skin structure infection

             16   studies, as well as significant safety data

             17   including pulmonary data, as well as a lot of other

             18   data at a higher dose.

             19             From the preclinical animal model data,

             20   there is significant PK/PD data specifically

             21   looking at various organisms and various models of

             22   endocarditis at various dosing intervals, and then

                                                                           130

              1   from the Phase II analysis, we not only have the

              2   old Lilly bacteremia and endocarditis data, but

              3   Cubist's own bacteremia data.

              4             It is with that combination that we feel

              5   that we can move appropriately into this very tough

              6   indication. Let's remind ourselves that there

              7   hasn't been a registration trial for endocarditis

              8   in 20 years.  We feel that if one is going to work,

              9   that this is the more likely one to do so, but only

             10   in the background of the significant amount of

             11   scientific and medical and ethical and business

             12   considerations.

             13             DR. POWERS:  I hadn't thought about it

             14   that way, Barry, but maybe one way to look at this

             15   process is the traditional and perhaps the best way

             16   to manage risk about determining whether a drug is

             17   safe and effective is in Phase III randomized,

             18   controlled trials.

             19             What we are hearing is that is also a

             20   difficult way to manage risk, so what we are trying

             21   to do is say if we only have one Phase III

             22   randomized, controlled trial, is there some of this

                                                                           131

              1   other information that we can then use to give us a

              2   better level of certainty about whether that drug

              3   is safe and effective or not.

              4             The concept of using other information

              5   isn't new, and the Code of Federal Regulations

              6   actually specifies that in certain situations, you

              7   can accept one clinical trial plus supportive

              8   information, and part of what we are trying to get

              9   at today is okay, if we don't want to do two big

             10   Phase III clinical trials, what is the level of

             11   that information that is done earlier in the

             12   clinical development program that would give us

             13   that level of certainty, maybe not equal to what we

             14   would get in a Phase III randomized trial.

             15             George, one of the points you bring up is

             16   making Phase II trials more robust, and one of the

             17   things I can think of right off the bat is putting

             18   controls in those, that uncontrolled Phase II

             19   trials, would you rather see a 100-patient

             20   uncontrolled trial or a controlled trial with 50

             21   and 50, which one of those would actually be able

             22   to give you more information.

                                                                           132

              1             Jack, Mike Dudley has had his hand up for

              2   about a half an hour.  I don't think you can see

              3   him beyond the podium.

              4             DR. EDWARDS:  I am sorry, Mike.

              5             DR. DUDLEY:  Let me go back to the

              6   endocarditis question, as well, because I think

              7   there is two points that are relevant I think

              8   towards bridging with the animal model and the

              9   observations in humans, and then I think also to

             10   address, I think, what George exhorted us to do

             11   earlier about an action item.

             12             One is I completely agree with the idea of

             13   controls.  The trial that George Drusano mentioned

             14   yesterday, that involves cefonicid in right-sided

             15   endocarditis where there was failures, was done at

             16   an institution with a lot of experience on treating

             17   right-sided endocarditis in San Francisco General

             18   Hospital.

             19             They know what the response is in patients

             20   who are getting adequate antibiotic therapy with

             21   clearance of cultures and defervescence of fever.

             22   In that study, those patients did not respond

                                                                           133

              1   within 4 days.  When they were immediately switched

              2   on to nafsone [ph], immediately responded in all of

              3   the cases.

              4             I do know that in industry, it is

              5   oftentimes, particularly in thinking about

              6   staphylococcal disease, that in a clinical trial,

              7   in a Phase II trial where you might have a

              8   comparative group like that, where you can look for

              9   those, is that one can get a very quick read across

             10   dose and across patients with respect to response.

             11             These patients, as you know, have

             12   staphylococcal bacteremia for several weeks before

             13   they finally present to be treated, so one is able

             14   to assure adequate safety in those individuals that

             15   if you are on a failing regimen, you can switch on

             16   to something else.

             17             So, I do think that there are examples

             18   where you can go into those diseases and

             19   particularly with good animal model data, which in

             20   that case, it actually did work in the bunnies

             21   because the drug was only 92 percent bound in

             22   rabbits, so therefore, they saw efficacy.

                                                                           134

              1             With respect to the other point of

              2   preclinical models and looking at, we have had

              3   experience with an anti-MRSA cephalosporin whereas

              4   if you go into the rabbit model of endocarditis and

              5   you dose it, so that you are 25 percent time above

              6   MIC, which according to Bill's slide is still

              7   within the--for anti-MRSA cefs or beta-lactams for

              8   staphylococci, is an effective regimen within the

              9   thigh, you do get efficacy in the rabbit model.

             10             You get about a 3-log drop in the MRSA in

             11   the rabbit over the 4-day treatment period starting

             12   from a CFU per gram of vegetation of 10
                                                                                                8.  However,

             13   if you go to 33 percent time above MIC, you can get

             14   a 6-log drop in those organisms with 6 out of 8 of

             15   the rabbits, if I remember, having sterile

             16   vegetations by 4 days.

             17             So, what I am saying is it depends on what

             18   the endpoint is, and what we have to do in the

             19   animal models now is decide what is the endpoint in

             20   the animal model that corresponds to the outcome

             21   within patients.

             22             So, whether you want to take the clinical

                                                                           135

              1   endpoint or whether you want to take the

              2   microbiologic endpoint, we do have to close Chuck's

              3   loop.

              4             We have to go back and look and say for an

              5   acceptable clinical outcome or microbiologic

              6   outcome within patients, what does that correspond

              7   to in terms of exposure, and then what does that

              8   exposure then go back into the animal model to get,

              9   is it the static dose, is it a 2-log drop, or is it

             10   a 3-log drop.

             11             We have the information now out there.

             12   You can go back as best you can from the

             13   literature, but there are better studies now,

             14   particularly in the thigh model, where we could go

             15   back now for fluoroquinolones and say is it a 2-log

             16   drop, is it a 3-log drop that we need to get in the

             17   models to do that.

             18             Azoles are an example where the clinically

             19   effective dose of fluconazole corresponds to just a

             20   1-log drop in the CFU in the kidneys of a mouse in

             21   that model, but yet we can really force the dose up

             22   and get a 5-log drop if we really want to.

                                                                           136

              1             So, calibrating those animal models to the

              2   outcomes that are seen in patients would be a

              3   helpful way to make them at least one step closer

              4   as being a surrogate marker.

              5             DR. POWERS:  But what we see, on the other

              6   hand, is the target has become everything.  So, in

              7   other words, a company starts off and says, well, I

              8   need to get the AUC over MIC of 40, so I will go

              9   back and I will pick the endpoint that gets me

             10   there rather than doing it the other way around of

             11   saying I am going to pick an endpoint of 2-log

             12   kill, what target do I need to get to 2-log kill.

             13             The literature says 40, I am going to go

             14   back, and I didn't make it for 2-log kill for 40,

             15   so I am going to change it to static.

             16             I think that is one of the issues that we

             17   are getting at today, is what are the ways in which

             18   we can tell what the endpoint ought to be.

             19             DR. EDWARDS:  George.

             20             DR. DRUSANO:  I would like to kind of get

             21   back to George Talbot's issue about robust Phase

             22   II's, and I am going to, after lunch, show you some

                                                                           137

              1   bridging studies where the same drug was used

              2   preclinically and also in a clinical circumstance

              3   with significant correlation.  However, let me put

              4   this out there.

              5             Let's do some war gaming.  Suppose a

              6   sponsor goes and does a robust Phase II trial.

              7   Sponsors are different, so some sponsor might want

              8   to see an 80 percent target attainment right with

              9   the dose that they take, some 85, some 90, some 95.

             10   Maybe it is dependent on whether it is your mom or

             11   your mother-in-law that is going to get treated.

             12             But at the end of the day, suppose that

             13   they actually do pick a dose that is going to give

             14   you a 90 or 95 percent target attainment rate for

             15   whatever you choose.

             16             You then take that into your robust Phase

             17   II trial, and everybody dose well.  Now, what is

             18   the inference from both the regulatory perspective

             19   and from the company perspective that should be

             20   drawn from such an outcome?

             21             Now, I have my own ideas, but I would

             22   certainly like to hear some of the sponsors around

                                                                           138

              1   this table say something, and I would certainly

              2   like to hear the FDA response and to how they would

              3   respond to such a clinical trial, because if you

              4   are getting 95 percent responses, it is not that

              5   you won't get a relationship, but the likelihood

              6   decreases as your obvious response rate goes up.

              7             So, what would you guys do with that?

              8             DR. POWERS:  I am not sure I understand

              9   the question, George.

             10             DR. DRUSANO:  Okay.  Let's back 15 yards.

             11             DR. POWERS:  So, you have got a cure rate

             12   of 95 percent, and we are supposed to be unhappy

             13   about that?

             14             DR. DRUSANO:  The point is, you know, we

             15   say we want to validate.  If you say, you know, you

             16   go to the animal data, right, and you want to see a

             17   pharmacodynamic relationship in Phase II, but you

             18   have chosen the dose correctly, is that okay?  I

             19   mean if you get that 95 percent response rate, but

             20   you don't get a pharmacodynamic relationship, does

             21   that count as a validation of the preclinical data?

             22             DR. POWERS:  But one of the other things

                                                                           139

              1   you might get out of that is what was the safety

              2   between those different doses, as well.  So, again,

              3   we can't forget there is more than just the

              4   efficacy piece.

              5             DR. DRUSANO:  Oh, absolutely, but as I

              6   think somebody else pointed out, if you are going

              7   to do 50 to 100 patients, and you have 50 of them

              8   on controls, your probability of getting a safety

              9   relationship approaches zero from the left.

             10             I mean we did 272 patients in the first

             11   prospective pharmacodynamic clinical trial

             12   published in the literature with the prospective

             13   analysis plan registered with the FDA, for levo,

             14   published in JAMA, and we saw zero relationship

             15   amongst 272 patients with good PK, and their

             16   toxicity outcomes.

             17             DR. POWERS:  Part of the issue about the

             18   critical path is I mean there are opposite

             19   examples. Five patients that got the drug in a

             20   Phase I trial, 2 of them become hypotensive.  So,

             21   the question is part of the critical path is

             22   selecting out the drugs you don't want to move

                                                                           140

              1   forward with, as well, so even though you don't

              2   answer every safety question, you can pick out the

              3   drugs that are highly likely to have significant

              4   safety problems, and do you want to go forward with

              5   those, or do you want to cull them out at that

              6   point.

              7             If you guys got the response, would you

              8   say that validated the preclinical data?

              9             DR. ZHENG:  I think it is difficult.  You

             10   can qualitate to say the drug works or not, but I

             11   think what we want is to verify that quantitative

             12   relationship.

             13             DR. DRUSANO:  Then, you need to have some

             14   failures, and therefore, then, you are not going to

             15   pick necessarily the right dose, you are going to

             16   have a wrong dose in order to get some failures.

             17             DR. ZHENG:  That is why I think Phase II

             18   study is important.  If you designed a Phase II

             19   trial very well, I think if the approach is so

             20   robust, you should be able to get some relationship

             21   from Phase II study.

             22             That is why I think we recommend

                                                                           141

              1   dose-ranging study in Phase II, because usually

              2   Phase III is a fixed dose and very often, as George

              3   said, because the response rate is so high, we

              4   couldn't identify that relationship.  We do have

              5   many, many examples.

              6             DR. EDWARDS:  John.

              7             DR. REX:  I want to inject two notes of

              8   caution. The first one is something that I was

              9   taught many years ago, which is that any dummy can

             10   measure an MIC.  The trick is to measure the right

             11   MIC, and everything we are talking about here today

             12   has MICs as part of the equation, but also has

             13   hidden in it this other choice, which is what is it

             14   that you are measuring in your PK/PD model, and we

             15   have had several discussions on that.

             16             I just want to make that point again, and

             17   I will make it really clearly.  I can only make it

             18   about MICs in particular, because you give me any

             19   bug and any drug, and tell me what MIC you want, I

             20   can measure that number for you, and I can make the

             21   MCI vary by 3 orders of magnitude by changing the

             22   way that I do the assay, 3 orders of magnitude. No

                                                                           142

              1   sweat.

              2             I am sure you can also do that with PK/PD

              3   parameter measurements, because MIC is built into

              4   that, ergo, I know I can vary the denominator by 3

              5   orders of magnitude, so surely I can fiddle around

              6   with the numerator, as well.

              7             So, keep that in mind when you are

              8   thinking about using this to drive what you are

              9   doing, and the whole thing has to come back to your

             10   clinical response.

             11             Let me reflect on what happened with

             12   fluconazole, a drug where we actually really have

             13   real human PK/PD observations.  We have them in

             14   esophageal candidiasis where we had the misfortune

             15   for those individuals of having high MIC isolates.

             16   We had the fortune of a variety of doses and blood

             17   levels.  We were able to establish a pretty strong

             18   Cardoso response curve for fluconazole in human

             19   beings at a time when we did not have animal data

             20   to support that idea.

             21             Then, David Andes came along and showed in

             22   an animal model that actually, what you might have

                                                                           143

              1   guessed was the breakpoint, turned out to actually

              2   be the breakpoint in his animals, and that actually

              3   then happened to validate the human scenario for

              4   invasive disease, not just esophageal disease, but

              5   invasive disease.

              6             So, all of it came together, but the

              7   closing of the loop was really a sloppy mess, and

              8   we never really closed the loop for the disease of

              9   greatest interest, which is invasive candidiasis.

             10             To this day, I can't tell you what the

             11   minimum effective dose of fluconazole is, nor do I

             12   care, I am not interested in playing a game of

             13   limbo with fluconazole in treating these patients.

             14             The other thing is that everybody in this

             15   room would handle the drug differently, so to find

             16   the minimum effective dose, I can find the minimum

             17   effective dose for George, okay, or the minimum

             18   effective dose of George, I am not sure which it

             19   is, but that doesn't mean that that is the correct

             20   dose for the next person with all of their various

             21   modifications.

             22             So, when we get into this issue of target

                                                                           144

              1   attainment rates and variabilities, you know, I

              2   think that we have to inject a great note of

              3   caution because we run the risk of making a whole

              4   series of very conservative assumptions, and

              5   getting to the end of the day saying, well, what

              6   you really need is 14 grams a day of the compound

              7   in order to guarantee a 90 percent target rate for

              8   all of the lefthanded redheads who have a variable

              9   modification of their handling of the drug.

             10             You have to be very careful about that

             11   because then that just drops out all the other

             12   possibilities for sub-MIC effects, there are just a

             13   whole host of other things that get buried in that,

             14   so that is my note of caution here. A lot of these

             15   numbers are very slippery, and do not get too hung

             16   up on 80 percent or 85 percent or 89 and

             17   three-quarter percent attainment rates.  I think

             18   that is a dangerous slippery slope.

             19             DR. EDWARDS:  Yes, please.

             20             DR. GRASELA:  I would like to make two

             21   points.  The first is I think as we go forward with

             22   the evolution of PK/PD information, it is going to

                                                                           145

              1   be really hard to validate things.  I think the

              2   comment that we are making is that if you were to

              3   take a quinolone into development right now, you

              4   could work a quinolone up really well, and there is

              5   such concordance between the in vitro animal and

              6   clinical data that you could take all the factors

              7   into consideration, protein binding, tissue

              8   distribution, and you could zero in with Monte

              9   Carlo simulation, zero in on what the dose is or

             10   the dose that is most likely to be the most

             11   effective, and if you don't have any toxicity

             12   limiting you on the other side, you could push the

             13   dose to the point where everybody who is going to

             14   get better, is going to get better.

             15             I turn that back around to the utility of

             16   a robust Phase II study, because I have an example

             17   in my presentation, but it took us pooling two,

             18   Phase III clinical trials, pooling the comparator

             19   quinolone, we had to throw it in there, as well, to

             20   get 68 well-studied cases of Strep pneumo.

             21             If you are looking for failures, you have

             22   to have large numbers, so again the more that we

                                                                           146

              1   learn, the harder this is going to become unless

              2   you are really going out into totally new targets

              3   where we don't know anything and we are starting

              4   all over again.

              5             Then, I would say yes, use the tools as

              6   much as we can, design a Phase II clinical trial

              7   that gives you the answer, but for some of the

              8   well-trod paths, this is going to be incredibly

              9   difficult, hence, George's question to you, if we

             10   developed a Phase II study and we had 95 percent

             11   response rate, would you take it and let us move

             12   forward to Phase III.

             13             DR. POWERS:  I think, Dennis, and one of

             14   the things maybe Chuck can talk about this, when we

             15   have looked at this internally, you point out

             16   quinolones, give us another example, that's about

             17   the only place where we have seen this

             18   characterization done in a way that would be

             19   useful.

             20             Part of this issue is we do want to try to

             21   see new targets, right, that is one of the goals

             22   here is to try to come up with new antimicrobials,

                                                                           147

              1   so we want to be able to utilize that information.

              2             The validation part, going backwards, is

              3   helpful for further indications for that drug if

              4   you want to study that, but let me give the example

              5   of what we have seen, where you see a Phase III

              6   trial, and the efficacy doesn't look so hot

              7   relative to the comparator.

              8             I walk over to the Biopharm people and say

              9   how did they pick this dose, because it looks to me

             10   like they didn't shoot for it, and everybody just

             11   shrugs their shoulders.  You know, you see Phil

             12   scratching his head going, you know, how did they

             13   come up with this.

             14             That is the position we don't want to be

             15   in.  There has got to be some rationale for going

             16   forward.

             17             The other issue here is, we keep

             18   acting--and John is pointing out some of the holes

             19   in the science here--as if we know everything there

             20   is to know about this and let's just call it a day.

             21   There are still some things we have to work out

             22   here about how useful this can be to us.

                                                                           148

              1             DR. GRASELA:  Let me just put one more

              2   point on the table.  The first is which of the

              3   furry individuals was John?  I couldn't get those

              4   on those two slides--I mean Phil--which one of

              5   those furry slides was John?

              6             The second question is how much, if

              7   discovery is going to come from the smaller

              8   companies, and that is something that was talked

              9   about before, and we talk about the body of

             10   knowledge and risk management, how much data from

             11   the compounds that fail will you allow for the

             12   support of future compounds?

             13             For instance, we have three or four

             14   possible leads.  You pick the best lead, the best

             15   biopharmaceutics' properties, et cetera, and work

             16   it up fully in animal models, in vitro, et cetera,

             17   and in the IND, toxicology studies, dose compounds

             18   fail.

             19             The question I would ask is how much can

             20   you bring of that hard work forward when you pick

             21   the backup compound that is very similar

             22   structurally, and has very similar properties?

                                                                           149

              1             DR. POWERS:  That is a really good

              2   question.  One of the parts of the critical path,

              3   though, when Dr. Woodcock talked about this a

              4   couple of weeks ago, is we see things other people

              5   do not at the Agency.  We can't publish that, we

              6   can't make it public because it is proprietary

              7   information, but one of the ideas of this end of

              8   Phase IIA  meetings is the idea of companies coming

              9   in and talk to us, so we can tell them, look, we

             10   don't think this is such a good idea.  And do they

             11   always listen to us?  That is a different story,

             12   but that we can actually try to give some of that

             13   advice based on what we have seen maybe with other

             14   compounds within the class.

             15             DR. EDWARDS:  John.

             16             DR. BRADLEY:  I just want to bring up a

             17   couple issues, one, I deal with pediatrics and they

             18   are a special population, and it's tougher to go

             19   through IRB, and virtually, every drug we get has

             20   gone through adult trials, so at least we have the

             21   opportunity to view some of these Phase II

             22   dose-ranging studies and see what doses fail in

                                                                           150

              1   adults, and don't even come near that.

              2             But, secondly, within pediatrics, there

              3   are different disease entities that we treat,

              4   perhaps the same organisms and the same drugs, but

              5   meningitis and cellulitis are two completely

              6   different concepts for me in terms of target

              7   attainment rates.

              8             A non-bacteremic cellulitis, I am willing

              9   to achieve 85, 90 percent.  That is probably not

             10   unrealistic, but for meningitis, I can't accept

             11   anything less than 100 percent.  I can't sacrifice

             12   any children, and if you look at, you know, George

             13   was mentioning everything is distribution, so

             14   distributions of MICs, of organisms that cause

             15   meningitis, distribution of PK serum values,

             16   distribution of what you get in the CSF, all of

             17   this stuff is very important in modeling how we

             18   come up with a dose to treat children with

             19   meningitis.

             20             I am not saying it isn't complicated, but

             21   these are all factors that we share with you in

             22   dialogue in coming up with the dose that we use,

                                                                           151

              1   but in Phase II, to use a dose in children which

              2   would fail is almost unethical.

              3             DR. POWERS:  That gets back to this issue

              4   of validating that PK/PD piece, but let's take a

              5   step back at what we are really trying to do here,

              6   and it is question I asked in the beginning.

              7             Are we trying to make these fine cuts

              8   between a drug that is 96 percent effective versus

              9   94 percent effective, or are we just trying to

             10   select this drug will make it, and this drug will

             11   not?

             12             Look, if it comes out 95 percent versus 90

             13   percent target attainment versus 20 percent, don't

             14   those tell you something?  I think that is getting

             15   to John Rex's point about you can monkey with the

             16   numbers to make it look like you want, but it might

             17   be a little tougher to make a 20 percent look like

             18   90 percent than it would be to make a 95 percent

             19   look like 90.

             20             DR. EDWARDS:  Yes.

             21             DR. TALBOT:  It seems to me that those are

             22   two very important questions, but they are

                                                                           152

              1   separate, (a), you know, which drugs can you kill

              2   clearly and without question in Phase II given the

              3   scientific and commercial constraints, and so

              4   forth, and then of those, based on efficacy, look

              5   as though they would almost certainly succeed, can

              6   you further differentiate those based on any safety

              7   information or exposure-response relationships, and

              8   also, then, how can you leverage what you have

              9   learned in a good Phase II and preclinical programs

             10   to optimally design an efficient Phase III program.

             11             But I think those are two different goals,

             12   both of which have major potential impacts.  They

             13   would potentially at least reduce cost.  They

             14   certainly might save time, of not time in clinical

             15   trials, time in review, multiple cycles, et cetera.

             16             I think to the extent they can do that,

             17   they reduce uncertainty, which as we discussed

             18   yesterday, is the bane of existence of senior

             19   management.

             20             DR. POWERS:  We have seen examples where

             21   these principles were not applied optimally, and

             22   companies went out and did 5 and 6 Phase III

                                                                           153

              1   trials, and the drug was not effective.  That would

              2   seem to be the biggest waste, first of all, it is

              3   bad for patients.  I tried to come back to that in

              4   the beginning, we don't want patients to be failing

              5   in serious and life-threatening diseases, but then

              6   get beyond that, there are the issues of that is

              7   not cost effective clearly to have done a big Phase

              8   III program and have a drug that does not look like

              9   it is effective in that setting.

             10             I thought Dr. Eisenstein's way of calling

             11   this "managing risk" was a really good way to look

             12   at it in terms of if you are a company, is it

             13   better to manage that risk in a smaller Phase II

             14   trial and find out what is going on than to get to

             15   a large Phase III program and get an unwanted

             16   surprise.

             17             DR. DRUSANO:  John, let me make one small

             18   proposal, and that is, if I am a sponsor and I go

             19   and do what everybody would say would be a robust

             20   Phase II trial, and I have taken my preclinical

             21   data and done the best job I can with it.

             22             I have chosen what everybody says is a

                                                                           154

              1   reasonable microbiological endpoint, I do a Monte

              2   Carlo simulation, I do an expectation over the MIC

              3   distribution.  I get a population target attainment

              4   rate, and it is something that everybody is happy

              5   with.

              6             I now go forward and do that robust Phase

              7   II.  You have, just from straight clinical and

              8   microbiological outcome, you have a point estimate

              9   of the response rate, and it is 95 percent

             10   confidence bound.

             11             In my view, and I would like to hear your

             12   response, is if the Monte Carlo simulation is

             13   somewhere in that 95 percent confidence bound, that

             14   would serve to me as a validation of the

             15   preclinical PK/PD even if you couldn't get a

             16   traditional, you know, exposure-response

             17   relationship because you had too few failures.

             18             So, how would you respond to that?

             19             DR. POWERS:  I don't think your question

             20   for us is the validation of the PK/PD target, is

             21   that coming back to the loop part, but what we are

             22   using it for in that particular drug product is to

                                                                           155

              1   decide am I going to go forward to a Phase III

              2   program.

              3             Let me turn it back and ask you, would you

              4   be happy with that information if you had that in a

              5   endocarditis trial--

              6             DR. DRUSANO:  I would be delighted.

              7             DR. POWERS:  That is the point for us.

              8   So, to then say, well, can we use it, go backwards

              9   and validate the PK/PD target, that may be useful

             10   for that company in terms of studying another

             11   disease, but in terms of going forward for that

             12   particular development program, it may not be as

             13   relevant.

             14             DR. DRUSANO:  Would you allow that, then,

             15   as a study that would serve as a supporting study

             16   for the single, well-controlled Phase III?

             17             DR. POWERS:  I keep saying, George, we

             18   have already done that.  We have already gotten to

             19   that point, and that is already specified in the

             20   CFR that one study plus supportive information, and

             21   we have already set the precedent of accepting

             22   that.

                                                                           156

              1             But one of the things that I want to get

              2   back to was I wrote down while Dr. Eisenstein was

              3   talking about this target attainment, isn't this

              4   dependent on the disease, and John Bradley brought

              5   that up, so I think some of this is going to

              6   be--so, in other words, if somebody brings us in

              7   something for complicated skin, and then they want

              8   to study their drug in endocarditis or meningitis,

              9   those may be different situations.

             10             DR. EDWARDS:  Mike.

             11             DR. DUDLEY:  I guess the usual situation

             12   might be, to take George's example further, would

             13   be that we may have a signal from preclinical

             14   studies, and even Phase I, that says maybe we are

             15   not quite sure about the safety at that point.

             16             So, in fact, you may want to go back and

             17   say, well, then, what is my, not AUC to MIC

             18   parameter, but what is the absolute AUC, and you

             19   still may want to study lower doses because that

             20   may save you from having that oops in Phase III

             21   that you have got an unacceptable safety signal in

             22   that, so you still have to go down, not that we are

                                                                           157

              1   going to purposely design trials that are going to

              2   have patients fail, but we are going to study them

              3   over a suitable dose-response curve even in Phase

              4   II, so that if we have signals from the preclinical

              5   and even Phase I setting, that we can start to look

              6   for that and do that risk management in terms of

              7   the low dose versus the high dose.

              8             DR. EDWARDS:  John.

              9             DR. LAZOR:  That was the point I was going

             10   to make, because you don't want to be faced with

             11   the oops in Phase III, and the question will come

             12   up, would a lower dose have been just as effective

             13   without the toxicity.

             14             DR. EDWARDS:  I am going to conclude this

             15   discussion now and by way of summary, let me say I

             16   think we have heard two things really clearly.  One

             17   is that some of the tools are available, and we are

             18   not using them in a way that is user-friendly for

             19   evaluation by the FDA.

             20             We have clearly discussed in depth the

             21   advantages of a robust Phase II clinical trial, and

             22   I think that point has come through very clearly. 

                                                                           158

              1   What isn't so clear to me is a mechanism that we

              2   have devised to answer the question of what do we

              3   need to get an endocarditis indication, not the

              4   details, but the process, and I don't think we are

              5   going to be able to discuss that further now, but

              6   maybe we will have a chance to come back to it

              7   later this afternoon.

              8             If I could end there, we are going to have

              9   an hour for lunch, and we will resume at 1:15.

             10             I have some announcements.  The panel

             11   members should leave with the FDA staff, who want

             12   to use that lunch facility.  Anyone returning this

             13   afternoon, please leave their badge at the table,

             14   and someone will be here to watch all the luggage.

             15             Thank you very much.

             16             [Whereupon, at 12:15 p.m., the proceedings

             17   were recessed, to be resumed at 1:15 p.m.]

                                                                           159

              1             A F T E R N O O N  P R O C E E D I N G S

              2                                                    [1:15 p.m.]

              3             DR. EDWARDS:  Before we start, we are

              4   running about 15 or so minutes behind, and we are

              5   going to run just a little bit later than the

              6   schedule shows, but not much, because some of us,

              7   myself included, have got to get to the airport at

              8   the right time, so don't worry about this dragging

              9   on a significant amount of time beyond what you see

             10   on your schedule there, but it will be a few

             11   minutes.

             12             Before we start the second part this

             13   afternoon, I want to ask Bob Powell to make a few

             14   comments from the podium.  I have not called on him

             15   in two sessions here, and I do not let him make his

             16   comments, which are very important, I am going to

             17   be persona non grata magna.  I am trying my best to

             18   stay out of that position.

             19             Bob has some very important points to

             20   make, so, Bob, let me let you do that now.

             21             DR. POWELL:  Thanks very much.

             22             Of course, when someone says that someone

                                                                           160

              1   has very important points to make, the skeptics

              2   will try and prove that that is not true, so I hope

              3   not to disappoint.

              4             Let me tell you I have spent some time in

              5   academics.  I spent about the last 16 or so, 17

              6   years in the industry, and then I came to the FDA

              7   in January.  My sense is that with regard to

              8   antibiotics, which I hadn't, aside from working on

              9   developing antibiotics and antifungals and

             10   antivirals some, my feeling is the conversation

             11   really hasn't changed much from about 10 or 15

             12   years ago, that I was hearing in terms of what is

             13   the number in terms of whether it should be AUMC or

             14   AUC ratios or peak or whatever, and I think what

             15   John Lazor said in the very beginning was

             16   absolutely key.

             17             He was talking about this feedback loop

             18   and that that doesn't exist, so that my curiosity

             19   about that, I started thinking about, well, why

             20   wasn't does that exist, because that is fixable,

             21   and it has been fixable for other therapeutic

             22   areas.

                                                                           161

              1             So, if you guys are stuck, then, you ought

              2   to try and do something different, and think about

              3   why that might be, because it is not the science,

              4   the science exists to do what you want to do, and

              5   yet why doesn't it get done.

              6             I would suggest to you that one of the

              7   main reasons is inside the drug company, it is not

              8   at the FDA, but the FDA can potentially help solve

              9   it.

             10             The people that develop the science,

             11   coming from preclinical through Phase I, all the

             12   stuff that you are talking about and that you are

             13   interested in, and that you want to get in here, by

             14   and large, the people that develop that work tend

             15   to have very little influence on the design and the

             16   execution of the Phase III trials, so that the

             17   piece that really needs to be fixed is inside drug

             18   companies, the people in experimental medicine or

             19   clinical pharmacology, or however you are

             20   organized, really have to have an impact on Phase

             21   IIB or Phase III trial designs, how they are put

             22   together.

                                                                           162

              1             For example, it is fairly uncommon in

              2   Phase III trials to have concentration data that

              3   could be collected in such a way to develop the

              4   correlations that you are looking for, or when

              5   there is an adverse drug effect, to have the case

              6   report forms.

              7             I want to give you some specifics.  The

              8   case report forms that can be designed in such a

              9   way that you know when the adverse effect occurs,

             10   when that is in relation to the dose, and to have

             11   it in the case report form, it is going to trigger

             12   someone to go out and draw a plasma level, if that

             13   is warranted, and also to describe the time that

             14   the adverse effect is there.

             15             I can tell you that is generally not

             16   present. People may be asking for it inside the

             17   company, but it is blocked.  Now, why is that?  The

             18   people that are running--and they may be in this

             19   room--the people that are running the Phase III

             20   trials may not be listening to the requests for

             21   dose ranging, and the other thing is that it is

             22   clearly the closer a drug gets to the marketplace,

                                                                           163

              1   so that for an antibiotic, that frequently occurs

              2   after Phase I, certainly in Phase II, marketing has

              3   a tremendous impact on the way those trials are

              4   going to be designed.

              5             If someone only wants one dose, that is

              6   hard to fight against.  Now, how can you fight

              7   against that?  There is actually a couple of

              8   leverage points.

              9             By the way, INDs, the target in an IND is

             10   the first dose in man is set 6 months before you

             11   start dosing. That is set up with the CRO, when we

             12   are going to take this sucker into humans.

             13             Now, when the company puts in the IND,

             14   they are trying to present as small a target to the

             15   FDA as they can. I can tell you that, because I did

             16   it, because they don't want to be stopped, so there

             17   is nothing on what the proof of principle strategy

             18   is, and there is nothing on what the dose finding

             19   strategy is.  It ain't there.

             20             If you want to have that dialogue, there

             21   is a provision to have a pre-IND meeting and

             22   discuss what the drug development strategy is,

                                                                           164

              1   where you can get into how we are going to do proof

              2   of principle and how we are going to do dose

              3   finding.  That is not done very often.

              4             The second thing is that at the end of

              5   Phase I, there will be a guidance that will be out

              6   shortly. It will be probably called an End of Phase

              7   IIA Guidance, but it is actually an end of Phase

              8   I/IIA guidance.

              9             That is an opportunity for the company to

             10   summarize the data quickly, you would have to be

             11   doing things on the fly, and come in and say what

             12   you have learned.

             13             Modeling and simulation can be used, if

             14   you are quickly putting the information together

             15   and you are doing PK/PD type relationships in Phase

             16   I or Phase IIA, that could be put together to then

             17   use along with--certainly PK/PD does not predict

             18   outcome--but compliance does.

             19             You talked about the neutropenic state of

             20   the patients or not.  All those sorts of things can

             21   be accounted for in trial design.  You were talking

             22   about why aren't these Phase III trials being more

                                                                           165

              1   efficient and using fewer subjects, why can't you

              2   just do one trial instead of two or three or four.

              3             Well, if you want to do that, then, my

              4   experience with the FDA over the years is that the

              5   people generally respond very well to good science,

              6   so that if you present a good scientific argument

              7   at the end of Phase I or the end of Phase II, IIA,

              8   or end of Phase II about what you are planning to

              9   do in Phase III, then, that is a great dialogue to

             10   have.

             11             Why doesn't that happen more often?  I

             12   don't know, but the reality is people that are

             13   interested in this topic are generally not as

             14   influential as they need to be inside companies.

             15             Now, I think you can use the FDA in the

             16   ways that I have talked about, to try and gain that

             17   leverage.  That is what I primarily wanted to speak

             18   about.

             19             Thanks.

             20             DR. EDWARDS:  Thank you very much.  That

             21   is a very important point, and in side discussions,

             22   it has come up on several occasions, especially

                                                                           166

              1   today, as well.

              2             I think we are going to have to go on.

              3   This afternoon's session is the current status of

              4   dose selection for the first part, and then the

              5   second part is improvement in dose selection

              6   concepts.  We will start now with the current

              7   status, and I am going to ask George Drusano to

              8   begin these presentations.  Most of the discussion

              9   is going to be at the end of all of the

             10   presentations actually, so, George, let me ask you

             11   to start off.  Thank you.

             12             III. Current Status of Dose Selection in

             13             Antimicrobial Drug Development Programs

             14                       Academic Perspective

             15             DR. DRUSANO:  Thank you, Jack.  I have

             16   already been told that if I go over, I will be

             17   shot, so that is clear, and I would first like to

             18   start by thanking the Agency for providing the

             19   invitation to give this presentation on getting the

             20   dose right and the view from academia.

             21             [Slide.]

             22             Well, what are the determinants of

                                                                           167

              1   "getting the dose right?"  Well, first, you have to

              2   answer a question, and the first question that

              3   needs to be addressed is what outcome is it that

              4   you desire.

              5             Multiple outcomes are reasonable.  You

              6   want a good clinical outcome, microbiological

              7   outcome.  You want to suppress resistance, you want

              8   to have minimization of concentration-related

              9   toxicities.

             10             Well, one or more of those is what you

             11   want you want to accomplish.  Now, what are the

             12   determinants of getting the dose right?

             13             [Slide.]

             14             Well, we can do all of those things and

             15   then dose choice becomes an issue basically in the

             16   Phase I time frame. Well, it is actually earlier,

             17   but the proper data becomes available at this time,

             18   because I, for one, I think that dose scaling,

             19   allometric scaling, or PD/PK, is useful, but it can

             20   be a little dangerous.

             21             You really want to have human PK.  Now,

             22   because of this, the choice of outcome is limited. 

                                                                           168

              1   Clinical outcome cannot at this time be an outcome

              2   measure because you haven't at this time put the

              3   drug into a patient that is infected.

              4             So, the most common measure, then, is a

              5   microbiological outcome determined either from in

              6   vitro or animal model data.

              7             [Slide.]

              8             Now, I point this out to you.  This comes

              9   from our laboratory.  By request, the name of the

             10   drug has been changed to drug X, and all the data I

             11   am going to show you today is from one single drug

             12   and different manifestations of studies.

             13             So, what one can see here is if you do

             14   multiple doses of this particular drug--and this is

             15   Pseudomonas aeruginosa--this a mouse thigh

             16   infection model.  You start out with around 7.9

             17   logs of organisms, in the presence of granulocytes,

             18   by the way, you can see that you need to have about

             19   45 to 1 AUC to MIC ratio to achieve stasis, about

             20   80 for a log kill, and up to 130 for 2 log kills.

             21             Bacteremia usually gets shut off somewhere

             22   between a 1 log and 1 log kill in this

                                                                           169

              1   circumstance.  So, remember that number of about

              2   80, so it is somewhere between 80 and 130.

              3             [Slide.]

              4             So, once a target exposure is chosen, what

              5   other information do you like to have?  Well, the

              6   first thing that you would like to have is to

              7   identify the sources of variability in the

              8   circumstance.

              9             So, as every is want to point out, I am a

             10   little on the porky side, so that if you take a

             11   look at somebody like Dr. Powers, and you gave him

             12   a dose of drug, and you gave me that same dose of

             13   drug, we would have two very different profiles of

             14   drug, because he is thin and I am not.

             15             So, how do we deal with this?  We actually

             16   take populations of individuals and give them doses

             17   of drug, and we do population pharmacokinetic

             18   analysis.  This quantifies the variability in how

             19   the drug dose is handled by a population.

             20             We also have different target pathogens,

             21   and for each target pathogen, there is a

             22   distribution of MICs for the drug in question.  So,

                                                                           170

              1   we have to have relatively large databases of these

              2   MICs for the target pathogen that we care about,

              3   and you also have to know your target product

              4   profile, because it is going to make a difference

              5   whether you are treating skin and skin structure,

              6   meningitis, endocarditis, pulmonary infections.

              7             The other thing that we need to know is

              8   that, in general, only free drug is

              9   microbiologically active.  I don't want to go back

             10   into that, we all know that there are times when it

             11   is not quite mathematical, but, in general, to a

             12   high degree of certitude, you want to pay attention

             13   to free drug.

             14             [Slide.]

             15             Now, we can evaluate how well a specific

             16   drug dose will attain the desired pharmacodynamic

             17   target, and we do this through Monte Carlo

             18   simulation, and all the Monte Carlo simulation is

             19   doing for you is to give you some measure of the

             20   variability of the drug exposure in the population.

             21   You have to remember the limitations.

             22             At this point, we are almost always

                                                                           171

              1   looking at volunteer data, so that is going to be

              2   skewed relative to the data that one sees in

              3   honest-to-God target populations. Usually, but not

              4   always, that skew is conservative, so that the

              5   clearances are, in general, higher, making the

              6   inferences drawn somewhat conservative.

              7             [Slide.]

              8             So, just to take a 1 log drop, so the AUC

              9   to MIC we saw for that was 81.  On the x axis, you

             10   see the MICs.  We have a distribution of MICs for

             11   Enterobacter cloacae and for Pseudomonas

             12   aeruginosa.

             13             We then do a 10,000 subject Monte Carlo

             14   simulation, and what one sees is that at this MIC,

             15   it's 100 percent target attainment, 100 percent,

             16   100 percent, and then it falls off the end of the

             17   table.  When you get down to 1, you have got about

             18   a 50 percent target attainment, and goes down to

             19   zero.

             20             Now, is that any good?  The answer is if

             21   all the MICs were out here, that would be

             22   spectacular, if all the MIC distribution was out

                                                                           172

              1   here, it would really stink, but we can actually

              2   get an idea of how good it is by doing a target

              3   expectation.

              4             So, you just take the fraction of

              5   organisms at that MIC, times the target attainment

              6   rate at that MIC, this one times that one, this one

              7   times that one, and so on, and you add it all up.

              8   It's a fancy name for taking a weighted average.

              9             So, when we do that for Pseudomonas, this

             10   particular dose of drug gives you an expectation of

             11   hitting 81 at about 66 percent.  For Enterobacter,

             12   that number is about 88 percent.  Remember, that is

             13   a little on the conservative side because this is

             14   volunteer data.

             15             [Slide.]

             16             Okay.  What are the questions now?  First,

             17   was the target correct, because as we talked about

             18   earlier, this thing has a 95 percent confidence

             19   bound around it.  There is no one right target, and

             20   it even may change by organism and by site.

             21             The second question, is the target

             22   attainment rate adequate?  Well, we will talk about

                                                                           173

              1   that in a second.

              2             So, the target, a 1 log decline is

              3   reasonably conservative, but you have to be careful

              4   about the infection site.

              5             Now, the answer to the second question

              6   depends on the patient and the consequences of

              7   being wrong.  For instance, if you have a

              8   granulocytopenic patient, the consequences of being

              9   wrong are severe.  The same thing for a meningitis

             10   patient.  If you have an uncomplicated skin and

             11   skin structure patient, it is not so bad that you

             12   could be wrong.

             13             Now, is it adequate, the target attainment

             14   rate?  Well, that depends on whether you are

             15   treating your mother or your mother-in-law, it's in

             16   the eye of the beholder.

             17             [Slide.]

             18             So, now, what now?  Now, it is important

             19   to recapitulate the analysis in real patients in

             20   the Phase II environment.  Why?

             21             Well, the PK that you got from that is

             22   determined in the target population.  Two, the

                                                                           174

              1   correlation, if you can make it, with preclinical

              2   animal or in vitro data, provides near certainty of

              3   "no surprises" in Phase III except for toxicity

              4   issues, and you are seeing real world organisms,

              5   and these can be gauged against the original MIC

              6   distributions.

              7             Again, when you do this, it actually,

              8   usually comes out conservative.  So, Monte Carlo

              9   simulations can then be recalculated with real data

             10   before you move into the Phase III environment.

             11             [Slide.]

             12             Here is an example of a drug that was

             13   taken from a nosocomial pneumonia study, and 750

             14   milligrams of this drug was given as a 1.5 hour

             15   constant rate I.V. infusion.  There were 58

             16   patients that were studied.

             17             The sampling design was a stochastic

             18   design that we put together at our group, and we

             19   only really had 6 samples, but we were able to

             20   capture a lot of the system information with those

             21   6 samples.

             22             I only point out the clearance here for

                                                                           175

              1   this drug, that the clearance in a normal volunteer

              2   population would be about 11 to 12 liters per hour.

              3   The mean and median clearance, I apologize for

              4   this, is between 6 and 7 liters per hour.  So, this

              5   is telling you of the difference between volunteer

              6   populations and true target populations

              7   particularly in terms of important factors like the

              8   drug clearance.

              9             [Slide.]

             10             We were then able to have 47 of those 58

             11   patients where we had a recovered pathogen from

             12   that nosocomial infection study, the MIC to the

             13   drug in question, an outcome that was a

             14   microbiological outcome in this particular

             15   instance, and the PK, so that we could do the

             16   population modeling, get MAP Bayesian estimates for

             17   each of the patients, and calculate a specific AUC

             18   to MIC ratio.

             19             The first thing we did was we took

             20   classification regression tree analysis to try to

             21   find a breakpoint, and the breakpoint that we came

             22   up with was a breakpoint of 87, which was really

                                                                           176

              1   quite close to the AUC to MIC of 81, that we had

              2   gotten in the mouse thigh model for a 1-log drop.

              3             We then did some model building.  There

              4   was a preregistered analysis plan, and with that

              5   preregistered analysis plan, there were only two

              6   things that had an impact upon the microbiological

              7   outcome.

              8             One was whether or not you attained the

              9   AUC to MIC of 87.  The second was the age of the

             10   patient, and the model was statistically

             11   significant.  We did look at the positive and

             12   negative predictive values, the sensitivity and the

             13   specificity, and it was quite reasonable, and these

             14   data will be published in the May 1st issue of the

             15   Journal of Infectious Diseases.

             16             [Slide.]

             17             For those who like pictures, here, on the

             18   x axis is the age of the patient; on the y axis is

             19   the probability of organism eradication, and this

             20   curve is for those that attained the breakpoint

             21   value of 87, and this curve is for those who did

             22   not.

                                                                           177

              1             As you can see, there is clear separation

              2   between these curves, and actually, when you look

              3   at it, what you can show is that if you are above

              4   65 years of age, this is statistically

              5   significantly worse.  So, there is actually a

              6   breakpoint in age, as well, as it is really the

              7   older patients that benefit the most from attaining

              8   the breakpoint AUC to MIC value of 87.

              9             [Slide.]

             10             We can then close the loop.  We have

             11   identified an AUC to MIC breakpoint in patients

             12   that is quite concordant with the number that we

             13   got out of the mouse thigh infection model, and now

             14   we can redo our AUC to MIC target of 87 and do the

             15   Monte Carlo simulation and the expectation.

             16             Now, this is a little different when you

             17   actually start to take a look at this distribution.

             18             [Slide.]

             19             When you do the expectations for this AUC

             20   to MIC breakpoint for Pseudomonas, now it is 72

             21   percent, and for Enterobacter, the target

             22   attainment rate is around 92 percent.

                                                                           178

              1             [Slide.]

              2             What about resistance suppression, can we

              3   do this?

              4             [Slide.]

              5             These are data that were published from

              6   our laboratory in the Journal of Clinical

              7   Investigation last year, and what you see is

              8   oftentimes when we beat on a large population of

              9   organisms, we see an initial fall followed by

             10   regrowth.

             11             This is the effect of having one drug

             12   exposure on two disparate populations of organisms,

             13   one that is susceptible and one that is less

             14   susceptible, and when you deconvolute the effect of

             15   the single exposure on the two populations, a funny

             16   thing happens on the way to the forum.

             17             When you look at the sensitive population,

             18   you really, really have an impact.  When you look

             19   at the exposure and its impact with the resistant

             20   population, you see unrestricted amplification of

             21   that resistant subpopulation, so that when you put

             22   the results of that single drug exposure on both

                                                                           179

              1   populations together, it's not at issue where this

              2   comes from.

              3             [Slide.]

              4             We population modeled a number of

              5   different exposures simultaneously using the Blue

              6   Horizon machine out at UC/SD Supercomputer Center.

              7   These are the point estimates of the parameters.  I

              8   have been enjoined by the Agency from not showing

              9   the five differential equations we used to do this,

             10   because nobody wanted to make everybody nauseous

             11   after lunch.

             12             So, we really did do it, that's what we

             13   got.

             14             [Slide.]

             15             Just to show you, you can then show that

             16   this is the MAP Bayesian posteriors predicted

             17   observed plot.  This is for the total bacterial

             18   count.  What you see is the r
                                                                               2 is about 0.93, the

             19   slope near 1, and we have a relatively small Y

             20   intersect.

             21             So, we did a good job, the model did a

             22   good job of describing the effect of the drug on

                                                                           180

              1   the total population.

              2             [Slide.]

              3             Here is the effect of the drug on the

              4   resistant population, again, slope near 1, small y

              5   intersect, and an r
                                                              2 of 0.94.  So, we were able to

              6   do this for this drug and Pseudomonas aeruginosa.

              7             [Slide.]

              8             You can then use those numbers to

              9   calculate an AUC to MIC that will shut off the

             10   growth of the resistant mutants, and that number is

             11   about 157 to 1 of total drug. It's about 110 to 1

             12   of free drug.

             13             [Slide.]

             14             We decided to do a prospective validation.

             15   Now, I am showing you the AUC to MIC of 52, which

             16   we calculated would optimally amplify the resistant

             17   mutants, and the solid lines are not best-fit

             18   lines, they are prediction lines from what we had

             19   done previously, and we scattered around those

             20   prediction lines what happened to the total

             21   population and the resistant population.

             22             If you actually look, you would say that,

                                                                           181

              1   hey, that's not so bad if you did not look at the

              2   resistant subpopulation.  You would show that you

              3   were able to drop the total population about a half

              4   a log with that AUC to MIC ratio.

              5             When you, however, look at the resistant

              6   population, you are amplifying the resistant

              7   mutants.  You are killing off a few of the

              8   susceptible organisms, but you are replacing them

              9   with resistant organisms.

             10             So, again, we were able to predict quite

             11   nicely, what was going to happen to the resistant

             12   mutant population as a function of a suboptimal

             13   exposure.

             14             [Slide.]

             15             When we used 157 to 1 to shut off the

             16   amplification of the resistant mutant population,

             17   it did not amplify.  I will also tell you we have

             18   recapitulated this study in an in vitro

             19   hollow-fiber infection model with another drug of

             20   the same class, and with the same Pseudomonas

             21   aeruginosa, and you find exactly the same thing.

             22             So, in that circumstance, you can do this.

                                                                           182

              1             [Slide.]

              2             So, we were able to determine how the

              3   overall population responds to pressure from this

              4   drug.  More importantly, we were able to model the

              5   resistant population, choose a dose based on

              6   simulation to suppress the resistant mutants, and

              7   the prospective validation, the only one in the

              8   literature of which I am aware, demonstrated that

              9   the doses chosen to encourage and suppress the

             10   resistant mutants did, indeed, work.

             11             [Slide.]

             12             We are almost done.  What about having a

             13   high probability of attaining a good outcome while

             14   not encumbering the patient with a high probability

             15   of a concentration-related toxicity?

             16             Now, we will use aminoglycosides for this

             17   where there has been developed concentration-effect

             18   and concentration-toxicity probability

             19   relationships.

             20             [Slide.]

             21             The efficacy probability function was

             22   generated by Angela Kashuba and Joe Bertino.  They

                                                                           183

              1   were kind enough to ask me to do the math on this,

              2   published in AAC in '99, and the toxicity

              3   probability function was developed out of Mike

              4   Rybak's lab as part of a randomized, double-blind

              5   trial, and again they were kind enough to ask me to

              6   do the math.

              7             [Slide.]

              8             So, for the effect, you see on the x axis

              9   AUC to MIC ratio, and the probability of resolution

             10   by different days of therapy.  For this, we will

             11   use 7 days of therapy.

             12             [Slide.]

             13             For the toxicity, this again was published

             14   in AAC, and you see on the x axis the daily area

             15   under the curve of aminoglycoside with and without

             16   concurrent vancomycin.  For this, we will use the

             17   without vancomycin curve, but you can clearly

             18   relate the probability of nephrotoxicity to the

             19   daily exposure to the aminoglycoside.

             20             [Slide.]

             21             But here we have both of them together,

             22   and on the x axis, one is AUC to MIC, one is AUC,

                                                                           184

              1   and so in order to get around that, we put AUC on

              2   the x axis in all instances, and then probabilities

              3   of both effect and toxicity, and then we sorted out

              4   by MIC, so that if you have a nice, sensitive E.

              5   coli or Klebsiella, where you have an MIC of 0.25,

              6   you can get a 90 percent or so probability of

              7   resolution, and not encumber much more than 1 or 2

              8   percent probability of toxicity.

              9             As you go to 0.5, now you can see it is

             10   around there, it is around 3 or 4 percent

             11   intersecting the toxicity probability curve, so it

             12   is a little narrower of a therapeutic index.

             13             When you go 1, now the wheels start to

             14   come up, and what you see is in order to have an 90

             15   percent probability, you have to encumber the

             16   patient with about a 33 percent probability that

             17   they are going to be toxic.

             18             At 2, you may as well not even bother

             19   because to get 90, is out here, and at that point,

             20   you have a 95 percent probability of getting toxic.

             21             So, what this is telling you is we can do

             22   this. This is the simple way to do it.  There is a

                                                                           185

              1   fully stochastic way that has been worked out with

              2   multiple model stochastic control with linear

              3   dynamics and quadratic cost functional, but this is

              4   just the easy way to do it.  It can be done.

              5             [Slide.]

              6             So, why go through all this mathematical

              7   nonsense? Because at the end of the therapy is a

              8   patient who would appreciate getting better without

              9   toxicity.  Also, because choosing the dose

             10   correctly, this allows trials with anti-infectives

             11   to have the lowest Phase III failure rate of any

             12   therapeutic area.

             13             In so doing, we can push the development

             14   of these drugs faster, cheaper, and better, so that

             15   we can keep those in the industry that are still

             16   in, and hopefully, attract back some of the people

             17   in industry that have abandoned this therapeutic

             18   area.

             19             In the words of my favorite Hollywood

             20   movie star, "That's all, folks."

             21             DR. EDWARDS:  Thank you very much, George.

             22   That was a very nice discussion.

                                                                           186

              1             We are going to move right along now to

              2   Dennis Grasela from BMS.

              3             Dennis, please go right ahead.

              4                       Industry Perspective

              5             DR. GRASELA:  I would like to begin by

              6   thanking the conveners of this meeting for the

              7   opportunity to speak here, and to also say that

              8   this is very difficult presentation, for two

              9   reasons.

             10             One, it is an hour after lunch, and, two,

             11   it follows Dr. Drusano.

             12             [Slide.]

             13             The selection of dose for the industry is

             14   a critical milestone in the drug development

             15   process, for lots of reasons, most of which have

             16   been discussed today, but of all those reasons that

             17   deal with the patient, with outcome, with

             18   regulators, with everything else, there is whole

             19   other large component of the industry that is

             20   mobilized when you say what the dose is.

             21             It determines how much is manufactured,

             22   what dosage forms are produced, for some insoluble

                                                                           187

              1   drugs it defines whether or not a dose formulation

              2   can be produced, and in some cases, a valuable drug

              3   may not be taken forward if we think the dose is

              4   600, the solubility is brick dust, and they say

              5   they can't put any more than 10 milligrams into a

              6   pill.  This is an incredible challenge for all of

              7   us.

              8             In summarizing yesterday, Dr. Edwards

              9   punted a little bit, but I will summarize it in one

             10   way - we are all in this together, and this is

             11   really hard to do, and I hope that more of these

             12   discussions happen, so that we can get it.  I don't

             13   think we will ever get it completely right, but we

             14   will get as good at it as we possibly can.

             15             [Slide.]

             16             There are three parts to my presentation.

             17   First, I would like to talk about the way that we

             18   have embraced exposure-response approach to dose

             19   selection at Bristol-Myers Squibb.

             20             Second, the optimist's view of the factors

             21   that are driving the use of PK/PD based drug

             22   development.

                                                                           188

              1             Third, my view of potential cost savings.

              2   Again, this is my perspective.

              3             [Slide.]

              4             As I begin the exposure-response

              5   discussion, I just want to preface it by saying it

              6   is assumed, I want to assume two things.  One, that

              7   we know a lot about the product, we factored in

              8   protein binding, we factored in tissue

              9   distribution, we factored in all of the things that

             10   we have talked about are important to know about

             11   the drug biopharmaceutics, et cetera.

             12             The second piece is to ask your indulgence

             13   in that some of the examples I will show, the data

             14   became available well after the decisions were

             15   made, but we utilized all that information for the

             16   second compound that came along with it, which was

             17   the driver for my question about how much can you

             18   bring forward from what you know before to get it

             19   right the second time around, particularly when

             20   development or discovery compounds come in

             21   clusters, you are usually trying to pick between

             22   one, two, or three compounds.

                                                                           189

              1             I will digress by saying the FDA has

              2   helped a lot by the concept of the screening IND,

              3   which enables us to take some uncertainty and some

              4   risk out of it by taking more than one compound in

              5   at a time.

              6             [Slide.]

              7             The road to dose selection, in my view, is

              8   really four basic principles.

              9             First, is you need to know the

             10   microbiological hurdle, and that can be figured out

             11   in some ways by the in vitro MIC values.  Given Dr.

             12   Rex's comment earlier on, I am not so sure how much

             13   we know about the hurdle after the smoke clears.

             14             The second is to define the

             15   pharmacodynamically-linked parameter and the target

             16   value as best we can from in vitro hollow-fiber

             17   models and in vivo animal models of infection.

             18             The third is to use PK/PD modeling in

             19   proof of concept studies to examine the

             20   exposure-response relationship.  This is really

             21   hard to do.  It is often about how you can not only

             22   garner support from your clinical research

                                                                           190

              1   colleagues, as indicated by Dr. Powell, but it is

              2   also how responsive the clinical sites can be to

              3   collect the minutia, and that is the way they think

              4   about the information we are trying to collect is

              5   minutia.  Heck, they are trying to just make sure

              6   the patient shows up that day and they can remember

              7   what day of the week the person actually came in.

              8   We are asking them to write down what time they

              9   took the dose.

             10             Writing the case report forms is

             11   incredibly difficult.  For one study, we needed to

             12   know what the time of their last meal was.  They

             13   told me about the bowl of ice cream they had the

             14   night before.  It was a once-a-day drug. I wanted

             15   to know what time breakfast and the meal was the

             16   day before that, and unless you are doing real-time

             17   data clean-up, this is incredibly difficult because

             18   what you get at the end of the day is a lot of

             19   unusable data, and you wind up, when you consider

             20   that, as Dr. Drusano had talked about, you need an

             21   organism, you need a patient that has some outcome.

             22   You actually need to know what his exposure is, and

                                                                           191

              1   it all has to happen in the same person, and you

              2   need to know the date and time of the dose and when

              3   those samples were collected for that individual.

              4             When you take all of that together, the

              5   recovery rate of subjects is incredibly low,

              6   despite how many patients you include in your Phase

              7   II and III clinical trials.

              8             When all that is said and done, we try to

              9   combine all that information, use Monte Carlo

             10   simulation to define the dose and schedule for

             11   Phase III clinical trials.  I might add that the

             12   guys that manufacture the stuff want to know this

             13   actually before you actually got it into your first

             14   patient.

             15             [Slide.]

             16             The hurdle.  The MIC distribution. I am

             17   sure if I have asked people in the room when they

             18   got their first discovery compound, they had an MIC

             19   90.  What they forgot to tell you it was an on an N

             20   of 10 isolates.

             21             I think if we looked across the history of

             22   compounds, you realize that the MIC 90 floats. 

                                                                           192

              1   Sometimes they go up, sometimes they go down.  The

              2   only message is here, and we have the luxury of our

              3   SENTRY program, so that we know a lot about the MIC

              4   distribution of our compounds as we bring them in,

              5   but you need to know that you need to continually

              6   monitor that to make sure you are not way off on

              7   that assessment, and you also need to know what the

              8   shape of that curve looks like.

              9             This distribution is very different than

             10   when it is skewed to the left or skewed to the

             11   right.

             12             [Slide.]

             13             The second part that I talked about is the

             14   in vitro models, and we have heard a number of

             15   eloquent presentations about the in vitro

             16   hollow-fiber.  This happens to be an example out of

             17   Phil Lister's lab for one of our quinolones,

             18   essentially doing dose fractionation studies, et

             19   cetera, from the history of quinolones.

             20             I hope we can agree that the AUC to MIC

             21   ratio is the target.  In vitro hollow-fiber gives

             22   you a window of free drug AUC to MIC ratios, and I

                                                                           193

              1   don't want to debate free drug.  From an industry

              2   perspective, I am always going to err on the

              3   conservative side, and if I think free drug is

              4   going to work, I am going to calculate the dose

              5   based on free drug.  I can always be a little wrong

              6   the other way, but I am going to calculate it based

              7   on free drug.

              8             This data suggested that AUC to MIC ratio

              9   of about 30 is what we needed for the pneumococcus.

             10             [Slide.]

             11             We next went to an animal model, and this

             12   is with Dave Nikalel's [ph] lab, and this is in a

             13   neutropenic mouse model.  Essentially, an AUC to

             14   MIC ratio of somewhere between 30 and 50 gives you

             15   100 percent survival with a free AUC to MIC ratio

             16   of somewhere between 30 and 36, 37, so we are

             17   getting information.

             18             [Slide.]

             19             We know what the MIC distribution is, we

             20   know what the in vitro hollow-fiber is telling us,

             21   and we know what the animal models are showing.

             22             Now, here is the hard part.  For this

                                                                           194

              1   particular quinolone, we introduced population

              2   pharmacokinetics into Phase II and Phase III.  This

              3   picture is actually combining two, Phase III trials

              4   in two different respiratory infections and

              5   throwing in the comparator quinolone to get enough

              6   information to get an exposure-response

              7   relationship.

              8             Essentially, this is 58 organisms and 58

              9   patients that we had all of the information come

             10   together on.  Basically, what it shows is that an

             11   AUC to MIC ratio above 33 gives you a high

             12   probability of clinical response, hence, the

             13   feedback loop gets closed to around a free AUC to

             14   MIC ratio of about 30 for this quinolone and the

             15   pneumococcus.

             16             [Slide.]

             17             When we use that target and look at the

             18   target hit rates using Monte Carlo simulation, it

             19   suggested if the target is 30, that the dose we had

             20   selected for this drug provides a 94 percent

             21   response rate--I am sorry, not a response rate--but

             22   a target hit rate for this compound.

                                                                           195

              1             When we looked at the same patients that

              2   we pooled from those two, Phase III clinical

              3   trials, we had microbiologic eradication in 92

              4   percent of those subjects.

              5             I would just like to digress for a minute.

              6   This is talking a lot about the efficacy piece of

              7   the equation, but dose selection continues on

              8   through Phase III and the post-marketing

              9   surveillance.

             10             For low-frequency safety issues that may

             11   be related to pharmacogenomic piece, that needs to

             12   be picked up in Phase III and post-marketing

             13   surveillance, and needs to continue to be looked

             14   at.

             15             In addition, with this information, as

             16   MICs begin to drift as the compounds are on the

             17   market, it provides the opportunity, in my view,

             18   for a course adjustment, perhaps a dose adjustment

             19   unless there is major shifts in the MIC values.

             20             [Slide.]

             21             Now, from my optimistic opinion, what are

             22   the factors driving the use of PK/PD based drug

                                                                           196

              1   development? John Lazor called it "science-based

              2   drug development," I will call it "knowledge-based

              3   drug development."  I think we are heading in the

              4   same direction.

              5             These principles allow dose selection,

              6   more importantly, dose conformation.  They allow

              7   you the ability to examine the effect of

              8   administering a dose not studied during the

              9   development process.

             10             What is the probability of if you lower

             11   the dose or raise the dose, what is the probability

             12   of your target hit rates?

             13             I think we can use this as select target

             14   indications.  I think Dr. Powers was alluding to

             15   this a little bit.  If your drug isn't going to get

             16   the job done, you probably ought to not go there,

             17   or you need to combine it with another drug to fill

             18   your spectrum gap, and just an overall enhanced

             19   understanding of the drug.

             20             [Slide.]

             21             Regarding that enhanced understanding of

             22   the drug, I had indicated we included population PK

                                                                           197

              1   into our Phase II and III trials for this

              2   particular drug.  I am a fan of population PK for a

              3   number of reasons.

              4             One, I think it can help explain

              5   differences in response among individuals receiving

              6   the same dose.  Is it a covariate, is it a genomic?

              7   It can help identify at-risk subpopulations and

              8   define risk-benefit ratios and/or risk management

              9   strategies.

             10             We talked a little bit about the

             11   clarithromycin study yesterday, and I am intrigued,

             12   I will read up on that, but I wonder whether the

             13   folks that had toxicity from clarithromycin were

             14   predisposed to that by some reason, and knowing

             15   that might enable us to do a risk assessment in

             16   terms of using that dose should it be warranted in

             17   selective patients.

             18             Also, to help guide the use of

             19   pharmacogenomics, if you don't have an exposure

             20   reason for an adverse event, and you don't have an

             21   individual demographic reason for that adverse

             22   event, can it lead you down the path of a genomic

                                                                           198

              1   reason for that event.

              2             [Slide.]

              3             What are the external factors?  We talked

              4   about some of those today.  There is FDA

              5   expectation and there are opportunities.  The

              6   expectations are set through the guidances and the

              7   opportunities, in part, are provided by the

              8   opportunities to discuss the plan, the pre-IND

              9   meeting, the End of Phase IIA meeting, and other

             10   venues.

             11             The FDA Modernization Act of 1997, which I

             12   will talk about in next couple of slides, also

             13   provides opportunities to streamline the drug

             14   development process, but there are also global

             15   expectations, data-driven responses to regulatory

             16   questions.  Regulatory agencies contain really

             17   smart folks that come up with really good

             18   questions.  Those questions are really hard to

             19   answer when you don't have data.

             20             The other piece that is becoming more

             21   important as you do risk management assessments or

             22   "what if" scenarios. The stronger your base, the

                                                                           199

              1   stronger your foundation, the more able you are to

              2   answer those questions in a collaborative way.

              3             Also, the notion of benefit-risk

              4   assessment.  Should you run into a low-frequency

              5   toxicity, what are you going to do?  You need to

              6   come up with risk-benefit assessment for that, for

              7   yourself, for the company, and for the public at

              8   large.

              9             [Slide.]

             10             Just coming back to the FDA Modernization

             11   Act.  Most of you probably know much about it.

             12   There are two elements - Section 111, which talks

             13   about bridging PK studies, bridging PK studies in

             14   pediatrics.

             15             [Slide.]

             16             And Section 115, which we have talked

             17   about a lot, and that is the single adequate and

             18   well-controlled trial when there is supporting

             19   PK/PD information.

             20             [Slide.]

             21             Potential cost benefits.  These are the

             22   arguments that I try to use when I am running into

                                                                           200

              1   resistance to incorporate population PK into some

              2   of the trials.  One is that population PK can

              3   sometimes obviate the need for selected clinical

              4   trials, the age-gender study, renal impairment

              5   studies, or modified renal impairment studies,

              6   these are easy wins if you collect the data

              7   appropriately.

              8             To position the company to be able to use

              9   ICH E5 guidelines, to use PK bridging studies for

             10   submissions in Japan and other regions, and to also

             11   position the sponsor to use FDAMA, rewrite issues,

             12   Sections 111 and 115, as potential benefits.

             13             [Slide.]

             14             Other cost benefits are selection of

             15   indications based on PK/PD evaluation of the

             16   antimicrobial spectrum.  I alluded to this a few

             17   seconds ago, in that if your drug is not going to

             18   be able to get the job done, it is better to know

             19   now, and not pursue that indication.

             20             Smaller sample sizes associated with

             21   exposure-response versus dose-response.

             22             Selection of optimal dose may lower sample

                                                                           201

              1   size requirements for non-inferiority trials.

              2             [Slide.]

              3             I am not a statistician, but this is my

              4   sort of fundamental approach at looking at this.

              5             If you have an anticipated response rate

              6   for comparators of 85 percent, and you have a

              7   projected response rate or target hit rate for your

              8   compound of 80, 85, 90, or 95 percent, your sample

              9   size are listed to your right.

             10             If you remember the Monte Carlo simulation

             11   for our compound, it suggested the hit rate was 94

             12   percent.  The clinical trials data for Strep pneumo

             13   suggests that the efficacy was at 92 percent.  If

             14   you assume the 90 percent difference, your sample

             15   size would be 83 per arm versus 219 per arm.  That

             16   is a substantial savings in my view if you have the

             17   gumption to go there.

             18             [Slide.]

             19             Cost benefits.  Higher quality

             20   submissions.  I consider a submission like a

             21   manuscript.  You either pay now or pay later.  If

             22   you put together a really good submission or really

                                                                           202

              1   good manuscript, your reviews are minimal and you

              2   are published quickly.

              3             Similarly, if you are a reviewer and you

              4   get a really good manuscript, you are likely to

              5   review it very quickly and get it in the

              6   literature; if it's not, you are likely to have a

              7   real struggle time with it.

              8             I think a good submission will always

              9   facilitate a regulatory review.  It will enhance

             10   the relationship with our regulatory colleagues and

             11   minimize post-submission questions.  Once you have

             12   made your submission, it is sometimes difficult to

             13   go back and pick through all that data to answer

             14   very specific questions.

             15             I think it can facilitate transition with

             16   novel dosage forms based on PK studies if you know

             17   the PK/PD relationships and provide a basis for

             18   market-driven differentiation.

             19             [Slide.]

             20             In summary, selecting the optimum dose for

             21   the treatment of infection is important in order to

             22   maximize efficacy, minimize toxicity, and minimize

                                                                           203

              1   resistance development.

              2             I think you have seen that we have the

              3   tools to at least get an idea of how to do this

              4   best, and using an exposure-response approach to

              5   dose selection can facilitate knowledge-based

              6   decisionmaking, optimize trial designs, and

              7   streamline development and post-development costs.

              8             Thank you.

              9             DR. EDWARDS:  Thank you very much, Dennis,

             10   that was a very nice discussion.

             11             We will now move to Frank Pelsor from the

             12   FDA.

             13             Frank.

             14                         FDA Perspective

             15             DR. PELSOR:  Good afternoon.  I will be

             16   presenting an FDA perspective on the current status

             17   of dose selection and antimicrobial drug

             18   development programs.

             19             [Slide.]

             20             I have two objectives for my presentation

             21   this afternoon.  The first is to discuss why a

             22   well-developed rationale for dose selection is

                                                                           204

              1   important to the antimicrobial drug development

              2   process, and secondly, to discuss some of the

              3   Office of Clinical Pharmacology and

              4   Biopharmaceutics experience with dose selection and

              5   antimicrobial drug product applications.

              6             [Slide.]

              7             Why is dose selection important?  We have

              8   had this question come up several times during the

              9   course of the couple of days here, and I feel like

             10   I am preaching to the choir, but nevertheless, stay

             11   with me on this, and then I will take up some

             12   examples that I think will be informative.

             13             Antimicrobial drug therapy is a complex

             14   endeavor that includes more components than the

             15   target of the drug, the bacteria.  The patient

             16   status, for example, immunocompetence, and the

             17   adaptability of bacterial must be taken into

             18   consideration when selecting an antimicrobial drug

             19   regimen for therapy.

             20             In addition, the drug itself may have

             21   complementary effects, such as an inflammatory

             22   activity which will be beneficial to the patient. 

                                                                           205

              1   However, for our discussion today, we must limit

              2   our focus.  At some risk of oversimplification, our

              3   attention must be focused by distilling

              4   antimicrobial drug therapy into a couple of

              5   concepts.

              6             The first concept.  For an effective

              7   drug-bacterial interaction, the requirement is to

              8   deliver free drug to the site where the infectious

              9   organism is located. Drug delivery can be

             10   accomplished by varying the magnitude and timing of

             11   input.

             12             The second concept is that we must deliver

             13   effective therapy while remaining within the domain

             14   of acceptable toxicity.  Adjusting the inputs can

             15   help manage the toxicity.

             16             [Slide.]

             17             The next slide, which is already up,

             18   illustrates the relationship between these two

             19   concepts using dose-response curves for efficacy

             20   and toxicity.

             21             Traditionally, the dose for an

             22   antimicrobial drug is selected from the upper

                                                                           206

              1   plateau of the efficacy dose-response curve, and

              2   that is the top arrow, and the toxicity has been

              3   relatively low, the second arrow.

              4             In contrast, newer antimicrobial drugs

              5   appear to be accompanied by dose-response curves

              6   for toxicity that are closer to the dose-response

              7   curves for efficacy.  This shift in curves is

              8   illustrated by the center curve.

              9             [Slide.]

             10             For cases where management of these

             11   unwanted toxic effects may be possible, perhaps

             12   through dose reduction, it is not clear what effect

             13   the dose reduction will have on efficacy of the

             14   antimicrobial drug, because we usually have

             15   information for a single dose only.

             16             In addition, for special populations where

             17   dose adjustment may be necessary, it is not always

             18   clear what effect the dose adjustment will have on

             19   efficacy.  Ultimately, failure to have the

             20   necessary understanding of the relationship between

             21   effective dose response and toxic dose response can

             22   result in delaying drug product approval or even in

                                                                           207

              1   denying approval.

              2             [Slide.]

              3             Drug toxicity management attempts to

              4   maintain optimal drug therapy within constraints

              5   imposed by the drug's toxicity.  We know that we

              6   can use dose adjustment for higher risk patients,

              7   such as pediatric patients and patients with renal

              8   and/or hepatic impairment to achieve desired

              9   exposures.

             10             In order to deliver optimal therapy in

             11   these situations, we must have a thorough

             12   understanding of the location of the dose efficacy

             13   response curve.  Appropriate interpretation and use

             14   of pharmacokinetic and pharmacodynamic information

             15   can help us to locate the dose.

             16             As part of our review of information that

             17   sponsors submit to the Agency, we asked some

             18   critical questions about the rationale for dose

             19   selection with an emphasis on exposure-response

             20   relationship, we asked the following:  Are the dose

             21   and dosing regimen consistent with the known

             22   relationship between dose or concentration and

                                                                           208

              1   response?  And, secondly, are there significant

              2   risks related to the clinical pharmacology issues,

              3   for example, any changes in exposure related to

              4   intrinsic or extrinsic factors, and we try to

              5   determine how these risks should be managed,

              6   perhaps through dose and/or dosage adjustment.

              7             [Slide.]

              8             To this point in the presentation, I have

              9   been describing the importance of dose selection,

             10   however, we recognize that dose selection is an

             11   intermediate output of a process.  Therefore, I

             12   want to shift gears now and focus on the process.

             13             As Agency reviewers, we expect to see dose

             14   selection rationale based on a multi-stage process

             15   shown in this diagram.  Following the diagram in

             16   clockwise fashion, each stage serves as a building

             17   block for the next.

             18             Proceeding from preclinical in vitro and

             19   animal model studies through Phase I human

             20   pharmacokinetic studies, and Phase II human dose

             21   and activity studies, we should have explored the

             22   PK and PD indices and the targets.  By the time we

                                                                           209

              1   establish proof of concept in Phase II, we should

              2   understand pertinent PK and PD relationships and

              3   potential doses that may be selected for Phase III

              4   clinical trials.

              5             Phase III clinical trials should be

              6   designed in such a way that the data coming out of

              7   the trials not only can be used to test hypotheses,

              8   but that the data provides feedback about the

              9   relationship between PK/PD indices and the dose.

             10             The process also allows for modification

             11   of future studies by permitting feedback at various

             12   stages, but I want to emphasize we do not view the

             13   process as bidirectional.  I am separating

             14   bidirectional now from feedback.  That is, the

             15   selection of dose followed by construction of a

             16   database to support the dose is not appropriate in

             17   our view.

             18             Ultimately, we would like to appreciate

             19   how the various pieces of the puzzle fit together,

             20   the pharmacokinetics, the pharmacodynamics, the

             21   relationships, the dose, the efficacy, and safety,

             22   and how the information can be further transformed

                                                                           210

              1   into knowledge for us in evaluating other

              2   antimicrobial drug products.

              3             [Slide.]

              4             The rationale for dose selection is highly

              5   variable in the regulatory packages that we see.

              6   It really is not always clear to us how the sponsor

              7   selected the dose to pursue an indication.

              8             We have seen some well-described

              9   rationales here today, and we have see

             10   well-described rationales in submissions.  I will

             11   discuss an example later.  However, we have seen

             12   rationales that have been based on information that

             13   really is not informative.  I will discuss as

             14   example, as well.

             15             The third example that I will present is

             16   an example where it appeared to us that there were

             17   other issues driving the dose selection, such as

             18   marketplace and patient compliance or convenience.

             19   Marketing and patient management issues certainly

             20   merit consideration, however, the focus of our

             21   discipline and our review is on a PK/PD based

             22   rationale.

                                                                           211

              1             [Slide.]

              2             This slide describes the framework in

              3   which we review a sponsor's package with respect to

              4   dose selection.  I have taken it from knowledge

              5   engineering and data mining literature.  You may be

              6   more familiar with it in terms of the knowledge

              7   pyramid.  I call it the Data Information Knowledge

              8   Continuum, however, I have added a dimension

              9   described how the continuum relates to levels of

             10   understanding.

             11             (Slide.)

             12             This is the first example of the

             13   rationale.  This slide illustrates a

             14   not-too-uncommon description of the dose-selection

             15   rationale.  The PK/PD target for time above MIC is

             16   derived from the mean concentration time profile

             17   and mean MIC values.

             18             The sponsor tells us that the

             19   100-milligram dose was selected because it provides

             20   concentrates greater than the MIC for the

             21   appropriate pathogen for 40 percent or about 10

             22   hours in a 24-hour dosing interval.

                                                                           212

              1             It turns out that the Phase III clinical

              2   trials provided the necessary evidence to support

              3   approval of the product, but in terms of being able

              4   to understand the rationale for the dose selection,

              5   this really is what I would call a black-box

              6   approach.

              7             The dose may be effective, but the

              8   description of the rationale really is not

              9   sufficient to be of any help to us.

             10             (Slide.)

             11             In this slide, I am giving an example

             12   where we have a package.  In this case, the Agency

             13   reviewers analyzed the information, which contained

             14   MICs for pertinent organisms, drug concentration

             15   time profiles from Phase I studies, and protein

             16   binding data to determine if the dosing regimen

             17   tested by the sponsor could be supported.

             18             Our finding was that dose would not be

             19   appropriate to test in a clinical trial for the

             20   indication being sought. The results of the

             21   clinical trial revealed that the drug was inferior

             22   to the control.

                                                                           213

              1             The example points out the detailed

              2   consideration of PK and PD information for dose

              3   selection can be a powerful tool in antimicrobial

              4   drug development.  The ability to identify drugs

              5   early that will fail should be important knowledge

              6   to have.

              7             [Slide.]

              8             Lastly, this is an example where the

              9   package contained detailed information for

             10   microbiological data in vitro and animal model

             11   data, and Phase I pharmacokinetics data.  The Phase

             12   II study design included several doses based on an

             13   analysis and information we were able to understand

             14   the relationships that results in the doses

             15   selected for Phase II.

             16             [Slide.]

             17             In summary, I have shared with you the

             18   view that we feel strongly that the rationale for

             19   dose selection of antimicrobial drug products

             20   should be linked to the supporting studies and

             21   data.

             22             The basis for dose selection is critical

                                                                           214

              1   to understanding how to manage changes in patient

              2   drug exposure while maintaining acceptable safety.

              3   However, it usually is not clear to us where we are

              4   on the dose-response curve.  Should the aim of dose

              5   selection be the search for a single point to find

              6   the dose with the highest probability of efficacy?

              7             Further attempts to relate PK and PD

              8   targets to clinical outcome are rare.

              9             [Slide.]

             10             I have also shared with you that we see

             11   large differences in the level of detail included

             12   in rationale to support dose selection.  The

             13   rationale is seldom presented in terms of a data

             14   information knowledge continuum.  Often the dose

             15   selection rationale is based on PK/PD targets

             16   derived from mean data, so that we have no

             17   appreciation for the variation.

             18             The rationale for dose selection is not

             19   always apparent and dose adjustments based on PK/PD

             20   targets is often difficult to assess.

             21             Thank you.

             22             DR. EDWARDS:  Thank you very much, Frank. 

                                                                           215

              1   I really apologize for that technical problem.

              2   That is next to the worst nightmare, the worst

              3   being that the slides don't appear at all.

              4             We are going to move right along and now

              5   enter the second phase of the discussion, which is

              6   an exploration of methodology that we might utilize

              7   to enhance the current tools which are available.

              8             Let me begin right away with Hartmut from

              9   the University of Florida.  Please go right ahead.

             10       IV. Improvement in Dose Selections Through Clinical

             11              Applications of PK/PD in Antimicrobial

             12                    Drug Development Programs

             13                       Academic Perspective

             14             DR. DERENDORF:  Thank you.

             15             First of all, let me also on behalf of

             16   ISAP thank the FDA for hosting this meeting and

             17   making it possible.  I think this is so important

             18   to bring everybody together who has interest in

             19   this field and have this exchange of ideas, and the

             20   discussion so far has been great.

             21             [Slide.]

             22             What I would like to do in the next 10, 15

                                                                           216

              1   minutes is make some proposals of how we can do

              2   things better, and let me start by this very basic

              3   slide that shows you again what we are doing with

              4   PK/PD.

              5             We are trying to break down the final

              6   outcome that we have, and that, of course, is the

              7   relationship between effect as a function of time,

              8   and PK/PD separates the contributions that we get

              9   from exposure, which is the time-concentration

             10   relationship, and the response, which is the

             11   concentration-effect relationship.

             12             I would like to point out that we are in a

             13   very unique situation with anti-infectives, because

             14   that is one of the few cases where we can look at

             15   the pharmacodynamics separately, we can move it

             16   outside the body and look at it and study it in an

             17   in vitro system or the animal model.  That is truly

             18   unique and that is something that we should take

             19   full advantage of.

             20             The pharmacodynamic portion here, this is

             21   what microbiology contributes, and we have to find

             22   intelligent ways to link that to the exposure.

                                                                           217

              1             [Slide.]

              2             If you look historically at the field in

              3   the last 10, 15 years, three indices, as they are

              4   called, have emerged and are used today that link

              5   the PK to the PD, and they are, as you know, the

              6   time above MIC, the peak MIC ratio or the area

              7   under the curve MIC ratio, and depending on the

              8   group of drugs, we have certain parameters, certain

              9   indices that we prefer and use.

             10             However, if you look at them closely, you

             11   see that for the PK information, in all cases, we

             12   use the serum concentration, and for the PD

             13   information, in all cases, we use the MIC, so this

             14   is our raw data that goes into these indices.

             15             As was pointed out by John Rex very

             16   eloquently earlier, the MIC has its problems, and I

             17   will come to that.

             18             So, what can we do better?  This is the

             19   gold standard, this is what we are doing right now.

             20   It works to a certain extent as we have seen.

             21   Sometimes it works very well, sometimes it doesn't

             22   work, and what can we do differently.

                                                                           218

              1             [Slide.]

              2             Let's start with the PK.  We use the serum

              3   level.  Is that the right way to go?  Well, there

              4   are certainly two areas where we can improve.  One

              5   has been addressed here several times, and that is

              6   protein binding, and I am glad that there is

              7   consensus now that we really should use unbound

              8   concentration.

              9             If you go to the literature, the data is

             10   overwhelming, not only in the anti-infectives

             11   field, it is a concept that holds true in all of

             12   pharmacology, that the unbound concentrations are

             13   responsible for the activity. There may be some

             14   exceptions, but this is truly rare.

             15             [Slide.]

             16             This is not a new finding.  This is an old

             17   study from 1973, where a bunch of penicillins were

             18   compared with increasing degrees of protein

             19   binding.  We have ampicillin here with 22 percent,

             20   all the way up to cloxacillin with 95 percent, and

             21   you see for each of these compounds three bars.

             22             The one on the left is the MIC in broth in

                                                                           219

              1   the absence of proteins.  The one in the middle is

              2   the MIC in serum in the presence of proteins, and

              3   you will see as you come to the drugs with the high

              4   protein binding, you need much more drug to kill

              5   the bug.

              6             The third bar on the right is the unbound

              7   concentration of the sample in the middle, and that

              8   agrees very nicely with the MIC in broth, so

              9   showing that it is, indeed, the unbound

             10   concentration.

             11             Again, there are many, many studies that

             12   have shown the same thing, so I really think we can

             13   put this issue to rest now.

             14             [Slide.]

             15             The second issue that is tougher to deal

             16   with is the question of local exposure.  In all of

             17   these indices, we use serum concentrations, and the

             18   simple reason is it is easy to measure.  It is easy

             19   to get a blood sample, however, that is not usually

             20   where the infection is.

             21             If you look at drug distribution in this

             22   cartoon here, we can separate the body in the

                                                                           220

              1   vascular and extravascular spaces, and as you know,

              2   the infection is usually in the extracellular space

              3   here.

              4             So, if we look at this fluid, that also

              5   contains protein, and we apply the concept that we

              6   should use unbound concentrations.  The target

              7   concentration that usually is applicable to

              8   anti-infectives is the unbound concentration in the

              9   extracellular fluid or the free tissue level, as it

             10   is called.

             11             There have been numerous ways to measure

             12   this experimentally, mainly with blister fluid

             13   data.  Fortunately, there is a better method that

             14   we have used in our group extensively, and that is

             15   microdialysis.

             16             [Slide.]

             17             Microdialysis works with a probe that you

             18   stick at any site where you want to know the local

             19   concentration, and it measures directly the unbound

             20   concentrations, so it measures what you want to

             21   know, and it has tremendous potential.

             22             [Slide.]

                                                                           221

              1             To show you one example here, this is a

              2   comparison of two oral cephalosporins, cefpodoxime

              3   and cefixime.  In white, you see, after oral

              4   administration, the serum concentrations.

              5             If you do the PK, you find that the AUC is

              6   exactly identical of the two, however, if you

              7   measure the local free concentrations, and the

              8   tissue in this case is the muscle, these are the

              9   purple lines, and you clearly see that this drug

             10   cefpodoxime has about twice as much local exposure

             11   at the tissue level than the cefixime has.

             12             So, the total serum concentration can fool

             13   you, and this allows you to measure locally.

             14             [Slide.]

             15             Let's move on to the pharmacodynamics.

             16   MIC has its problems, we heard about that already.

             17   It is imprecise, it has an effect of 2-fold when

             18   you determine it, so that right there makes any

             19   calculation questionable.

             20             It is monodimensional, and I will come

             21   back to that, what that means.

             22             It is used as a threshold.  I think this

                                                                           222

              1   is conceptually the biggest problem.  We draw a

              2   line in serum concentration, and this is where we

              3   want to be, or we want to be above that line.  That

              4   is not really what happens in pharmacology.  We

              5   rarely do have black and white events, as we imply,

              6   with this threshold.

              7             We have learned to get around this issue.

              8   When the MIC doesn't work, then, we have some

              9   patches, such as post-antibiotic effect or sub-MIC

             10   effect that we use to explain what it should be.

             11             [Slide.]

             12             So, how can we do it better?  Well, the

             13   better way is to use kill curves, I believe, and we

             14   have seen several models.  This is the one that we

             15   are using.  We can reproduce any kind of exposure

             16   profile that you are interested in.

             17             What we are doing, we are doing the

             18   microdialysis and then reproduce the unbound

             19   concentration profiles in this in vitro model.

             20   Then, we can compare different dosing regimens and

             21   see what they do.

             22             [Slide.]

                                                                           223

              1             This is an example of a penicillin,

              2   piperacillin. We have in the top row here 2 grams

              3   and 4 grams.  We can look at multiple doses, once a

              4   day, q8, and 6 times a day treatments here, and you

              5   will always see two curves.  The black curve, the

              6   top curve is the control, and the red is treated

              7   curve under the same conditions.

              8             So, we can then compare the effects and,

              9   for example, if you compare these two pictures

             10   here, the 2 grams 6 times a day, you get an

             11   excellent response, whereas, with the 4 grams 3

             12   times a day you get a marginal response.

             13             Now, the total daily dose is the same,

             14   it's 12 grams a day, so you can deduce from this

             15   experiment that giving the drug more frequently

             16   will give you more effect, and that, is course,

             17   consistent with the approach of constant rate

             18   infusions with beta-lactams.

             19             [Slide.]

             20             Now, what other problems does the MIC

             21   have?  The MIC sometimes can give you misleading

             22   information, that is what I mean by

                                                                           224

              1   monodimensional.  This is a simple comparison of

              2   two bugs here.  On the left you see Strep

              3   pneumoniae with an MIC of 20; on the right,

              4   Hemophilus, and these are a bunch of kill curves

              5   with increasing concentrations of ceftriaxone.

              6             If I pick up the concentration of 10

              7   nanograms product milliliter, we see here, this is

              8   this blue curve, that for this concentration,

              9   clearly, the effect on Hemophilus is stronger.

             10             [Slide.]

             11             However, if you to high concentration, you

             12   get an exact opposite response.  The effect on

             13   Strep pneumoniae is better than on Hemophilus.

             14   Same bug, same drug, same MIC, you get different

             15   outcomes depending on concentration.  The reason is

             16   they have different kill rates.

             17             [Slide.]

             18             Another example of that here.  This is a

             19   simulation of a situation where you have exactly

             20   the same PK in these two cases here.  You have also

             21   exactly the same growth rate of the bacteria. You

             22   have the same MIC.  So, all these three indices

                                                                           225

              1   that we just looked at would be identical.

              2             However, these two bugs have different

              3   EC50, different sensitivity, and different maximum

              4   kill rates, therefore, the outcome, which is the

              5   pink curve here, is different in these two

              6   situations.  You would not pick them up by just

              7   looking at the three conventional indices.

              8             [Slide.]

              9             Another example of a penem antibiotic.

             10   This is a PK/PD study to look at the effect of food

             11   intake.  In the top curve, you have, for the same

             12   dose, the pharmacokinetics after a meal.  On the

             13   bottom, the fasted situation, you clearly see the

             14   impact that the food intake has.

             15             On first glance, you would think, well,

             16   this is clearly a lower exposure, therefore, the

             17   response probably is less.  However, if you do the

             18   PK/PD modeling, then, you will be surprised to see

             19   that actually this exposure profile will result in

             20   a better effect than the one that you see here.

             21   The reason is that you get an extended release by

             22   the food.  You cut off the peak levels, but yet,

                                                                           226

              1   over time, the release covers better.

              2             [Slide.]

              3             Another example again where you can get

              4   fooled.  This is a comparison of piperacillin in

              5   healthy subjects versus intensive care patients and

              6   again using the method of microdialysis.  You see

              7   on the left here, the serum concentrations is on

              8   the open symbols, and the concentration in muscle,

              9   and in comparison here, the exposure in patients.

             10             Clearly, if you compare these two down the

             11   same scale, the exposure in patients is lower, but

             12   you also see that the half-life is longer, and then

             13   again results in a better effect in the patients

             14   than compared to the healthy subjects.

             15             [Slide.]

             16             These are the kill curves when you use

             17   these exposure profiles and reproduce them in vitro

             18   and incubate the material, you clearly get a better

             19   effect here in patients than you do in the healthy

             20   subjects.

             21             [Slide.]

             22             So, to sum up, I believe that a simple

                                                                           227

              1   comparison of serum concentration and MIC is

              2   helpful, but frequently not sufficient to evaluate

              3   the PK/PD relationships of anti-infective agents.

              4             We need to consider protein binding, that

              5   we need to consider tissue distribution when we

              6   talk about the exposure, and that microdialysis may

              7   be a method that can help us.  There are other

              8   methods also are emerging, such as imaging, and I

              9   think a lot of new developments are going on in

             10   this area.

             11             PK/PD analysis based on MIC alone can be

             12   misleading, and kill curve analysis provides more

             13   detailed information about the PK/PD relationships

             14   than simple MIC determination.

             15             [Slide.]

             16             In general, I believe that intelligent

             17   PK/PD modeling can help to streamline rational

             18   clinical dose selection.  It has clearly its

             19   limitations and the final dose that comes out of

             20   these predictions needs to be confirmed in a

             21   clinical trial.  That is always necessary.

             22             [Slide.]

                                                                           228

              1             I would like for those of you interested

              2   in this topic to mention this meeting.  This will

              3   happen in September this year in Nurnberg, Germany.

              4   "Dosing the Magic Bullets" is in honor of Paul

              5   Erhlich's 150th birthday.  Outside the room, there

              6   are flyers of the meeting, and it is going to be a

              7   very, very exciting program.

              8             [Slide.]

              9             I would also like to thank all of the

             10   people in my group who have contributed to the data

             11   that I have shown you.

             12             Thank you.

             13             DR. EDWARDS:  Thank you very much.

             14             I am going to deviate from the schedule at

             15   the moment.  I think we would probably benefit by

             16   taking a 10-minute break.  We have no break

             17   scheduled for this afternoon.  Is that acceptable?

             18   I think so.  So, let us come back in 10 minutes.

             19             [Break.]

             20             DR. EDWARDS:  We are on a tight schedule

             21   at the end of the day.  My estimate is that we are

             22   going to be finishing sometime between 4:00 and

                                                                           229

              1   4:15.  So, if that helps some of you, if possible,

              2   we will be on the earlier side of that, but I think

              3   we are going to be right in that zone.

              4             I know for absolute certain we are going

              5   to be finished by 4:30, at least I will be, so that

              6   we can count on for sure, but I am hoping more for

              7   between 4:00 and 4:15.

              8             We will just go right ahead.  I told Greg

              9   that, as he was walking up right ready to present,

             10   and I call a break, it is a little bit like calling

             11   time out just before the last field goal to win the

             12   football game or lose the football game, so he has

             13   assured me he is ready to go now, and I am sorry

             14   for that.

             15                       Industry Perspective

             16             DR. WINCHELL:  First of all, I would like

             17   to start with the standard disclaimer that it is

             18   not at all clear to me that my perspective is that

             19   of the industry.  In fact, it is not at all clear

             20   to me that my perspective even matches that of most

             21   of the rest of Merck, hence, the sub-title of my

             22   talk.

                                                                           230

              1             Also, one of the problems with speaking

              2   this late in the program, of course, is everybody

              3   has already covered everything I am going to say.

              4   Therefore, and given the time, I will try to be

              5   brief.

              6             [Slide.]

              7             Moving into it, first of all, the dose

              8   selection in drug development, a point that has

              9   been made numerous times, but can't be emphasized

             10   enough, of course, is that what we are trying to

             11   identify is not just the dose, of course, but the

             12   dosing regimen.

             13             Another point, though, is that it is not

             14   just a single decision that we are making through

             15   the course of drug development, but it is actually

             16   a series of decisions we are making before each of

             17   the stages of trial - Phase IIA, Phase IIB, if you

             18   do both of them, Phase III, and then what you put

             19   in the label for marking the product.  Hopefully,

             20   each of these decisions is made with increasing

             21   certainty.

             22             Then, beyond that, in the label, you want

                                                                           231

              1   to consider, unless your drug has an unusually wide

              2   therapeutic index, you want to know whether you

              3   need dosing adjustments in the subpopulations.

              4   Even beyond that, if you really have a narrow

              5   therapeutic index drug, you have to take your

              6   dosing adjustments down to the level of

              7   individualization.

              8             In fact, the value of PK/PD actually

              9   matches that, as well, with a very wide therapeutic

             10   index drug, you really don't care about the PK/PD,

             11   you just give them a large enough dose, so the vast

             12   majority of your population gets a response, and

             13   you don't worry about it.

             14             The other angle is on the

             15   individualization where you are actually using

             16   PK/PD and therapeutic drug monitoring to pick the

             17   dose for an individual patient.  Of course, the

             18   vast majority of drugs and the vast majority of

             19   antibiotics falls somewhere in between those two.

             20             [Slide.]

             21             This is sort of my generic PK/PD slide,

             22   and I actually like Hartmut's more, so I may have

                                                                           232

              1   to steal it from him.  I would like to make two

              2   points with this.

              3             First of all, in dividing pharmacokinetics

              4   and pharmacodynamics, both kinetics and dynamics,

              5   coming from an engineering background, are related

              6   to time.  They are time-dependent events.  So, what

              7   we are interested in, in pharmacokinetics, is drug

              8   concentration with time, and what we are interested

              9   in, in pharmacodynamics, is drug effect with time.

             10             The other point, too, is, with an

             11   engineering background, is thinking of this really

             12   as a black box.  When you are doing just

             13   dose-response relationships, you really have a

             14   large black box where you are going from the two

             15   ends, from dosage to effect.

             16             One of the objectives of PK/PD really, as

             17   I see it, is to divide that black box into two,

             18   pharmacokinetic angle, where you have the

             19   intermediate measure of drug concentration with

             20   time, and then the second black box.

             21             [Slide.]

             22             To amplify on that a little bit, to start

                                                                           233

              1   with pharmacokinetics, obviously, we all know that

              2   pharmacokinetics depends on many different factors,

              3   however, as I think George demonstrated very amply,

              4   I think we are pretty good at this point at

              5   characterizing the pharmacokinetics of drugs, and

              6   with population pharmacokinetics, including the

              7   variability of that drug, so it is critically

              8   important.

              9             But that part of it, I think we really

             10   know how to do, and, of course, there is the added

             11   complication of the concentration site of action,

             12   but that is already I think, I knew would be

             13   covered amply by other speakers, and I won't touch

             14   on that, but I would add the point that if you

             15   aren't able to measure drug at the site of action,

             16   you can incorporate distribution to tissue into

             17   your pharmacodynamic side of the model.

             18             On the other hand, pharmacodynamics, I

             19   think, is the more difficult part, and here I have

             20   just listed a bunch of the factors I think

             21   influence, sort of off the top of my head,

             22   pharmacodynamics.

                                                                           234

              1             I have seen other lists that are probably

              2   more comprehensive than this, but the key point is

              3   you are treating a population of bugs in a

              4   population of patients, and there is a whole series

              5   of steps that leads to your drug effect, any one of

              6   which could be the rate-limiting step.

              7             In addition, you have a whole bunch of

              8   other factors that modulate that effect, and we are

              9   trying to reduce all of this into a simple

             10   pharmacodynamic model.  The key is it really isn't

             11   that simple.

             12             [Slide.]

             13             So, this is my attempt, and I think it is

             14   pretty consistent with what was said before, to

             15   summarizing what the current paradigm is for

             16   identifying antimicrobial PK/PD, and I think it is

             17   quite consistent with what has been presented

             18   before.

             19             The first part is characterizing the

             20   activity in non-clinical studies, in vitro systems,

             21   animal models, all of that has been covered,

             22   identifying the metric of exposure.  At least until

                                                                           235

              1   now, the standard has been to identify, you know,

              2   what is the index of exposure.

              3             Population PK/PD modeling in Phase I, and

              4   using Monte Carlo simulation, and then I think the

              5   hard part and the part that I am addressing is

              6   confirming these regimens and exposure metrics in

              7   clinical trials.

              8             So, again, the first part I think has

              9   really been covered,, where I think we still have

             10   room for improvement is in the last part - once you

             11   get into the clinic, what do you do with your

             12   clinical PK, clinical PK/PD information to improve

             13   how you select dose.

             14             [Slide.]

             15             So, I have listed here what I see are four

             16   of the opportunities for improvement.  The first

             17   one has been touched on by two of the previous

             18   speakers, Hartmut and Dr. Benincosa, is to use more

             19   dynamic PK/PD models.  This goes back to what I

             20   said before, and I will amplify in a minute. You

             21   really want to take time into account, if you can,

             22   in what the drug effect is.

                                                                           236

              1             The second is really a no-brainer, which

              2   is the increased use of simulation at all stages of

              3   development.  I really think simulation is one of

              4   the really underused tools in clinical drug

              5   development, and it is not just to go from Phase I

              6   to Phase II.

              7             As you go through drug development, there

              8   are many, many opportunities to use the data you

              9   have, whatever it is, preclinical data, data from

             10   other compounds, data from compounds that died

             11   previously, or whatever, there are many

             12   opportunities to use simulation to help guide the

             13   design of your next study, and I really don't think

             14   overall we make full use of that.

             15             Of course, the major caveat is that your

             16   simulation is always only as good as the data by

             17   which it is driven, so in order to do good

             18   simulation, you have to develop good data

             19   throughout the course.

             20             If you are working off bad in vitro data,

             21   you are going to get bad simulations.  If you do a

             22   bad job of collecting population PK in Phase II,

                                                                           237

              1   you are going to have bad simulations for Phase

              2   III.  So, you must have good data to drive your

              3   simulation.

              4             Another point which has also been raised,

              5   but it is easy to say that we really need to do it,

              6   and that is to include population PK/PD in your

              7   Phase II and Phase III trials, and actually, the

              8   Phase II is really the most critical, in my view,

              9   because that is where, if at all possible, you have

             10   the most dynamic dose range, which I will get into

             11   as well in a minute, but there is just no getting

             12   around it.

             13             If you want to use concentration data,

             14   what we have heard about a lot today is getting to

             15   that Phase II and Phase III, especially the Phase

             16   II, dose selection, but unless you do the

             17   population PK/PD in Phase II to drive your Phase

             18   III selection, and then do it in Phase III to

             19   confirm all of that, you really have lost an

             20   opportunity.

             21             The other issue where I think we can do

             22   better at that level in our pharmacokinetic

                                                                           238

              1   analysis is really you can characterize key

              2   factors, and here, I have picked three, and it

              3   reflects the fact that I work largely in

              4   antivirals, what I picked.

              5             But the key is to think about your drug

              6   and what the key factors are, and then try to build

              7   those into the model and into your analysis.

              8   Again, these are just three examples, but you

              9   really have to think about what it is.

             10             You could do something else like the

             11   natural history of disease is another aspect that

             12   you are able to build in.  The other one that

             13   George did, which is the amplification of a

             14   resistant subpopulation.  All of those can be built

             15   into your pharmacokinetic and pharmacodynamic

             16   models.

             17             [Slide.]

             18             So, to amplify a little bit on the dynamic

             19   aspects of PK/PD modeling, I think I start from a

             20   fundamental philosophical point, for antimicrobial

             21   response of actually almost any pharmacodynamic

             22   endpoint, which is these responses are a function,

                                                                           239

              1   often complex, of drug concentrations over time at

              2   the site of action, so that is why I have trouble,

              3   at least initially, with focusing on exposure

              4   metrics like time above MIC or AUC over MIC,

              5   because I really don't think that they fully

              6   account for the dynamics.

              7             Again, there has been discussion of the

              8   limitations of MIC, which I think are widely

              9   recognized.  I do think there are good relative

             10   measures of what kind of exposure it takes for

             11   comparing either within pathogens or even across

             12   pathogens, but I think the human physiological

             13   system is sufficiently different that it is not a

             14   direct correlation, as has been raised many times

             15   before.

             16             Nevertheless, I think that they can be

             17   useful, in fact, they have obviously been very

             18   useful, however, starting out particularly in the

             19   Phase I/Phase II part of drug development, it is

             20   sort of where you want to end up, in my view, not

             21   where you want to start.  If you can do it, you

             22   would rather start with a more complex model that

                                                                           240

              1   incorporates the dynamics.

              2             Of course, having PK/PD models, such as

              3   Dr. Derendorf has described, and Lisa, as well,

              4   that you can incorporate both concentration and

              5   time into the model can be more useful.

              6             It allows you to account for more complex

              7   data, for example, post-antibiotic effect, there is

              8   really nothing about post-antibiotic effect that is

              9   magic.  It is really quite easy to conceive of a

             10   model in PK/PD terms that will explain

             11   post-antibiotic effect.

             12             Again, having time in there allows you to

             13   do more robust simulations.  You can get much

             14   better information if you have time information in

             15   there.  Of course, the price you have to pay to

             16   have more robust data is it requires more data to

             17   drive these PK/PD models.

             18             [Slide.]

             19             Now, again, characterizing key factors, I

             20   have just listed three, but as Dr. Powell started

             21   to mention, taking into account factors other than

             22   just taking PK/PD are very important for

                                                                           241

              1   understanding.

              2             Adherence, of course, is critical in the

              3   HIV world, but I think it is also relevant for

              4   antibiotics.  Any time you have ambulatory patients

              5   who are not taking observed dosing, adherence can

              6   be a critical factor, and, in fact, can completely

              7   obscure, in the cases of HIV, can completely

              8   obscure your PK/PD relationship, because whether

              9   somebody responds or not, or develops resistance or

             10   not, is much more a function of whether they were

             11   adherent to therapy than what their PK/PD exposure

             12   was on the day they came in for PK day and had an

             13   observed dose.

             14             But also you can flip that around, if you

             15   have a dynamic PK/PD model, you can actually assess

             16   the effect, and this has been done again in HIV.

             17             You can assess the effect of adherence

             18   patterns if you are able to gather them, by

             19   following or simulating what you think the

             20   concentration over time is for the adherence

             21   pattern that you have either measured or somehow

             22   assessed or even simulated in that patient.  You

                                                                           242

              1   can get some idea of what the forgiveness or what

              2   the effect of that non-adherence is on your

              3   outcomes.

              4             Likewise, emergence of resistance is

              5   another one that can be characterized.  Emergence

              6   can actually be a fairly difficult stochastic

              7   model, but also a much simpler problem which has

              8   been addressed here is just treatment of resistant

              9   pathogens, which is actually fairly easy to

             10   accommodate in PK/PD analyses.

             11             The last one is host factors and any of

             12   the factors like that, factors on my list

             13   previously, which can be incorporated into your

             14   population PK/PD analysis as covariates, for

             15   example, neutrophilic, you can incorporate it any

             16   way you want, neutropenic or not, or neutrophil

             17   counts, or whatever you want to do.

             18

             19             Now, of course, you can do these kinds of

             20   analysis just looking at your standard

             21   dose-response data, but what you gain by

             22   incorporating it instead into a PK/PD analysis is

                                                                           243

              1   you see what the effect on drug concentration is.

              2             So, for a neutropenic patient, what is the

              3   concentration that is required to treat a given

              4   organism in that patient, and what is the

              5   concentration that is required in the

              6   non-neutropenic patient.

              7             So, incorporating these covariates into

              8   population PK/PD modeling, and this is what you

              9   really gain by doing it in a large population in

             10   Phase III, only in that very rich dataset or very

             11   extensive dataset do you really have the numbers to

             12   have any chance of identifying what the important

             13   covariates are.

             14             [Slide.]

             15             Of course, there are challenges in trying

             16   to do all of this, and the first one I think we

             17   have hit on in the discussion a number of times.  I

             18   think the biggest single challenge scientifically

             19   in trying to do this in anti-infectives is the

             20   limited dynamic range that you have in clinical

             21   studies.

             22             In the studies that you do, you have a

                                                                           244

              1   limited dose range often, and even more important

              2   for trying to separate out or validate effects, is

              3   you have a limited number of regimens.  It is very

              4   hard to get people to do two times a day or three

              5   times a day if people think it might be a

              6   once-a-day drug, and it is very hard to get a

              7   range.

              8             I think something that is not listed here,

              9   that also came up in antimicrobials, is you

             10   actually have a limited dynamic range in your

             11   pharmacodynamic endpoints, as well.  When your

             12   endpoints or, say, microbiology either are killed

             13   or not killed, just a dichotomous answer, there is

             14   really not that much information there, especially

             15   if it is only measured at one time point.

             16             So, I think all of those combine to say it

             17   is really hard to get the necessary dynamic range

             18   you need to be able to identify what these are.

             19             It is even more difficult, of course, in

             20   serious infections, which again is an area I have

             21   worked most in, both in antifungals and HIV, when

             22   you have serious infections, of course, the

                                                                           245

              1   benefit-risk is such that you don't want to take

              2   the chance of being low, so the difficulty is you

              3   immediately try to get as high as you can on the

              4   dose-response curve, and it becomes very difficult,

              5   then, to characterize where that curve starts to

              6   fall off and where you start to lose efficacy.

              7             This came up before, it is hard to do a

              8   robust Phase II study in those cases because you

              9   just can't generate the data that is on the steep

             10   part of the dose-response curve.

             11             Finally, a point that Dr. Craig, who

             12   apparently has left, raised earlier is it is very

             13   difficult because of this limited dynamic range, to

             14   actually validate, if you choose them, those

             15   metrics that you have chosen preclinically.

             16             [Slide.]

             17             I have a slide to illustrate that.  This

             18   is for antifungal caspofungin where I have plotted

             19   over three different doses AUC versus C24, so

             20   trough concentration, of course, that could be time

             21   above MIC, you get very much the same plot.  But

             22   what you see, because it was the same regimen, at

                                                                           246

              1   the same length of infusion, what you see is a very

              2   good correlation between C24 and AUC.

              3             So, based on this information in this

              4   study, in this Phase II study, you really can't

              5   differentiate between whether it is C24 or AUC,

              6   which, of course, becomes important, say, when a

              7   patient has renal impairment.  Now you have changed

              8   the relationship between C24 and AUC, and you don't

              9   know, on the basis of this data at least, which one

             10   is going to be really driving efficacy.

             11             [Slide.]

             12             An area, which I don't think we have

             13   touched on as much today, are the practical issues

             14   in trying to do this, in particular, the practical

             15   issues in trying to do population PK.  To me, the

             16   number one issue on this is timing, and that hasn't

             17   come up at all.

             18             If you are trying to drive dose decisions

             19   using PK/PD, it is essential that you deliver the

             20   data and the analysis in time to make the decision.

             21   It does no good to do a PK/PD analysis and not

             22   deliver, because they are going to make the

                                                                           247

              1   decision already, they are not going to wait for

              2   the PK/PD analysis, and you are not going to hold

              3   up going from Phase I to II, or from II to III

              4   waiting for a PK/PD analysis to be done.

              5             It is a very, very difficult problem, I

              6   think it is the hardest one.  You have to plan way

              7   ahead, you have to take extraordinary measures to

              8   get your data in-house in time and get people to do

              9   the analysis in real time, so that you can deliver

             10   a PK/PD analysis in time for people to use it for

             11   effective decisionmaking with respect to dose, and

             12   that is one of the hardest.

             13             Of course, another one, especially for

             14   population PK, which Dennis touched on, are the

             15   logistics and resources are very difficult.  Again,

             16   it is the quality of the data that this really

             17   drives.  It is very difficult to generate quality

             18   population PK data.

             19             You have to have accurate sampling times,

             20   you have to have accurate sample handling, and it

             21   can be very difficult, particularly in a Phase III

             22   setting.  I actually think anti-infectives people

                                                                           248

              1   are used to doing this, and it is easier to do, as

              2   opposed, to say, in an obesity clinic where it can

              3   be very difficult to get accurate PK/PD data.

              4             The last one, I think once you get into

              5   combination therapy, PK/PD becomes extraordinarily

              6   difficult.  It is really hard to differentiate, in

              7   clinical studies, it is hard to differentiate out

              8   what the relative contributions of different drugs

              9   are in combination therapy.

             10             So, then, I would like to finish with just

             11   a brief review of sort of the history of drug

             12   development of anti-infectives at Merck.  Actually,

             13   I missed a point, which was a key point, which is

             14   the organizational inertia and resistance, which

             15   Dr. Powell was hitting on.

             16             One of the reasons why it is difficult to

             17   do population PK/PD is just it is hard to get our

             18   colleagues in clinical research and senior research

             19   management to agree to do this.  Part of it is just

             20   inertia, although in some cases, it is outright

             21   resistance.

             22             But nevertheless, my staff and I, we spend

                                                                           249

              1   an inordinate amount of time on the phone trying to

              2   convince people to draw samples when we want a

              3   sample, allow them to do all of that, go back even

              4   to preclinical to convince our preclinical

              5   colleagues to draw the samples, to do the right

              6   experiments to generate the PK/PD.

              7             So, again, I think that this is another

              8   major stumbling block at least in the industrial

              9   setting in trying to get this done.  My experience

             10   at Merck at least has been that you really have to

             11   win this battle incrementally, as well as

             12   therapeutic area by therapeutic area.

             13             You have to take what they--in terms of

             14   sampling, deliver on that value, then, turn around

             15   and they will be much more amenable to doing it the

             16   next time if you can show value from whatever you

             17   can convince them to do the first time.

             18             [Slide.]

             19             So, with that background, I would then go

             20   to my summary and how this has evolved at Merck.

             21             Starting with indinavir, our protease

             22   inhibitor, in this case actually, we did not

                                                                           250

              1   prospective PK/PD, in fact, all of the PK/PD

              2   analyses that have been done for indinavir were

              3   done post hoc, well after the dosing decisions were

              4   made--well, five patients, was it, six.

              5             One of the reasons this happened, just as

              6   an instructive, is we actually had a bad experience

              7   in one of the previous HIV drugs, an RTI we were

              8   developing earlier, where we generated a set, in a

              9   Phase II study, we generated a set of PK data.

             10             It was just completely useless because it

             11   was not collected properly, we didn't have good

             12   sampling times.  It basically was just noise, and,

             13   in fact, when you tried to do a PK analysis, it

             14   just added noise to the data and was a worse

             15   analysis than just doing dose.

             16             As a result of that, we lost credibility

             17   with our Anti-infective group, so when it came to

             18   developing the protease inhibitor, they were much

             19   less willing to put any faith in, yes, you can

             20   draw, you can actually do this in a clinical

             21   setting.  So, a lesson learned there.

             22             The second one is our carbapenem.  In this

                                                                           251

              1   case, the beginning part, we actually followed much

              2   of the paradigm that was listed, you know, just

              3   another carbapenem, you know, time above MIC was

              4   largely assumed to be the driving force, and our

              5   dose selection was based on time above MIC as

              6   measured in the animal models done by Dr. Craig, as

              7   I recall, and human PK, and that was the dose

              8   selection.

              9             However, we didn't do, after that, we

             10   didn't do any Phase II or Phase III population

             11   pharmacokinetics, after that, we relied just on

             12   dose.

             13             That was in, say, that was approved in

             14   '91, so it was developed in 2000-2001 time frame.

             15             With caspofungin, on the other hand, we

             16   did manage to convince them to do prospective

             17   population PK/PD in all of the clinical trials, and

             18   that worked very well, and it turned out to be very

             19   supportive actually of the dosing selections in

             20   this case.

             21             That actually was a little bit of an easy

             22   case, however, because it is an I.V. drug, so it is

                                                                           252

              1   relatively easy to characterize, and it is given in

              2   a hospital setting, so compliance isn't an issue.

              3             So, it was very useful, but it was also

              4   one of the easiest cases in which you are able to

              5   characterize population PK/PD.

              6             Just as a sign of the progress we are

              7   making at least at Merck, one of the drugs we now

              8   have in early clinical development, we started

              9   early, we did human PK predictions to select the

             10   initial dose based on preclinical data that we got

             11   started.

             12             We are doing prospective population PK/PD

             13   and we have it already built into the plans to use

             14   it for selecting dosing regimens for our subsequent

             15   trials.  We are using MEMS-caps or planning to use

             16   MEMS-caps in order to capture compliance data, to

             17   try to incorporate that into the PK/PD modeling.

             18             So, I think what this shows is again you

             19   can see the incremental increase in the acceptance

             20   of these approaches just within the Anti-infective

             21   group at Merck, and again it is a question of

             22   showing value and then delivering on that value,

                                                                           253

              1   and avoiding the kind of mistakes we made early in

              2   the HIV program.

              3             Basically, that's it, and I have just

              4   tried to give you some aspect at least at Merck as

              5   to what the issues and opportunities are in this

              6   area.

              7             DR. EDWARDS:  Thank you very much, Greg.

              8             I will now call on our last speaker, Jenny

              9   Zheng from FDA, to comment on the FDA perspective.

             10                         FDA Perspective

             11             DR. ZHENG:  Good afternoon, everybody.

             12             I will talk about improvement in dose

             13   selection from FDA's perspective.  The views

             14   expressed in this presentation actually are based

             15   on FDA's current experience.

             16             [Slide.]

             17             I will, first, very briefly discuss the

             18   studies we need to evaluate the dose selection and

             19   then move on to the second topic, which is use,

             20   model-based, a quantitative  approach to dose

             21   selection.

             22             The use of PK/PD for dose selection.  Dose

                                                                           254

              1   selection is very challenging.  You have to

              2   integrate both efficacy and the safety.  The dose

              3   should be selected to demonstrate at such a dose, a

              4   drug is not only efficacious, but also safe, so

              5   integration of both are important, however, the

              6   safety consideration is not the main topic of this

              7   presentation.  I am going to focus on the dose

              8   selection from efficacy perspective.

              9             Resistance is a special issue for

             10   antimicrobial agents, so it is important and should

             11   be considered for dose selection.

             12             [Slide.]

             13             First, I will discuss the studies we need

             14   for evaluation, the dose selection.  It comes from

             15   four areas, first, microbiology in vitro data,

             16   which include all the susceptibility data, protein

             17   binding, post-antibiotic effect data, and

             18   preclinical data including the PK/PD studies in

             19   various animal and in vitro model.

             20             The primary objective of those studies is

             21   to identify the important PK/PD indices and

             22   estimate the magnitude of the PK/PD indices.

                                                                           255

              1             [Slide.]

              2             PK study from Phase I is necessary for

              3   dose selection except the PK study in healthy

              4   subjects, we would recommend PK study in special

              5   population will be conducted earlier, so the

              6   information from that study can also be used for

              7   dose selection.

              8             Phase II studies are very important for

              9   dose selection.  For efficacy perspective, there

             10   are proof or concept studies.  They provide a lot

             11   of information on efficacy, on the other hand, the

             12   well-designed Phase II dose-ranging study can be

             13   very informative to define the PK/PD relationship

             14   in human, which currently have very limited data.

             15             From safety perspective, Phase II studies

             16   are the only studies actually we can use to

             17   identify any dose-related adverse events, because

             18   most of the time, in the Phase III, it is going to

             19   be a fixed dose study.

             20             So, integration of efficacy and the safety

             21   from Phase II study is important to optimize the

             22   dose for Phase III.

                                                                           256

              1             [Slide.]

              2             Dose selection is such an important issue,

              3   so they would recommend early communication with

              4   us.  The desirable time could be prior to Phase II

              5   and Phase III study.

              6             [Slide.]

              7             At the current time, as Frank discussed in

              8   his presentation, the rationale for dose selection

              9   in the antimicrobial drug application is variable.

             10   Very often the dose actually is selected based on

             11   mean PK data in relation to MIC for relevant

             12   pathogens.

             13             Two issues are associated with this

             14   approach.  One is that the PK variability is not

             15   considered.  The underlying assumption is that

             16   exposure is the same among the population at the

             17   dose, obviously, it is not correct.

             18             So, a second issue is it is not always

             19   clear how high the concentration should be above

             20   MIC, or how long the concentration should be above

             21   MIC.  In the situation where you have to decide a

             22   dose between X or 2X, this approach barely give you

                                                                           257

              1   any clue of which one you are supposed to choose.

              2             So, I will discuss a quantitative

              3   approach.  This is not new, especially as the last

              4   speaker of the presentation, but I would emphasize

              5   the utility of this method for dose selection.

              6             [Slide.]

              7             Quantitative approach is to use modeling

              8   and the simulation, to quantitatively predict the

              9   outcome.  The advantage is quantitative, so you can

             10   use the model to make prediction, to predict the

             11   outcome.

             12             Secondly, the decision is more

             13   transparent, a decision can be made more

             14   objectively than subjectively, so it may reduce the

             15   chance of making human error.

             16             [Slide.]

             17             Quantitative approach is about predicting

             18   outcome. What we are predicting here is if the drug

             19   can kill the bacteria or inhibit bacterial growth

             20   at a certain dose in a human who is infected by the

             21   pathogen.

             22             Assuming a subject is infected by the

                                                                           258

              1   pathogen, and dose X is given to the subject,

              2   according to the PK and the protein binding, you

              3   can predict unbound drug exposure for this subject,

              4   but the question still remains if the pathogen can

              5   be killed at such dose.

              6             So, to answer that question, we have to

              7   rely on PK/PD relationship obtained from

              8   preclinical study.  It has been found that some

              9   PK/PD indices actually do correlate with bacterial

             10   killing or bacterial inhibiting effect, so a PK/PD

             11   index can be obtained from those studies to be used

             12   to predict the bacterial killing and the inhibiting

             13   effect.

             14             Knowing the PK, knowing the MIC of

             15   pathogen, you can calculate the PK/PD index from

             16   this subject, compare that value based on PK/PD

             17   indices you found from the preclinical study.  If

             18   PK/PD index for unbound drug in subject is above

             19   the PK/PD index obtained from preclinical study, we

             20   would assume the pathogen can be killed at such a

             21   dose.

             22             [Slide.]

                                                                           259

              1             The goal of this approach is to predict

              2   the percent of patients who could reach a PK/PD

              3   target at a range of doses.  Gathering information

              4   from the trial is important, however, design the

              5   trial right to get the right information is more

              6   important.

              7             So, the results from this analysis can

              8   actually guide you to design your trial.  If the

              9   objective of the study is to demonstrate efficacy

             10   at a fixed dose, you can chose a dose such that the

             11   majority of subjects actually could reach the PK/PD

             12   target, however, if the objective is to design the

             13   Phase II dose-ranging study to define PK/PD

             14   relationship, you probably can choose the doses

             15   which should be defensible [?] with regard to

             16   percent of subjects reach the PK/PD target.

             17             By doing that, you increase the power of

             18   detecting the relationship.

             19             [Slide.]

             20             In the next couple of slides, I will use a

             21   simulated example to illustrate this methodology.

             22   Again, this is not new, but I just want to

                                                                           260

              1   emphasize the utility for dose selection.

              2             PK data from Phase I can be used to

              3   establish the PK models, which include estimation

              4   on not only PK parameters, and also its viability,

              5   so the model can be used to generate a PK profile

              6   at a dosage even not studied.

              7             So, the advantage of this approach is you

              8   can explore many scenarios.  The concentration time

              9   profile for this simulated drug at dose X, actually

             10   are generated and presented in this figure.  As you

             11   can see, these are actually very different among

             12   the population, even the same dose is given.

             13             Knowing the MIC value of a pathogen, you

             14   can calculate PK/PD indices, but for this example,

             15   I use MIC 90. I know it's a single value, but it is

             16   a more conservative consideration.  It represents

             17   some distribution above this MIC, but again it is

             18   more conservative approach.

             19             So, based on the individual profile and

             20   MIC 90, you can calculate the important PK/PD

             21   index, which had been found from preclinical study.

             22             [Slide.]

                                                                           261

              1             So, the distribution of that PK/PD index,

              2   here, I just assume, I use the MIC as the key PK/PD

              3   index.  So, distribution of that PK/PD index at

              4   such dose is presented in this histogram.

              5             Assuming from the preclinical study, the

              6   AUC-MIC ratio of 35 or greater is associated with

              7   desirable bacteria killing effect, we can calculate

              8   percent of subjects who actually can reach this

              9   target level.  At dose X, it is 77 percent.

             10             [Slide.]

             11             Using the same approach, you can calculate

             12   the percent of subjects who reach that PK/PD target

             13   in a range of doses for different pathogens with

             14   different MIC values. I will use the term

             15   "response" to represent the percent of subjects

             16   with AUC-MIC greater than 35 for the purpose of

             17   explaining these slides.

             18             In general, the dose-response relationship

             19   can be described by the Emax model, meaning that at

             20   certain point, increasing dose will not result in

             21   any benefit with regard to response.

             22             The second point is the dose-response

                                                                           262

              1   relationship is going to be very different.  It

              2   depends on the MIC value of the pathogen.  So, at a

              3   certain dose, the response could be very different,

              4   depends on MIC of the pathogen.

              5             The right vertical line represents the

              6   dose X.  As you can see, at such a dose, for the

              7   pathogen with MIC 0.5 micrograms, the response is

              8   in the range of 80 percent, but the response rates

              9   actually are very low for the other two pathogens

             10   with MIC value of 1 and 2, which is not very

             11   different from 0.5 actually, only 1-fold, 4-fold

             12   difference.

             13             So, knowing your target right is very

             14   important for your dose strategy here.  Knowing the

             15   dose-response relationship is very informative for

             16   dose selection.  For example, I am using a pathogen

             17   with MIC 90, 0.5 microgram per ml as an the example

             18   here.

             19             You wouldn't choose dose X 1 because the

             20   response is going to be too low.  You probably

             21   don't want to choose the dose X 2 either, because

             22   even though the response probably is high, but the

                                                                           263

              1   dose itself if not very robust.  A little change in

              2   the dose would result in dramatic difference in

              3   response rate, so it may not be a good dose either.

              4             Definitely, you don't want to pick up the

              5   dose X 3 because it is unnecessarily too high.

              6   Selecting this dose, you may have a safety problem.

              7   The dose X 4 probably is reasonable.  At first, it

              8   provides pretty good response, and it is on the

              9   flat curve.  It is a little bit more robust.

             10             So, knowing where your dose is on the

             11   curve is important for successful development of

             12   the drug.

             13             [Slide.]

             14             Successful treatment of infection in most

             15   interaction of host drug and the bacteria.  The

             16   factors we have considered in this model include

             17   the pharmacokinetics, protein binding,

             18   susceptibility of the pathogen, and PK/PD

             19   relationship, however, many other factors have not

             20   been considered in this methodology.  They could be

             21   tissue penetration.  We have heard the issue with

             22   that.  The post-antibiotic effects, some killing

                                                                           264

              1   rate, and more importantly, we have not considered

              2   immune system [?] in this model, but we all think

              3   it is important.

              4             So, the factors not being considered may

              5   represent the potential limitation of this

              6   approach.

              7             [Slide.]

              8             The most significant issue is we use PK/PD

              9   from animal to predict the outcome in human, so the

             10   issue is the predictability of PK/PD relationship

             11   in animal for treatment of infections in human is

             12   not clear, because most of the time the PK/PD

             13   relationship for most of the drugs was established

             14   only in human.

             15             So, to improve our understanding, this

             16   PK/PD relationship in human, we need to gather more

             17   data from Phase II studies and from Phase III

             18   studies.

             19             Again, designing the trial is more

             20   important than gathering the information.

             21             [Slide.]

             22             In summary, a model based on quantitative

                                                                           265

              1   approach is informative for dose selection,

              2   however, it raises the potential limitation, the

              3   dose should be selected based on the totality of

              4   the available data.

              5             Dose selection is an ongoing process.  At

              6   first, you can gather microbiology preclinical data

              7   to integrate with Phase I data, and doing the

              8   analysis to help you to design the Phase II.

              9             The results from the Phase II study can be

             10   very useful to confirm the hypothesis you generate

             11   from the early development process, and integration

             12   of both safety and efficacy can help you to design

             13   the dose for Phase III.

             14             Selected dose for Phase III can be tested

             15   actually in the trial.  The results from the Phase

             16   III trial can further confirm the hypotheses you

             17   use at the beginning.

             18             [Slide.]

             19             So, in the future, we would like to see

             20   more well-designed, dose-ranging Phase II studies

             21   in appropriate infection.  From those studies, it

             22   can provide PK/PD relationship in human and also

                                                                           266

              1   safety information.

              2             The improvement of the model used in

              3   quantitative approach is needed except as a

              4   prospective, we need to consider the safety and

              5   resistance.  I wish we could have all the

              6   relationships presented by George Drusano in his

              7   presentation.  He present all the prospective.  We

              8   wish we could have the relationship before the drug

              9   is approved other than after that.

             10             So, evaluation of indices other than

             11   AUC-MIC, Cmax-MIC, and the time of MIC is

             12   encouraged because we know those parameters have

             13   some limitation as the previous speaker has talked

             14   about.

             15             Finally, development of optimal duration

             16   of therapy should be considered.

             17             Thank you.

             18             DR. EDWARDS:  Thank you very much, Jenny.

             19             Let me just update the schedule.  I am now

             20   predicting 4:15 as the absolute end unless there is

             21   a dramatic change in that estimation, so we are

             22   going to be shooting for that now.

                                                                           267

              1             Now I would like to open the discussion

              2   for both of these sessions this afternoon.

              3                            Discussion

              4             DR. LAZOR:  We heard this afternoon, and

              5   actually this morning, as well, that is important.

              6   Data translates into information, and information

              7   into knowledge.  I also heard that sometimes the

              8   data are difficult to obtain because of the

              9   practicalities involved.

             10             What progress has been made over the years

             11   to actually collect good data?

             12

             13             DR. EDWARDS:  Dennis.

             14             DR. GRASELA:  I think the progress that we

             15   have made has been around the prospective design of

             16   protocols and case report forms, and the use of

             17   real-time data assembly to have a few back-loop

             18   back to the sites as early as we possibly can.

             19             One of the things that I have noticed is

             20   that study coordinators, God bless their souls, are

             21   incredibly efficient and incredibly consistent, and

             22   if they got it right at the investigators meeting,

                                                                           268

              1   they will do a great job.  If they got it wrong or

              2   that the lag time is too long between when you

              3   design your trial and when they actually enroll

              4   their first patient, and they don't get it right,

              5   they will do it wrong every time after that.

              6             So, the thing that we have paid a lot of

              7   attention to, or tried to pay a lot of attention

              8   to, is to do things in a very prospective manner.

              9   When I first came to BMS, the population PK

             10   consisted of a boatload of data coming to us,

             11   dumped on the table, and saying save us, we don't

             12   think we have the right dose.

             13             Now we have gone to a prospective

             14   approach, which I think will ultimately keep us in

             15   good stead, but it is always the balance of--one of

             16   the things we didn't talk about is enriching

             17   trials, and sometimes if you are going to enrich

             18   your trials with a certain population or, for

             19   instance, penicillin-resistant Strep pneumo, if you

             20   are going to enrich your trial, sometimes it can be

             21   done in the U.S., sometimes it has to be done in

             22   the far reaches of the world, that can make the

                                                                           269

              1   infrastructures really bad, and even shipping the

              2   samples, it can cost you thousands of dollars just

              3   to have someone walk on site to pick up the box to

              4   bring it to you.  So, there are a lot of logistic

              5   challenges with doing studies in developing worlds.

              6             DR. SCHENTAG:  The interesting thing is I

              7   have been approached over the years by sponsors all

              8   over the place that say what do I need to do, and

              9   then when you tell them what you need to do during

             10   the course of a clinical trial, either Phase II or

             11   III, I have done it in both, they always say, no,

             12   that is impossible, the investigators will never do

             13   it.

             14             I say every time, okay, if you fund this,

             15   I will get the investigators to do it, and that is

             16   true, you have to usually pay them a little bit

             17   more, and usually you have to watch them and work

             18   with them, and you have to be in real type contact

             19   with them.

             20             The other thing, and I agree exactly with

             21   what Dennis said, too, is that if you are willing

             22   to interact all the way along in the sampling

                                                                           270

              1   process, you can get very good samplings out of the

              2   clinical investigators all over this country

              3   anyway, and I haven't tried it overseas, but I

              4   don't have any reason at the moment to believe it

              5   couldn't be done there also.

              6             It is just a matter of you need to commit

              7   as much effort to it as you do to collecting the

              8   patients in the first place.  You can't just put it

              9   in the protocol and expect it to be done right,

             10   because most of the time it isn't, but I have never

             11   seen a situation yet where we couldn't get the data

             12   if there was real-time commitment to it.

             13             DR. EDWARDS:  John.

             14             DR. POWERS:  It seems what people are

             15   saying is that if it takes that commitment, it

             16   seems like basic human nature that nobody is going

             17   to make that commitment unless there is something

             18   in it for you.

             19             So, the question would be--I was just

             20   writing this down, thinking here what is the lesion

             21   here--is it a lack of consensus on how to do it

             22   right, is it a lack of perceived value from

                                                                           271

              1   industry either because it costs too much, it takes

              2   too much time, or both, or is it because they can't

              3   use it for marketing or labeling in a way that they

              4   find useful, or is it a perception that FDA won't

              5   find this useful, I can't use it to move my drug

              6   forward, so I am not going to do it?

              7             Greg, can I ask you that one to start off

              8   and maybe some other folks can try to tackle that?

              9             DR. WINCHELL:  I don't think it is because

             10   it doesn't drive labeling, because actually it has

             11   been quite successful.  A lot of population PK/PD

             12   information is making its way into the labels.

             13             I think it is people, at least don't

             14   understand the value--we haven't demonstrated the

             15   value of it is what I think is probably the major--

             16             DR. SCHENTAG:  To?

             17             DR. WINCHELL:  To management and also--

             18             DR. SCHENTAG:  To management.

             19             DR. WINCHELL:  Again to clinicians within

             20   therapeutic areas actually.  As was pointed out,

             21   clin-pharm, clinical PK, et cetera, are involved

             22   only up to a certain point, and then it goes in,

                                                                           272

              1   and then, at least the way Merck is structured,

              2   when you get to the clinicians, it is a different

              3   therapeutic area each time, and you have to fight

              4   the same battles and win the same victories with

              5   each therapeutic area as you go along, and it is

              6   just very difficult to do.

              7             DR. DUDLEY:  I think Dennis' comments are

              8   spot on, and I have taken the approach--the

              9   contract research business is very competitive, as

             10   you know, and if they can't get it done, then, it's

             11   go talk to the next group they get on the list.

             12             A lot of centers that are now doing

             13   studies particularly in the antifungal area now, a

             14   lot of those initial trials are taking place

             15   offshore, they are taking place in research units

             16   in South America and South Africa. They are very

             17   conscientious about getting those types of studies

             18   done now, as well.

             19             So, I think it probably depends on the

             20   company and their commitment to doing it right, but

             21   I think you can insist on it and get the contract

             22   research organizations to respond or else you go

                                                                           273

              1   down the list.

              2             DR. EDWARDS:  Bob.

              3             DR. POWELL:  You were asking where the

              4   critical lesion is.  Really, all the pieces that

              5   you discussed, but probably the most important

              6   thing to understand about an organization is how

              7   decisions are made.

              8             So, for example, you are at the FDA.

              9   Decisions are made on NDAs in terms of go/no go,

             10   approval or not approval, where what's in the label

             11   by the physicians that are running the therapeutic

             12   areas, where everything else, is my perception, is

             13   advisory to that, to those people.

             14             Would you agree, more or less?

             15             DR. POWERS:  I like to think we work as a

             16   team.

             17             DR. POWELL:  But that is the option of the

             18   person that is running the therapeutic area, and

             19   some do it more than others.  It is the same in the

             20   industry, so that in some therapeutic areas--and

             21   people have alluded to this--that people will be

             22   very team-oriented and inclusive, and some, for

                                                                           274

              1   sure, one of the characteristics in drug

              2   development is that people want to focus.

              3             You can track responsibility for making

              4   decisions usually down to one person for a given

              5   type of decision, and it is kind of up to that

              6   person's style.  Now, organizations, I would say,

              7   in drug development, some organizations have

              8   evolved a style because of horrendous mistakes that

              9   have been made by one person where they are more

             10   inclusive, but others aren't, but it comes down to

             11   that is the piece, how decisions are made.

             12             The other thing is that a lot of times the

             13   people in clinical pharmacology, they may want to

             14   be influential, but for whatever reason, they

             15   haven't evolved to be as influential inside the

             16   drug company as the Phase III physicians.

             17             People may want to disagree with that, but

             18   that is my perception.

             19             DR. POWERS:  So, then comes up the next

             20   question, then, so outside of tying up those

             21   people, throwing them in the middle of the floor

             22   here, and say why don't you accept this, what would

                                                                           275

              1   it take to convince those people?  Would anybody go

              2   forward with an anti-infective program without

              3   chucking their bug into a test tube and seeing what

              4   the MICs were?

              5             DR. POWELL:  Well, I am not sure those

              6   people are here.  Are there Phase III people here?

              7             DR. POWERS:  Let me go back.  How does

              8   something like an MIC become so accepted, and how

              9   do we get PK/PD to be accepted in a similar way

             10   that people will see its value?

             11             DR. POWELL:  It is just as simple.  The

             12   clinical trialist mentality that has driven really

             13   the FDA over the last 30 years has driven much of

             14   clinical development.  That is changing, but it is

             15   changing ever so slowly, and as people have said,

             16   you have to take that on, on a person-by-person,

             17   project-by-project, therapeutic area-by-therapeutic

             18   area.

             19             Now, I would say that, in my experience,

             20   infectious disease people are kind of akin to

             21   oncologists, they tend to be pretty conservative in

             22   terms of we will do this trial the way we did the

                                                                           276

              1   last trial, or the way someone else did the trial,

              2   where sometimes I have seen people in other

              3   therapeutic areas be much more amenable to change.

              4             DR. EDWARDS:  Hartmut.

              5             DR. DERENDORF:  I think a lot of things

              6   are done because they are done that way, you know,

              7   people just don't think about it, they do it like

              8   the other person before them has done it, and if

              9   that worked, then, they are encouraged to do it

             10   that way.

             11             So, from that perspective, I think the

             12   role that you play, and that the FDA plays, is

             13   extremely important, because that sets the tone,

             14   and that new Critical Path document that just came

             15   out, in my opinion, is a milestone, because it

             16   really presents an opportunity that you can shape

             17   drug development in this country.  I mean that you

             18   are not just drug police and monitoring

             19   organization, but you truly have an active role in

             20   bringing better products to the people quicker.

             21             DR. POWELL:  There is another leverage

             22   point, and that is regulatory affairs inside the

                                                                           277

              1   company.  They will usually also be very

              2   conservative, but I think that they pay a lot of

              3   attention to what the FDA says, so that if we can

              4   start having End of Phase I/IIA meetings or End of

              5   Phase II meetings, then, what you say, as the

              6   regulator, at that meeting, and what you are

              7   looking for, there is a lot of attention that is

              8   paid to that inside the company, so regulatory is a

              9   leverage point.

             10             DR. BENINCOSA:  I would like to echo what

             11   Dr. Powell was saying.  Certainly, in the last

             12   year, industry has taken notice to both the

             13   opportunity for the End of Phase IIA meeting, which

             14   was just mentioned, his appointment, as well as Dr.

             15   Stanski's appointment, so the receptivity to this

             16   knowledge-based drug development, the message is

             17   coming clear, and that is only going to help us.

             18             I think perhaps in the past, the focus of

             19   the traditional End of Phase II meeting, that

             20   clinical pharmacology issues, we have not had the

             21   opportunity to really discuss them as much, and so

             22   the End of Phase I/IIA meeting gives us that

                                                                           278

              1   opportunity and puts the issue of dose selection in

              2   a forum where we can have these discussions, so it

              3   will only advance our abilities to do this in the

              4   industry.

              5             DR. EDWARDS:  John.

              6             DR. REX:  I want to follow on to answer

              7   John's question specifically about what does it

              8   take to influence. Let me start with the

              9   observation that the FDA doesn't have a brain, AZ

             10   doesn't have a brain, Pfizer doesn't have a brain,

             11   there are no corporate or federal neurons.  It is

             12   all about individual people and what they believe

             13   and what they think.

             14             This is actually one of the reasons why

             15   guidances are so valuable, and it is particularly

             16   valuable when guidances contain instructive

             17   exemplars, instructive examples.

             18             I was looking at one of the draft--I will

             19   use that ugly word--draft guidances from whenever

             20   the last ones were published, and buried in there I

             21   found some really lovely little vignettes about

             22   stories about a given case where somebody had done

                                                                           279

              1   or not done something and the consequences of it.

              2             I really enjoyed Hartmut's examples of

              3   things that worked and didn't work.  We have had

              4   several of those today, and that kind of thing is

              5   what educates people, and that is what I can use to

              6   educate senior management.

              7             Before I joined AstraZeneca, my personal

              8   cross to bear was that I was a hospital

              9   epidemiologist, and I had to convince

             10   cardiovascular surgeons that they needed to do

             11   something different from what they were doing, and

             12   you can't tell a cardiovascular surgeon anything,

             13   but they are physicians, and they are very

             14   interested in outcomes and data.

             15             So, when I started to show them their

             16   personal data, then, all of a sudden I had their

             17   attention because it was alive and real to them.

             18             The same thing is true of my bosses, of

             19   Lisa's bosses.  Once you show them real data, of

             20   real examples, not made-up stuff, and I know that

             21   you guys, you can't name names, but you can

             22   summarize themes and provide examples.

                                                                           280

              1             The things that you can put in those

              2   documents and white papers and guidances, that we

              3   can use as examples, you know, Greg has got some

              4   great stories about how things worked and didn't

              5   work, and to have those in front of you when you

              6   are trying to tell a tale is incredibly useful.

              7             So, that is the thing that I would like to

              8   say, is that, you know, this is why these documents

              9   are so important to us on the other side.  It is

             10   because it gives us examples.

             11             DR. EDWARDS:  George.

             12             DR. TALBOT:  I would like to echo what

             13   John was just saying.  I was operating at a

             14   slightly higher--well, my example was at a slightly

             15   higher cerebral level, because I wasn't thinking of

             16   neurons, I was thinking of memory.

             17             I was going to point out two things.  One

             18   is that in many companies, there is a lack of

             19   institutional memory, and that is also relevant

             20   because again, we talk about PhRMA, but as we

             21   discussed yesterday, PhRMA isn't pharma, it's

             22   bigger companies maybe with a longer history and

                                                                           281

              1   better institutional memory, it's smaller companies

              2   who are perhaps new and coming into this, and they

              3   vary tremendously, in my experience, in terms of

              4   their expertise and their institutional memory.

              5             So, I think John's point about how useful,

              6   if not official guidances, can be, but at least

              7   summaries, communications that can be used in

              8   educational processes, I agree with that.  I think

              9   all those documents are helpful in those situations

             10   where institutional memory may be missing.

             11             DR. EDWARDS:  Phil.

             12             DR. COLANGELO:  During the course of

             13   today, I seemed to have fixated a little bit on

             14   Phase II studies. There has been a lot of

             15   discussion about Phase II studies and to Phase IIA

             16   meetings, et cetera, and I think intellectually,

             17   scientifically, we can all understand and

             18   rationalize the importance of Phase II studies

             19   within a development program.

             20             I guess, though, my curious question to

             21   the industry colleagues and those in academia is

             22   what type of obstacles, I guess, do you all face in

                                                                           282

              1   terms of trying to convince your management folks

              2   and investigators as to doing Phase II types of

              3   trials.

              4             George Drusano kicked around the concept

              5   of robust Phase II studies, and I think that needs

              6   to be clearly defined as we try to move forward to

              7   get better ideas about rational dose regimen

              8   selection.

              9             So, I just would like to throw it back to

             10   you all and maybe here at this point in time, what

             11   types of hurdles or obstacles you may face within

             12   your own companies or within your investigators in

             13   terms of doing Phase II trials.

             14             DR. EDWARDS:  Before we have a response, I

             15   would just like to add something to that, Phil, and

             16   ask, in addition to what you are asking, whether it

             17   is possible to convince management that robust

             18   Phase II trials are cost saving, and whether there

             19   is a structured way to approach them with that

             20   particular concept in mind.

             21             Yes, Mike.

             22             DR. DUDLEY:  I think that if you look at

                                                                           283

              1   what happens in small companies, I can't comment on

              2   big companies, one of the real sentinel events or

              3   the milestones, of course, are filing the IND.

              4   That is really sort of the Good Housekeeping Seal,

              5   and not being put on clinical hold, but filing the

              6   IND and being able to go into Phase I, that is a

              7   real milestone.

              8             Then, I think the other is the Phase IIA,

              9   the first time going into patients and the

             10   so-called, as we have talked about, the proof of

             11   concept Phase IIA efficacy study.

             12             So, that is why that first Phase IIA study

             13   is so critical.  I think that management,

             14   especially rookies in the area, they will recognize

             15   that it is going to cost some money to go to Phase

             16   IIA anyway, so I think that by now, with the

             17   guidances being in place, and I think the comment

             18   that was made earlier is that guidances are very

             19   important, the stories of the autopsies of failed

             20   programs and what have happened that results in

             21   problem, then, all strengthen the idea that it is

             22   going to cost money, but you are enabling yourself

                                                                           284

              1   to, at a critical value creation point, by having a

              2   successful Phase IIA study, because you are going

              3   to want to go on from there with some fairly good

              4   certainty about where your dose is going.

              5             So, I think that the timing of where these

              6   studies are placed is actually pretty good at least

              7   in the smaller company, because you are positioned

              8   to do those studies where there is more emphasis on

              9   getting success and rather than on the cost-cutting

             10   types of things that may come later.

             11             DR. POWERS:  Also, one of the things it

             12   seems to us, that we have heard about, is that for

             13   smaller companies, that getting capital is an

             14   important thing.  Is doing those Phase II trials

             15   and showing some proof of principle helpful to them

             16   in terms of doing that?

             17             DR. DUDLEY:  Absolutely, absolutely, and I

             18   think that there is pushback oftentimes, do we

             19   really need to do that, can we cut the cost of the

             20   trial, and so forth, but I think with the ideas

             21   that that analysis is going to really, a successful

             22   Phase IIA is going to be a value creation point, so

                                                                           285

              1   it is worth the investment.

              2             You have heard pay me now or pay me later

              3   a lot of times in the meeting today, and I think it

              4   does help, because it is an insurance policy that

              5   you will have usable data from that Phase IIA

              6   study, because if you don't have a good result, you

              7   don't have a way of getting yourself out of it, and

              8   understanding why.

              9             DR. COLANGELO:  The perception, at least

             10   my perception about Big Pharma is that there is

             11   really no sequential process, you know, Phase I,

             12   II, III, it is really a simultaneous process and

             13   that it would at least seem to us that learning

             14   from Phase II first, before going to Phase III, I

             15   think would be helpful in describing, as well as

             16   transparency about Phase II, what you have learned

             17   from Phase II with us also would be helpful.

             18             But I know that there are deadlines and

             19   milestones that need to be reached, so again, I

             20   just welcome your comments about what types of

             21   problems there are with that.

             22             DR. EDWARDS:  Jerry.

                                                                           286

              1             DR. SCHENTAG:  I would just like to add

              2   something about money to this, because budgets for

              3   these things are, of course, always an issue.  It

              4   cost about the same per patient, and, you know,

              5   budgets go per patient, it costs about the same per

              6   patient to add on PK/PD done properly to a Phase

              7   II, as it does to a Phase III, but because Phase

              8   III's are typically huge trials, and Phase II's are

              9   smaller, it makes it financially more attractive to

             10   do this in Phase II, or the other possibility,

             11   which is you do it in a subset of the Phase III

             12   patients, like in some centers, not in others, so

             13   it is not a money difference that prevents it from

             14   being done in Phase III or forces it to be done in

             15   Phase II.  There are other things that have to be

             16   done.

             17             It is only about 15 to 20 percent more to

             18   add PK/PD on to a typical trial budget anyway.

             19   Most of the expense is not the cost of doing the

             20   PK/PD, it is the cost of paying for that patient

             21   and paying that investigator for his time.

             22             Anyway, I just thought I would add that.

                                                                           287

              1             DR. EDWARDS:  Yes, Dennis.

              2             DR. GRASELA:  I would like to address it

              3   from actually two sides.  The first is a real

              4   candid question, and please don't take offense.

              5             If you have two well-controlled clinical

              6   trials and they are successful, they have met their

              7   non-inferiority endpoints, and you have no idea how

              8   they have selected the dose, will the drug get

              9   approved, yes or no?

             10             DR. POWERS:  The answer is yes.

             11             DR. GRASELA:  So, we talk about some of

             12   the difficulties we have with our trialist friends,

             13   and that is the carrot that they will always stand

             14   up.  When they get pushed back from the

             15   investigators about doing some of these things,

             16   because some investigators will actually tell us,

             17   well, I have a trial from company X and your trial,

             18   you want PK and a whole bunch of other things, and

             19   you are only to give me a few thousand dollars, and

             20   I can do company's B and have twice the throughput

             21   because I am not burdening my study coordinator

             22   with that extra work.  I am going to do trial B,

                                                                           288

              1   and the patients are going to go there, so they may

              2   accept your trial, get IRB approval, they enroll

              3   those subjects.

              4             So, as long as the Agency says two

              5   well-controlled trials and you are approved, our

              6   trialist friends are always going to hold that up.

              7             The second piece of the coin is I have

              8   been to a couple of End of Phase II meetings, and

              9   the agendas are so packed that I don't get to talk

             10   to my friend Phil Colangelo about what we are doing

             11   in the clinical pharmacology piece of things, there

             12   is zero time.

             13             So, I have been encouraged with the IIA

             14   piece.  Now, coming back to IIB, I have been very

             15   fortunate, I have an incredible relationship with

             16   my VP of Clinical Research, and I have been able to

             17   introduce population PK across the board in

             18   infectious diseases.

             19             The problem I am having right now is

             20   delivering, because when the database is cleaned,

             21   our clinical colleagues push a button, and they get

             22   their endpoints, their confidence intervals, and

                                                                           289

              1   they are done, and if you are not very prospective

              2   in the way you collect the data, you are just

              3   beginning to do that PK/PD analysis, and it takes

              4   time to do that.

              5             So, therefore, you cannot deliver by the

              6   time of the end of the Phase II meeting, and they

              7   say, well, we couldn't use it for decisionmaking

              8   anyway, why should we fund this.  That is our

              9   problem, that is actually not your problem.

             10             I have a good example of when we have done

             11   it with quinolone, and it was very successful, and

             12   we had it in time for the IIA meeting.  That took a

             13   lot of work, and it was very rewarding, and it sets

             14   the precedent.

             15             The third piece is coming back to the

             16   guidances and really good examples, because I think

             17   what we are getting nods from my colleagues is you

             18   have to reeducate the next person.

             19             When I branched into the antiviral part of

             20   our pipeline, until they made all the mistakes the

             21   first time, I was like salmon swimming up a

             22   waterfall.  Now that they have got the hard

                                                                           290

              1   questions, and I have been able to answer them with

              2   data, now I have no problem, they are calling me up

              3   and saying, okay, you need to do this for the next

              4   compound, so it is those wins that help the

              5   snowball run, but also having documents which have

              6   examples of when it didn't work, and having the

              7   folks at the Agency at large meetings like an end

              8   of Phase II where senior vice presidents of

              9   clinical research are present, and you folks start

             10   to say, gee, how are you selecting the dose, what

             11   is going on here, I mean you are going to Phase

             12   III, but we are not certain, it is not transparent

             13   to us, or where is your population PK, that really

             14   sends a really strong message.

             15             DR. POWERS:  There is an old proverb that

             16   says you should learn from other people's mistakes

             17   because you won't have time to make them all

             18   yourself.

             19             It would seem we have talked a lot about

             20   managing risk.  Is there no trepidation on the part

             21   of management that they are about to select a dose

             22   and go into a Phase III trial, and spend millions

                                                                           291

              1   of dollars, and not know whether they are going to

              2   get those two adequate and well-controlled trials

              3   to work, isn't that enough of a risk to make them

              4   want to know these things before they move ahead?

              5   I guess not.

              6             DR. TALBOT:  I have a comment on that.  I

              7   would like my current pharmaceutical colleagues to

              8   comment, but it is my recollection that senior

              9   management says we have to have the NDA filed by X,

             10   how are you going to do it?

             11             DR. POWERS:  This has got some timeline

             12   deadline, and you are going to get there no matter

             13   what else happens, but if you don't get there

             14   because your drug didn't work, then, what have you

             15   accomplished?

             16             DR. POWELL:  There is marked differences

             17   in company culture in that regard.  I have worked

             18   in one company that clearly time was the piece that

             19   was most valued in terms of development.  You hit

             20   the times and if you stood in the way of them, you

             21   got into trouble.

             22             When Pfizer ate [?] us a couple of years

                                                                           292

              1   ago, I was really amazed by their culture, and Lisa

              2   may want to speak about this, but they literally

              3   stopped development at the Phase II point, or they

              4   used to, for on the order of a year to try and get

              5   the decision right in terms of whether to go

              6   forward or not, and, secondly, what the Phase III

              7   trial would be designed.

              8             So, I mean you see these radical

              9   differences between companies in terms of exactly

             10   what you are saying. It is very variable.

             11             DR. TALBOT:  I think another element,

             12   though, just to go back to Dennis' question about,

             13   you know, if you have two non-inferiority studies

             14   that are successful, does it matter how you got

             15   there, I think an important distinction to make in

             16   the whole thought process is whether you are

             17   dealing with another fluoroquinolone or whether you

             18   are dealing with a new compound with a novel

             19   mechanism of action, and in the latter case I

             20   suspect it's--well, hopefully, it is going to be

             21   easier to convince people of the necessity of

             22   having an ironclad dose rationale selection.

                                                                           293

              1             But not to minimize all the other

              2   excellent points that have been made about the

              3   obstacles both at the investigative site level and

              4   at the company management level and at the project

              5   team level, that impede the utilization of this

              6   tool.

              7             DR. COLANGELO:  I think that in Phase II,

              8   not only do we want to obviously explore efficacy,

              9   but we may be able to go to a lower dose that give

             10   us the same efficacy, but with more acceptable

             11   safety.  You know, there is a lot of

             12   advantages--disadvantages obviously--but, you know,

             13   I think at the end of the day, it seems like a lot

             14   of times our questions are, well, golly, couldn't

             15   they have gotten by with a lower dose at the end of

             16   a Phase III evaluation.

             17             DR. EDWARDS:  Yes.

             18             DR. TULKENS:  May I maybe comment on that

             19   and maybe just try to rephrase the discussion?  We

             20   started the meeting by saying we are lack of good

             21   drugs for bad bugs, so the resistance problem is

             22   something that is driving us today.

                                                                           294

              1             Now, if I hear you, Phil, I am afraid of

              2   one thing.  About 10 years ago or 8 years ago,

              3   companies were looking for low doses, and they were

              4   introducing low doses of anything they could do

              5   including antibiotics.  I am pretty confident that

              6   looking for low doses for antibiotics is going to

              7   drive resistance, and I would submit to you the

              8   following thing, is that actually taking a PK/PD

              9   parameter suggests the static dose might even be

             10   dangerous because the bacteria are still there, and

             11   that is the best way to actually construct

             12   resistance or select resistance.

             13             I was surprised to see here among all the

             14   various presentations that resistance was spoken

             15   about, but very few data were given.  The only ones

             16   that I really saw was those of George Drusano.

             17             I would submit again to you that we should

             18   perhaps, at the level of the regulatory, try to

             19   introduce a concept which is, first of all, try to

             20   see whether we minimize the risk of resistance, is

             21   there anything that could demonstrate that first?

             22             Second, is the dose which is given to

                                                                           295

              1   patients going to eradicate, eradicate the bacteria

              2   very effectively, because once again, if we do not

              3   eradicate, we know that is the best way to drive

              4   for resistance, and this is what has been done over

              5   the last 20 years, because we identified dose that

              6   were effective.  We were curing patients, weren't

              7   we?

              8             But yet we have seen the resistance

              9   emerging.  Sometimes we do see it very quickly in

             10   one patient, that is with the gram-negative

             11   bacteria, sometimes in the population basis, and

             12   you have to face the reality that--let's take

             13   macrolides.  Macrolide resistance has gone from

             14   almost a few persons to 30, 35 persons in about 10,

             15   15 years.

             16             We believe, or I believe at least, I

             17   submit to you that this is partially due to the low

             18   doses of many of the antibiotics that were used.

             19   So, in other words, my point here, which I tried to

             20   make, is that we should perhaps in the PK/PD

             21   modeling system that we use, introduce the concept

             22   that resistance is an issue, and not satisfy

                                                                           296

              1   yourselves by just efficacy for the patient, it

              2   should also be efficacy on a population basis, and

              3   efficacy for the years to come.

              4             DR. EDWARDS:  Thank you very much.

              5             I think I would like to take that comment,

              6   if I could, in this formal part of the discussion.

              7   It really goes back to where we started in a way.

              8             So, I am going to do that.  I am going to

              9   actually need 10 minutes myself, if I could get

             10   that, in that I feel that we really need to

             11   verbally produce a summary here that hopefully will

             12   engender some feedback, so that we can put a

             13   summary into a written form, that will be

             14   representative and not more or less personal as I

             15   am going to do it now, and it really will be only

             16   10 minutes, but I will need 10 minutes.

             17                        Concluding Remarks

             18             Before I start, I need to thank everyone

             19   involved with this meeting including the FDA, ISAP,

             20   the NIH, and the CDC who came, the IDSA.  There are

             21   a number of individuals I need to single out, and

             22   they include John Powers and Mark Goldberger, Ed

                                                                           297

              1   Cox, Renata Albrecht, Janice Soreth, John Lazor,

              2   and the FDA Biopharm staff.

              3             I would like to thank Bob Powell,

              4   unfortunately, he just left us, for his comments.

              5   Dana Schuhly and Christine Moser, and all the FDA

              6   staff for their support, and Bob Guidos from the

              7   IDSA, and certainly Leo Chang, who I have been

              8   extremely concerned about this physical welfare

              9   maintaining through this entire time.  Leo has just

             10   done a tremendous job of helping us keep all this

             11   together.

             12             And especially all of you who took the

             13   effort to come down and spend your valuable time at

             14   this meeting over this extremely important topic.

             15             The amount of effort that I know John

             16   Powers put into getting this meeting together is

             17   extraordinary, and I really appreciate all of that,

             18   John.  I know from talking to John late at night

             19   and early in the morning, and on Saturdays and

             20   Sundays, what kind of effort has gone into this,

             21   and we thank you very much.

             22             I would suggest if any of you have any

                                                                           298

              1   feedback to give to John, that you probably not

              2   call him after 10 o'clock tonight.

              3             I am about to extemporaneously, in front

              4   of an audience with collectively hundreds of years

              5   of experience in this area, summarize 24

              6   presentations and 7 discussions that have occurred

              7   over the last two days, and if anyone would like to

              8   do this instead of me, would you please raise your

              9   hand.

             10             I didn't have any takers there.

             11             Let me go ahead and, as I say, what I am

             12   going to say is at this moment, my own

             13   interpretation, which hopefully will be modified as

             14   we try to produce a written summary, so we will

             15   have to take the comments in that context.

             16             We are going to shift gears and go all the

             17   way back now to yesterday morning, where we started

             18   out with a much more policy orientation to the

             19   discussions.  During that part of the discussion,

             20   the question was raised are we in a crisis here now

             21   with the availability of anti--particularly

             22   bacterials--but anti-infectives in general and

                                                                           299

              1   resistance.

              2             The point was brought up by George Talbot

              3   that a crisis really is in the eye of the beholder

              4   and it depends on how you define the crisis, and we

              5   discussed the issues that it is clear that Big

              6   Pharma is leaving the anti-infective development,

              7   not wholly, but a significant contingent of Big

              8   Pharma is leaving that.

              9             We have documentation to that effect.

             10   Again, we refer to the fact that the paper that was

             11   written, studying the exodus, was just went on the

             12   web site yesterday, and, in fact, it did, the

             13   Spillberg paper.

             14             So, that is a fact, and I think we

             15   understand many of the pressures that they are

             16   under and many of the reasons why it is happening.

             17             It is also a fact that there has been a

             18   decline, no matter how you analyze it, in the new

             19   molecular entities for anti-infectives.  So, over

             20   the past several years, there is a documented

             21   decline.

             22             We discussed the issue that it may be

                                                                           300

              1   comparable to what is going on in classes of drugs

              2   outside of anti-infectives, but we also mentioned

              3   the fact that that may not be very relevant to the

              4   fact that there is, indeed, a decline.

              5             We also know that the level of resistance

              6   is increasing.  That is a fact.  The point was

              7   brought up by Mark Goldberger that there are lots

              8   of antibiotics that are effective against most of

              9   the resistant drugs we have at the present time,

             10   however, the impact of this emerging resistance is

             11   palpably felt in the clinical setting.

             12             One thing we haven't heard from during

             13   this meeting is a lot of testimony to that effect,

             14   but all of us who are seeing patients on a daily

             15   basis would use a definition of the word "crisis"

             16   that would fit perhaps best with again a term

             17   George has so nicely provide for us, and that is

             18   that we are in a brewing crisis, and we mentioned

             19   the fact that we are all about here working on

             20   trying to prevent a potential catastrophe that has

             21   not yet happened in its fullest extent.

             22             We reviewed the progress that has occurred

                                                                           301

              1   since the last meeting of November 2002, and I am

              2   just going to briefly mention again that the IDSA

              3   has done many things which were enumerated on a

              4   long list by George, and have led to the final

              5   stages of the development of a white paper, which

              6   will go to Congress, and pressure will be put on

              7   our legislature and legislators to provide

              8   incentives where it is felt at the present time is

              9   going to be the strongest lever in maintaining an

             10   active anti-infective program, and the IDSA is very

             11   close to actually writing legislation at the

             12   present moment.

             13             The FDA, Ed gave us a very beautiful

             14   summary of all of the activity which has gone on in

             15   the FDA, and that has been very impressive.  It is

             16   clear that the FDA is completely aware of the--I

             17   will use the term "brewing crisis," and has been

             18   very responsive, but it is all so clear that none

             19   of us want to see a compromise in safety and

             20   efficacy.

             21             So, therefore, we have a very intricate,

             22   detailed problem.  As someone said earlier, we are

                                                                           302

              1   in a field that does not have a lot of easy

              2   solutions.  It is a hard field.  It can be

              3   characterized by the fact that there is a high

              4   attrition rate of drugs developing, we are dealing

              5   with human safety issues, and there are no easy

              6   answers to most of the questions we are trying to

              7   address.

              8             So, I think all of us, and I am really

              9   speaking certainly from the IDSA standpoint,

             10   genuinely appreciate the openness that has occurred

             11   with the FDA, and I would like to really use the

             12   thought John expressed just a moment ago, is that

             13   FDA is becoming a team, part of the team of

             14   clinical development rather than the police, as

             15   someone else used the term, and we feel that.

             16             At the same time, guidance is a word that

             17   has been used probably more than any other single

             18   noun during these discussions, and as I mentioned

             19   earlier today, the IDSA, in its summary, will

             20   undoubtedly reflect on how useful getting the

             21   guidances into a form that can go up for public

             22   analysis and then in a finalized form by the end of

                                                                           303

              1   2004, how useful that would be.

              2             The FDA has told us that the five

              3   guidances that are they are developing, which

              4   include the resistant pathogens, meningitis, acute

              5   otitis, sinusitis, and AECB are moving along, and I

              6   think everyone is--again, the issue was brought of

              7   what does industry do with the guidances, how do

              8   they use them.  Whatever the answer to that

              9   question is I don't think any of us have, but we

             10   just do know they want them, and that they are of

             11   value I think for many reasons, which we actually

             12   haven't probed the depth of during these

             13   discussions.

             14             So, as we got into the surrogate

             15   endpoints, I am going to briefly summarize what I

             16   think was conceptually important about that

             17   discussion, and that they are clearly a mechanism

             18   of reducing costs of drug development, they are

             19   desirable.

             20             That doesn't need to be elaborated upon,

             21   but there are very few of them that are in use at

             22   the present time, and they are difficult to

                                                                           304

              1   validate, but they must be validated to be useful.

              2             Centered around the surrogate endpoints

              3   again were two points that the IDSA will again

              4   reflect on as selected for use, very useful in the

              5   near future, and that would be a consideration of

              6   the prosthetic joint infection guidance, if you

              7   will, using the surrogate endpoint of bacterial

              8   eradication, and the development of a strategy to

              9   study staphylococcal bacteremia.

             10             The precise mechanism to complete those

             11   strategies may perhaps best go to the

             12   Anti-Infective Advisory Committee unless another

             13   mechanism can be devised to try to bring those two

             14   entities to a form of definition.

             15             There is one point that I need to mention

             16   that is just a little out of context right now, and

             17   that is a point that was brought up by John Rex and

             18   others, and that is that as we try to influence our

             19   societal structure through the legislature, that we

             20   need to think about that, not only as it pertains

             21   to the United States, but also globally, because of

             22   overlap we have with international regulatory and

                                                                           305

              1   areas where industry is involved in anti-infective

              2   development.  So, I just want to make sure I got

              3   that point out.

              4             The point was brought up also that the NIH

              5   may be an area where development of surrogates can

              6   be facilitated. Dennis Dixon has suggested some

              7   examples.  This may be an area that a great deal of

              8   more focus could be put on in the future in terms

              9   of expansion of the role of NIH in helping develop

             10   validated surrogates.

             11             As we moved into the PK/PD discussions, it

             12   is without necessity to say that dose selection is

             13   an extremely important component for clinical

             14   trials and clinical drug development, and that

             15   PK/PD is useful for dose selection.

             16             That may be its greatest area of

             17   usefulness.  We reflected on many occasions on the

             18   desirability of robust Phase II clinical trials,

             19   and I think that bringing that out in this

             20   discussion was very useful.

             21             There may be other tools that are on the

             22   horizon that may facilitate the development of the

                                                                           306

              1   PK/PD science, and area still needs, and will need,

              2   clinical validation. The importance of the pre-IND

              3   and Phase IIA and pre-NDA discussions was brought

              4   out within the context of the fact that we do have

              5   a lot of tools for PK/PD that aren't being utilized

              6   as extensively as they might be within the context

              7   of utilizing those discussions to their maximum

              8   benefit.

              9             As we moved into the dose selection area,

             10   it became clear that there are highly developed and

             11   scientifically sophisticated strategies for dose

             12   selection at the present time, the two examples we

             13   reflected on most acutely are population PK/PD and

             14   modeling, population PK/PD modeling and the Monte

             15   Carlo simulations.

             16             The tools again it was mentioned were not

             17   always brought forward by sponsors in a way that

             18   provides a complete picture for analysis of data

             19   for approval.  Again, it was emphasized that the

             20   modeling and the tools that are under development

             21   are going to have to be validated by clinical data.

             22             I am not going to go into some of the

                                                                           307

              1   details of the fact that it is obvious that the

              2   PK/PD data cannot be based on MIC alone or area

              3   under the curve versus MIC or the Cmax compared to

              4   MIC or the time over the MIC.  These parameters

              5   have to be integrated and paired with other data.

              6             The final comments that I will make, and

              7   these really are the final comments, is that a

              8   problem has been perceived as arising.  There have

              9   been very strong attempts to discover the details

             10   and the pace and the development of the problem.

             11             Certainly, industry, FDA, and certainly

             12   the IDSA are aware of the brewing problem, and one

             13   thing that is clear, and was an interesting

             14   discussion I overheard is that those of us involved

             15   with all of this are passionate about

             16   anti-infectives.

             17             I really don't need to mention that to

             18   this audience because that is the reason all of us

             19   are here and have spent a lot of time from other

             20   activities to confront these issues, but we are

             21   talking about a class of drugs her that in many

             22   ways is different from many other classes and that

                                                                           308

              1   they are acutely life-saving drugs.

              2             They work in all age populations of

              3   patients.  They can prevent things such as

              4   blindness and amputations, an additionally, they

              5   have been responsible for many of the advances that

              6   have occurred in surgery and in cancer chemotherapy

              7   development.

              8             We are faced with the unique problem that

              9   these drugs define their own life span and that the

             10   thought leaders at the present time have a tendency

             11   to be very conservative with the use of the new

             12   drugs, but part of that conservatism is based on

             13   the fact that the pipeline is so small at the

             14   present time.

             15             So, I am going to conclude now and once

             16   again thank you all very much for participation in

             17   this meeting, and I am sure this will not be the

             18   last meeting on this topic, and we look forward to

             19   developing plans for the next one.

             20             Thank you.

             21             [Whereupon, at 4:20 p.m., the meeting

             22   concluded.]