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 s