1

DEPARTMENT OF HEALTH AND HUMAN SERVICES

FOOD AND DRUG ADMINISTRATION

CENTER FOR DRUG EVALUATION AND RESEARCH

ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE

Wednesday, April 14, 2004

8:30 a.m.

Advisors and Consultants Staff Conference Room

5630 Fishers Lane

Rockville, Maryland

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PARTICIPANTS

Arthur H. Kibbe, Ph.D., Chair

Hilda F. Scharen, M.S., Executive Secretary

MEMBERS:

Gerald P. Migliaccio, Industry Representative

Marvin C. Meyer, Ph.D.

Patrick P. DeLuca, Ph.D.

Charles Cooney, Ph.D.

Melvin V. Koch, Ph.D.

Cynthia R.D. Selassie, Ph.D.

Nozer Singpurwalla, Ph.D.

Jurgen Venitz, M.D., Ph.D.

Marc Swadener, Ed.D., Consumer Representative

SPECIAL GOVERNMENT EMPLOYEES:

Paul H. Fackler, Ph.D.

Gordon Amidon, Ph.D., M.A.

Judy Boehlert, Ph.D.

Leslie Benet

Charles DiLiberti

Laszlo Endrenyi

FDA:

Gary Buehler, R.Ph.

Ajaz Hussain, Ph.D.

Helen Winkle

Lawrence Yu, Ph.D.

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C O N T E N T S

Call to Order, Arthur Kibbe, Ph.D. 4

Conflict of Interest Statement,

Hilda Scharen, M.S. 4

Bioequivalence of Highly Variable Drugs,

Lawrence Yu, Ph.D. 8

Why Bioequivalence of Highly Variable Drugs is an

Issue,

Charles DiLiberti, M.S. 11

Highly Variable Drugs: Sources of Variability,

Gordon L. Amidon, Ph.D. 35

Clinical Implications of Highly Variable

Dr. leslie Benet 60

Bioequivalence Methods for Highly Variable Drugs,

Laszlo Endrenyi, Ph.D. 81

Bioequivalence of Highly Variable Drugs Case

Studies,

Barbara Davit, Ph.D. 99

FDA Perspectives, Sam Haidar, Ph.D. 125

Bioequivalence of Highly Variable Drugs Q&A,

Dale Conner, Pharm.D. 130

Bioinequivalence: Concept and Definition,

Lawrence Yu, Ph.D. 176

Statistical Demonstrations of Bioinequivalence,

Donald Schuirmann, M.S. 182

Update\--Topical Bioequivalence, Lawrence Yu, Ph.D. 225

Establishing Bioequivalence of Topical

Dermatological

Products, Robert Lionberger, Ph.D. 225

Future Topics\--Nanotechnology, Nakissa Sadrieh,

Ph.D. 257

Conclusions and Summary Remarks, Ajaz Hussain,

Ph.D. 270

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

2 Call to Order

3 DR. KIBBE: By the clock on the wall, I

4 think we are at 8:30. It looks like our

5 electronics are working so we will be in good

6 shape. We need to start off with the reading of

7 the conflict of interest statement.

8 Conflict of Interest Statement

9 MS. SCHAREN: Good morning. I am Hilda

10 Scharen. I am the executive secretary for the

11 Advisory Committee for Pharmaceutical Science and I

12 am going to be going through the conflict of

13 interest statement for the committee.

14 The following announcement addresses the

15 issue of conflict of interest with respect to this

16 meeting and is made a part of the record to

17 preclude even the appearance of such at this

18 meeting.

19 Based on the agenda, it has been

20 determined that the topics of today\'s meetings are

21 issues of broad applicability and there are no

22 products being approved at this meeting. Unlike

23 issues before a committee in which a particular

24 product is discussed, issues of broader

25 applicability involve many industrial sponsors and

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1 academic institutions. All special government

2 employees have been screened for their financial

3 interests as they may apply to the general topics

4 at hand.

5 To determine if any conflict of interest

6 existed, the agency has reviewed the agenda and all

7 relevant financial interests reported by the

8 meeting participants. The Food and Drug

9 Administration has granted general matters waivers

10 to the special government employees participating

11 in this meeting who require a waiver under Title

12 XVIII, United States Code Section 208.

13 A copy of the waiver statements may be

14 obtained by submitting a written request to the

15 agency\'s Freedom of Information Office, Room 12A-15

16 of the Parklawn Building.

17 Because general topics impact so many

18 entities, it is not prudent to recite all potential

19 conflicts of interest as they may apply to each

20 member and consultant and guest speaker. FDA

21 acknowledges that there may be potential conflicts

22 of interest but, because of the general nature of

23 the discussion before the committee, these

24 potential conflicts are mitigated.

25 With respect to FDA\'s invited industry

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1 representative, we would like to disclose that

2 Gerald Migliaccio is participating in this meeting

3 as an industry representative, acting on behalf of

4 regulated industry. Mr. Migliaccio is employed by

5 Pfizer. Dr. Paul Fackler is participating in this

6 meeting as an acting industry representative. Dr.

7 Fackler is employed by Teva Pharmaceuticals U.S.A.

8 In the event that the discussions involve

9 any other products or firms, not already on the

10 agenda, for which FDA participants have a financial

11 interest, the participants\' involvement and their

12 exclusion will be noted for the record. With

13 respect to all other participants, we ask in the

14 interest of fairness that they address any current

15 or previous financial involvement with any firm

16 whose product they may wish to comment upon. Thank

17 you.

18 DR. KIBBE: Thank you, Hilda. Just so

19 that our audience knows who all is here, I would

20 like to ask everybody to introduce themselves and

21 give their affiliation. We will start with Dr. Yu.

22 Lawrence?

23 DR. YU: Lawrence Yu, Director for

24 Science, Office of Generic Drugs, Office of

25 Pharmaceutical Science, CDER, FDA.

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1 DR. BUEHLER: Gary Buehler, Director,

2 Office of Generic Drugs, Office of Pharmaceutical

3 Science, CDER.

4 DR. HUSSAIN: Ajaz Hussain, Deputy

5 Director, Office of Pharmaceutical Science, CDER.

6 MS. WINKLE: Helen Winkle, Director,

7 Office of Pharmaceutical Science, CDER.

8 DR. AMIDON: Gordon Amidon, University of

9 Michigan.

10 DR. VENITZ: Jurgen Venitz, Virginia

11 Commonwealth University.

12 DR. SELASSIE: Cynthia Selassie, Pomona

13 College.

14 DR. BOEHLERT: Judy Boehlert, and I have

15 my own pharmaceutical consulting business.

16 DR. SWADENER: Marc Swadener, consumer

17 representative, retired from University of

18 Colorado, Boulder.

19 DR. KIBBE: I am Art Kibbe and I am

20 Professor of Pharmaceutical Sciences at Wilkes

21 University.

22 DR. MEYER: Marvin Meyer, formerly

23 University of Tennessee professor, now living in

24 Boca Raton, Florida.

25 DR. SINGPURWALLA: Nozer Singpurwalla,

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1 George Washington University.

2 DR. KOCH: Mel Koch, the Director for the

3 Center for Process Analytical Chemistry at the

4 University of Washington.

5 DR. COONEY: Charles Cooney, Professor of

6 Chemical and Biochemical Engineering at MIT.

7 DR. DELUCA: Pat DeLuca, University of

8 Kentucky.

9 MR. MIGLIACCIO: Gerry Migliaccio, Pfizer.

10 DR. FACKLER: Paul Fackler, industry

11 representative, Teva Pharmaceuticals.

12 DR. KIBBE: Thank you. We are going to

13 start this morning and Dr. Yu will set us up for

14 our discussion. Lawrence?

15 Bioequivalence of Highly Variable Drugs

16 DR. YU: Good morning. My slides I guess

17 are in a different file so I will give my

18 introduction without the slides.

19 Dr. Kibbe, Chair of the FDA Advisory

20 Committee for Pharmaceutical Science, members of

21 the FDA Advisory Committee for Pharmaceutical

22 Science, distinguished speakers, distinguished

23 guests and distinguished audience, I am Lawrence

24 Yu. I am Director for Science, Office of Generic

25 Drugs, Office of Pharmaceutical Science, CDER, FDA.

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1 This morning it gives me great pleasure

2 and privilege to introduce to you the first topic

3 of bioequivalence, bioequivalence of highly

4 variable drugs. The objectives of this discussion

5 are to explore and define bioequivalence issues of

6 highly variable drugs, to discuss and to debate

7 potential approaches in resolving them,

8 specifically the pros and cons of the solutions and

9 the benefits and limitations of these potential

10 approaches.

11 The bioequivalence issues of highly

12 variable drugs have been discussed in many

13 conferences and meetings nationally and

14 internationally. The issue is obvious because of

15 the high variability of the drugs or drug products

16 that require a large number of subjects or

17 volunteers in order to pass the confidence interval

18 of 80-125 percent. Despite many, many discussions,

19 despite many, many publications in scientific

20 literature, to date there is no consensus and no

21 solutions have ever been reached. In fact, there

22 is no regulatory definition with respect to the

23 high variability drugs or drug products. So, there

24 are various approaches in resolving this in the

25 scientific literature, for example, expansion of

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1 the bioequivalence limits; for example, using

2 scaling approaches.

3 We have invited a panel of distinguished

4 speakers this morning to discuss this issue related

5 to the bioequivalence of highly variable drugs from

6 various perspectives, from practical difficulties

7 of bioequivalence of highly variable issues, from

8 mechanistic understanding of what causes the high

9 variability of drug or drug products, from

10 understanding of different approaches to resolve

11 understanding of clinical implications why high

12 variability drugs are safer, from case studies and,

13 finally, from regulatory options.

14 At the end of these presentations you will

15 be asked to discuss or address the following

16 questions. First, what is actually the definition

17 for highly variable drugs or drug products?

18 Second, with respect to expansion of

19 bioequivalence limits, what additional information

20 should we gather in order to answer this question?

21 We also ask you to comment on scaling approaches.

22 With this introduction, I want to turn the

23 podium over to our first speaker, Charlie

24 DiLiberti. Charlie?

25 Why Bioequivalence of Highly Variable Drugs

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1 is an Issue

2 MR. DILIBERTI: Thank you, Dr. Yu. Before

3 I start I need to disclose the potential conflict

4 of interest in that I am employed by Barr and I am

5 also a shareholder and option holder in the firm.

6 Also, before I get into the actual

7 discussion I would like to say that in the context

8 of preparing this presentation I had numerous

9 discussions with many of my colleagues in the

10 industry and, based on the feedback that i got from

11 them, it seems to me that the views that I am about

12 to portray in my presentation are quite widely held

13 in the industry.

14 \[Slide\]

15 With that, let\'s start off with the

16 definition of highly variable drugs. Oftentimes,

17 highly variable drugs are defined in the context of

18 within-subject variability in terms of a

19 bioequivalence study. I would like to take it one

20 step further and look at variability within the

21 patient and what does this high level of

22 variability mean to an individual patient taking

23 the drugs.

24 Commonly, the often used definition of

25 highly variable drugs is those drugs whose

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1 intra-subject or, as I characterize it here as

2 intra-patient, coefficient of variation, or CV, is

3 approximately 30 percent or more. I will use that

4 as my guideline for the rest of this presentation.

5 \[Slide\]

6 What are the current criteria? Just very

7 briefly, for bioequivalence they involve a

8 comparison between test and reference product,

9 involving the natural log transformation of the

10 data. The current criteria are that the 90 percent

11 confidence intervals around the geometric mean

12 test/reference ratios have to fall entirely within

13 the range of 80-125 percent.

14 These criteria really apply to all drugs

15 here, in the U.S., regardless of the inherent

16 variability of the drugs. These criteria do have

17 other implications. For example, they can be used

18 by innovator and, for that matter, generic firms to

19 justify a substantial formulation change so it is

20 not just in the context of approving a generic.

21 \[Slide\]

22 This really speaks to the crux of the

23 issue with highly variable drugs in that it

24 portrays the number of subjects that you would have

25 to plan on using in a two-way crossover

13

1 bioequivalence study given a particular

2 intra-subject CV. You can see that for very low CV

3 drugs the number of subjects required is fairly

4 small and quite manageable from a practical

5 standpoint but, as the CV increases, you can see

6 that the number of subjects required can increase

7 to quite large numbers, possibly in the hundreds.

8 \[Slide\]

9 Why do we possibly need alternative

10 criteria for highly variable drugs? Well, first of

11 all, we have an ethical mandate to minimize human

12 experimentation. Second of all, the prohibitive

13 size of some bioequivalence studies for some highly

14 variable drugs impacts on the availability of a

15 generic version of that drug, which may mean that

16 in the absence of a generic many Americans can\'t

17 afford the reference product so they may go either

18 untreated or they may be subdividing their doses

19 contrary to the prescription.

20 Also, changing criteria will reduce the

21 number of participants in the BE studies and I

22 think it can\'t be done without compromising the

23 safety and efficacy of the product. Also, there is

24 experience elsewhere in the world with criteria

25 other than 80-125 percent.

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1 \[Slide\]

2 This slide shows some of the

3 bioequivalence criteria in other countries and

4 regions in the world. These are not specific to

5 highly variable drugs and in many cases they don\'t

6 apply necessarily to all drugs. That is why I have

7 \"most drugs\" or \"some drugs\" listed here. But,

8 certainly, there is experience with certain drugs

9 in these different regions with confidence

10 intervals that are either wider than 80-125 or, in

11 the case of Canada for many drugs there is no

12 confidence interval criterion, just a point

13 estimate criterion.

14 \[Slide\]

15 What types of drugs are highly variable?

16 Well, the types of drugs really cut across all

17 therapeutic classes and include both new and older

18 products. The potential savings to American

19 consumers could possibly be in the billions of

20 dollars if generics are approved. In saying this,

21 I want to be clear that the bioequivalence issues

22 for many of these drugs are not the only barriers

23 to getting a generic. In some cases there might be

24 patent issues or formulation issues as well, but

25 still the bioequivalence issues do represent some

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1 sort of a barrier.

2 What are some examples? This is a very

3 brief list and the list can go on and on but just

4 to give you some kind of representative examples of

5 drugs that cut across many therapeutic areas, some

6 of which are on-patent, some off-patent, just to

7 give a flavor.

8 \[Slide\]

9 Another issue is that as of last year we

10 now have to meet confidence interval criteria for

11 fed bioequivalence studies. So now the variability

12 under the fed state is of concern. There is

13 generally very little information available on the

14 variability of drugs in the fed state, and we have

15 found that some drugs do show more variability

16 under fed conditions than under fasting conditions,

17 leading to the potential for bioequivalence

18 failures because they may be under-powered. What I

19 am trying to get across here is that because of the

20 lack of information on many drugs under fed

21 conditions, there may in fact be many more highly

22 variable drugs than we are led to believe.

23 \[Slide\]

24 Why aren\'t the current criteria

25 appropriate for some highly variable drugs? Well,

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1 I will start off by saying that the current

2 criteria are, I believe, appropriate for drugs with

3 low to moderate variability because the

4 dose-to-dose variability that a patient would

5 experience is comparable and consistent with the

6 width of the criteria.

7 However, in the case of highly variable

8 drugs this is not true where the dose-to-dose

9 variability experienced by a patient may often be

10 much larger than the width of the criteria. I will

11 illustrate this point later on with some graphs.

12 Highly variable drugs are oftentimes wide

13 therapeutic index drugs. In other words, they have

14 shallow response curves and wide safety margins. I

15 want to qualify this statement by saying when I say

16 highly variable drugs, highly variable in a patient

17 with respect to the parameter that is variable. If

18 a patient experiences high variability, that means

19 that the drug is safe and effective despite this

20 wide variability in the patient. Therefore, I

21 believe that modifying bioequivalence criteria on

22 highly variable drugs to reduce the number of

23 participants in bioequivalence studies could be

24 accomplished while still maintaining safety and

25 efficacy assurance.

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1 \[Slide\]

2 Different highly variable drugs may

3 require different approaches. One size may not fit

4 all. As we can see from the earlier power graphs

5 that I had plotted, obviously the number of

6 subjects required for a drug with, say, 30 percent

7 coefficient of variation is very different from the

8 number of subjects required for a drug with, say,

9 70 percent intra-subject CV. And, there are other

10 considerations that we have to take into account.

11 \[Slide\]

12 Probably one of the more important

13 considerations is whether the drug accumulates in a

14 patient at steady state. Let\'s first take the case

15 of a drug that does not experience significant

16 accumulation to steady state in a patient. These

17 are typically short half-life drugs, in other

18 words, short half-life with respect to the dosing

19 interval. Here are some examples. We could

20 possibly consider some sort of modification to the

21 criteria for both AUC and Cmax because an actual

22 patient would experience significant dose-to-dose

23 variability for both Cmax and AUC because neither

24 is smoothed out at steady state. Therefore, the

25 drug could be considered to exhibit a wide

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1 dose-to-dose variation in blood levels irrespective

2 of chronic dosing.

3 The same sort of logic could potentially

4 apply to a highly variable drug that is not dosed

5 chronically. One particular application of the

6 scenario of a relatively short half-life drug that

7 does not undergo accumulation might be the case of

8 a parent drug with a short half-life and high

9 variability where there is also a metabolite that

10 needs to be measured which has a much longer

11 half-life and low variability. I could easily

12 envision the case where the confidence interval

13 criteria are somehow modified to accommodate the

14 higher variability of the parent drug but, in the

15 same compound, the current 80-125 criteria could be

16 applied to the metabolite.

17 \[Slide\]

18 Now let\'s look at the case of accumulation

19 to steady state. Typically, this is a case where a

20 drug is used chronically and with a half-life long

21 relative to the dosing interval so there is some

22 accumulation going on. Here are a few examples.

23 In this case, because the accumulation

24 process will tend to reduce the fluctuation in AUC

25 and Cmax, both at steady state, actually in

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1 essence, the drug to a patient may not really be

2 highly variable because the variability may be

3 small at steady state. However, the Cmax and AUC I

4 think need to be looked at in a different light.

5 At steady state the test/reference ratio for two

6 drugs, assuming linear accumulation, will be about

7 the same as the test/reference ratio that we see in

8 a single dose study because the accumulation

9 process preserves that test/reference ratio.

10 However, for Cmax, generally speaking, the

11 test/reference ratio that we see at single dose

12 conditions will be the most extreme and the

13 test/reference ratio observed upon accumulation to

14 steady state will go closer and closer to unity,

15 one. So, that is why I think we potentially need

16 to consider these two cases differently in the case

17 of a drug that accumulates.

18 \[Slide\]

19 The other possibility with drugs subject

20 to accumulation is to actually conduct the steady

21 state study but this has all sorts of practical

22 limitations for some drugs, including toxicity.

23 \[Slide\]

24 What I have tried to do in this graph is

25 to get some sense of the magnitude of day-to-day

20

1 fluctuations in a pharmacokinetic parameter\--I have

2 plotted this as if it were Cmax but it could

3 equally apply to AUC\--in the case of a drug that

4 does not undergo accumulation.

5 What is plotted here, in orange, is

6 simulated data representing the sequential

7 day-to-day Cmax\'s that might be seen in a given

8 patient taking a single drug over the course of 30

9 days where the drug has a true mean of 100 percent.

10 In fact, the sample mean here for this set of 30

11 data points is 100 and is the geometric mean, and

12 the CV of this data set is 10 percent. So, you can

13 see that the drug is fairly well controlled within

14 a fairly narrow range. Just as a yardstick for

15 variability, I have plotted the bioequivalence

16 limits, the 80 percent limit and the 125 percent

17 limit. I want to make it clear these limits do not

18 apply to individual day-to-day values, but I am

19 just plotting them here to give some sense of

20 scaling.

21 What I have plotted here, in the green, is

22 a different formulation, formulation B of the same

23 drug that has a mean here of 125. So, it is a 25

24 percent higher mean than this. CV is still 10

25 percent. So, this could be seen to represent the

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1 magnitude of change that one would expect upon

2 switching a patient from one formulation to a

3 second formulation with a higher mean. You can see

4 that there is some degree of overlap between the

5 second formulation and the first but, just

6 eyeballing this, it is not too hard to see that

7 there is visually some discernible shift in the

8 overall levels.

9 \[Slide\]

10 Let\'s see what the case looks like for a

11 drug with 30 percent intra-subject CV. You can see

12 here that there are many more excursions on a

13 single formulation outside the range of 80-125

14 percent. Overall, there is much more overlap

15 between formulation B and formulation A despite the

16 fact that these two formulations differ by 25

17 percent.

18 \[Slide\]

19 Let\'s increase the variability one notch

20 further to 50 percent CV, and we can see even more

21 day-to-day excursions in Cmax for a patient on a

22 given formulation, many of them outside 80-125.

23 You can see now that the overlap between

24 formulation B and formulation A, again a 25 percent

25 difference here, is almost not discernible at all

22

1 to the eye.

2 \[Slide\]

3 Finally, let\'s turn it up one notch

4 further to 70 percent intra-subject CV. With a

5 drug that is this variable you end up, while on a

6 single formulation with no switch involved, with a

7 range of Cmax values that could be as far as a

8 5-10-fold range day-to-day. So, there are wide

9 swings in the Cmax\'s achieved for a given subject.

10 In light of this, suppose that this is a

11 reference drug that is already approved by the

12 agency and known to be safe and effective, that

13 safety and efficacy is true in spite of the wide

14 variability from day-to-day so, therefore, the drug

15 cannot have a narrow therapeutic index and must

16 necessarily have a relatively wide therapeutic

17 index if it is safe and effective despite such wide

18 variation.

19 Also, you can see that the switch-over

20 product, formulation B, again a 25 percent higher

21 mean, is virtually indistinguishable now from the

22 range of blood levels that you see with formulation

23 A.

24 I think that the criteria, which are still

25 plotted here, 80-125 percent, need to be

23

1 commensurate with the degree of overlap that we are

2 trying to achieve between formulations. Even

3 though these are the criteria, I would like to

4 point out that in order to pass the criteria the

5 actual observed mean in a bioequivalence study

6 generally has to be in a very narrow range, maybe 5

7 or 10 percent deviant from 100. Outside of that,

8 your chances of passing a bioequivalence study on a

9 very variable drug are very, very poor.

10 \[Slide\]

11 There are certain special considerations

12 that we need to take into account in the discussion

13 of highly variable drugs, one of which is where

14 parallel studies are conducted for long half-life

15 drugs.

16 Oftentimes you can\'t do a crossover study

17 because the wash-out period would be too long.

18 Powering parallel studies depends on between

19 subject variability rather than within subject

20 variability. Between subject variability is often

21 large, necessitating large bioequivalence studies

22 just as with highly variable drugs. However, the

23 high between subject variability does not

24 necessarily imply high within subject variability.

25 Instead, it could be due to inter-individual

24

1 differences in absorption, metabolism, etc. So,

2 these drugs, from a clinical perspective, may not

3 really be highly variable but we are still faced

4 with the powering problems in terms of conducting

5 bio studies. In these cases, generally speaking,

6 multiple dose studies are not feasible, and we

7 might consider some sort of alternative criteria

8 for such studies.

9 \[Slide\]

10 A second issue that arises and is directly

11 related to the issue of highly variable drugs is

12 the issue of pooling data from multiple dosing

13 groups. Because of the large number of subjects

14 often required for highly variable drugs,

15 oftentimes you have to split up dosing into

16 multiple dosing groups.

17 Currently, the FDA requires a statistical

18 test for the poolability of the data from these

19 multiple dosing groups and the test is a measure of

20 the significance of the group by treatment

21 interaction terms in the analysis of variance. If

22 this interaction term is statistically significant,

23 then you are not permitted to pool the data from

24 the multiple dosing groups. The consequence of

25 this is that each group is then evaluated on its

25

1 own merit and, because each group is generally

2 considerably smaller than the total pool of

3 subjects, each group will be grossly under-powered

4 to achieve bioequivalence and, therefore, if you do

5 have a statistically significant interaction term,

6 overall you are likely to have failed the criteria.

7 This procedure results in discarding and

8 having to repeat about 5 percent of studies based

9 on random chance alone, even if there is no genuine

10 underlying effect. The concern here I think is

11 that even if there were some sort of underlying

12 explanation for the statistical significance of the

13 interaction term, for example differences in

14 demographics among the dosing groups, I believe

15 that there is no reason not to use the data from

16 all the dosing groups because had they been dosed

17 together in a single group it would be perfectly

18 usable and we wouldn\'t be having this discussion.

19 \[Slide\]

20 Conclusions\--while the current

21 bioequivalence acceptance criteria I believe are

22 appropriate for drugs with ordinary variability, I

23 believe that they may not be appropriate for some

24 highly variable drugs.

25 Current bioequivalence acceptance criteria

26

1 make it difficult or impossible to develop generics

2 in some cases, which has the public health issue of

3 effectively denying treatment to many patients

4 because of affordability issues.

5 I believe that practical, scientifically

6 sound alternative bioequivalence acceptance

7 criteria could be implemented for highly variable

8 drugs to reduce the bioequivalence study size while

9 still maintaining assurance of safety and efficacy.

10 Different approaches may be needed for

11 different types of drugs depending on accumulation

12 following multiple dosing, and also depending on

13 the variability of the drug. And, other related

14 situations, i.e., the issue of parallel studies and

15 multiple dosing groups should also be considered in

16 conjunction with any changes to acceptance criteria

17 for highly variable drugs. Thank you.

18 DR. KIBBE: Does anybody on the panel have

19 questions for our presenter to clarify information?

20 Nozer?

21 DR. SINGPURWALLA: Certainly, I do. I

22 have four questions and five comments. Do I have

23 time?

24 DR. KIBBE: You have until everybody

25 leaves to go to the airport!

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1 DR. SINGPURWALLA: The first question is a

2 question of clarification. What is Cmax? when

3 somebody puts C and a max I think of the maximum.

4 MR. DILIBERTI: That represents the

5 maximum because concentration achieved within a

6 given patient or subject over the course\--

7 DR. SINGPURWALLA: So, it is maximum blood

8 concentration?

9 MR. DILIBERTI: Yes, it is maximum blood

10 concentration.

11 DR. SINGPURWALLA: Thank you. What is

12 AUC?

13 MR. DILIBERTI: Area under the curve,

14 which is generally taken to be a measure of the

15 extent of absorption.

16 DR. SINGPURWALLA: The third question is

17 why did you take natural logs?

18 MR. DILIBERTI: It is conventional in the

19 analysis of bioequivalence data to do a log

20 transformation. This is already established as

21 standard\--

22 DR. SINGPURWALLA: Log transformation of

23 the whole data or just the maximum?

24 MR. DILIBERTI: You would log transform

25 each of the individual Cmax\'s and then follow that

28

1 by appropriate analysis of variance. The same log

2 transformation also applies to the individual AUCs

3 prior to analysis of variance.

4 DR. SINGPURWALLA: Well, I can see doing a

5 log transformation of all the data to get

6 approximate normality if the distribution is log

7 normal.

8 MR. DILIBERTI: Yes, that is true.

9 DR. SINGPURWALLA: Just taking the log of

10 the maximum\--I don\'t know. By geometric mean, you

11 mean product divided by\--what do you exactly mean?

12 MR. DILIBERTI: The geometric mean is what

13 results from the log transformation. You do the

14 log transformation and conduct analysis of

15 variance. From the analysis of variance you get a

16 least-squares mean on a log transformed variable.

17 When you back-transform that by exponentiating it

18 you end up with, in essence, a geometric mean.

19 DR. SINGPURWALLA: Okay. Now we will go

20 to comments. As somebody who is new to all this

21 and doesn\'t know, the thought that first comes to

22 my mind is that this HVD, highly variable drug,

23 should really be looked at as a bivariate problem.

24 You have two variables. One variable is the extent

25 of absorption and the other variable is the rate of

29

1 absorption. So, I would look at it as a surface

2 because the following is possible, suppose you have

3 a drug which has a low variability with respect to

4 absorption but high variability with respect to

5 extent of absorption, how do you classify it? So,

6 what we need is a better measure of classifying a

7 highly variable drug which is a bivariate measure.

8 That is the first comment.

9 You proposed, I think, abolishing the

10 confidence limit notion.

11 MR. DILIBERTI: No, I didn\'t. I am not

12 here to propose solutions to the problem; I am just

13 here to really identify what the concerns and

14 problems are.

15 DR. SINGPURWALLA: Okay, but do you have

16 any sense of what is an alternative?

17 MR. DILIBERTI: Various alternatives have

18 been proposed, including reference scaling or some

19 fixed point scaling that is different from 80-125\--

20 DR. SINGPURWALLA: But you are not putting

21 those forward?

22 MR. DILIBERTI: I am not really here to

23 discuss that.

24 DR. SINGPURWALLA: So, your basic focus is

25 criticizing what is there but without an

30

1 alternative in mind?

2 MR. DILIBERTI: Right, I think many of the

3 later speakers will address the issue of potential

4 solutions.

5 DR. SINGPURWALLA: Now, in these charts

6 that you showed, how did you choose the particular

7 patient whose charts you were showing?

8 MR. DILIBERTI: It is simulated data. It

9 is log normally distributed random independent

10 variables. It is not patient data. I am sorry, I

11 thought that that was clear. It is entirely a

12 computer simulation just to give some sense of the

13 relative magnitude of the variability.

14 DR. SINGPURWALLA: Well, I didn\'t get that

15 message. I thought that was a real patient\--

16 MR. DILIBERTI: No, no, no.

17 DR. SINGPURWALLA: \--those data you were

18 showing.

19 MR. DILIBERTI: No.

20 DR. SINGPURWALLA: But you don\'t need to

21 show it because if it is simulated we can

22 appreciate it. The last point is when you talked

23 about pooling the data between two groups, how is a

24 group defined? What constitutes a group?

25 MR. DILIBERTI: By the day on which dosing

31

1 occurs. For example, it may be impractical to dose

2 100 patients or subjects in a clinic all on the

3 same day. So, you may have to dose half of them

4 today and maybe the other half several weeks from

5 today.

6 DR. SINGPURWALLA: So the groups are

7 random depending on who shows up.

8 MR. DILIBERTI: Essentially, yes.

9 DR. SINGPURWALLA: Suppose one were to

10 think about forming these groups based on some

11 other, you know biological or\--defining a group in

12 a certain way, conceivably you could justify

13 pooling. This is completely random.

14 MR. DILIBERTI: Right, and I believe that

15 the way that the groups are conventionally arranged

16 in a typical bioequivalence study pooling may be

17 justified even if you do have a statistically

18 significant interaction term.

19 DR. SINGPURWALLA: See, what I am afraid

20 of is that if you did this on some other day and

21 you had the same policy of pooling at random you

22 may see a completely different result in the sense

23 that the point you are making may not be made.

24 Well, thank you.

25 MR. DILIBERTI: Thank you.

32

1 DR. KIBBE: Anybody else? Go ahead.

2 DR. SELASSIE: You mentioned that

3 potential savings to patients are in the billions

4 of dollars if generics are approved. Can you tell

5 me or do you have an idea of what percentage would

6 actually be the lack of savings due to the fact

7 that there are no generics for each of these as

8 opposed to other patent issues?

9 MR. DILIBERTI: That is very difficult to

10 assess because, for example, in looking at patents

11 you need to look even beyond the \"Orange Book.\"

12 Some of these formulations have patents that are

13 not listed in the \"Orange Book.\" So, to compile

14 data like that would be a Herculean task. However,

15 I do know from personal experience that the

16 difficulties in meeting bioequivalence criteria do,

17 in fact, pose a very real barrier to the

18 development of some generics.

19 DR. MEYER: If I could give an example, if

20 your wife is on premarine you know you insurance

21 co-pays \$20.00, because there is no generic

22 currently available because of bioequivalence

23 issues, instead of \$5.00.

24 MR. DILIBERTI: Right.

25 DR. MEYER: Since my light is on I will

33

1 just add that I do agree with you about pooling

2 data together. A clinical trial, after all, has a

3 patient come in to a doctor\'s office; they take a

4 measurement. A week later another patient comes in

5 and now you have two groups, and you don\'t analyze

6 those separately. So, unless there is really some

7 reason to think that two groups of 50 can\'t be put

8 together to make one group of 100, I think it is

9 silly not to put them together.

10 DR. KIBBE: Paul?

11 DR. FACKLER: If I could just make a

12 couple of comments, one addressing the issue of AUC

13 and Cmax, there are very few drugs where I think

14 Cmax is not highly variable but AUC is. I would

15 say that from our experience it is the other way

16 around.

17 DR. SINGPURWALLA: I am sorry, I missed

18 that. You are saying that the two are correlated.

19 DR. FACKLER: I am saying that there are

20 very few examples of drugs that are highly variable

21 on AUC but not highly variable at Cmax. Generally

22 it is the other way around, AUC is not as variable

23 as Cmax.

24 DR. SINGPURWALLA: So, it makes my point

25 that you may have a bivariate situation.

34

1 DR. FACKLER: Yes, absolutely.

2 DR. SINGPURWALLA: Thanks.

3 DR. FACKLER: One of the things I wanted

4 to ask Charlie was on the simulated data you

5 represented 80 percent and 125 percent. I am

6 wondering did you happen to calculate the

7 confidence intervals for the simulated data sets to

8 show where the 90 percent confidence intervals

9 would have resulted? Because I am certain they are

10 far beyond 80-125.

11 MR. DILIBERTI: That is right. No, I did

12 not go through that calculation.

13 DR. FACKLER: The last point I wanted to

14 make was that on the graph of the number of

15 subjects needed to get to 80 percent power versus

16 the variability, it is important to recognize that

17 80 percent power means that one out of five studies

18 under those conditions will fail to show

19 bioequivalence, or only four out of five will. So,

20 even if a product is tested against itself with,

21 for instance, 30 percent variability, using the

22 number of subjects in that particular graph one out

23 of five studies will fail to show that the product

24 against itself is bioequivalent.

25 DR. KIBBE: Shall we move along? I think,

35

1 Gordon, you are up.

2 Highly Variable Drugs: Sources of Variability

3 DR. AMIDON: I am going to talk about

4 sources of variability and emphasize mechanisms of

5 absorption and focus on bioequivalence from an

6 absorption point of view. It is the approach I

7 have been taking for the past 10 to 15 years.

8 \[Slide\]

9 If you think about bioequivalence where we

10 are comparing drug products, then the question of

11 bioequivalence is really a dissolution question.

12 Right, the same drug? So, we should be looking at

13 mechanism and dissolution and processes that are

14 controlling absorption and develop our tests around

15 that mechanism, what is controlling the process.

16 Of course, plasma levels are the gold

17 standard. Our business is to ensure that plasma

18 levels match the innovator product used in the

19 clinical testing. That is the criterion, no

20 question about that; no argument about that. The

21 question is what test.

22 \[Slide\]

23 So, I want to show some of the factors.

24 We tend to focus on bioequivalence from a plasma

25 level point of view over here. We focus on the

36

1 plasma which is the gold standard. But if

2 absorption is controlled by the dissolution

3 process, dissolution controls the presentation of

4 drug along the gastrointestinal tract and,

5 therefore, controls the rate and extent of

6 absorption. If the rate and extent of absorption

7 is the same, then the plasma levels will be the

8 same. So, in the question of bioequivalence then

9 the real scientific issue is how do we set a

10 dissolution standard? My position may be a little

11 extreme because no one seems to want to think about

12 that very much but that is the reality of the

13 science.

14 \[Slide\]

15 So, I think if you have two drug products

16 that present the same concentration profile along

17 the gastrointestinal tract, they will have the same

18 rate and extent of absorption and systemic

19 availability. You may want to think about that,

20 the same rate and extent of absorption implies the

21 same systemic availability. So, we need to focus

22 on product.

23 \[Slide\]

24 Some of the processes in the

25 gastrointestinal tract that can lead to the

37

1 variability\--and I will just illustrate some of the

2 processes here\--would be the gastric emptying,

3 intestinal transit, luminal concentration both of

4 pH and surfactants, phospholipids, presence or

5 absence of food. When you think about it, there

6 are a lot of sources of variability just in the

7 gastrointestinal tract.

8 \[Slide\]

9 Systemic availability\--what should our

10 testing ensure? It is the gold standard, no

11 question about it. But the question then is what

12 is the best test? What is the best test to ensure

13 plasma levels? And, when plasma levels are

14 difficult to measure or, in the case of highly

15 variable drugs where it requires a lot of subjects,

16 then I think it really requires us to think what is

17 the source of that variability and then what type

18 of test might we set.

19 I would argue that if two highly variable

20 drug products dissolve the same way in the

21 gastrointestinal tract they will be bioequivalent.

22 It might require 100 subjects to show that. I

23 think that is unnecessary. I think you just do it

24 with a dissolution test and the answer will be far

25 simpler.

38

1 \[Slide\]

2 So, what are some of the physicochemical

3 factors? Clearly, particle size and distribution;

4 wetting and solid-liquid contact; and, of course,

5 in some cases chemical instability such as prodrugs

6 and esterases and peptidases in the

7 gastrointestinal tract can lead to highly variable

8 absorption and, hence, systemic availability.

9 \[Slide\]

10 I just put one graph in here showing the

11 dependence here of dissolution time, ranging up to

12 30 hours, and gastrointestinal transit time as a

13 function of particle size. I can\'t manipulate this

14 in this presentation but the dissolution time

15 increases dramatically as the drug solubility

16 decreases. Particle size becomes a critical factor

17 for low solubility drugs. Of course, everyone

18 realizes that but it is not particle size that we

19 put into the formulation, it is the particle size

20 that comes out of the formulation in the

21 gastrointestinal tract. So, those process

22 variables are important.

23 \[Slide\]

24 Some of the factors in the

25 gastrointestinal tract then are gastric emptying,

39

1 intestinal transit, position dependent permeability

2 along the gastrointestinal tract\--duodenum,

3 jejunum, ileum and colon and, of course, intestinal

4 mucosal cell metabolism, and in particular CYP3A4

5 which is highly expressed and differentially

6 expressed along the gastrointestinal tract, and

7 potentially PGP expression along the

8 gastrointestinal tract.

9 \[Slide\]

10 To give you an example of variability in

11 gastric emptying rates, we can just look at the

12 light blue because that is administered with 200

13 ml, the approximate glass of water that we use. We

14 used 200 ml here because we did this before we got

15 involved in drug regulatory standards and realized

16 that a glass of water was the U.S. standard; not

17 the standard in Japan. We are trying to figure

18 that out, what is a glass of water in Japan. So,

19 with 200 ml you can see that the variability in

20 gastric emptying. Depending on when you dose in

21 the fasting state, it ranges from 5 minutes to

22 about 22 minutes. There is about a 4-fold

23 variation in gastric emptying rate depending on

24 when you administer to a particular subject. This

25 is because of the different contractual activities

40

1 in the fasted state, shown here as phase 1, 2, 3

2 and 4.

3 \[Slide\]

4 Clearly, intestinal transit\--again, this

5 is a movie but I can\'t show it with this

6 presentation\--transit through the gastrointestinal

7 tract where the drug is released in the duodenum.

8 It has a very short transit time, maybe 10, 15

9 minutes through the duodenum, jejunum, ileum and

10 colon. The dissolution rate, particularly of a low

11 permeability drug where the permeability appears to

12 be the rate-determining step to absorption, the

13 permeability profile along the gastrointestinal

14 tract is very important.

15 \[Slide\]

16 There are about 10 L of fluid processed in

17 the gastrointestinal tract per day, actually

18 depending on which book you read, 8 to 10. Of the

19 10 L that are processed, only about 2 L are

20 actually ingested as external. The other 8 L are

21 ourselves. We are continually secreting and

22 reabsorbing not only fluids but cells and proteins

23 and other ions that are secreted into the intestine

24 so there is a tremendous amount of variability and,

25 of course, food has a large impact on that as well.

41

1 So, that is a major factor that can be involved in

2 the variability and dissolution and absorption in

3 the gastrointestinal tract.

4 \[Slide\]

5 I show here just ranitidine, a low

6 permeability drug. This is animal data. I don\'t

7 have human data and, in fact, it is very hard to

8 get human data although there is some data

9 available. The duodenum, jejunum, ileum\--there is

10 a significant difference in permeability. So, you

11 can envision a slowly dissolving ranitidine

12 product\--I don\'t know if there are any, but

13 releasing in the ileum would have very poor

14 absorption. So, dissolution for a low permeability

15 drug is probably more important because, in

16 general, the permeability in the upper part of the

17 gastrointestinal tract is more important or higher,

18 I should say.

19 You know, we used to use language like

20 \"rapidly but incompletely absorbed.\" You would see

21 that in the literature after analysis of

22 pharmacokinetic data and I would say how can that

23 be? It doesn\'t make sense to me. If it is rapid

24 it should be well absorbed. Right? Clearly, there

25 has to be position-dependent permeability and the

42

1 absorption rate must decrease dramatically at some

2 point very quickly after the drug is administered.

3 Presumably, that is the result of drug getting into

4 the ileum or distal in the small intestine where

5 there is lower absorption.

6 \[Slide\]

7 PGP\--this is some immunoquantitation

8 results on CYP3A4 showing the variation in the

9 duodenum, ileum and colon, much less in the colon

10 so that there is less metabolism, particularly if

11 there is a controlled release formulation releasing

12 drug in the colon and, of course, much more in the

13 liver. I don\'t know, maybe Leslie is going to say

14 more about the metabolism source of variability,

15 maybe not. You are shaking your head, no.

16 \[Slide\]

17 I am going to propose that we classify the

18 drugs, highly variable drugs using BCS. Here is

19 what I think we would see. We need to actually

20 look at particular drugs. In fact, I would like to

21 see a list of drugs perhaps based on the

22 variability of reference products, whatever we

23 could find today, develop a list of highly variable

24 drugs or that we think might be highly variable,

25 and then look at their properties and decide what

43

1 are the likely sources of variability.

2 Anyway, I know there are certain so-called

3 highly variable drugs that are Class I drugs. They

4 have to be low dose, low solubility drugs but they

5 are soluble enough to dissolve in a glass of water.

6 That is our criteria at the present time. So, if

7 those drug products dissolve rapidly\--if they do; I

8 don\'t know if they do, we should look at that and

9 it is over; there is no issue. It is all biologic

10 variability; nothing to do with the product

11 variability. Again, that is a hypothesis.

12 Probably the majority of the drugs that

13 are highly variable are in Class II where there is

14 low solubility, potentially Class IV for some

15 higher molecular weight compounds. There, the

16 solubility-dissolution metabolism interaction can

17 be difficult to separate and that is where we would

18 need to look more carefully at the drug products to

19 determine whether it is the solubility and

20 dissolution variability or whether it is a

21 metabolism variability that is leading to the high

22 variability in plasma levels.

23 \[Slide\]

24 So, I think that the BCS classification

25 can help focus on the source of the high

44

1 variability. Then, in the case of rapid

2 dissolution of Class I and Class III drugs a

3 dissolution standard may be enough. There may not

4 be too many highly variable drugs because I think

5 the majority would be the low solubility Class II

6 or Class IV drugs and there I think metabolism

7 and/or dissolution can be the source of

8 variability. In the case of metabolism, the

9 metabolism variation would be due to the

10 variability and dissolution and presentation along

11 the gastrointestinal tract. So, again, it comes

12 back to a dissolution issues.

13 In fact, I would propose that we look more

14 carefully at the highly variable drugs, the sources

15 of variability, again asking the critical question

16 what is the best test? What is the best test? I

17 will go back to the original implementation of BCS

18 in the case of high solubility, high permeability,

19 rapidly dissolving drugs. Plasma levels are

20 telling us nothing about the product differences.

21 It is only telling us about gastric emptying

22 differences at the time of administration of

23 patients or subjects. So, again, focusing on

24 dissolution and classification I think can help us

25 unravel and simplify some\--maybe not all. Maybe

45

1 not all of the highly variable drugs can be

2 simplified this way but I think some of them can be

3 simplified this way. For those drugs that are

4 complicated, we just say they are complicated.

5 Take a drug like premarine. You have already

6 mentioned that, Marvin. I think that premarine is

7 a complicated drug. That is life; that is the way

8 it is. It is too complicated for us to unravel

9 today because of the way we regulate drugs and

10 approve drugs. So. I am happy to answer any

11 questions by the committee.

12 DR. KIBBE: Questions, folks? Jurgen?

13 DR. VENITZ: I agree with you, I am very

14 much in favor of identifying sources of variability

15 and what you are presenting are obvious sources of

16 variability, and it always bothers me when we talk

17 about highly variable drugs and they are defined

18 phenologically. All we are doing is a clinical

19 study. We are measuring Cmax and AUC and we find

20 that they vary a lot, and that is the end of it,

21 and now let\'s change criteria to see whether they

22 can fit bioequivalence. So, I agree with you on

23 that.

24 What I won\'t agree with you, at least not

25 fully, is that it is all a dissolution issue. I

46

1 think you are ignoring, in my mind at least, the

2 effects that excipients may have that could be very

3 different between formulations so that may not have

4 an impact on dissolution but may have an impact on

5 pH, may have an impact on permeability and may have

6 an impact on GI metabolism. Now, I don\'t know

7 whether that is a significant problem or not but I

8 think it is more than dissolution that you are

9 looking at. It doesn\'t preclude what you are

10 recommending, which is basically do dissolution

11 tests and find out if that is an issue and then see

12 how that matches your in vivo data. That is just a

13 comment.

14 DR. AMIDON: If we extend the dissolution

15 to dissolution of the excipient, that is, the

16 dissolution of the excipient and the drug, then I

17 think we would be okay; I think my statement would

18 be okay.

19 DR. VENITZ: But if you have products that

20 have different excipients, that is my point.

21 DR. AMIDON: Yes, okay.

22 DR. VENITZ: As you said, life is

23 complicated. Sometimes it works; sometimes it

24 doesn\'t.

25 DR. AMIDON: Right. So, that is the

47

1 function of what is the source of the variability.

2 DR. VENITZ: Yes.

3 DR. KIBBE: Ajaz?

4 DR. HUSSAIN: I worked with Gordon for

5 many years on developing the BCS guideline, and so

6 forth, and we actually did examine that very

7 question of excipients and their impact not only on

8 the dissolution process but on permeability and

9 metabolism and it is a serious issue and I think we

10 learn more about transport every day. Therefore,

11 clearly, I think when Gordon mentioned dissolution,

12 we have discussed that so many times and we always

13 include that as a source of variability and that

14 has to be considered.

15 But, Gordon, I wanted to push you in a

16 different direction. One of the hesitations as we

17 developed the BCS guidance was the reliability of

18 the in vitro dissolution test. We were not

19 confident that the current test really was good

20 enough to extend it to the slower releasing

21 products. So, that was the reason we crafted

22 rapidly dissolving and said dissolution is not rate

23 limiting and, therefore, we can rely on the current

24 dissolution test to do that.

25 I think as we move forward here, I think

48

1 what we have done with the PAT initiative is to

2 sort of say, all right, let\'s really ask the

3 question what are the criteria variables, what are

4 the root causes of this. So, go back to the basics

5 as to particle size, and so forth, and if you

6 really understand those relationships then you have

7 a better link between your formulation and your

8 excipients; you have your process directed to the

9 clinical relevance. So, that is the opportunity

10 that technology is offering us to do that without

11 having to do an artificial in vitro test where

12 questions keep continuing and increasing with

13 respect to the relevance of that in vitro test.

14 DR. AMIDON: I certainly obviously agree,

15 Ajaz. We have talked about these issues for many

16 years. I did use the word in vivo dissolution.

17 There is a big step from in vitro to in vivo. I

18 don\'t think it is magic; it is just complicated and

19 I think we can figure that out. I think we can

20 determine for any particular drug what might be a

21 good representative dissolution test, and I might

22 call that a bioequivalence dissolution test rather

23 than a QC, quality control, dissolution test. But

24 you are absolutely right. The issue is really in

25 vivo dissolution and how do we capture that in some

49

1 in vitro methodology. I don\'t think we have

2 thought about that very hard at all. I am not sure

3 why. We use the term dissolution very generically

4 when it should be much more specific.

5 DR. KIBBE: Les wants to comment and then

6 Nozer. Can you make a comment, Les, because you

7 are not part of the committee?

8 DR. BENET: They said as a visitor I can.

9 I wanted to comment on BCS and what Jurgen brought

10 up in terms of the excipients. When we initiated

11 BCS I was very strong concerning the potential for

12 excipients on Class I drugs and we have written the

13 rules to make sure that these excipients don\'t have

14 an effect. In fact, I now recognize that with

15 Class I drugs that is not a problem, that the

16 excipients won\'t be a problem in terms of affecting

17 at least the transporters. But they will be a

18 problem with Class III drugs.

19 So, so far I have been very opposed to

20 moving the Class III drugs because I can make a

21 Class III formulation that will pass dissolution,

22 any dissolution, and fail. The reason is that

23 Class III drugs need uptake transporters to get

24 absorbed and, therefore, I can block an uptake

25 transporter in the gut with a substance that has no

50

1 dissolution criteria. So, I still think we are a

2 little early in translating this dissolution

3 criteria beyond Class I, but I think we were

4 correct in Class I and the extra safeguards we put

5 in actually really turn out not to be necessary.

6 DR. SINGPURWALLA: I like this concept of

7 looking at the causes of variability. I see this

8 as a first step towards going to a Bayesian

9 alternative for the existing methodology that was

10 criticized by the first speaker. But I do have a

11 question perhaps both for you and also for the

12 first speaker. Has anybody looked at the

13 reliability of the testing instrument itself?

14 Because if the testing instrument itself shows a

15 large variability\--if the instrument itself shows a

16 large variability then you don\'t know whether the

17 variability is coming from the instrument or from

18 the particular drug or the combination of the

19 instrument, the drug and the patient.

20 DR. KIBBE: Anybody? Who wants to handle

21 that?

22 DR. VENITZ: I think by instrument what

23 you mean is the human being used in those studies.

24 Are you talking about dissolution or are you

25 talking about in vivo?

51

1 DR. SINGPURWALLA: Both.

2 DR. VENITZ: Well, then let\'s talk about

3 in vivo and I will leave it up to you to talk about

4 dissolution. What you are looking at is the Cmax\'s

5 and the areas under the curves. They do not only

6 depend upon absorption and dissolution; they depend

7 on everything that happens after the drug gets in

8 the body, which is something we are not interested

9 in. If that contributes significantly to the

10 variability, then you are looking at primarily

11 variability and disposition which determines why we

12 have a highly variable drug, not because there is

13 variability in absorption. So, your instrument

14 would be a very noisy instrument I think, to use

15 your lingo.

16 DR. SINGPURWALLA: Right. You have an

17 instrument by which you measure these things, like

18 a thermometer. If your thermometer is bad\--

19 DR. VENITZ: I am saying that for some

20 drugs it could well be that you have a very noisy

21 instrument and the noise is not related to what you

22 are trying to measure.

23 DR. SINGPURWALLA: Exactly.

24 DR. KIBBE: Let me just take the

25 prerogative of the chair for half a second and then

52

1 I will let you speak. It is very difficult for us

2 to understand the real noise level of the

3 instrument. The instrument is the bioequivalency

4 test itself and the agency gets submissions with

5 bioequivalency tests that are passed. The question

6 is how many were done that failed before the one

7 that passed, and what was done to make that work?

8 I think if you go back and we got a bunch

9 of data together, which we can\'t but it would be

10 interesting to look at, we would find that the

11 instrument is very crude and the reason we live

12 with it is that it is close to the clinical

13 therapeutic outcomes that we really want to measure

14 in terms of steps away from that outcome. What

15 Gordon is recommending is that we even eliminate

16 the human from our decision-making process, which

17 brings us further away from the ultimate goal which

18 is to know that it therapeutically equivalent, and

19 we have to be sure that our predictor is going to

20 hold true. Those are the problems I think that we

21 all have been struggling with for 25 years.

22 DR. HUSSAIN: Now I have three comments.

23 With respect to the instrument variability, I think

24 it is a very important question. In the case of

25 bioequivalence testing we try to minimize that and

53

1 try to make it more precise and more accurate by

2 doing a crossover study. We test the two products

3 in the same patient in a crossover fashion. So,

4 that is our attempt to minimize that. The other

5 attempt that we had to minimize is to get a group

6 of more similar individuals but we wanted to move

7 away from that in the general population because

8 the crossover is a way to minimize that. I also

9 pointed out with respect to variability the

10 dissolution test. I think as we think about that,

11 we need to address that.

12 But the point I think, going back to the

13 key question, is what are the important questions

14 here? Dr. Kibbe\'s comment was, in a sense,

15 bioequivalence. For therapeutic equivalence our

16 approach is very simple. First you need to be

17 pharmaceutically equivalent and then, if there is a

18 need, you do a bio study. For example, for

19 pharmaceutical equivalence for solutions you don\'t

20 need a bio study. So pharmaceutical equivalence,

21 bioequivalence and then therapeutic

22 equivalence\--those come together to define that. I

23 could sort of generalize what Gordon has said, in a

24 sense if we understand our formulations, if we

25 understand our processes, if we understand the

54

1 mechanisms, pharmaceutical equivalence essentially

2 is defining therapeutic equivalence.

3 DR. AMIDON: To come back to your question

4 about the dissolution apparatus, there is a range

5 of dissolution apparatus in the USP that are used

6 internationally, and you can study many of the

7 variables that change in vivo by pH and surfactants

8 in those apparatus. The apparatus themselves have

9 been proven perhaps historically to be very

10 reliable, although you could argue maybe today that

11 we could design a better apparatus but that is very

12 complicated because these things are used in many

13 companies internationally with defined procedures

14 that are approved by the regulatory agencies and

15 making change in an apparatus is a very complex

16 process.

17 But, yes, we can study the various

18 variables in vivo and I think that a dissolution

19 test that included changes in pH and surfactant to

20 reflect what is happening in vivo is something we

21 should do. We don\'t do that; we just do fixed pH

22 and follow the dissolution as a function of time.

23 So, I don\'t think we use our apparatus very

24 insightfully actually.

25 DR. KIBBE: I would argue that the way we

55

1 use dissolution is reliable but insensitive, and we

2 need to do a lot more to be able to make that

3 conversion. Anybody else?

4 DR. MEYER: Gordon, I listened to the PAT

5 stuff all day yesterday and what I got out of it is

6 that it is applicable to this so the idea of why do

7 we have variability\--right now we are proposing to

8 potentially change our release specifications

9 because our product is too variable and that is not

10 acceptable in the manufacturing arena. You go back

11 and figure out why it is too variable. I wonder

12 how much data is really available on if I gave

13 myself a rapidly absorbed drug once for the next

14 three weeks, what would my profiles look like? I

15 don\'t know that there is a lot of data that shows

16 reproducibility in a subject, unless it was the old

17 multiple dose studies where the drug was

18 essentially eliminated in 24 hours.

19 So, I think we need some more information.

20 I don\'t know, maybe the agency does this, but when

21 the innovator firms do special populations and they

22 find the elderly are different than the young, do

23 they have to then go further and explain is that

24 gastrointestinal pH, is it transit, is it

25 metabolism, what is the reason for it. Because I

56

1 think then we can get some background information

2 on source of variability.

3 Just to bounce off an idea which is

4 undoubtedly ludicrous, do we need in a sense to

5 prescreen some subjects so we have a calibrated man

6 or, if you will, a USP man or woman that is then

7 allowed into the study so if they have less

8 variability they get into our study? Could we do

9 that? One thing that really troubles me is the

10 current policy, and I understand why it is and I

11 think I support it, of having different mechanisms

12 of release tested against each other in a

13 bioequivalence study, an oral study versus a

14 particular dosage form. Intestinal transit can

15 have a profound difference on those two so if you

16 have a uniform man, that uniform man may show them

17 to be equal but if you throw in a vegetarian, that

18 vegetarian might show the oral tablet is excreted

19 in four hours and the other person may take much

20 longer. So, just some support really for the idea

21 of knowing where the problems are; can we reduce

22 variability somehow; are subjects legitimately\--is

23 that a viable approach?

24 DR. AMIDON: I don\'t know, I am not sure I

25 would want to take on preselecting subjects because

57

1 what criteria are you going to use? Normal in what

2 sense?

3 DR. MEYER: I am thinking more in terms

4 of, say, rapid metabolism or poor metabolism. We

5 do that now somewhat routinely.

6 DR. AMIDON: Right.

7 DR. MEYER: So, we might give a

8 panel\--CROs now, they use the same subjects over

9 and over again anyway. Let\'s characterize them

10 first before they are allowed into subsequent

11 studies.

12 DR. KIBBE: Paul, go ahead.

13 DR. FACKLER: If I can just comment on

14 that, we used to do bioequivalence studies in males

15 only and restricted their ages from 18 to 45, I

16 believe. The agency has recently requested that BE

17 studies be done in a larger group of people, more

18 representative of the American population so we now

19 include females and we include the elderly, and it

20 just makes the variability problem that much worse.

21 I mean, I agree completely that ideally if we would

22 get 15 people all exactly the same way, all exactly

23 with the same physical habits, generally with the

24 same diet, it would make BE studies easier to pass

25 because we have reduced the variability in the

58

1 subjects. But the agency has been going, at least

2 recently, in the opposite direction, making these

3 products in particular less likely to pass against

4 themselves again.

5 DR. KIBBE: It is my impression, and I am

6 sure the FDA people will correct me, that they are

7 trying to get two answers using one study, and that

8 is, are the two formulations behaving the same,

9 should be their behavior independent of the

10 subjects studied, and are there variabilities

11 between product-subject interactions that might be

12 significant in special populations. I think it is

13 really hard to do that in one study, and that is

14 one of the problems you are running into. What I

15 think Gordon is suggesting is if we understood the

16 variables we might not have to use that blunt a

17 tool to estimate what will happen in the average

18 patient.

19 I would love to see us be able to do that.

20 There was a wonderful report done\--Les will

21 remember because he is almost as old as I am\--by

22 the agency that looked at dissolution and tried to

23 correlate it with bioequivalency data that they had

24 almost twenty years ago and there was absolutely no

25 way that dissolution predicted any of the results

59

1 that they got on those studies. So, it is more

2 complicated than it first appears.

3 DR. AMIDON: I got involved in this

4 process about that time, and my position is you

5 just did the wrong test. Okay? That is the

6 problem. So, it is a matter of refining the

7 dissolution test to make it more relevant to the

8 variables that we need to control to ensure

9 bioequivalence. We haven\'t done enough of that.

10 DR. KIBBE: Ajaz, you have a comment?

11 DR. HUSSAIN: The key aspect I think is

12 that we need to keep the focus on asking the right

13 questions and if a bioequivalence study is only

14 for, you know, males 18 to 45, is that the right

15 question from the public health aspect because the

16 product is going to be used in all populations?

17 So, you really have to go and look at the

18 fundamentals of what is a bioequivalence study. If

19 it is just confidence interval criteria, then that

20 is one aspect.

21 DR. SINGPURWALLA: Why not have a separate

22 set of drugs for different categories of people?

23 Like, you know, you have cholesterol drugs 20 mg,

24 10 mg and you specify your milligrams based on the

25 population.

60

1 DR. HUSSAIN: That is a major aspect of

2 dose finding and then labeling that goes into the

3 new drug development process itself. The

4 bioequivalence essentially has been a quality

5 assurance approach to making sure that a

6 pharmaceutically equal product has an in vivo rate

7 and extent of absorption similar to the innovator.

8 That is one of the main reasons for doing the bio

9 study, to make sure that your assumptions and your

10 in vitro methods are more reliable or at least

11 conform from that perspective.

12 DR. KIBBE: Thank you. Unless someone

13 else has a comment we will let you off the hook for

14 a few minutes, and go to Dr. Benet who will

15 enlighten us.

16 Clinical Implications of Highly Variable Drugs

17 DR. BENET: I am older!

18 \[Laughter\]

19 Thank you. It is a pleasure to be here.

20 I think the last two times I have appeared before

21 this committee I stayed in my office but it is nice

22 to be here in person, and I thank you for the

23 opportunity.

24

25 We have been discussing at an

61

1 international level, I was reminded as I heard

2 this, for 15 years\--we held our first sort of

3 consensus conference in 1989 to try to develop

4 standards for bioequivalence and we are still at

5 it.

6 \[Slide\]

7 This was said by the first speaker but

8 this is a slide that is now maybe 12 years old, or

9 at least parts of it. The current U.S. Procrustean

10 bioequivalence guidelines: the manufacturer of the

11 test product must show using two one-sided tests

12 that a 90 percent confidence interval for the ratio

13 of the mean response\--usually the area under the

14 curve and Cmax\--of its product to that of the

15 reference product is within the limits of 0.8 and

16 1.25 using log transformed data. It is

17 Procrustean, and those of you who don\'t remember

18 your mythology, the Procrustes himself was a robber

19 that took people when they came through his gate

20 and put them on his bed, the Procrustean bed. If

21 they were too long he cut off their feet. If they

22 were too short he stretched them out until they fit

23 the bed. And, that is exactly what we have,

24 Procrustean guidelines that say all drugs must fit

25 the same criteria no matter what the issues are.

62

1 Now, BCS, biopharmaceutical classification

2 system, is non-Procrustean. It is an advance and

3 the obvious answer, Arthur, to why a study failed

4 in looking at dissolution is that we didn\'t

5 understand the flawed classifications. So, the

6 only time dissolution is going to have any

7 relevance to bioequivalence or bioavailability is

8 for Class I and Class III drugs. Since we looked

9 at all drugs about 20 years ago, we were obviously

10 going to fail. So, we are making some advances.

11 But I strongly believe and have suggested over a

12 number of years that there need to be other

13 non-Procrustean advances and that is what I will

14 talk about today.

15 \[Slide\]

16 What are we trying to solve? What are the

17 bioequivalence issues and what concerns patients

18 and clinicians so that they have confidence in the

19 generic drugs that are approved by the regulatory

20 agencies so that they feel there are no questions

21 related to their therapeutic efficacy?

22 It doesn\'t help to tell them\--and that is

23 a true fact, it doesn\'t help to tell them that

24 there has never been a drug that passed the U.S.

25 FDA bioequivalence issues that ever caused any

63

1 therapeutic problems in a prospective study. That

2 doesn\'t help them because they always say, well, it

3 is the next drug and they have a lot of emphasis

4 out there from people who would like them to

5 question the bioequivalence criteria. So, this is

6 always in my mind, that one of the major issues

7 that we face is not necessarily scientific but it

8 is creating an environment where the American

9 public has confidence in the regulations that we

10 use and the drugs that we say can go on the market.

11 But what we have done and what our

12 concerns are now with therapeutic index drugs, NTI,

13 we need to have practitioners have assurance that

14 transferring a patient from one drug product to

15 another yields comparable safety and efficacy, and

16 a few years ago we termed that switchability and we

17 developed or tried to develop a number of

18 statistical criteria to approach that. The issues

19 we are facing today are for a wide therapeutic

20 index, highly variable drugs which do not have to

21 study an excessive number of patients to prove that

22 two equivalent products meet the preset one size

23 fits all statistical criteria. So, these are the

24 issues I want to address and ask the committee to

25 take cognizance of.

64

1 \[Slide\]

2 Now, it was not obvious a few years ago

3 but it is very obvious today that if you have a

4 narrow therapeutic index drug it is very easy to

5 pass the bioequivalence criteria, and that is

6 because narrow therapeutic index drugs, by

7 definition, must have small intra-subject

8 variability. If this were not true for narrow

9 therapeutic index drugs, patients would routinely

10 experience cycles of toxicity and lack of efficacy,

11 and therapeutic monitoring would be useless. So,

12 in fact, it is not an issue. Narrow therapeutic

13 drugs we take care of and we do very well from a

14 scientific issue. We might not have the

15 confidence, and I will come back and address that.

16 \[Slide\]

17 Let\'s look at some narrow therapeutic

18 index drugs. They have high inter-subject

19 variability and they have low intra-subject

20 variability. That is why we don\'t have to worry;

21 when we get the patient to the right place, they

22 stay there. The question was are they all Class I,

23 Class II. Theophylline is a Class I drug. So,

24 there are drugs on this list that are Class I drugs

25 although most of them are Class II drugs.

65

1 Getting back to the reliability of the

2 instrument, I would just like to make a comment.

3 Look at the warfarin sodium intra-subject

4 variability. The clinical measure that the

5 clinician uses to judge the status of the patient

6 in terms of his blood thinning capability, the INH

7 measurement, is significantly more variable. So,

8 in fact, what the clinician does in testing if the

9 drug is working is more variable than the patient

10 is going to experience from dose to dose in terms

11 of the criteria for this particular drug. So,

12 these are interesting questions.

13 \[Slide\]

14 Now, we tried to address this

15 switchability issue over a long period of time with

16 the concept called individual bioequivalence, and I

17 chaired the expert panel for about three years and

18 tried to address this issue. The ideas about

19 individual bioequivalence were that we were going

20 to get these promises, we would address the correct

21 question, switchability in a patient. We would

22 consider the potential for subject by formulation

23 interaction. There would be incentive for less

24 variable test products. Scaling would be based on

25 variability of the reference product both for

66

1 highly variable drugs and for certain

2 agency-defined narrow therapeutic range drugs.

3 And, we would encourage the use of subjects more

4 representative of the general population.

5 In fact, none of that worked and we gave

6 up on it. So, did it address the correct question?

7 Well, the question was, was there even a question

8 and was there any necessity for this at all, and

9 there is no evidence that the present regulations

10 are inadequate and that we need to be more rigorous

11 in our definition related to switchability.

12 \[Slide\]

13 Consider that the subject by formulation

14 interaction turned out to be an unintelligible

15 parameter from both the agency and the exterior

16 scientific community.

17 Incentive for less variable test products,

18 yes, but that could be solved by average

19 bioequivalence scaling and that is what at least I

20 am here to talk about today.

21 Scaling based on variability of the

22 reference product both for highly variable drugs

23 and for certain agency-defined narrow therapeutic

24 index drugs, again average bioequivalence with

25 scaling could solve this issue.

67

1 Encourage the use of subjects more

2 representative of the general population, that was

3 a good hope but it completely failed in terms of

4 how people designed their study. So, it didn\'t

5 work.

6 \[Slide\]

7 I recognized in Lawrence\'s introduction

8 that the FDA doesn\'t have a definition for highly

9 variable drugs. This is the consensus definition

10 that came out of a number of international

11 workshops, highly variable drugs should be those

12 when the intra-subject variability is equal or

13 greater than 30 percent. The idea is that for wide

14 therapeutic index highly variable drugs we should

15 not have to study an excessive number of patients

16 to prove that two equivalent products meet this

17 preset one size fits all statistical criteria.

18 This is because, by definition, again

19 highly variable approved drugs must have a wide

20 therapeutic index, otherwise there would have been

21 significant safety issues and lack of efficacy

22 during Phase III testing. In fact, highly variable

23 drugs fall out; don\'t get to the market. They fall

24 out in Phase II because the company can\'t prove

25 that they work and they can\'t prove that they are

68

1 safe. So, we don\'t have highly variable narrow

2 therapeutic index drugs. We only have drugs that,

3 with this tremendous variability that we

4 potentially saw in the first speaker\'s slide, don\'t

5 have any problems. And, those individual patients

6 having very high levels one time, low levels the

7 next time, high areas under the curve one time, low

8 areas under the curve the next time get through.

9 In fact, for those highly variable drugs we don\'t

10 need to worry about the genetic differences in

11 their enzymes. It has already been shown that,

12 yes, there are tremendous differences. Somebody is

13 going to have very high levels because they lack

14 the enzyme; somebody is going to have very low

15 levels but still they are safe and effective

16 because they are wide therapeutic index drugs.

17 \[Slide\]

18 But it makes it very difficult, as was

19 also pointed out by the first speaker, to get them

20 to be bioequivalent and here is my champion or what

21 I think is the champion from the data that I have

22 seen, and this is progesterone which I believe is

23 the poster drug for highly variable variability. A

24 repeat measures study of the innovator\'s product

25 was carried out in 12 healthy post-menopausal

69

1 females and it yielded intra-subject variability in

2 an AUC of 61 percent for the coefficient of

3 variation and intra-subject coefficient of

4 variation for Cmax of 98 percent.

5 If you did the calculations, it came out

6 that you needed 300 women just to meet the

7 statistical criteria and, in fact, this was not a

8 study that a generic company, or at least the

9 company interested in this, could afford to carry

10 out because, for sure, we know that the way we

11 design the studies there is a chance, even if you

12 had the right numbers, that one out of ten or one

13 out of five studies would fail just on statistical

14 chance and you have carried out a study with 300

15 people in it to prove that this highly variable

16 drug is bioequivalent. This is the issue that we

17 are asking you to talk about today, and can we

18 solve this problem so that we don\'t have highly

19 variable, very safe, wide therapeutic index drugs

20 for which we can\'t prove bioequivalence because of

21 the inherent variability of the innovator product.

22 \[Slide\]

23 I appeared before this committee three and

24 a half years ago to give the recommendations of the

25 FDA expert panel on individual bioequivalence, and

70

1 these are some of the recommendations. One that I

2 didn\'t put on here is that all generic drug studies

3 must be submitted to the agency, and I am very

4 pleased that that has happened and congratulations

5 to the agency.

6 Our recommendations at that time were that

7 sponsors may see bioequivalence approval using

8 either average bioequivalence or individual

9 bioequivalence, and we recommended that the subject

10 by formulation parameter be deleted since no one

11 knew what to do with it and we couldn\'t justify it

12 statistically.

13 We asked that scaling for average

14 bioequivalence be considered, that the agency and

15 the statistical group go into this and it be

16 something to be followed up and presented to this

17 advisory committee at some time in the future.

18 We recommended at that time that if an IBE

19 study, individual bioequivalence study, was carried

20 out and the test product fails you could not then

21 reanalyze with average bioequivalence because in

22 those days we said you had to pick one or the

23 other.

24 Here is something that we recommended that

25 I want to bring up again today because this has to

71

1 do with confidence. We recommended the point

2 estimate criteria be added, and we added this not

3 on any scientific basis that we are going to rule

4 out products, we said that these criteria are

5 always met today and what we have is a conception

6 or a view outside that it would be possible to have

7 products that differ by 25 percent, and that we

8 would be well served if we would say let\'s put a

9 point estimate criterion in addition to our

10 criteria\--AUCs of at least plus/minus 15 for point

11 estimate criteria and Cmax plus/minus 20 percent no

12 matter what you do, and if you have narrow

13 therapeutic index drugs make it even smaller, make

14 the point estimate plus/minus 10 percent for AUC

15 and plus/minus 15 percent for Cmax.

16 \[Slide\]

17 So, what I am suggesting here today and

18 what I am recommending to the committee to do is

19 ask the agency to develop methodology, and we are

20 going to hear some, to allow approval based on

21 weighting of average bioequivalence analytical for

22 highly variable drugs so that we can bring some

23 drugs to the market that can\'t be studied because

24 of the progesterone example. Also, that the point

25 estimate criteria be added to the criteria because,

72

1 in fact, all products will pass these criteria at

2 the present time and we won\'t be harmed, or we will

3 increase the confidence of those that say, you

4 know, you could have two products that differ by 50

5 percent because look at what the FDA criteria say.

6 Now, the FDA criteria, as they used to be

7 written two years ago, were easily misinterpreted

8 but that also changed two years ago and now the

9 criteria are written in a way that no clinician can

10 understand them in the first place so they won\'t be

11 misinterpreted.

12 \[Laughter\]

13 They still say exactly the same thing but

14 they can\'t be misinterpreted to say you could have

15 two products that differ by 50 percent. So, these

16 are my recommendations. Thank you for listening to

17 me.

18 DR. KIBBE: Questions for Dr. Benet?

19 DR. SINGPURWALLA: I have a comment not

20 just to you but to everyone else. This example of

21 highly variable drugs shows, to me, how the drug

22 industry is buried under the tombstone of

23 frequentist methods. Such methods ignore clinical

24 and biopharmaceutical knowledge, and it is bogged

25 down by its own weight.

73

1 DR. BENET: I disagree.

2 DR. SINGPURWALLA: Why?

3 DR. BENET: I think you are coming to this

4 fresh and that is good, but what we are interested

5 in is safety and efficacy, and in all cases

6 measures of safety and efficacy are more variable

7 than any pharmacokinetic measure. What we are

8 really interested in, what the agency is interested

9 in is safety and efficacy.

10 DR. SINGPURWALLA: Who said that Bayesian

11 methods do not incorporate high variability? It is

12 these confidence intervals and these confidence

13 limits, and the comment you make is a failure to

14 understand Bayesian methods.

15 DR. BENET: I understand Bayesian methods.

16 DR. SINGPURWALLA: No, you don\'t; you

17 wouldn\'t say this.

18 DR. BENET: Well, I welcome the

19 committee\'s spending the time discussing this with

20 you and if you adjourn I get to go home.

21 \[Laughter\]

22 DR. MEYER: I think I agree with

23 everything you have said and it embarrasses me no

24 end to say that!

25 \[Laughter\]

74

1 Is there still going to be a perceived

2 problem when you have, let\'s say, a Cmax point

3 estimate of plus/minus 15 percent? Isn\'t that

4 going to solicit illustrations of, well, look, my

5 Cmax was 115 units and their Cmax was 85 and the

6 high and low can be switched in the marketplace?

7 DR. BENET: I think we are never going to

8 get around that. There are always going to be

9 people who will take the present situation and use

10 it to their marketing advantage. So, I don\'t think

11 we can get around that. You know, we have the same

12 issues today. I am not sure that everyone on the

13 committee is aware that in terms of BCS Class I,

14 where you don\'t have to do a clinical study\--I

15 don\'t know of a generic company that has used that

16 for exactly the reason you are bringing up, Marvin.

17 They would be afraid that someone will go out there

18 and say this product has never been tested in

19 humans; it was approved on the basis of a

20 dissolution. You have confidence in this product

21 so that people that use BCS Class I at the present

22 time are the innovators who use it when they have a

23 SUPAC change or something like that. So, I think

24 we are always going to face that, and I think what

25 we need to do is just try to do the best job that

75

1 we can in making it happen.

2 DR. KIBBE: Let me just ask about an

3 application of one of your recommendations to your

4 own example. If you use methodology that is

5 developed as a weighted average, how would that

6 play out with progesterone? In other words, what

7 kind of numbers would we start to work with?

8 DR. BENET: I mean, I do agree with

9 weighting to the variability of the innovator

10 product. In other words, that would be the term in

11 the denominator that you would weight. But there

12 are different statistical issues that have to be

13 addressed that I can\'t do so we need the expert

14 statisticians to tell us how to approach that. But

15 that is what I want. I would want a weighting on

16 the variability of the innovator product in terms

17 of the coefficient of variation for Cmax as one

18 criterion and for AUC as another criterion.

19 DR. KIBBE: I have always found

20 intellectually attractive the concept of three ways

21 where we could look at variability and then compare

22 it to the generic. Is that going to help us get to

23 the numbers that we need to make these kinds of

24 decisions?

25 DR. BENET: Well, there is going to have

76

1 to be some measure of intra-subject variability.

2 We need to know that, and I have requested the

3 agency for many years to make this a requirement

4 for new drugs, that a measure of intra-subject

5 variability in humans or even in patients be

6 included in the approval process and be included in

7 the package insert. So, we do have to have that

8 measure some place.

9 I am very encouraged, even though the

10 agency does not require that, that we are starting

11 to see with many new products, when you look at

12 their package insert, measures of intra-subject

13 variability included because it is important

14 criteria and value that clinicians want to know.

15 What is the inherent pharmacokinetic variability so

16 that then I can say is the pharmacodynamic

17 variability more than this inherent pharmacokinetic

18 variability. If they don\'t know the inherent

19 pharmacokinetic variability, then they have a tough

20 time making any decision about whether the change

21 in efficacy is related to pharmacokinetics or to

22 real variability. So, somebody has to do this,

23 Arthur, and I think that has to come out of what

24 you recommend.

25 DR. MEYER: Les, you put a little bit less

77

1 weight on Cmax than you do on AUC; there is a less

2 stringent requirement. Is that because Cmax is

3 more variable because we don\'t measure it very

4 precisely, or is it because Cmax is less important

5 than AUC? And, I would quarrel that we don\'t have

6 enough data for the latter conclusion.

7 DR. BENET: Well, in some cases we do but,

8 as was initially discussed, it is confounded. As

9 we all know, Cmax is a very confounded measure and

10 the agency and many academics have spent years and

11 years in trying to develop a new measure. None of

12 them turned out to be any better. So, it is very

13 confounded and, as was stated, is always more

14 highly variable than AUC. I know of no case.

15 DR. MEYER: But it is the only measure we

16 have that has any component of rate in it.

17 DR. BENET: That is correct, but it is

18 more variable.

19 DR. VENITZ: Les, I agree with your

20 additional recommendation to put constraints on the

21 point estimates. You mentioned one of the reasons

22 being that the public needs to be reassured that,

23 indeed, no matter whether it is unintelligible

24 regulation or not, we do have generics that are

25 bioequivalent.

78

1 What I am personally not certain about is

2 whether I agree with the reference scaling\--and,

3 again, we are going to have some more presentations

4 on that\--because you are now, in my mind,

5 aggregating variance and mean differences, and I am

6 not sure whether one can offset the other. In

7 other words, if you have a large mean difference,

8 can that be offset by differences in variance?

9 When we had the discussion last time with IBE,

10 surprisingly there were drugs out there in the

11 database that the FDA provided us with that passed

12 IBE but wouldn\'t have passed ABE, which I think was

13 counter-intuitive for most of us, at least on the

14 committee, in terms that we expected IBE to be much

15 more conservative than ABE and it didn\'t turn out

16 that way. So, I still personally withhold judgment

17 on the reference scaling but I am very much in

18 favor of putting in additional constraints.

19 DR. BENET: Let me just answer that. I

20 think having the additional constraints solves part

21 of the problem.

22 DR. VENITZ: Yes, that was the reason why

23 I think the committee at that time went along with

24 that because we were worried about the IBE not

25 being conservative enough. Right now you are

79

1 basically breaking drugs down into two categories,

2 NTIs and non-NTIs, in terms of the criteria that

3 you are going to use or that you are proposing to

4 be used for BE assessment.

5 DR. BENET: Yes.

6 DR. VENITZ: Can you think of additional

7 criteria along the lines that we heard Gordon talk

8 about, that if we understand where the variability

9 comes from we might use different criteria? In

10 other words, is NTI the only thing that we have in

11 some decision tree that decides which way we are

12 going to go?

13 DR. BENET: As I said, the NTI statement

14 there has nothing to do with science because it is

15 easy to prove bioequivalence of NTI drugs. It just

16 has to do with confidence. So, that is why I made

17 it lower, because it is easy to pass.

18 I definitely believe that as we progress

19 we are going to have different criteria, and I

20 think BCS has a real potential for it. I have a

21 big list, my BCS list, and I looked to see what

22 drugs were there and that is why I made sure that

23 theophylline was a Class I drug. I think as we

24 progress\--and I presented to the agency last

25 November my newest concepts in terms of using BCS

80

1 or some sort of variant of BCS to actually predict

2 drug disposition, and I think we are going to

3 progress a lot in the next few years.

4 DR. KIBBE: Nozer?

5 DR. SINGPURWALLA: Well, just a general

6 comment. I was pleased to hear you acknowledge

7 that newcomers can identify things like

8 confounding, but I also think that newcomers can

9 look at an old problem and come up with new methods

10 of addressing that. Therefore, I urge you to pay

11 more attention to alternate methods and not get

12 committed to an old, archaic notion of confidence

13 intervals. These have been criticized in the

14 literature. And, what we see here is repeated use

15 of confidence limits, and the difficulty that

16 confidence limits poses both to the FDA and also to

17 the drug industry in getting their drugs approved.

18 So, I am going to urge you to start paying more

19 attention to alternatives and don\'t dismiss it.

20 DR. BENET: I don\'t dismiss it, and my

21 colleague, Dr. Scheiner, has spent a lot of time

22 informing the committee and the agency of these

23 approaches and the Bayesian approach, and I think

24 we are all well aware of it and do recognize it.

25 It is important to have fresh eyes and fresh views

81

1 of these kinds of issues, but it is also important

2 to recognize that the agency\'s criteria are safety

3 and efficacy, and when we have criteria that have

4 never failed it is tough to say that we move beyond

5 that criteria to untested criteria in terms of this

6 particular issue. So, that is why the agency must

7 be very careful in the changes that they make.

8 DR. KIBBE: Thank you, Les. We have one

9 more speaker before the break. Dr. Endrenyi,

10 welcome.

11 Bioequivalence Methods for Highly Variable Drugs

12 DR. ENDRENYI: Thank you.

13 \[Slide\]

14 This presentation was put together with

15 Laszlo Tothfalusi and I would like to acknowledge

16 that.

17 \[Slide\]

18 I would like to raise a number of

19 questions which I believe that this committee will

20 have to make recommendations about eventually that,

21 certainly, the agency ought to consider. I would

22 like to go through the first part fairly quickly

23 because much of that has already been considered.

24 So, we have the usual criterion of comparing two

25 formulations and the confidence limits for the

82

1 ratio of geometric means should be between 0.8 and

2 1.25. This has already been stated.

3 \[Slide\]

4 It has also been stated that for highly

5 variable drugs this presents a problem because with

6 large variations it is very easy to hit that 0.8 to

7 1.25 and, therefore, many subjects may be needed in

8 order to satisfy that.

9 \[Slide\]

10 For the purpose of this presentation but

11 not necessarily as the final word at all, the

12 coefficient of variation has been considered

13 exceeding 30 percent for highly variable drugs.

14 \[Slide\]

15 This slide would simply ask is there an

16 issue and this has already been asked and the

17 answer was probably yes. In this case, two

18 formulations of isoptin are considered in the same

19 subject repeatedly, and two different occasions

20 different relationships between the two

21 formulations were obtained. So, it looks as though

22 the drug is not really bioequivalent with itself

23 and that is a concern, but this has already been

24 demonstrated by Dr. DiLiberti.

25 \[Slide\]

83

1 This is perhaps more recent. This was

2 obtained from Diane Potvin, from MDS, who

3 demonstrated that, indeed, things look reasonable

4 as long as the intra-individual CV is up to about

5 70 percent but beyond that it is very difficult to

6 satisfy the criteria. There are many, many studies

7 submitted that failed.

8 \[Slide\]

9 Then she went on, very kindly, to look at

10 details of these highly variable drugs. From this,

11 one could conclude that there is a relationship

12 between the coefficient of variation and failure

13 rate, higher failure rate with higher coefficient

14 of variation. Mind you, these are all submitted

15 studies so this analysis is still biased because

16 the company submitted them in the hope that they

17 would pass, so these are not all studies at all.

18 The second conclusion is that, indeed,

19 AUCs fail less frequently than Cmax\'s but they

20 still fail with a high frequency. So, the

21 variation of AUCs should not be dismissed.

22 \[Slide\]

23 Study condition\--perhaps I would omit this

24 almost entirely because it is considering single

25 dosing versus steady state. In the U.S. this is a

84

1 non-issue because U.S. goes by single

2 administration even though it has been demonstrated

3 and we know that frequently in steady state we get

4 lower variation\--not frequently but not always.

5 \[Slide\]

6 This is a study showing that and in the

7 U.S. I think this is largely at the moment

8 irrelevant.

9 \[Slide\]

10 Study designs, which one to choose? A 2 X

11 2 traditional or replicate design? It need not be

12 a 4-period replicate design; it could be 3.

13 \[Slide\]

14 Now, the advantage of replicate designs

15 includes that one gets clear estimates of

16 within-subject variations. Particularly the

17 concern would be to get a clear estimate of

18 within-subject variation for the reference product.

19 I would note that this design is favored by K.K.

20 Midha who has worked long years and is certainly

21 one of the foremost experts on the bioequivalence

22 of highly variable drugs and drug products. So,

23 his voice ought to be respected.

24 Secondly, on the other hand, my concern is

25 that one can have a pooled criterion which could

85

1 have better properties, pooled criterion related to

2 the test and reference products together.

3 There are issues that these replicate

4 design studies can be evaluated by various

5 procedures, and a question is whether these

6 procedures would give the same results and,

7 therefore, would agencies be able to check how

8 those results would be calculated and were

9 calculated.

10 Another question arises, namely, is a test

11 comparing the variations of test and reference

12 products useful; is it needed? Or, perhaps is an

13 estimate of these variations simply sufficient or

14 is that needed?

15 \[Slide\]

16 Turning to the 2 X 2 crossovers, they are

17 simple; simple to execute, simple to evaluate. An

18 advantage is that there are many studies on file

19 and they could be evaluated retrospectively.

20 Another comment is that the ratio of

21 within-subject variabilities could be estimated.

22 There are procedures that would permit this even

23 from 2 X 2 crossover studies. For example, the

24 procedure suggested here by Guilbaud and Gould is

25 to have for each subject the sum of the test and

86

1 reference response, AUC or Cmax in this case, and

2 then the difference of the two; plot them against

3 each other, have a linear regression and evaluate

4 the slope, and then apply the slope in that fashion

5 which gives the ratio of the estimated variances.

6 So, it would be possible to evaluate this ratio

7 from 2 X 2 crossovers. However, features of this

8 procedure have not been studied and they ought to

9 be evaluated.

10 \[Slide\]

11 Now, various possible methods of

12 evaluation, the usual procedure is unscaled average

13 bioequivalence with a criterion of 0.8 to 1.25 for

14 the ratio of geometric means, the GMR. It is also

15 possible to apply unscaled average bioequivalence

16 with expanded bioequivalence limits. One way of

17 doing it is to present these bioequivalence limits.

18 It has been shown that some jurisdictions do this.

19 For example, the ratio of GMR could be between 0.75

20 and 1.33 or 0.7 to 1.43. This is one possibility

21 which is practiced in some areas, or to expand the

22 bioequivalence limits flexibly depending on the

23 estimated variation. I shall talk more about these

24 procedures.

25 Another approach is the scaled average

87

1 bioequivalence and, again, I shall refer to this

2 and shall talk about this, and I also should

3 mention scaled individual bioequivalence for

4 comparisons only.

5 \[Slide\]

6 To talk about unscaled average

7 bioequivalence\--these scissors are supposed to be

8 less than or equal signs so instead of scissors, it

9 is less than or equal\--the unscaled average, as we

10 have seen\--this is a bit more formalized but, as

11 you see here, the ratio of geometric means should

12 be between, say, 0.8 or 1.25 or 0.75 and 1.33.

13 This is the same statement as saying that the

14 logarithmic bioequivalence limits should be plus

15 and minus and in between is the difference of the

16 logarithmic means, and that is a useful way to look

17 at it. Now, the procedure is simple but as the 0.8

18 and 1.25 limits were arbitrary so would be any

19 other criteria.

20 But another concern is that whatever way

21 it would be decided, if this is the way to go, then

22 0.75 to 1.33 is a partial solution because it may

23 help drugs with, say, 30, 40 percent intra-subject,

24 intra-individual variation but not those which have

25 higher variations and 50, 60 percent would still be

88

1 the cut off.

2 \[Slide\]

3 Another approach would be to expand the

4 limits in proportion to the estimated variation.

5 This has been suggested by Boddy and coworkers.

6 So, here there is a proportionality factor, and the

7 other factor is the estimated standard deviation,

8 intra-subject variation. This procedure has the

9 advantage that the usual testing procedure can be

10 applied with some proviso. The statistical power

11 is independent of the variation and the statistical

12 power is higher, much higher than the unscaled

13 average bioequivalence with the usual criterion so

14 we need fewer subjects.

15 On the other hand, the criterion is that

16 bioequivalence limits, as shown there, are really

17 random variables because they include the estimated

18 standard deviation, estimated intra-subject

19 variation. So, the limit itself is a variable.

20 Therefore, the two one-sided test procedure is not

21 quite correct, however, it is becoming

22 approximately correct with large samples.

23 \[Slide\]

24 Scaled average bioequivalence is very

25 similar to the previous one except that the S from

89

1 the bioequivalence limits, here, came over to the

2 measure that we apply. So, it is formally very

3 similar and we have developed and have recommended

4 procedures for setting the bioequivalence limits.

5 Again, the advantages are that the

6 statistical power is independent of the variation

7 and with the same sample size is much higher than

8 the unscaled average bioequivalence. I am going to

9 demonstrate this. There is a sensible

10 interpretation. The first interpretation is very

11 similar to that applied with individual

12 bioequivalence, namely, the expected change to

13 switching is being compared with the expected

14 difference between replicate administrations and

15 one can make sense of that.

16 A second interpretation is that the

17 standardized effect size is being applied which is

18 a clinical interpretation. There are procedures to

19 evaluate confidence limits. If it is a 2 X 2

20 crossover, then non-central t-test can be applied,

21 or there is a procedure recommended by Hyslop and

22 her coworkers which is somewhat more involved but

23 still reasonable I think.

24 \[Slide\]

25 This is a demonstration comparing the

90

1 procedures and effectiveness of various approaches.

2 They include the scaled individual bioequivalence,

3 scaled average bioequivalence and unscaled average

4 bioequivalence. You see the probability of

5 acceptance. These are results of simulations. It

6 amounts to the probability of acceptance at various

7 distances between the two means. The first thing

8 you can see is that for individual bioequivalence

9 the range is very wide. Ranges are much narrower

10 with scaled average bioequivalence. So, this wide

11 range raised the concern of Dr. Benet. The second

12 observation is that scaled average bioequivalence

13 is, indeed, much more powerful than unscaled

14 average bioequivalence. So, we again need fewer

15 people.

16 \[Slide\]

17 What is the limiting variation for highly

18 variable drugs? This is obviously a subject of

19 regulatory decision, as are the others. The

20 procedure could be that we apply unscaled average

21 bioequivalence if the variation is less than the

22 cut-off measure and use some kind of a different

23 procedure appropriate for highly variable drugs if

24 the variation is higher.

25 Perhaps I should go down here. This is

91

1 the same kind of mixed model that was suggested for

2 individual bioequivalence but, just as Dr. Benet

3 suggested, it is not reasonable that a sponsor

4 should play both ways. The sponsor should declare

5 the intention of using one procedure or the other

6 in the protocol.

7 I wouldn\'t necessarily dismiss these other

8 possibilities. For example, K. Midha recommends 25

9 percent. The outcome of those probabilities that

10 you have seen on the previous slide depend on how

11 you set these limiting variations. Obviously, 30

12 percent is stricter than 25 percent. In all cases

13 you and the agency will ask what is the practically

14 reasonable criterion that one can live with, the

15 agency can live with and the industry can live

16 with, and the public can live with. So, don\'t

17 necessarily set everything on the 30 percent; do

18 consider what the effect of, say, 25 percent would

19 be.

20 \[Slide\]

21 Now, this method of the secondary

22 criterion has arisen in connection with the

23 features of individual bioequivalence. So, we talk

24 about two approaches, that of individual

25 bioequivalence and today we are talking about

92

1 highly variable drugs. There are two very

2 different concerns.

3 First of all, we have already seen that

4 for highly variable drugs the potential variation

5 is smaller than with individual bioequivalence. In

6 the case of individual bioequivalence the

7 deviations arose because the regulatory criterion

8 was changed. A much more liberal regulatory

9 criterion was introduced whereas in the case of

10 highly variable drugs it is a natural change of the

11 variability between the two means. You know this

12 very well. With the usual kind of drug the

13 variation between the means just fluctuates

14 slightly. Most of the differences are probably

15 between the two means and are within the range of

16 10 percent. But with highly variable drugs those

17 means also fluctuate much more. So, to impose a

18 constraint of 10-15 percent on this natural

19 variation means that the natural fluctuation is

20 altered so the sources of the concern are very

21 different. Whereas in the case of individual

22 bioequivalence you have to deal with the criterion,

23 here you have to deal with the natural variation.

24 \[Slide\]

25 So, I would like to raise some caution.

93

1 In addition, the imposition of the secondary

2 criterion has serious consequences. I present this

3 from my life earlier when I dealt with individual

4 bioequivalence because we had the results then; I

5 don\'t have many results for average bioequivalence.

6 But, again, here you have the results for

7 individual bioequivalence. This is the probability

8 curve for the constrained criterion alone and this

9 is then the application of the combined criterion.

10 The combined criterion is expected and

11 does always run below the two separate criteria.

12 But when the GMR criterion is highly constricting,

13 as in this case, then the combined criterion is

14 really a GMR criterion essentially and has nothing

15 to do, or very little to do with the bioequivalence

16 criterion. So, if you were to consider the

17 secondary criterion, then this slide suggests to do

18 it with great caution and after serious

19 consideration.

20 \[Slide\]

21 Here are the questions again which I have

22 raised for the committee\'s consideration and for

23 the agency\'s consideration. They certainly suggest

24 that many of these issues require further

25 consideration and further investigation.

94

1 Originally I wanted to end with this loose and

2 compliant mode, however, I looked at the questions

3 being raised and, since after this I may have to

4 shut up, I would like to call attention to question

5 2(b) in which the application of scaling is

6 combined with the application of this secondary

7 criterion. I would like to call your attention to

8 the fact that these are two separate questions.

9 Both of them ought to be studied further but, to my

10 mind, the restriction criterion is much more

11 controversial and requires thorough exploration for

12 its need as well as for its application. So, I

13 would recommend a separation of those questions.

14 Also, I have a question about reference

15 scaling. I would certainly like to be an advocate

16 for scaling, but whether the scaling ought to be

17 reference scaling I would like again to be a

18 subject for study. Thank you.

19 DR. KIBBE: Thank you. Questions?

20 Jurgen?

21 DR. VENITZ: I have a question about your

22 first simulation slide where you compare the IBE to

23 the ABE and scaled ABE. My question basically is

24 that you are assuming for the purposes of

25 simulation that the COVs for both test and

95

1 reference are the same, 40 percent. Is that

2 correct?

3 DR. ENDRENYI: Yes.

4 DR. VENITZ: What would happen if you had

5 differences in COVs between test and reference? In

6 other words, let\'s assume that the test product has

7 much less intra-individual variability than the

8 reference, how would that affect your curves?

9 DR. ENDRENYI: It does affect the curves,

10 but mainly the curve of the individual

11 bioequivalence. It affects little the average

12 bioequivalence curve.

13 DR. VENITZ: What about the scaled average

14 BE?

15 DR. ENDRENYI: The same. But that is an

16 artifact in a way because here we consider the

17 scaling by reference product so we didn\'t

18 have\--these were 4-period studies. Your question

19 is relevant if you consider the 2-period studies.

20 DR. VENITZ: Right, right.

21 DR. ENDRENYI: Which we haven\'t done, but

22 that is an interesting question. It would be worth

23 investigating.

24 DR. VENITZ: So, the answer that you are

25 using then is the reference variation.

96

1 DR. ENDRENYI: That is right.

2 DR. VENITZ: So, you are assuming that you

3 know but you wouldn\'t necessary do a 2 X 2\--

4 DR. ENDRENYI: No, the estimated

5 reference.

6 DR. VENITZ: So, you could get that from a

7 2 X 2 design?

8 DR. ENDRENYI: Well, it is a different

9 interpretation. Yes, we could but it has to be

10 validated whether it works or not. We haven\'t done

11 that.

12 DR. KIBBE: Anybody else? Ajaz, do I see

13 you leaning forward? No? Go ahead.

14 DR. SINGPURWALLA: I just have a technical

15 comment. Somewhere in your slides you had a

16 restricted maximum likelihood. Right?

17 DR. ENDRENYI: Yes, as a possible

18 procedure.

19 DR. SINGPURWALLA: As a possible

20 procedure?

21 DR. ENDRENYI: Yes.

22 DR. SINGPURWALLA: Well, this is a

23 technical comment, the maximum likelihood is

24 advocated because of its asymptotic properties in

25 the sense that it converges to the center. You

97

1 know, you get the central limit theorem. When you

2 restrict your maximum there is no assurance that

3 you converge, the central limit theorem.

4 Therefore, the value of that process cannot be

5 really evaluated. I don\'t know what impact all

6 that has on the proposals you have made but I just

7 want to caution you.

8 DR. ENDRENYI: You are absolutely right,

9 but the point I think was that in the case of

10 replicate design probably the procedure of

11 evaluation would have to be defined very clearly

12 and very strictly, otherwise one can go in all

13 different directions and that will be another task

14 if the agency goes that way.

15 DR. KIBBE: Go ahead.

16 DR. BENET: Just a quick follow-up on

17 Laszlo\'s comment, I think it would be worthwhile if

18 the agency went back and looked at the content

19 uniformity criteria and published two sets of data.

20 I think it would be worthwhile to go back and look

21 at the bioequivalence data and look and see how

22 often it falls within certain criteria. You have a

23 big database and it would be nice to see what those

24 numbers were, and I think that would be useful for

25 the committee on the secondary criteria.

98

1 DR. YU: Actually, you will see that in

2 the last talk. Sam is going to talk about data.

3 DR. KIBBE: It is always good to have data

4 when we are having a discussion. No one else?

5 Marv?

6 DR. MEYER: This is probably a

7 statistically ignorant question but under the

8 scaled condition, however you want to scale it, is

9 it possible to have a product with a scale

10 confidence limit that was, say, 60-90? If so, then

11 let\'s say the ratio would be somewhere around 75

12 percent and that wouldn\'t be acceptable. So,

13 without the point estimate constraint you have a

14 potential for allowing 60-90 approved and 120-140

15 to be approved.

16 DR. ENDRENYI: No\--

17 DR. MEYER: Two different studies?

18 DR. ENDRENYI: In two different

19 studies\--within each study it should be one and I

20 wouldn\'t envision between study variation and I

21 don\'t\--I doubt it very much.

22 DR. MEYER: Even if the test product only

23 released 70 percent of its dose and the innovator

24 released 100 percent of its dose the true ratio

25 would be 0.7 and you wouldn\'t know that; you would

99

1 be looking for 1.0. It is not possible?

2 DR. ENDRENYI: No, I think if it is

3 inter-study variation, then with the low variation

4 drugs each of them would be between 0.8 and 1.25

5 but the two in comparison with each other could be

6 quite different. That is equally possible but it

7 is not likely. If it is the same reference

8 product, then it is not possible.

9 DR. KIBBE: I see no other questions.

10 Thank you very much. We will take our break now.

11 We will be back at 10:52.

12 \[Brief recess\]

13 DR. KIBBE: We have open public hearing at

14 one o\'clock but there are no presentations to be

15 made at that time so what we will be able to do is

16 modify our schedule to try to get everything done

17 and get back on schedule. I know there is a lot of

18 interest in what we are talking about so we might

19 allow our speakers a little extra time and some

20 questions and answers to go a little further. I

21 see our next speaker is at the podium, ready to go,

22 Barbara Davit.

23 Bioequivalence of Highly Variable

24 Drugs Case Studies

25 DR. DAVIT: I am pleased to be able to

100

1 respond to one of the questions that Les raised, in

2 that we do have a survey of some of the data that

3 has been submitted to the Division of

4 Bioequivalence.

5 \[Slide\]

6 When Dale and I were talking about putting

7 this presentation together for the advisory

8 committee, one of the things we thought we would

9 consider is looking at what has been submitted to

10 the Division of Bioequivalence and to answer the

11 question of whether highly variability is a

12 significant issue in these bioequivalence studies

13 in ANDA submissions.

14 By looking at these data and focusing on

15 some case studies, we thought also we could maybe

16 answer the questions in a limited number of cases

17 of what is contributing to the variability or what

18 are some of the sources of this variability.

19 \[Slide\]

20 So, what we were trying to do is see if

21 there is a significant problem with highly variable

22 drugs, and I would like to mention, first of all,

23 that this obviously represents a biased sample

24 because we receive predominantly studies that have

25 passed the 90 percent confidence interval criteria.

101

1 So we obviously don\'t see the big picture like

2 people from industry would be seeing. We don\'t see

3 what percentage that is of the total number of

4 drugs in a company\'s pipeline for example.

5 But of the submissions we saw, which are

6 passing studies, what percentage were for highly

7 variable drugs? Did these studies involve

8 enrolling a large number of subjects because that

9 has been one of the issues that has been raised

10 today, the large number of subjects that might be

11 necessary to show bioequivalence for these generic

12 products of highly variable drugs? Also, how

13 narrow and wide are these 90 percent confidence

14 intervals? That goes along with how many subjects

15 are necessary for a passing study.

16 \[Slide\]

17 We collected data from all the

18 bioequivalence studies that were submitted to the

19 Division of Bioequivalence in 2003. We used the

20 root mean square error as an estimate of

21 intra-subject variability. I realize this is just

22 a rough estimate and it is not a pure estimate of

23 the intra-subject variability but, unfortunately,

24 most of the studies that we had to look at were

25 two-way crossover studies so the best estimate that

102

1 we could get of the intra-subject variability was

2 the root mean square error.

3 We defined a highly variable drug as one

4 with a root mean square error which is greater than

5 0.3, representing 30 percent intra-subject

6 variability. The data that I am going to present

7 is only solid oral dosage forms, and I would like

8 to point out that all the studies that I am going

9 to be presenting passed our 90 percent confidence

10 interval criteria, but that is because for the most

11 part we don\'t receive submissions of studies where

12 the product did not pass bioequivalence criteria.

13 \[Slide\]

14 First of all from 2003, this was a total

15 of 212 in vivo bioequivalence studies. Of these

16 212, looking at only those studies in which the

17 root mean square error of AUC or Cmax was greater

18 than 0.3, in 15.5 percent of these studies, AUC or

19 Cmax, was greater than 0.3. In other words, in

20 about 15 percent of our studies the drug would

21 qualify as having highly variable characteristics.

22 Most of this was due to Cmax and this has been

23 discussed today. So, in about 13 percent of the

24 total only Cmax was highly variable. There were no

25 studies in which only AUC was highly variable. But

103

1 there were 5 studies in which both AUC and Cmax

2 were highly variable, and this was 2.5 percent of

3 the total.

4 \[Slide\]

5 This goes along with the previous slide

6 and it just shows the number of studies in which we

7 saw a root mean square error of a particular value

8 for Cmax. There is an error in this particular

9 slide in your handout but this is the correct

10 slide. Really, obviously, for most of the Cmax

11 values the root mean square error is below 0.3. I

12 have a line here representing 0.3. I think I said

13 earlier that 15 percent of all the studies, 15.5

14 percent of all the studies that came in had a root

15 mean square error for Cmax of greater than 0.3.

16 \[Slide\]

17 This is for AUC. Of course, the AUC is a

18 lot less variable than Cmax. Really, for the most

19 part the root mean square errors were hovering

20 around 0.1 to 0.15, so quite a bit less variability

21 in AUC than Cmax.

22 \[Slide\]

23 One of the questions that we wanted to ask

24 was what is contributing to this variability.

25 Since for a lot of products we look at

104

1 bioequivalence studies in fasted subjects as well

2 as fed subjects, we wanted to see what impact was

3 having on variability. I mentioned 33 studies.

4 This represented a total of 24 of the ANDAs that

5 were submitted and reviewed in 2003. Of these,

6 both AUC or Cmax were highly variable in both the

7 fed and fasted studies. In 8 of these the

8 pharmacokinetic parameters were highly variable in

9 only the fed study, and for 7 the PK parameters

10 were highly variable in only the fasted study. But

11 this is a little bit skewed too because we have

12 submissions, for whatever reason, which contain

13 only a fed study and submissions that contain only

14 a fasted study\--not a lot but it does happen.

15 \[Slide\]

16 This shows some of our data. I think

17 these are all the Cmax values from the 212 studies

18 I was talking about in which Cmax was variable in

19 only the fed study and not the fasting study. So,

20 this would suggest, of course, that we are seeing

21 variability because of food effects. I am not

22 giving the names of the drugs but I have

23 illustrated them by class.

24 There is a variety of reasons I think for

25 the variability. Some of these are prodrugs. We

105

1 have a number of angiotensin converting enzyme

2 inhibitors and most of these are prodrugs.

3 Generally the parent is present at low

4 concentrations so this could contribute to the

5 variability. A number of these drugs also are

6 highly metabolized and this would contribute to the

7 variability. But, in this case, obviously there

8 was a food effect. The variability was observed in

9 the fed state, not in the fasting state. In these

10 studies too the number of subjects ranged from

11 about 27 to 51 I guess, so all over the place in

12 terms of numbers of subjects.

13 \[Slide\]

14 It is pretty unusual to only see a highly

15 variable Cmax in the fasting study and not the fed

16 study, and this occurred in only two cases last

17 year. These were both angiotensin converting

18 enzyme inhibitors, both prodrugs. For one of them

19 the bioequivalence was based on measuring the

20 parent. For the other one the company could not

21 measure the parent despite I guess a number of

22 attempts. This is actually true for pretty much

23 everyone who has worked with this particular drug.

24 So, the bioequivalence here is only based on the

25 metabolite. But that is quite rare. In the vast

106

1 majority of submissions that we have the

2 bioequivalence is based on the parent.

3 \[Slide\]

4 This table shows the Cmax data where Cmax

5 was highly variably in both fed and fasted studies.

6 So, for this drug product obviously there will be

7 highly variable regardless of whether it is the fed

8 study or the fasted BE study. This was six drug

9 products, various drug classes, various reasons for

10 variability; some prodrugs, some highly metabolized

11 drugs; some drugs that undergo extensive first-pass

12 metabolism. The number of subjects varied from I

13 guess 18 to 57.

14 \[Slide\]

15 Finally, this table is for two-way

16 crossover studies and shows the data for which both

17 AUC and Cmax were highly variable, and this was for

18 four drug products. For the one that I have shown

19 in yellow, for this particular product both AUC and

20 Cmax met the highly variable criteria in both the

21 fed and the fasting state. For the other drugs

22 there was high variability in the fed but not

23 necessarily the fasted, or Cmax and not necessarily

24 AUC. So, this was four drugs that fell in this

25 class. The number of subjects that the companies

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1 used varied from 26 to 62.

2 \[Slide\]

3 In trying to explore some of the sources

4 for this variability, we wanted to compare the

5 intra-subject variability for the test versus the

6 reference product. We don\'t see very many

7 replicate design studies anymore. In this

8 particular class of drugs we only had two

9 submissions last year so these are the data from

10 the two submissions.

11 These data are a good sign because what

12 they show is that the variability, based on the

13 root mean square error, was comparable for the test

14 and the reference product for both of these drug

15 products. That is obviously what we are looking

16 for because we want to see people achieve a generic

17 product that is the same as the reference product.

18 So, in this case I would say the variability was

19 comparable, test versus reference.

20 One study used 33 subjects. The other,

21 this would obviously fall into a category where it

22 necessitated a lot of subjects because this was not

23 only 77 subjects, it was also a replicate design so

24 it meant that each of these 77 subjects received

25 the drug product four times, on four occasions.

108

1 So, this was quite an extensive study.

2 \[Slide\]

3 Another question we wanted to ask was are

4 there ever cases in which the pharmacokinetic

5 variability is a function of the drug product as

6 opposed to the drug substance. We found two

7 instances last year, two different drug products

8 and I will call them drug C and drug D. This was

9 the same RLD for both studies for drug C and the

10 same RLD for both studies with drug D. Drug C was

11 an extended release tablet. Drug D was an

12 immediate release tablet.

13 We will look at drug C first. In one

14 study, conducted by one applicant, using I guess 33

15 subjects in the fasted and 35 subjects in the fed,

16 this product would not qualify as a highly variable

17 drug. Notice root mean square errors of 0.18,

18 0.11, 0.21, 0.24. However, for the same reference

19 product, in other words it is the same product,

20 different formulation, another company, 0.31, 0.38,

21 0.25, 0.34.

22 This could be due to a number of reasons.

23 I looked at the data and, obviously, the extended

24 release dosage forms are more complex than the

25 immediate release dosage forms and the two

109

1 formulations were quite different. So, there could

2 have been, you know, differences in variability due

3 to the formulation. Also, the bioequivalence

4 studies were done at different sites. I looked at

5 the assays. They were both LCMS assays. I didn\'t

6 get the specifics of the extraction methods but I

7 noticed that the two studies had different limits

8 of quantitation and there were different doses in

9 the two studies. I am not sure how much of a

10 factor this was. This was an extended release

11 product for which I believe there were three

12 different strengths. One company submitted a study

13 on the highest strength and I think used two times

14 15 mg, which was 30. The other company did studies

15 on 5 mg and used 4 times 5 mg, which was 20. So,

16 different doses in the two studies. So, there are

17 all these factors that could be contributing to the

18 variability. At least, those are the factors I

19 could think of.

20 Drug D\--this was an interesting issue.

21 Once again, in the hands of one sponsor, one

22 applicant, we saw root mean square errors of 0.16,

23 0.25, 0.13 and 0.2; the other applicant, 0.38,

24 0.55, 0.22 and 0.24. This was an immediate release

25 product and I noticed that the formulations of

110

1 these two were qualitatively identical;

2 quantitatively there were some differences.

3 These were done at two different sites and

4 in this particular application the bioanaytical

5 methods were done at a CRO that we have had some

6 issues with in the past. They seemed to be having

7 problems with some of their data. So, it could

8 have been a contributing factor here.

9 I would like to stress that of all the

10 applications that we saw last year, these were the

11 only four in which we saw that there was a

12 difference which was possibly due to drug

13 formulation or possibly due to where the studies

14 were done that was contributing to the high

15 variability.

16 \[Slide\]

17 Then we thought we would look at how many

18 study subjects are usually enrolled in these

19 studies. Once again, I emphasize that this is

20 really a biased sample because we only see the

21 studies that have passed. We don\'t know how many

22 tries this represents. We don\'t know how many

23 studies were done where the company just couldn\'t

24 get the study to pass the confidence interval

25 criteria so these are just the passed studies.

111

1 I was expecting to see a much bigger

2 increase in the number of subjects as we went above

3 0.3 and we really didn\'t. This could probably be

4 in part because, as you know, the root mean square

5 error is not really a true estimate of variability;

6 it is just a rough estimate. But in general, I

7 guess of all the studies that came in last year,

8 that came in and were reviewed, there were only 14

9 that enrolled more than 50 subjects, and for those

10 that met our high variability criteria there were

11 only 5 that enrolled more than 50 subjects. I

12 think there are about 14 with root mean square

13 errors greater than 0.3 that enrolled more than 40

14 subjects. But in some cases we are seeing high

15 numbers of subjects but this particular graph shows

16 that it is possible for companies to do a study

17 with under 40 subjects with a drug that is

18 considered highly variable and still pass

19 confidence interval criteria.

20 \[Slide\]

21 Then I wanted to see what would happen if

22 we plotted the width of the confidence interval

23 versus the number of subjects, and this was done

24 for Cmax. These are the 33 bioequivalent studies

25 in which the root mean square error of the Cmax was

112

1 greater than 0.3. Really not a big surprise. As

2 the number of subjects increased the width of the

3 confidence interval became narrower, suggesting, as

4 has been mentioned this morning, that with a higher

5 number of subjects it is much easier to meet the

6 confidence interval criteria because the confidence

7 interval of the product becomes narrower.

8 \[Slide\]

9 These are the data for AUC. We have data

10 from a combination of fed and fasted studies. I

11 would like to point out the two at the top. These

12 are fed bioequivalence studies and they don\'t meet

13 our present confidence interval criteria, but these

14 studies were submitted before the new food guidance

15 was put into effect, which was in January. If a

16 study was submitted before January of 2003, we were

17 evaluating the study based on our old criteria for

18 fed bioequivalence studies, meaning that only the

19 point estimate had to fall within the limits of 0.8

20 to 1.25. That is why, if you look at the

21 confidence intervals, if these studies had been

22 done later these would not have met our criteria

23 but they met our criteria at the time.

24 Once again, you can see a trend where, as

25 the number of subjects increases, the confidence

113

1 interval narrows. I suspect that these two

2 products, with more study subjects, probably would

3 have been able to squeeze into the 0.8 to 1.25

4 confidence interval.

5 \[Slide\]

6 In conclusion, I would just like to sum up

7 that these are observations from the data that we

8 looked at from 2003, and 15.5 percent of all the

9 bioequivalence studies that were submitted and

10 reviewed last year were for drugs that met the

11 highly variable criteria. Cmax was more variable

12 than AUC. In general, higher pharmacokinetic

13 variability occurred in the fed bioequivalence

14 studies. The two replicate design studies that we

15 were able to look at showed comparable

16 pharmacokinetic variability for the generic and the

17 RLD product.

18 \[Slide\]

19 In two cases for two drug products the

20 variability was associated with the formulation or

21 other factors in conducting the bioequivalence

22 studies. In general, the width of the 90 percent

23 confidence interval narrowed as the number of

24 subjects increased. Of the 212 passing

25 bioequivalence studies, only 14 enrolled more than

114

1 50 subjects. Of the 33 passing bioequivalence

2 studies of highly variable drugs, only 5 enrolled

3 more than 50 subjects.

4 \[Slide\]

5 I would like to acknowledge the members of

6 our working group at the FDA. This is a group of

7 individuals who have been discussing the highly

8 variable drug issues and what types of

9 presentations to put together for the advisory

10 committee meeting today. I would like to give a

11 special thanks to Devvrat Patel, one of our

12 reviewers in the Division of Bioequivalence, who

13 collected all the data that I showed you today. I

14 would also like to thank all of our reviewers for

15 their hard work in putting the reviews together

16 from which Dev was able to collect these data.

17 Thank you for your attention.

18 DR. KIBBE: Questions, folks? Go ahead,

19 Jurgen.

20 DR. VENITZ: Just one clarification. This

21 was an interesting presentation, Barbara but just

22 one clarification, the root mean square error that

23 you calculated, is that the pooled intra-individual

24 variability across test and reference?

25 DR. DAVIT: Yes, it is.

115

1 DR. VENITZ: Then if you go back to your

2 slide number 14, this is where you look at the

3 effect the drug product may have and you compare

4 the extended release and the immediate release. In

5 case number one, I guess manufacturer number one,

6 it looks like it is a low variability drug and for

7 manufacturer number two is a high variability drug.

8 DR. DAVIT: Right.

9 DR. VENITZ: Could that indicate that the

10 test product for manufacturer two actually has a

11 higher variability and the reference drug still has

12 the same, whatever variability, it has?

13 DR. DAVIT: Oh, absolutely. I mean, yes,

14 there is no way to tell.

15 DR. VENITZ: So, this might then

16 contradict one of the statements that you made

17 later on because you are saying that test and

18 reference in the replicate design studies\--

19 DR. DAVIT: For those two products.

20 DR. VENITZ: Right, for those two products

21 it could well be that the test product has higher

22 variability than the reference product.

23 DR. DAVIT: For this product, yes, it is a

24 possibility.

25 DR. VENITZ: But all the replicate design

116

1 studies that you looked at\--

2 DR. DAVIT: Which was only two.

3 DR. VENITZ: Right, you found for those

4 two at least that test and reference had the same

5 intra-individual variability.

6 DR. DAVIT: Yes.

7 DR. VENITZ: How does that compare to the

8 overall experience, going back beyond your survey?

9 Do you have any idea? Because I know they talked

10 about this in 2001 the last time we met.

11 DR. DAVIT: You know, that is a really

12 good question and we didn\'t have the time for this

13 presentation. We were only able to collect data

14 from last year. We do have a lot of replicate

15 design data from 2000, 2001 and I guess some from

16 2002 and I think we would like to expand this study

17 and go back a number of years because we would have

18 more replicate design studies to compare test and

19 reference variability. Yes, this is all we have,

20 unfortunately.

21 DR. BENET: First of all, what you

22 presented is very interesting but it wasn\'t what I

23 asked for. So, let me make clear what I think the

24 committee could use. There is an issue about the

25 point estimate. In 1999 Commissioner Heaney

117

1 published in JAMA an article where she looked at

2 all the drugs approved in \'97, showed the content

3 uniformity and the Cmax and AUC with the means and

4 the standard deviations. What I am asking you to

5 do is to go back and give the committee information

6 on the point estimates. Where are the point

7 estimates on all those studies? How much

8 variability? Are you going to do that?

9 DR. YU: That is actually going to be

10 presented by the next speaker.

11 DR. BENET: You set me up. Barbara said

12 she was answering my question! Many of you saw the

13 MDS abstract at the AAPS in November of 2002. If

14 you didn\'t, I have two slides here that I talk

15 about all the time. MDS looked at 800 fasting

16 studies in terms of approval or non-approval. Of

17 course, you can have a highly variable drug that 12

18 people pass because sometimes statistics work.

19 I think the most interesting piece of data

20 from that is that they looked at the number of

21 subjects enrolled and how many studies failed.

22 When they looked at 49-60 subjects enrolled in a

23 study, 68 percent of the studies failed. When they

24 looked at greater than 60 subjects 12 percent of

25 the studies failed.

118

1 Now, why is that? It has nothing to do

2 with statistics. It has nothing to do with going

3 back and saying how are you going to run the study.

4 It has to do with generic companies CEOs, and I

5 have seen it many times. The scientists say to the

6 company \"we have run the preliminary study. We ran

7 six. We need 96 people to make sure that we meet

8 the confidence intervals,\" and the president says,

9 \"96 people? Do you know how much that costs? I am

10 feeling lucky, run 24.\" And, that is exactly what

11 happens. If the 24 they get it. If the 24 doesn\'t

12 pass, they either give up or they run another

13 study. So, you can\'t conclude anything from the

14 data that you are seeing here in terms of

15 variability and the ability to pass. I want to

16 warn you on that. I think it is really important

17 to realize that until you start to see all the

18 data, which you will now, you really can\'t make

19 comments about whether highly variable drugs can

20 pass or whether you could have a progesterone study

21 that passed based on 50 subjects. You could

22 easily; you just have to be lucky and lots of

23 people are lucky.

24 DR. DAVIT: Oh, I agree. I thought the

25 exact same thing when I looked at all these studies

119

1 with the number of subjects and number 24 and 25

2 came up again and again and I wondered if it was

3 something like that.

4 DR. KIBBE: I just have a question about

5 the data. You had 212 studies you analyzed but

6 that wasn\'t for 212 different compounds\--

7 DR. DAVIT: Right.

8 DR. KIBBE: \--there were multiple

9 submissions for the same compound.

10 DR. DAVIT: Right.

11 DR. KIBBE: So, the question that I come

12 back to is on that early slide where you showed

13 five studies had AUC and Cmax problems, which

14 represented 2.5 percent. How many drugs was that?

15 DR. DAVIT: Oh, that was five different

16 drugs.

17 DR. KIBBE: Five different drugs, not just

18 five studies by different companies?

19 DR. DAVIT: Right, it was five different

20 drugs.

21 DR. KIBBE: That isn\'t the same though for

22 the 33 with AUC or Cmax?

23 DR. DAVIT: Correct.

24 DR. KIBBE: There would be cases where you

25 had studies where there were multiple studies

120

1 showing the same drug having variability in each

2 one of the studies?

3 DR. DAVIT: Right. I actually had a slide

4 like that at one point and I took it out. But the

5 answer to your question is yes.

6 DR. DELUCA: I noticed that your data is

7 just for the approved drugs.

8 DR. DAVIT: Yes.

9 DR. DELUCA: But I had a question. Maybe

10 Les\--with the data he just mentioned because he has

11 data there of approved and non-approved, you have

12 212 here, is there a feel in relation to how many

13 drugs were not approved that did not meet the

14 specs? Maybe the industry or the data that Les has

15 might be able to give an estimate of what that

16 might be.

17 DR. BENET: Well, this is something that

18 Laszlo said. I mean, the MDS data obviously\--you

19 know, what they showed was that if you had CVs less

20 than 30 percent, only like one-quarter of the

21 studies failed. If you had CVs greater than 30

22 percent, 62 percent failed. And, Laszlo was giving

23 you the data for greater than 35 percent and all of

24 them failed. But, again, these could have easily

25 been under-powered. I think most of these are

121

1 under-powered in terms of the studies that MDS ran

2 but it is 800 studies. I mean, they got data from

3 800 studies.

4 DR. SINGPURWALLA: I want to respond to

5 Dr. Benet again before he goes away.

6 \[Laughter\]

7 Now, you raised this dichotomy of the

8 surprise that the test passed and then it failed,

9 or something like that. I am not sure exactly how

10 you said it. But there is a procedure in

11 statistics called sequential analysis which I am

12 sure you are aware of. The government, not the FDA

13 but the Department of Defense uses this procedure

14 for acceptance sampling of products, whatever

15 product they are interested in. The whole idea

16 behind that is you test one item at a time and you

17 make a decision either to accept or to reject. If

18 you cannot make a decision to accept or to reject,

19 you take another sample. You keep taking a sample

20 until you make a decision, let\'s say, to accept.

21 The government then buys tons and tons of

22 transistors or whatever it is based on this nice,

23 little sequential test, codified an put out as

24 military standard 414, version C, which is how I

25 last remember it.

122

1 Now, if you use that particular procedure

2 and let\'s say the procedure says accept and, for

3 fun, you don\'t accept and go on testing more, guess

4 what happens. The procedure leads to rejection.

5 So, an early acceptance could be a bad thing had no

6 tested more. It seems that the same phenomenon is

7 happening here. The culprit there again is this

8 concept of type 1 and type 2 errors that come into

9 play. These procedures have been discussed and

10 shown to be incoherent. I suspect similar things

11 are happening here. Thank you.

12 DR. KIBBE: Paul?

13 DR. FACKLER: I have a couple of comments.

14 Ordinarily I agree with Les but I think he might

15 have over-simplified the generic industry.

16 Admittedly, a study with 96 subjects costs a lot of

17 money and there is a statistical probability that

18 with a highly variable drug you will pass with 10

19 or 12 subjects. Decisions are made based on a lot

20 of factors. Part of it is the probability of

21 passing. Part of it is the economics of what a

22 product might bring back to a generic company. I

23 will leave it at that.

24 As far as the analysis that the FDA has

25 done, the generic industry has been asked to submit

123

1 failed studies and I believe it is almost part of

2 the Federal Register now that those are required.

3 But those are failed studies on products that are

4 submitted to FDA. There are a number of products

5 that the generic industry works on that never come

6 to FDA because the BE studies haven\'t been able to

7 be passed. So, MDS have a larger data bank of

8 studies than FDA but, of course, it is confidential

9 information and MDS can\'t really share all of the

10 details about that with FDA.

11 A couple of other comments, the one slide

12 that showed the two products that had differing

13 root mean squares, you suggested it might be

14 formulation differences that caused the difference

15 in the variabilities. I am not sure that you can

16 draw that conclusion. You did qualify it by saying

17 that there could be other reasons for those errors.

18 It could be as simple as different populations of

19 patients or subjects in these cases. We have seen

20 examples where doing a highly variable product by

21 one CRO can give a dramatically different

22 variability than another CRO just because of the

23 variability of the subject population that the CROs

24 are able to gather. A CRO in the inner city is

25 going to have a dramatically different patient

124

1 population than a CRO in the country in the

2 northern part of a very isolated corner of the

3 United States.

4 Then, I wanted to make the same comment

5 about the slide that showed only five studies with

6 more than 50 subjects. The studies with 50, 60,

7 70, 80 and 90 subjects often fail and FDA never

8 becomes aware of those. Those projects are often

9 dropped after two or three failures because there

10 doesn\'t seem to be a way to meet the 0.8 to 1.25

11 confidence intervals.

12 I would suggest, if the resources are

13 there, the FDA go back and look at all those

14 replicate design studies that were submitted two

15 and three years ago when we were looking at IBE as

16 a possibility and scaled bioequivalence. I think

17 you will find that the variability between test

18 product and test product is really not different

19 than the variability you see between the reference

20 product in those replicate design studies.

21 DR. KIBBE: Anybody else? Ajaz?

22 DR. HUSSAIN: I think I just want to put

23 some issues back, important issues back on the

24 table. I think one of the reasons we wanted Gordon

25 Amidon to come and speak here I think was to focus

125

1 on what the root causes of variability are.

2 Because often we have these discussions, and so

3 forth, and we get so bogged down in the numbers and

4 the statistics that we forget what the real

5 questions are that we were really asking. So, I

6 just want to remind us.

7 DR. KIBBE: Anybody else? No? Thank you.

8 Now Dr. Sam Haidar.

9 FDA Perspectives

10 DR. HAIDAR: Good morning, everyone.

11 \[Slide\]

12 For my talk I will present regulatory

13 perspectives on the issue of bioequivalence of

14 highly variable drugs.

15 \[Slide\]

16 We are interested in this issue because it

17 has several potential benefits, including reduction

18 in regulatory burden and easier market access for

19 drugs which are safe and effective but also highly

20 variable.

21 \[Slide\]

22 Initially I would like to present a quick

23 overview of the regulatory requirements if

24 different agencies including the FDA. For example,

25 Health Canada, CPMP in Europe and the FDA

126

1 equivalent in Japan.

2 \[Slide\]

3 The FDA criteria for bioequivalence has

4 been more precisely defined earlier so I will just

5 repeat that we have 80-125 percent limits on the 90

6 percent confidence interval for both AUC and Cmax.

7 These criteria are applied to drugs of low and high

8 variability.

9 \[Slide\]

10 In contrast, Health Canada has the same

11 criterion on AUC, the 80-125, however, no

12 confidence interval criteria for Cmax. They just

13 have a constraint on the point estimate test to

14 fall between 80 and 125. In June of last year,

15 these criteria were judged flexible enough to

16 handle highly variable drugs by an expert advisory

17 committee meeting.

18 \[Slide\]

19 In Europe they have the same limits on the

20 confidence interval for AUC and Cmax, however, they

21 do make an exception in certain cases with regard

22 to Cmax where wider limits are acceptable and they

23 cite the 75-133 as an example.

24 \[Slide\]

25 In Japan also they have the 80-125 percent

127

1 limits for AUC and Cmax, however, in cases of

2 failure they do allow for add-on studies.

3 \[Slide\]

4 From this, we conclude that major

5 regulatory agencies do have some flexibility in

6 their regulations to handle special cases of the

7 highly variable drugs. To evaluate the performance

8 of the FDA criteria a survey was taken of ANDA

9 submissions between 1996 and 2001. I will present

10 the point estimate distribution for Cmax and AUC.

11 \[Slide\]

12 This is the point estimate distribution

13 for AUC and we have the percent of total studies

14 submitted. We can see that there is a clustering

15 around the ratio of 1.0 and with a closer look we

16 saw that for 95 percent of the studies\--the in vivo

17 bioequivalence studies, 95 percent were within

18 plus/minus 10 percent.

19 \[Slide\]

20 For Cmax, which is a more variable

21 parameter, it is expressed with a wider

22 distribution. However, we also see a clustering

23 around the ratio of 1.0. In the case of Cmax, 85

24 percent of the studies were within plus/minus 10

25 percent.

128

1 \[Slide\]

2 From this data set we created a subset

3 that included highly variable drugs and highly

4 variable drug products. We see a somewhat similar

5 distribution, also clustering around a ratio of 1.0

6 for the AUC.

7 \[Slide\]

8 The same is true for Cmax but also to a

9 lesser extent as expressed by the greater

10 distribution.

11 \[Slide\]

12 From this we conclude that although the

13 FDA criteria allow for a mean difference of

14 plus/minus 20 percent, the vast majority of the

15 submissions were within plus/minus 10 percent.

16 This was also observed for highly variable drugs.

17 \[Slide\]

18 In dealing with the special case of highly

19 variable drugs there are several options, including

20 a scaling approach based on intra-subject

21 variability in Cmax and AUC, or direct expansion of

22 the regulatory limits.

23 \[Slide\]

24 For scaling approaches, they would result

25 in a reduction in sample size. The limits are not

129

1 fixed but they are defined as a function of the

2 variability. There may also be a need for a point

3 estimate constraint.

4 \[Slide\]

5 In contrast, direct expansion of the

6 limits, which may be applied only to Cmax or Cmax

7 and AUC, the limits are fixed, for example 70 to

8 143, for drugs which are considered highly variable

9 or are classified as highly variable. There may

10 also be a need for a point estimate constraint in

11 this approach as well.

12 A major concern with this method for drugs

13 which are borderline around the 30 percent cut-off

14 is how do we classify those drugs, and who does it?

15 Because, obviously, there are major commercial

16 advantages with a drug being classified as highly

17 variable under those circumstances.

18 \[Slide\]

19 A study conducted by Walter Hauck, which

20 was supported by the FDA when the food effect

21 guidance was under development\--they wanted to look

22 at the impact of expanded limits around Cmax on

23 study design since fed studies in general tend to

24 be more highly variable. The interval test

25 evaluated was 70-143 around Cmax. The outcome was

130

1 60 percent reduction in sample size on average. A

2 concern was expressed in that study that Cmax

3 ratios of up to 128 percent still passed using this

4 limit.

5 \[Slide\]

6 Finally, if a decision is made to modify

7 the regulations to accommodate highly variable

8 drugs, we feel like either approach would result in

9 a significant reduction in sample size although an

10 additional regulatory criterion might be needed

11 constraining the point estimate. However, based on

12 our previous experience, it is very likely that a

13 clustering around a ratio of 1.0 would still be

14 observed although, in theory, it could fluctuate to

15 a greater extent.

16 \[Slide\]

17 Now Dr. Dale Conner would chair the

18 question and answer session.

19 Bioequivalence of Highly Variable Drugs Q&A

20 DR. CONNER: Good morning. I was asked to

21 simply not have a presentation but come up and

22 field questions. You know, anything on this topic

23 is fair game I think, although you can try and get

24 some other ones in if you like. However, I decided

25 to start it off to get the ball rolling by making a

131

1 few remarks. First off, I can truly say when I sit

2 through a lot of advisory committee topics and

3 discussions I am not extremely stimulated by them.

4 I hate to admit that but sometimes some of the

5 topics to me, personally, are not very exciting.

6 This one however I found extremely exciting from

7 beginning to end, and perhaps that is just because

8 it is bioequivalence; it is what I do all the time

9 and it is a problem that has been discussed for a

10 long time and, due to the experts and this

11 committee, we are finally starting to make some

12 progress towards doing something about it.

13 So, I would like to thank both the

14 committee and all the excellent speakers who really

15 gave us quite a lot to think about and discuss.

16 Because there were so many issues, I sat there with

17 my little list of points that I was going to make

18 which, hopefully, wouldn\'t have taken very long but

19 it started to expand at a very alarming rate with

20 each of the speakers and the excellent points they

21 were making. I consider it kind of a scaling

22 effect that my points were expanding with the

23 variability and quality of the speakers. So, I

24 will try and keep it to a minimum and perhaps be a

25 little Procrustean in cutting off both ends.

132

1 These comments, these points I am making

2 are my own take on it so one shouldn\'t necessarily

3 interpret this as FDA policy or even FDA thinking,

4 but I tend to deal with these types of questions on

5 a day-to-day basis in a practical sense and it

6 really seems to me, and what should have come out

7 of this if you get down to the real issue, what we

8 are looking at here for the most part is an

9 economic issue. In other words, from the drug

10 company\'s point of view it is really economics.

11 These studies cost too much.

12 When I want to develop this drug\--if I am

13 a generic company and I want to develop this drug

14 and, as has been stated before, my statistician

15 comes back and say you have to do 120 subjects, I

16 am sure that the bean counters at the firm are very

17 alarmed and saying, \"my God, I\'m used to paying for

18 a 24-subject trial and you\'ve just told me it\'s

19 five times as expensive.\" For a small company that

20 could mean three or four other products that I

21 don\'t have the funds to develop. So, they have to

22 make a choice. Is this product worth all that

23 money to spend or should I do four or five other

24 ones and forget about this?

25 It doesn\'t necessarily mean those products

133

1 aren\'t going to be developed by someone and be on

2 the market but it will decrease the players. Only

3 those with deep pockets will be able to develop it.

4 So, of course, in the marketplace you will have

5 very much lower competition, which is not good for

6 the consumer.

7 So, there is a variety of economic

8 considerations that this brings into play and if we

9 consistent somehow, using scientifically valid,

10 good regulatory methods, alleviate some of that,

11 that would be good. The FDA has a motivation as

12 well. As has been mentioned, we have a mandate to

13 eliminate or decrease unnecessary or excessive

14 human testing so we have a motivation as well. Our

15 motivation isn\'t strictly economic but we don\'t

16 want to expose normal subjects or patients in these

17 trials anymore than we have to because although

18 most of these trials are very low risk, they are

19 not no risk. So, it is up to us to develop

20 scientifically valid ways to determine

21 bioequivalence with confidence, yet efficiently

22 with the least number of subjects we can get away

23 with. That is our motivation.

24 So, you could simply say from the firm\'s

25 point of view because of this criterion, because of

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1 this inflexible criterion I am having to do an

2 unreasonable or excessive number of subjects. Now,

3 what is that? I mean, how do you define

4 \"unreasonable\" or \"excessive?\" I am sure that some

5 people say that anything above 24 is unreasonable

6 and if I asked everybody in this room what is an

7 unreasonable number of subjects, what is the

8 maximum number of subject you think you should have

9 to do in any bioequivalence study for any product,

10 I would probably get as many answers as there are

11 people in this room.

12 So, one of the ways you could start is

13 simply empirically saying, okay, I am going to set

14 a number of samples that I don\'t want to go above,

15 no matter what. A very simple way would be to work

16 your way backward from that number. Say, 60 was

17 the highest you ever wanted to do, work your way

18 back saying, well, this is the variability I am

19 looking at. This is the allowable true mean

20 difference. This is the power I want. Work your

21 way back and through simulation you get a set of

22 criteria that would achieve that goal, static

23 criteria. You could do something like that.

24 Static criteria, I think as the last

25 couple of talks have outlined, in this case has

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1 some problems because you have boundary conditions,

2 things where a drug product in one CRO\'s hands is

3 highly variable. You know, you go to another CRO

4 and it is not highly variable. So, who gets the

5 benefit of your highly variable technique? When

6 you look at creating a method to deal with this you

7 don\'t want to reward highly variable. You don\'t

8 want sponsors, whether they intend to or not, to

9 force themselves into the highly variable state

10 just to get the benefit of whatever techniques you

11 are dealing with. So, you want to adequately deal

12 with the problem without, whether unintentionally,

13 encouraging bad or highly variable formulations.

14 What was mentioned by Les in the

15 individual bioequivalence, that was an attempt to

16 promise the fact that system would encourage firms

17 to make lower variability products that still

18 matched and fit within the accepted criteria,

19 therapeutic criteria that were established in the

20 NDA. Even if we are not able to do that, we

21 certainly don\'t want to do the opposite. We

22 certainly don\'t want to unintentionally encourage

23 people to make their products or do their studies

24 in a more variable manner just to get a benefit and

25 an easier pass. So, just keep that in mind when

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1 you are considering anything. You don\'t want to be

2 counterproductive. You know, help people in one

3 way and then be counterproductive in another way

4 and, therefore, decrease the quality of the generic

5 and maybe even the innovator products that we are

6 putting out. So, that is something that always has

7 to be kept in mind.

8 The topic that also worries me\--I again

9 said in these scientific discussions\--I will make

10 another admission, a portion of my mind is always

11 on the scientific discussions and a portion of my

12 mind is, you know, on the practical sense of how

13 the heck am I going to implement this. Because the

14 scientists in industry, the firms and the review

15 staff at the FDA are the ones that are going to

16 have to live with this, are going to have to find a

17 way to implement these techniques, to make them

18 work, and a lot of times the little details that we

19 don\'t talk about in rooms like this are the things

20 that kill you, that make this an almost unworkable

21 system.

22 For example, we can all agree and discuss

23 that we like 30 percent as the cut-off but, again,

24 now do you determine that 30 percent? Is it

25 determined before you do any studies, from pilot

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1 studies? Is it determined from the literature? Is

2 it determined from the NDA? What happens when the

3 entire literature and available information says

4 that something is 28 percent and somebody does a

5 study and it is 34 on that one product? Every

6 other product that is done, similar product, is

7 still 29, what do you do in that case? Or the

8 opposite? You know, every other study has been 32,

9 33, 34. It is considered a highly variable drug.

10 Somehow you do one study, have one formulation and

11 it is 28. What do you do then? So, that really is

12 a very practical thing. These things that are on

13 the borderline could have the benefit or the remedy

14 applied to them or not applied to them depending

15 how their data comes out. It is probably an

16 advantage for a proper scaling method rather than

17 simply increasing the study confidence intervals.

18 With that said, I will field the questions

19 if there are any.

20 DR. KIBBE: Shall we start? Les wants to

21 ask a question.

22 DR. CONNER: Les gets very antsy unless he

23 talks about every ten minutes.

24 DR. KIBBE: If you do all the paperwork,

25 Les, you can sit at the table.

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1 DR. BENET: That is exactly why I am not

2 sitting at the table.

3 \[Laughter\]

4 Dale, going back to Sam\'s data and just

5 following up exactly what you said, you have some

6 products that passed where the point estimate on

7 Cmax was 1.2 and they were supposed to be in the

8 highly variable group. Have you gone back and

9 looked at that data? Was it a huge number of

10 subjects or was there no variability on that study?

11 DR. CONNER: Usually with that type there

12 are only like one or two instances. I mean, we

13 have done all sorts of periods and done that data,

14 and I actually like that way of presenting it

15 rather than the Heaney article\--

16 DR. BENET: I like that way too.

17 DR. CONNER: \--and subsequent article

18 which just gave point estimates. I always expect,

19 you know, that I am going to see that are out at

20 1.8 or 1.9 or, you know, kind of close to the edge

21 but not quite there, and I always react with horror

22 when I see that particular data point. When we

23 really go in\--I am not really sure; I would

24 actually have to direct it to Sam to put that

25 together because it doesn\'t make sense to me that

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1 something could have a point estimate that far out

2 and be highly variable unless they used a lot of

3 subjects\--I mean a lot. So, I will direct it to

4 him but, on its face, it doesn\'t seem to make sense

5 because I have seen that type of data presented in

6 other ways and when I looked into it, it was a low

7 variability product. It was something that just

8 squeaked by, had low variability and they used

9 sufficient subjects so even the alarmingly close

10 point estimate was still okay by our criteria.

11 DR. KIBBE: We are recording the activity

12 so you have to talk into a microphone.

13 DR. HAIDAR: I will have to go back and

14 look at that study but based on what I have seen

15 there were maybe one or two studies that were above

16 1.20, and the reasons could be large number of

17 subjects or just purely by chance.

18 DR. BENET: I agree they passed but I

19 think it would be instructive to go back and look

20 at those boundary conditions and see what are the

21 characteristics of those studies. I am sort of

22 thinking back to the generic drug scandal, you

23 know, where we saw some unbelievably low standard

24 deviations that nobody else ever saw at any other

25 time and I just think we ought to look at that data

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1 carefully.

2 DR. CONNER: Sometimes you can get low

3 standard deviations when you study the same drug

4 against the same drug.

5 DR. FACKLER: Can I address that point?

6 DR. KIBBE: Please, go ahead.

7 DR. FACKLER: Confidence intervals weren\'t

8 required for fed studies prior to 2002.

9 DR. DAVIT: I was just going to say the

10 same thing.

11 DR. FACKLER: A lot of the fed studies

12 from \'96 to 2002 only needed a point estimate to

13 pass so 1.20 was perfectly within FDA\'s

14 acceptability criteria.

15 DR. BENET: I am aware of that too but you

16 didn\'t separate them out, Sam? Those were both fed

17 and fasted conditions?

18 DR. DAVIT: It is everything.

19 DR. BENET: I think we need to separate

20 them out.

21 DR. HAIDAR: They were not separated.

22 This was our initial look.

23 DR. DAVIT: I would like to say too that

24 probably we didn\'t start seeing consistently fed

25 studies that passed confidence interval criteria

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1 until about six months ago. So, before that all

2 the fed studies were point estimate criteria.

3 DR. CONNER: So, we are doing an unusual

4 analysis. We are taking that data and calculating

5 confidence intervals but it was never designed or

6 powered to do that. So, in a way we are being

7 unfair to the data although, I mean, it is still

8 useful to look at it but, you know, to expect it to

9 pass confidence intervals when that was never the

10 intent and the statisticians that designed them

11 never powered it that way.

12 DR. BENET: Right, I can understand that.

13 If it was really true, then my recommendation would

14 be impossible so that is why I want to see data

15 that looks at that. I think you do too, or the

16 committee should too.

17 DR. CONNER: It is also important to

18 remember that point estimates\--you know, people

19 like to look at them because they are easy and they

20 seem to be the mean but you have to really look at

21 them very carefully because the say the statistics

22 work that isn\'t the true mean of the product. That

23 is simply an estimate of the center of the data of

24 your small sample of the universe. So, although it

25 is interesting to look at them and they can be a

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1 good indicator, you have to be very careful when

2 you look at point estimates because it is not the

3 true mean.

4 DR. KIBBE: Ajaz?

5 DR. HUSSAIN: I think Dale mentioned

6 something which I think is important and I want to

7 sort of repeat that because I think the whole

8 aspect of bioequivalence is to confirm that two

9 pharmaceutically equal products would behave as we

10 would expect them to behave. And, I think we keep

11 missing that discussion and I think this discussion

12 also will not get to that but I want to keep

13 pounding on that. If there are differences in the

14 variability of the product in terms of rate and

15 extent of absorption, that is the concern. That is

16 a regulatory decision that has to be evaluated,

17 whether a high level of variability compared to a

18 lower level of variability in the innovator product

19 is acceptable or not.

20 But the key aspect here is differences in

21 the two products of the same drug. The drug is the

22 same here. The formulation is different. That is

23 the focus of our entire discussion and, again, we

24 get into the discussion on numbers and so forth but

25 we never ask the question\--since generally drug

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1 approval is evaluation of the chemistry

2 manufacturing controls and then there is the

3 bioequivalence study which is one study. If we

4 remember the clay feet of the bioequivalence

5 argument that Prof. Levy has always argued, the

6 connection never gets discussed and somehow we have

7 to rethink that process.

8 DR. KIBBE: Nozer?

9 DR. SINGPURWALLA: First I would like to

10 comment on vocabulary. I prefer that you use the

11 word within-subject versus between subject instead

12 of this inter- and intra-, whatever it is.

13 DR. CONNER: I agree. I always get mixed

14 up by that too.

15 DR. SINGPURWALLA: I think that is a minor

16 comment. But the significant comment is in the

17 handout questions on your last slide.

18 DR. CONNER: Those aren\'t really my

19 questions. Those are the questions for the

20 committee.

21 DR. SINGPURWALLA: Right, but are we ready

22 to talk about these?

23 DR. KIBBE: We are ready if you are.

24 DR. SINGPURWALLA: Right, I am. Now, this

25 whole morning\'s presentation, which I agree with

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1 you was not boring but very interesting, makes one

2 point clear, that this problem of bioequivalence

3 and highly variable products calls for an

4 application of risk-based decision-making. The FDA

5 should serve as a benevolent decision-maker and

6 formulate the problem as one of decision-making

7 under uncertainty, keeping in mind the interests of

8 the population, of the subjects; keeping in mind

9 the interests of the drug companies or the

10 pharmaceutical companies and balancing and trading

11 off those risks.

12 You can retain the technology of scaling.

13 You can retain the investigation of causes of

14 variability. There is nothing in the framework

15 that denies those things. But what is really

16 needed is a change of mind set and a shift in the

17 paradigm. You have to get away from the notion of

18 confidence intervals which have, I am told, just

19 been introduced two or three years ago, and move on

20 into a paradigm of decision-making under certainty,

21 bringing in utilities, bringing in those kinds of

22 considerations into this problem, otherwise you are

23 just spinning your head against the wheel. That is

24 my comment.

25 DR. KIBBE: Anybody? Marvin?

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1 DR. MEYER: If we go with the static

2 change, 70-143 for example, that smacks a bit of

3 being arbitrary which is a problem to defend and,

4 without a point estimate, allows, according to

5 Walter Hauck, 128 percent to pass. That can be

6 taken care of by a point estimate such as Les

7 suggested. So, the arbitrariness of that bothers

8 me.

9 But if we go to what I think is more

10 scientific-based, based on the variability of the

11 reference, albeit necessary to do a replicate at

12 least on the reference, then you have a situation

13 where you have confidence limits varying by study,

14 by sponsor, by whatever else and then the

15 marketplace becomes chaotic because I am sure you

16 will have people arguing, well, our confidence

17 limits are narrower than their confidence limits

18 and we have, therefore, a better product. Of

19 course, then you will send out a letter and say you

20 can\'t say that. So, I don\'t know\--I guess I would

21 favor the scale because it has some elements of

22 being tied to real data, and then somehow figure

23 out\--I think Les said don\'t worry about what people

24 think in some sense. So, if the confidence limit

25 ranged within two sponsors, maybe it isn\'t going to

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1 be a big issue once people understand what you did.

2 Those are just some comments really.

3 DR. CONNER: Over the years I have been in

4 many meetings, internal and external, where we have

5 discussed widening or tightening the confidence

6 interval depending on the topic and the drug under

7 discussion. The tendency that always disturbs me,

8 and still disturbs me to this day, is people say,

9 oh well, it is more variable so we should widen the

10 confidence intervals; let\'s do 70, let\'s do

11 plus/minus 30. You query where did you get this

12 number. Well, it is wider. Well, how do you

13 support that? What makes you think that is wide

14 enough to deal with the problem? Maybe you have

15 gone too far. Or tightening the confidence

16 interval limits, static limits are the same. I

17 mean, what makes you think that is tight enough to

18 deal with the perceived problem? People tend to

19 just jump to the next\--you know, they say it is

20 going to be wider and they go to the next five or

21 ten. But we rarely ever have anyone come in and

22 support that with data. Maybe it is just because

23 the data is hard to come by but it disturbs me to

24 this day that most of these discussions are not

25 supported by any kind of scientific support that

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1 this change is truly going to be able to perceive

2 the problem.

3 You know there are decisions and there are

4 problems with scaling methods, especially if you do

5 mixed scaling where you have a transition point

6 where it goes from a constant or static limit to a

7 scaled limit, which we saw proposed in individual

8 bioequivalence. There are some boundary problems

9 around that transition point. Again, you know,

10 which side do I fit? Where do I get a better deal?

11 That type of situation. But I don\'t really think

12 it is as big a problem. You know several different

13 sponsors might have slightly different limits

14 because those limits are determined by their own

15 data, their study, their data. If, say, one CRO is

16 a little more sloppy\--I don\'t mean necessarily

17 negatively, and their variability of doing their

18 study is a little bit higher, that scaling would

19 account for that because you would have that across

20 the board for both reference and test. I mean,

21 scaling does have some properties that if it is

22 properly done it is probably a little more elegant

23 way to deal with this problem. Still, you have to

24 do it properly and you have to think it through

25 very carefully. You can\'t just jump into a method

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1 without careful study.

2 DR. KIBBE: Jurgen?

3 DR. VENITZ: I am trying to get us to

4 start working on question number one, and it has to

5 do with the comment that I made earlier. Gordon

6 talked about mechanisms. We heard Ajaz talking

7 about the need to understand where the variability

8 comes from, and that really is something that I

9 personally am missing. And, I won\'t even get into

10 my pet peeve about what is the clinical relevance

11 of all of this.

12 But if I can identify, and I think I am in

13 agreement with Nozer that we have to use risk-based

14 assessment. Well, risk to me means I have to

15 understand where are the key variability sources

16 that I am impacting on. What if the variability is

17 primarily driven by systemic metabolism, then the

18 area under the curve and Cmax do not reflect

19 primarily product performance. They reflect

20 something else which presumably is not affected by

21 changing products. So, is there any way that you

22 can incorporate that in some kind of algorithm,

23 some kind of decision tree where you decide what

24 rules you are going to use depending on what you

25 know about the drug? Maybe I am not as strongly

149

1 statistically Bayesian as you are, but I do believe

2 that the current system disregards anything that we

3 know about the product. It just says compare

4 product A to product B and roll the dice. It

5 ignores everything that we know about the

6 pharmaceutic characteristics of the drug substance

7 and what we might know about a specific product in

8 question, whether it is extended or immediate

9 release classification.

10 So, I would like for the FDA to think

11 about how you could come up with an algorithm, a

12 decision tree where you would incorporate that in

13 the early stages and then, by the time that you get

14 to the end of your tree, there are different rules

15 but those rules are then based on what you know

16 about the drug, not about something

17 arbitrary\--well, in order for me to avoid a large

18 number; in order for me to pass some arbitrary

19 criteria I have to do this. To me, it is the tail

20 wagging the dog as opposed to trying to use the

21 understanding that we have and a lot of those

22 products that you are looking at have been out for

23 a long time so we know a lot about them but we

24 ignore that when it comes to the bioequivalence

25 assessment.

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1 As far as scaling is concerned, the way I

2 understand it right now I am still wary about the

3 scaling and I really haven\'t formed an opinion yet.

4 In order to do the average bioequivalence scaling,

5 right now what you need and probably the most

6 important problem I guess is within-subject

7 variability in the reference product. How would

8 you get that? You couldn\'t get it from a 2 X 2

9 study design. So, whose responsibility then is it

10 to provide that information? Because it presumably

11 requires either a replicate design study or a

12 specific study just to identify the within-subject

13 variability in the innovator product. Whose

14 responsibility is that? Is the FDA going to pay

15 for all those studies?

16 DR. CONNER: Usually it is the sponsor\'s.

17 DR. VENITZ: Okay, so the generic company

18 has to do at least two studies or a replicate

19 design study.

20 DR. CONNER: With that approach, if that

21 is the type of scaling you designed requiring

22 replicate designs as we tried to do in the past,

23 probably a replicate design would be in order.

24 But, you know, there are a variety of things in the

25 literature and other proposals where that may or

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1 may not be necessary. But if you did pick that

2 type of approach, yes, the sponsors would end up

3 probably doing some type of replicate design.

4 DR. KIBBE: Lawrence?

5 DR. YU: I want to make a comment. I

6 guess a lot of speakers, especially FDA speakers

7 from the Office of Generic Drugs, paid a lot of

8 attention this morning to the generic application.

9 Yes, it is absolutely necessary that a part of the

10 requirement for generic approval for the market.

11 But I want to remind you that we are developing an

12 FDA policy to equally apply for innovator

13 manufacturers. What I specifically mean is that I

14 think we have data to show that innovators, during

15 the drug development process, during the approval

16 process or postmarketing, will make significant

17 changes, for example in excipients, formulation and

18 manufacturing facilities, and so on and so forth.

19 They will be required to conduct a bioequivalence

20 study to make sure they are equal. Therefore, for

21 highly variable drugs it is also equally applied

22 for the innovator, not just simply the generic

23 companies. I want to make sure that is

24 understandable.

25 Secondly, in terms of if we go forward, we

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1 are seeking your advice on which approach we should

2 take so that we can spend time on the right track

3 and then come back to you with recommendations on

4 what approach we should take. If the committee

5 advises us to move forward with the reference

6 scaling how do we determine within-subject

7 variability? That is an excellent question.

8 Certainly it would be very difficult to get a

9 two-way crossover study. We would have to go to

10 the three-way crossover study at least a

11 replicative design from the reference list product

12 to get the number. Thank you.

13 DR. KIBBE: Marv?

14 DR. MEYER: To kind of follow-up on

15 Jurgen\'s comment about what we know about the drug,

16 I think there are a couple of simple yardsticks.

17 If you can give a patient an intravenous and then

18 transfer them to IR, or if you can give them IR and

19 transfer them to CR, or back and forth, there is

20 probably no issue with Cmax there. If the product

21 is only available in one strength, 200 mg, and I

22 take it and small people take it and old people

23 take it once a day or twice a day, there is

24 probably no real issue with AUC or Cmax so you

25 could have somewhat less stringent requirements for

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1 those kinds of drugs.

2 One other comment, add-in designs\--it has

3 shown up here and there but we haven\'t really

4 addressed it and, to me, that seemed to be one

5 approach, provided that there are some constraints

6 on that and you don\'t just keep on adding three

7 subjects until you get it right. Add-ons have some

8 capability of eliminating excessive use of

9 subjects.

10 DR. CONNER: By add-on, I think you mean

11 sequential.

12 DR. MEYER: Yes.

13 DR. CONNER: In other words, you do the

14 first group, you look at the results, you make a

15 decision whether to go on or not.

16 DR. MEYER: Right, the point estimate

17 looks good\--

18 DR. CONNER: What we refer to as add-on

19 is, you know, you plan to do 24 subjects and you

20 get a whole lot of dropouts. You haven\'t looked at

21 the data; you have no decision based on the results

22 but you realize you are going to come up short so

23 you get some alternates, recruit some more and put

24 them in. That is what we consider an add-on. So,

25 there is no real decision based on results.

154

1 Whereas a sequential design is a plan ahead of time

2 to do a certain number and generally, as has been

3 mentioned before, the true sequential design where

4 you do one sample at a time is really not very

5 practical in these types of studies. It would take

6 you years maybe to do the right trial.

7 So, what we are talking about is a partial

8 sequential where you do groups. If you were going

9 to plan an overall 36, you were going to do 12 at a

10 time or 18 at a time, look at the results, make a

11 decision\--you know, a correct statistical penalty

12 for that look and that decision and then go on. We

13 don\'t currently accept that but we are working on

14 it. In several venues, PQRI and otherwise, we have

15 some working groups looking at that very carefully

16 and the proper statistics to do on that. So, we

17 hope to have some results on that pretty soon.

18 DR. KIBBE: Ajaz?

19 DR. HUSSAIN: No, I think I just wanted to

20 say a couple of things after Jurgen\'s comment.

21 From what he discussed, I think there are a

22 probably a few questions which are not on the

23 screen. So, are we asking the right question also

24 is the topic and I totally agree with him in a

25 sense because we continue to use the black box

155

1 approach. We don\'t know anything about it so we

2 have to pass through this goalpost and the goalpost

3 often tends to be arbitrary to start with.

4 Then also, I think we essentially move

5 towards a check box exercise because that is easy

6 to implement, and so forth. Clearly, I think you

7 have to balance the ease of the process of doing

8 something and the scientific rigor and so forth.

9 So, I think clearly as we move forward we will be

10 looking at what are the right questions also and

11 what are the right opportunities.

12 Two things that I think will open this up

13 further and new opportunities will come is the

14 prior knowledge. For example, currently if you

15 look at an ANDA submission or even an NDA

16 submission you don\'t have much information to make

17 decisions with respect to formulation, process and

18 so forth, what are the critical variables. In

19 ICH-Q8 we have essentially moved forward with

20 pharmaceutical development as a basis for making

21 more scientific, mechanistic based decisions. So,

22 I think we are trying to bring that know-how into

23 the agency to do that.

24 Also, I look at submissions of all

25 studies, all bio studies done as an opportunity to

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1 use all that knowledge to make more rational

2 decisions and set more appropriate specifications,

3 and so forth. So, clearly, I think there is

4 opportunity that is opening up and what you see in

5 front of you are questions of trying to make

6 decisions in the current mode and the future might

7 be quite different.

8 DR. KIBBE: Let me just throw out some

9 thoughts from listening to everyone. We have been

10 trying to take a complicated situation and make an

11 easy rule, a simple rule. I think Jurgen hit one

12 of the points dead-on, and that is, I think we

13 really need a decision tree that looks at the

14 characteristics of the product we are dealing with

15 and the therapeutic ranges that it is effective in.

16 We have lots of data on a lot of these products in

17 terms of their therapeutic concentrations in the

18 body and how wide that can be and still get

19 reasonably safe therapeutic effects.

20 To make a rule that only responds to the

21 fact that the product is variable and doesn\'t have

22 a basis for why we are allowing that variability or

23 why we shouldn\'t allow that variability just

24 doesn\'t sound good to me. The thought of going

25 outside the box with some solutions to some of

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1 these problems, instead of going straight to

2 another bio study and redesigning a bio study\--and

3 Gordon said, you know, what is wrong with designing

4 better dissolution testing? One thing no one ever

5 said is, well, what is wrong with a different

6 animal model? You know, I have had quite a bit of

7 success with the pig. The pig is a good animal

8 model for human absorption in the GI tract and that

9 is really what we are caring about\--and the

10 controls, the negative controls are always tasty.

11 \[Laughter\]

12 The question here is too complicated for a

13 simple answer and whether we have enough data to

14 get really a quality answer today is problematic.

15 I am intrigued by scaling but only when the

16 supportive data makes sense that we should scale to

17 allow something to happen.

18 I love three-way and four-way studies

19 because they get at what we have been trying to get

20 at for years, which is how variable is the product

21 and how variable are the people we test it on.

22 I worked for a couple of years in a

23 contract research lab. We did ten bio studies a

24 month and I will tell you that I don\'t think the

25 agency gets to see 40 percent of what we do, and I

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1 think a lot of the companies, when they find that

2 they can\'t successfully formulate, they kill it and

3 none of those studies show up. You might get some

4 useful qualitative information from the contract

5 research labs by just simply asking them to tell

6 you how many studies they do that never make it to

7 the agency so you have a handle on the denominator,

8 if you will.

9 I think you have a real bear by the tail

10 here and I wish I had as much confidence in

11 Bayesian that would answer every question as my

12 colleague does, but I think really we have to

13 apply\--we have to be willing for the agency not to

14 have a rule that everybody can look at in one

15 sentence and say I meet that rule or I don\'t meet

16 that rule. We have to be willing to say good

17 science supports my product, good science doesn\'t

18 support my product, and the agency can make a

19 decision based on a whole set of criteria.

20 The last thing is that there is lots of

21 information I would love to learn about the

22 process, supporting Gordon\'s idea of really

23 understanding the variables and understanding what

24 is going on so I can make better decisions and I am

25 stuck with the single question of who is going to

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1 pay for that, and I don\'t see everybody rushing to

2 the forefront to throw millions of dollars for

3 understanding it when what they really want to do

4 is get the product on the market.

5 DR. MEYER: A really quick follow-up to

6 Art, maybe there is a way for the innovator firms

7 to have a little expansion of exclusivity if they

8 seek answers to some of the questions you would

9 really like to know about mechanisms. It wouldn\'t

10 cost you a cent. It would cost the American public

11 a little bit but the return might be good.

12 DR. SINGPURWALLA: Well, I was very

13 pleased to hear Art talk about using decision trees

14 but was a little concerned when he said he doesn\'t

15 have that much faith in Bayesian methods. Well,

16 the two are isomorphic, my friend.

17 DR. KIBBE: Pat?

18 DR. DELUCA: Just a comment, it seems to

19 me that the innovator wants a drug approved so it

20 should be incumbent upon the innovator to seek

21 answers to why there is that high variability. I

22 don\'t know if we need to give any more exclusivity,

23 I think it ought to be incumbent upon them to do

24 this and to see whether that high variability is a

25 formulation or a physical property, as Gordon had

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1 outlined.

2 DR. MEYER: Pat, when I was talking about

3 exclusivity I was talking about something already

4 approved, much like the pediatric carrot\--if you do

5 pediatric studies you get an extra six months; if

6 you do mechanism studies you get another three

7 months, or whatever. And, if you are making a

8 million a day that is a pretty good incentive.

9 DR. KIBBE: I don\'t know where the

10 incentive is for the company. If I am an innovator

11 with an approved product on the market which has a

12 lot of variability but is still approved and it is

13 clinically effective, and it has been sold and now

14 I am producing X billion dollars worth of product

15 every year, do I really want to carefully define

16 that product so someone else can copy it? Or, do I

17 want to keep claiming that the trace elements in it

18 that come from the natural source are so important

19 if they have to be assays so that I don\'t have to

20 have the problem? I mean, I think Marvin is right,

21 you have to have an incentive for them to get that

22 data for you.

23 DR. HUSSAIN: I just want to point out

24 what Dr. Benet and Jurgen also pointed out, that I

25 think as you go through an approval decision to

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1 approve a new drug product, the basis of it is

2 safety and efficacy and the risk/benefit decision

3 that is made. Often when you go through that

4 process what results is that it is a safe and

5 efficacious product.

6 Now, PK variability, yes, we can measure

7 it. We know it is there and it may not have any

8 bearing on that and that is what Dr. Benet started

9 discussing, and so forth. So, keep in mind\--Jurgen

10 keeps raising his hand again\--what is the clinical

11 relevance. If we can measure it and it is highly

12 variable, if it is not relevant we should probably

13 not be measuring it.

14 DR. KIBBE: Paul, go ahead.

15 DR. FACKLER: I was just going to make a

16 comment along the same lines. We are aware of some

17 NDAs that have been approved without the BE studies

18 having to meet the traditional confidence intervals

19 on both Cmax and AUC\--

20 DR. HUSSAIN: Yes, it is done all the

21 time. It is a clinical decision; it is not a PK

22 decision.

23 DR. FACKLER: Right, where the Division

24 can say, you know, the Cmax isn\'t that relevant to

25 this therapeutic endpoint and, of course, then the

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1 generic industry still has to meet Cmax even when

2 reference versus reference can\'t possibly pass it.

3 So, I think it is a real problem for the generic

4 industry. Lawrence is right, these rules apply to

5 new drugs but the divisions have the authority I

6 suppose to waive a particular data requirement.

7 You know, as far as granting extra

8 exclusivity to find the variability in a new drug,

9 I don\'t think the generic industry is opposed to

10 doing replicate design studies, doing four-way

11 studies, to define the variability of the reference

12 product and I am guessing that the Division of

13 Bioequivalence would be interested to know the

14 variability in the test product they are

15 considering. So, I am sure there are lots of ways

16 of getting the information one needs to make a

17 decision about whether a product is highly

18 variable. I just wanted to point out that there

19 are products approved for which there is no way for

20 a generic product to gain approval without

21 reference scaling, wider goalposts, whatever the

22 committee decides to recommend. There needs to be

23 a process for a certain fraction of products that

24 are on the market today in the U.S.

25 DR. KIBBE: And I would hope that the FDA

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1 staffers will go away and give us a decision tree

2 with some understanding of the therapeutic outcomes

3 and the risk/benefit of that product, how narrow

4 the therapeutic range has to be, how variable it

5 is, and then the goalposts can move based on a

6 decision tree and not have us reestablish another

7 set that are just ticked. I think we have lived

8 quite well with 80-125 but it was still picked.

9 Someone came up and put that down.

10 The other point I just wanted to emphasize

11 is what Les said about how this plays out in the

12 public and among healthcare professionals, and his

13 concept of adding the point determination with what

14 would appear to be to them a narrower range or

15 allowable range might be something also to look

16 into.

17 DR. YU: I guess we will have to look into

18 long-term solutions, the short-term solutions,

19 long-term objectives and short-term objectives. I

20 think mechanisms understanding of the causes of

21 variability and having some kind of decision tree

22 which you imagine is a great idea. I think that is

23 long-term we ought to be looking for. We ought to

24 be looking at moving in this direction. We also

25 have to balance the short-term objectives. If we

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1 have not seen what the decision tree will look

2 like, how to implement them right now basically the

3 policy for the short-term is 80-125 percent

4 confidence interval, and you have already heard

5 that some drugs may be difficult to meet, maybe

6 never to be put on the market. So, I guess this is

7 a question put to the committee we will have to

8 discuss. In other words, we are waiting to also

9 develop the great idea of long-term objective

10 decision trees and then put basically, given this

11 short-term period for the next decade, you will not

12 have those products. So, that is a decision for

13 which we are seeking your advice. Thank you.

14 DR. CONNER: One comment, I mean, you

15 mentioned that we should come forward with a number

16 of sets of data, including the therapeutic range of

17 the product. Now, having been involved in at least

18 one working group where we were looking at NTI

19 drugs and saying, well, can we have a definition,

20 you know, that can always be supported for a given

21 drug or new drug to say what its therapeutic window

22 or therapeutic range is, we spent I think about

23 four or five years and realized we couldn\'t do it

24 because the data, even in an NDA, does not really

25 tell the true therapeutic range. I mean, they have

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1 some indicators that if I go up above a certain

2 point, you know, I don\'t get anymore efficacy and I

3 start to get something with disturbing side effects

4 but, you know, they usually don\'t have a full

5 characterization of therapeutic range. Plus, the

6 definition of what indicators I look for and, you

7 know, do I take a ratio and do I look at this

8 versus that, you know, for any given drug it is

9 just not there. Even if you had infinite amount of

10 data, it is very hard to decide what I should look

11 at and what I should use. So, it is easy to say I

12 want to know about the therapeutic range but the

13 data to determine it so that everyone will agree on

14 it, and determine it with certainty just isn\'t

15 there. We spent a long time really trying to do

16 that and finally gave up, unfortunately. We are

17 still interested in the topic but, you know, we

18 realize that it is a lot harder to do than most

19 people realize.

20 DR. KIBBE: Marvin?

21 DR. MEYER: Since it is in the interest of

22 everyone who is trying to sell a highly variable

23 drug, it would seem to me that a number of

24 companies would give a release to MDS, if asked, to

25 just have a disguised set of data\--you don\'t even

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1 have to say class of drugs.

2 I kind of favor number two, reference

3 scaling with a test of a reference stipulation, but

4 I would be interested in seeing the 66 percent of

5 studies that failed and what would you have to do

6 to your limits in order to get them to pass and

7 work with real data. Right now it is somewhat

8 hypothetical.

9 DR. CONNER: Well, that could be done and

10 it would provide more evidence and information

11 about the immediate problem but I don\'t think that

12 partial knowledge of those would get at the root

13 cause, which is what some members have said they

14 want to see. I mean, you really have to know what

15 the nature of those drug substances is and how they

16 are formulated. You have to know a lot of factors

17 and relate that to what you saw in trying to get at

18 the problem and its root causes.

19 DR. MEYER: Yes, that is a laudable goal

20 but I thought we wanted an answer while we are

21 still alive.

22 \[Laughter\]

23 DR. CONNER: I expect you will be around

24 for quite a while.

25 DR. KIBBE: Gary?

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1 DR. BUEHLER: We do want an answer while

2 you are still alive, Marvin. This is a big issue

3 for us. I am one that always says that we have to

4 bring difficult issues to the advisory committee

5 and, you know, we bring sort of soft balls to you.

6 I really made the point that this is a very

7 difficult scientific issue. It is an economic

8 issue. As was brought up today, clearly it is an

9 economic issue but the Office of Generic Drugs is

10 an economically driven office that makes scientific

11 decisions and makes these economic decisions in a

12 scientific way. We have products out there that we

13 can\'t get generics of, and that was made evident by

14 Dr. Fackler, from Teva, and he knows that probably

15 better than I do. But generic drugs are a big

16 political issue. They are a big, passionate issue

17 with the American public today. People see

18 generics as an answer to the high cost of

19 prescription drugs today.

20 So, what we are bringing to you today\--I

21 am not saying that a decision tree is not a good

22 idea; I think it is a great idea, but I agree with

23 my colleagues from the Office of Generic Drugs that

24 a decision tree can be an awfully long process to

25 put together and to gather the data from all the

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1 various drugs, and I am not sure I have the staff

2 to be able to do that within the next millennium or

3 so. So, what we would like from you today is some

4 direction as to which way to go. If you would be

5 able to provide that to us somehow, we would

6 appreciate that.

7 DR. KIBBE: I think we have heard from Dr.

8 Meyer that he prefers scaling. How many of us

9 think that that is an option for situations that

10 seem to be highly variable and need an evaluation

11 outside of the current rules?

12 DR. SINGPURWALLA: I am sorry, I think one

13 has to put things sometimes rather bluntly. I feel

14 that those questions that you have asked are the

15 wrong questions, or there should be additional

16 questions, namely, what are the alternatives? The

17 decision tree, as Jurgen puts it, is a very good

18 alternative.

19 The second point is the scaling. The

20 scaling has been talked, and talked, and talked

21 about. There is a simple reason why you do the

22 scaling. The scaling is a transformation which

23 tries to bring the variability down. If the

24 scaling does not bring the variability down no

25 statistician will do it. Its main purpose is

169

1 two-fold, to make everything look approximately

2 normal and, in the process, bring the variability

3 down. So, the scaling is a technical exercise

4 which nobody should question or criticize whenever

5 it is appropriate and it is not a debatable issue.

6 The issue that is really debatable is are

7 those the right kind of questions and do we want to

8 pursue that line of questioning. What I would like

9 to do, if Mr. Chairman would allow me sometime

10 later in the afternoon, is to ask everyone around

11 the table what do they mean by confidence limits;

12 what is its interpretation; and how is it

13 understood. And, I will make a bet that fifty

14 percent will get the answer wrong, at least. Thank

15 you.

16 DR. KIBBE: It is against federal

17 regulation to gamble in Washington, D.C.

18 \[Laughter\]

19 There will be no betting going on!

20 Jurgen, what do you think?

21 DR. VENITZ: I think it is time for lunch.

22 DR. KIBBE: No, no, no one is going to

23 lunch until we answer his question. What do we do

24 in the short term?

25 DR. VENITZ: Well, in the short term I

170

1 don\'t think there is anything wrong with reference

2 scaling the way I understand it. I had some

3 question about how you are going to get the

4 estimate for your within-subject reference

5 variability but if that is part of a replicate

6 design study or separate study, I think I can live

7 with that. I still think, as I said before with

8 Les, you should have additional constraints on the

9 point estimates, and it might just be for public

10 consumption so everybody on the outside that is the

11 recipient of whatever we come up with today feels

12 comfortable, yes, the rules are not being bent to

13 make bad products look good or, you know, highly

14 variable drugs look good. But other than that, I

15 can live with this as a band aid. I do think you

16 should start working on the long-term strategy,

17 which comes back to the decision aspects.

18 DR. KIBBE: Gordon, what do you think?

19 DR. AMIDON: I would agree with the

20 scaling. Again, the question of how you get the

21 reference scaling, I think the last point on the

22 reference scaling is a good starting point to look

23 at in trying to make that in a concrete decision

24 rule. I still think that the mechanism, you know,

25 what is going on with highly variable drugs, where

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1 the problems are, that is the real long-term

2 solution, understanding what the problem is and the

3 FDA should in some way put some resources into

4 that, and I think Marvin Meyer\'s suggestion is an

5 excellent one. You know, just provide some

6 incentive for industry to fund research into what

7 is happening with these drugs.

8 DR. KIBBE: Judy?

9 DR. BOEHLERT: I have thought about this a

10 lot and I also would agree with using the scaling

11 factors but I think you are still going to be in a

12 position of having to make decisions and having

13 some kind of decision tree, even if it is not

14 formal because Dr. Davit presented data this

15 morning that showed that the same product with two

16 different laboratories had different values. So,

17 you are going to be in that situation where one

18 manufacturer uses scaling and the other one doesn\'t

19 so you are going to have to make some decisions

20 around those issues. It is not so straightforward,

21 particularly when you get around that 30 percent

22 number.

23 DR. BUEHLER: Making decisions is \"an

24 understood\" for me so I can accept that. But I

25 echo Jurgen. We have to make sure that whatever we

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1 decide will provide a good scientific method so

2 that the generic products that go on the market as

3 a result of this are unequivocally bioequivalent

4 and, of course, safe and effective.

5 DR. KIBBE: My colleague who hardly ever

6 speaks?

7 DR. SELASSIE: I agree with what Jurgen

8 said because I think that there needs to be a

9 mechanistic basis as to what type of scaling

10 factors you use, and it seems to me that that is

11 really important and we need to understand the

12 physicochemical parameters that are involved in

13 dissolution and it seems like that is missing and

14 is arbitrary in trying to set some scaling factor,

15 and we are not taking those types of phenomena into

16 consideration. So, I think a decision tree in the

17 long-term would be a good idea but I guess in the

18 interim you can use something like reference

19 scaling.

20 DR. KIBBE: Marc?

21 DR. SWADENER: I think it is a little bit

22 naive to think that all of us here, at every stage

23 along the line, don\'t use a decision tree of some

24 sort. Formalizing it to the stage that people are

25 talking about here is a little diplomatic but we

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1 all have a decision tree that we use. Whether I

2 came here to this meeting or not, I used one.

3 Whether it was formalized or not is a different

4 question.

5 I do know enough about statistics to know

6 you can\'t believe them all the time. You have to

7 be very, very careful about them. So, I think I

8 would encourage looking at a decision tree not as

9 the short term as it will take time, and do the

10 best you can. I do agree with Jurgen, you really

11 have to look at what are the real fundamental

12 questions you are dealing with too. With my

13 representation on this committee, the public just

14 needs to know that what they are getting is safe

15 and will do what it says it will do. Now, that is

16 a very simple approach but they don\'t know all this

17 stuff and they are relying on you to do the best

18 you can. I don\'t see that you are not doing that.

19 DR. KIBBE: Dr. Koch?

20 DR. KOCH: Yes, I would agree with the

21 summary that you came up with and certainly stay

22 with the reference scaling short term but something

23 has to be put together to address the decision tree

24 approach.

25 DR. COONEY: I think that the change in

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1 limits is not acceptable because it is very

2 arbitrary and if there are options I think the use

3 of reference scaling makes fundamental sense.

4 Furthermore, a decision to do that is a decision

5 down the path of a decision tree so it is a logical

6 step to take and I want to underscore the

7 importance of continuing to gather the data and

8 establish the criteria around which these

9 individual decisions are made and you will be in a

10 better position to do this goring forward.

11 DR. KIBBE: Pat?

12 DR. DELUCA: I am certainly an advocate of

13 getting those drugs that are safe and effective to

14 the market. Certainly, the public would benefit

15 from those. I guess I favor in the short term\--I

16 think in the long term something more substantial

17 has to be done with regards to decision making, but

18 reference scaling I think is very important here.

19 Again, I don\'t like the arbitrary nature of

20 widening the limits but if that is something that

21 can be approached in the short term, then I would

22 be for it.

23 I still think that we need to encourage

24 the innovator to finance for the high variability

25 that exists. Whether it is offering incentives in

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1 some way, so be it but certainly an incentive if

2 they can reduce the variability. It seems to me

3 they gain something when the generic has to go into

4 the reference scaling, they have improved the

5 product so I think that is also an incentive to do

6 it.

7 DR. KIBBE: Our industry representatives

8 have a comment one way or the other? I would just

9 like to wrap up and go to lunch but with one

10 comment. I think if we go ahead and make a change

11 in the way we approve highly variable drugs, then I

12 think we ought to consider seriously also Les\'

13 other point which is to come up with something that

14 is going to reassure the public that the changes we

15 are making are not getting drugs that can vary by

16 50 percent on the marketplace but, rather, that

17 they really are tighter than that so they

18 understand it better. So, I would end with that.

19 With that said and no one else waving frantically

20 to get my attention, we will break for lunch and we

21 will be back at 1:40.

22 \[Whereupon, at 12:40 p.m. the proceedings

23 were recessed for lunch, to resume at 1:40 p.m.\]

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1 A F T E R N O O N P R O C E E D I N G S

2 \[Because the Chairman reconvened the

3 proceedings before 1:40 p.m., part of the text is missing.

4 There were no speakers who wished to speak

5 during the open public hearing but there was a public

6 submission from Zeb Horowitz, M.D.\]

7 Bioinequivalence: Concept and Definition

8 \[Slide\]

9 DR. YU: \...bioavailability is rate and

10 extent of absorption and it is the site of drug

11 action. So, normally you give a drug to a healthy

12 volunteer or patient and measure the plasma

13 concentration against time, as shown in this

14 figure. Then you get a Cmax here; you get the AUC,

15 AUC is area under the curve. Cmax is a surrogate

16 for the rate of drug absorption; AUC is basically

17 for the extent of absorption so this is defined as

18 bioavailability.

19 \[Slide\]

20 Bioequivalence basically is defined as the

21 absence of a significant difference in the rate and

22 extent to which the active ingredient or active

23 moiety in the pharmaceutical equivalents or

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1 pharmaceutical alternatives become available at the

2 site of drug action when administered at the same

3 molar dose under similar conditions in the

4 appropriately designed study. So, this basically

5 is the Federal Register Notice definition. So,

6 bioequivalence basically means the absence of a

7 significant difference in the rate and extent of

8 drug absorption.

9 \[Slide\]

10 This morning we discussed bioequivalence

11 and we said a 90 percent confidence interval for

12 the extent or AUC for the rate as a surrogate or

13 Cmax 80-125 percent. Now, passing or meeting the

14 bioequivalence standards allows marketing access

15 basically as one of the standards for approval of

16 the applications. Of course, you have to meet

17 very many other requirements, especially with

18 respect to the chemistry, manufacturing controls

19 with respect to quality of the products. So,

20 demonstration of bioequivalence makes the generic

21 to be approved and the innovator basically

22 demonstrates that the marketed formulation is

23 equivalent to the clinical formulation.

24 \[Slide\]

25 The question is why do we define the

178

1 bioinequivalence concept? What are you talking

2 about here? Why do you define this? It is because

3 FDA receives studies that attempt to reverse a

4 previous finding of bioequivalence. In other

5 words, you approve a product to put on the market

6 when some manufacturer conducts a study to show or

7 fails to show the bioequivalence. Also, in the

8 public literature there are claims of

9 bioinequivalence. In reality, it is simply a

10 failure to demonstrate bioequivalence. So, there

11 is a concept you need to clarify, what is called

12 bioequivalence and what is called bioinequivalence.

13 What is the difference when you fail to demonstrate

14 bioequivalence and bioinequivalence?

15 \[Slide\]

16 There are many reasons, as we discussed

17 this morning, for high variability\--under-powered

18 study designs, study samples, many, many reasons

19 that can make a study fail. Of course, the easiest

20 way, as we discussed this morning, is to use a

21 small number of subjects. So, it is easy to fail

22 to show bioequivalence by a small number of

23 subjects and, certainly, there will be other

24 considerations like study design, study sample,

25 data analysis. There are many, many other reasons

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1 and these are just several of them.

2 \[Slide\]

3 What should bioequivalence mean if we

4 define a definition for bioinequivalence? As we

5 said, bioequivalence leads to market access.

6 Basically a study that demonstrates bioequivalence

7 is clear and convincing evidence of equivalence.

8 Bioinequivalence may lead to market exclusion. Of

9 course, we have to consider many, many other

10 factors too as we discussed this morning\--safety,

11 efficacy, pharmacokinetics, pharmacodynamic

12 relationship and so on. But a bioequivalence study

13 demonstrated by equivalence is clear and convincing

14 evidence of potential problem for the specific

15 product.

16 \[Slide\]

17 So what do we specifically mean here? I

18 want to spend a little time on this slide. When

19 you conduct a study, if a study is properly

20 designed, the 90 percent confidence interval is

21 between 80 to 125 percent. Now, if this study is

22 under-powered and if this study has a small number

23 of subjects, there is a greater possibility that it

24 fails to demonstrate bioequivalence. What this

25 specifically means is simply that the manufacturer

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1 or sponsor does not use enough subjects for

2 example, of course, among many other reasons, to

3 conduct a study. If the study is powered enough,

4 there is a greater possibility that you can narrow

5 the confidence interval and make this a passing,

6 successful study. Coming back to so-called

7 inequivalence is to make sure that the test product

8 has a difference more than 20 percent or, for

9 example is underneath the 80 percent or above 125

10 percent. Of course, there is also the failure to

11 demonstrate a bioequivalence study because simply

12 the top limit above 80 or, on the other side of the

13 lower limit it may be below 125.

14 I think it is in the best interest of the

15 public and us, for clarification, that we want to

16 define the bioequivalence, bioinequivalence,

17 failure to demonstrate bioequivalence and failure

18 to demonstrate bioinequivalence. This is a concept

19 that we have to be very clear about because in many

20 cases in the published literature or studies

21 submitted to the Food and Drug Administration are

22 simply that. For example, the top limit is above

23 125 percent or the lower limit is below 80 percent

24 if you use enough power and increase the subjects

25 of the studies the study will become a successful,

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1 passing study instead of failure to demonstrate

2 bioequivalence.

3 Yet, because of confusion because there is

4 no clear definition with respect to bioequivalence,

5 in the end any study, whether the lower limit is

6 below 80 and upper limit is above 125, the sponsor

7 or other parties will have bioinequivalence. The

8 reality is simply to fail to demonstrate

9 bioequivalence. In other words, the true

10 difference is acceptable, however, the study is not

11 properly designed because it is under-powered, or

12 many, many other reasons where the confidence

13 interval does not meet the regulatory criteria,

14 which is 80-125 percent. At the end, the claim is

15 basically bioinequivalence and in reality, as I

16 said, is a failure to demonstrate bioequivalence.

17 So, I want to make it clear, I want to clarify the

18 concept.

19 \[Slide\]

20 So, the objective at FDA is to develop

21 bioinequivalence criteria that are scientifically

22 sound, statistically valid and fair to all parties

23 and, hopefully, easy to use.

24 With this introduction, I want to turn the

25 podium to Don and I am sure a lot of people know

182

1 him. He is the developer of the original 80-125

2 percent criteria for FDA standards. He will be

3 speaking about how to statistically establish

4 bioinequivalence.

5 DR. MEYER: A real quick question, I don\'t

6 quite catch the fail to demonstrate

7 bioinequivalence for the one where the right-hand

8 tail is barely across 80 but the point estimate is

9 well to the left of 80. It seems to me that still

10 is a bioinequivalent product.

11 DR. YU: This one?

12 DR. MEYER: Yes, with the point estimate

13 falling well to the left. It seems to me changing

14 the N won\'t help that one. It will just make the

15 confidence limits fall, totally bioinequivalent.

16 DR. YU: Yes, most likely if this study is

17 powered\--to increase, for example, the power of

18 this study this product is bioinequivalent. This

19 time it is because the confidence interval above 80

20 statistically speaking, as Don can clarify, failed

21 to demonstrate whether it is truly bioinequivalent

22 or not. I think that Don is the better person to

23 answer the question.

24 Statistical Demonstrations of Bioinequivalence

25 MR. SCHUIRMANN: One clarification to what

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1 Lawrence said, I did not have anything to do with

2 choosing 80-125 as the limits for bioequivalence.

3 \[Laughter\]

4 \[Slide\]

5 This presentation is joint work with

6 colleagues in the Quantitative Methods and Research

7 staff of the Office of Biostatistics and also in

8 the Office of Generic Drugs, and the bulk of the

9 presentation was put together by my colleague, Dr.

10 Qian Li, who originally was scheduled to give this

11 presentation but she just recently had a baby so

12 she is having a little deserved maternity leave.

13 \[Slide\]

14 We hope to go over the definition of

15 bioinequivalence, comments on claiming

16 bioinequivalence if you fail to show

17 bioequivalence, proposing a criterion to use for

18 one PK endpoint\--PK is pharmacokinetic, and talk

19 briefly about strategies when you are looking at

20 three pharmacokinetic endpoints.

21 \[Slide\]

22 The usual definition of the bioequivalence

23 interval on the ratio of the population geometric

24 mean of the test product over the population

25 geometric mean of the reference product is that it

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1 should fall within the limits of 80 percent to 125

2 percent. That is what is called the bioequivalence

3 interval. It is never correct to refer to that as

4 a confidence interval. So, it is obvious to define

5 the bioinequivalence region as just the complement.

6 If you are not in the bioequivalence interval, then

7 you are in the bioinequivalence region which

8 consists of the two disjoint regions.

9 \[Slide\]

10 So, the question that I first want to look

11 at is, is it appropriate to claim bioinequivalence

12 if a study fails to show bioequivalence? Two

13 products may, in fact, be bioequivalent but they

14 may not be shown to be bioequivalent by the study.

15 The primary reason for that is inadequate power.

16 There could possibly be other reasons.

17 \[Slide\]

18 In doing our standard testing for

19 bioequivalence, it is an application of statistical

20 hypotheses testing where we have a null hypothesis

21 that says either the ratio of geometric means is

22 too low, below 80 percent, or else it is too high,

23 above 125 percent, and we test that against the

24 alternative hypothesis that the ratio of geometric

25 means is within the interval. The way that we have

185

1 typically tested this statistical hypothesis is by

2 doing two one-sided statistical tests, and each of

3 those tests is carried out at the alpha equals 0.05

4 level of significance.

5 Now, it turns out that doing these two

6 one-sided tests\--it is an example of what is called

7 intersection union test\--is algebraically

8 equivalent to calculating a two-sided 90 percent

9 confidence interval and seeing whether it falls

10 within the equivalence interval. So, that is why

11 you hear a lot of talk about confidence intervals

12 today even though we are not using the confidence

13 interval as a confidence interval; we are using the

14 endpoints of the confidence interval as test

15 statistics. What we are doing here is statistical

16 hypothesis testing. As I said, the type 1

17 error\--we have to reject both one-sided null

18 hypotheses, both H-nought 1 and H-nought 2. If we

19 reject both one-sided null hypotheses, then we

20 conclude that this alternative is true, that is,

21 that we have average bioequivalence.

22 \[Slide\]

23 So, we need to reject the hypothesis of

24 inequivalence with high confidence and the

25 rejection region is selected to make the chance of

186

1 doing that incorrectly to be small, and that is the

2 level of significance which, as I said, is alpha

3 equals 0.05.

4 \[Slide\]

5 So, what is the error associated with

6 claiming inequivalence if you don\'t claim

7 equivalence? Well, if you are looking for a

8 procedure for testing to see if you have

9 inequivalence, then we need to control the error

10 wrongfully rejecting equivalence to be small. If

11 you are going to base it on the equivalence test,

12 that means you want the equivalence test to have

13 large power. However, the power for the

14 bioequivalence test, as you will in a moment, may

15 not be large overall values of the geometric mean

16 ratio in the equivalence region.

17 The testing for bioequivalence focuses on

18 controlling the type 1 error and then other aspects

19 of the test, such as high power if the alternative

20 is true, are gotten, if they can be. So, we may

21 not have adequate power to claim bioequivalence

22 even when bioequivalence is true.

23 \[Slide\]

24 Here is an example. If we had a product

25 and we are going to design a two-period, two

187

1 sequence bioequivalence trial, and we assume we

2 have within-subject variance of 0.04, that is to

3 say within-subject standard deviation of 0.2, and

4 we are willing to assume that the ratio of

5 geometric means deviates from 1 by no more than 5

6 percentage points and, if that is true, we want to

7 be at least 85 percent sure that we will reach a

8 conclusion of equivalence, you can then crank the

9 numbers and you come up with the sample size of 22

10 subjects.

11 Well, if you have 85 percent power, that

12 means you are 85 percent sure of concluding that

13 the products are equivalent. That means you could

14 have as much as a 15 percent chance of not

15 concluding that they are equivalent. So, even with

16 this design study you could have products that are

17 equivalent but you have as much as a 15 percent

18 chance, or even more, of failing to conclude that

19 they are equivalent. In fact, if the variance,

20 unbeknown to you, is higher than you thought or if

21 the geometric mean ratio deviates from 1 by more

22 than 5 percentage points, the power will be lower

23 so the chance of not concluding bioequivalence will

24 be higher. So, it should be apparent that that is

25 not a basis for concluding inequivalence.

188

1 \[Slide\]

2 The rejection region for the

3 bioequivalence test\--what do I mean by rejection?

4 I mean rejecting the hypothesis of inequivalence

5 and concluding equivalence\--is determined by the

6 variability associated with the point estimate of

7 the geometric mean ratio, which is illustrated here

8 on the log scale. The higher that standard

9 deviation of the estimator is, the further away

10 from the actual limits\--delta-2 here is 1.25;

11 delta-1 is 0.8\--the narrower that rejection region

12 will be and the lower the power will be. So, it

13 isn\'t enough for the point estimate to be within

14 the equivalence interval. It has to be comfortably

15 away from the edges of the equivalence interval in

16 order to conclude equivalence.

17 \[Slide\]

18 This is an example of power curves for the

19 test for equivalence. The blue lines correspond to

20 a standard deviation on the log scale of 0.5. The

21 red lines correspond to a standard deviation of 0.3

22 and the green lines correspond to a standard

23 deviation of 0.1. The solid lines are for a study

24 with 60 subjects. The corresponding dashed lines

25 are for a study with 30 subjects.

189

1 Let\'s take this example, a 60-subject

2 study but the standard deviation is 0.5, even if

3 you are exactly equivalent, you are identical,

4 there is a very good chance that you will not

5 conclude equivalence so that is no basis for

6 concluding inequivalence.

7 \[Slide\]

8 So, instead of trying to use the

9 equivalence test as a means for establishing

10 inequivalence, we need to develop a testing

11 procedure aimed specifically at inequivalence.

12 Here we have done that by reversing the hypotheses.

13 Now the null hypothesis, in statistical jargon, is

14 that the geometric mean ratio is within the

15 interval. The alternative is that it is either

16 below 80 percent or else it is above 125 percent.

17 So, once again try to test this hypothesis by doing

18 two one-sided tests, each at a level of 0.05, and

19 in the case of equivalence we had to reject both of

20 the one-sided hypotheses but in this case we have

21 to reject one or the other. So, we will reject

22 bioequivalence and conclude bioinequivalence if one

23 of these two one-sided hypotheses is rejected. It

24 says here under certain conditions and, in fact,

25 under most conditions the overall level of that

190

1 procedure will not be appreciably different from

2 0.05, however, we can find mathematically cases

3 where it could be higher. It could be as high as

4 0.1.

5 \[Slide\]

6 Before I showed you the region for

7 concluding equivalence and that is these lines,

8 here. Now the region for concluding inequivalence

9 is given by this line and this line. You have to

10 fall higher than this with the point estimate or

11 you have to fall lower than that in order to

12 conclude inequivalence. So, once more you need to

13 be comfortably away from the actual boundary before

14 you reach your conclusion.

15 \[Slide\]

16 This is what the power of that test looks

17 like. The color scheme and the solid and dashed

18 line scheme is the same before. These vertical

19 lines are the 0.8 to 1.25 lines and so if you are

20 in the interval, 0.8 to 1.25, the probability never

21 gets higher noticeably than 0.05. But if you are

22 outside of the interval, then you have a greater

23 chance of concluding bioinequivalence. I might add

24 that it is symmetric in the reciprocal of the

25 ratio. In other words, here is a ratio of 0.5 and

191

1 it has a certain high probability of concluding

2 inequivalence. To have the same probability over

3 on the other side, it would have to be equal to 2,

4 which is the reciprocal of 0.5.

5 \[Slide\]

6 We are going to try to control that error

7 to 0.05. It is a function of what in this slide is

8 designated sigma-T, which is the standard deviation

9 of the estimator that is used as the basis for the

10 test statistic. As that sigma-T gets larger you

11 could possibly have more than a 5 percent chance of

12 wrongfully concluding inequivalence.

13 \[Slide\]

14 Well, how big would the variance have to

15 be? Dr. Li ran some calculations. In this

16 example, here, N equals 10. A bioequivalence study

17 with only 10 subjects I don\'t think would even be

18 allowed. These studies tend to be considerably

19 larger than 10 subjects. But it just illustrates

20 the fact that even for that tiny sample size the

21 standard deviation, which is on the log scale, has

22 to be quite large before the chance of making the

23 wrong decision, that is to say concluding

24 inequivalence when, in fact, the products are

25 equivalent gets unacceptably high. If you have a

192

1 more reasonable number of subjects it has to be

2 astronomically high before you start running into

3 that problem.

4 There could be cases perhaps with a

5 parallel design of a highly variable drug where we

6 might have to do some adjustment to the

7 significance level, and there do exist methods in

8 the literature to do that.

9 \[Slide\]

10 So, that is the corresponding test to the

11 bioequivalence test for one parameter, but

12 generally we assess bioequivalence studies with

13 respect to three endpoints\--I said parameter,

14 didn\'t I? Pharmacokineticists love to use the word

15 \"parameter\" to describe AUC and Cmax; statisticians

16 don\'t. Typically, we require for an equivalent

17 study you have to show equivalence for area under

18 the curve to the last sampling time; area under the

19 curve extrapolated to infinity; and maximum

20 observed concentration. What are we going to do

21 about bioinequivalence? In concept the products

22 are bioinequivalent if they are bioinequivalent

23 with respect to just one of these three.

24 \[Slide\]

25 So, what statistical criteria shall we

193

1 use? We are looking at a number of strategies.

2 Strategy one is to say, well, if you conclude

3 bioequivalence with respect to just one of the

4 three pharmacokinetic endpoints, then you will

5 reach a conclusion of bioinequivalence. The things

6 in favor of that is that it is quite intuitive.

7 The arguments against it are that you now have

8 three chances\--if you have a case where the

9 products are close to being inequivalent but they

10 aren\'t inequivalent, then you have three chances to

11 make a mistake and you may inflate the overall type

12 1 error rate.

13 \[Slide\]

14 So, if you are worried about that, here is

15 another strategy which says, well, you have to show

16 that it is inequivalent with respect to all three

17 of the PK endpoints. Then you can tightly control

18 the type 1 error rate. Type 1 error in this case

19 means concluding inequivalence when, in fact, the

20 products are equivalent. But the argument against

21 this strategy is that it is not going to have

22 reasonable power against alternatives of interest.

23 \[Slide\]

24 Another possibility would be to say, well,

25 you need to prespecify which endpoint you are going

194

1 to look at. In the slide here AUC was used as an

2 example. Possibly Cmax would be another choice.

3 This will control the type 1 error but if the

4 endpoint you chose is not the endpoint for which

5 the products are inequivalent, then you are not

6 going to have a reasonable chance of reaching a

7 proper conclusion.

8 \[Slide\]

9 Other strategies require that you show

10 equivalence for all three but you adjust the alpha

11 levels so the overall level is maintained but you

12 have more power for each individual test. A method

13 to do this which doesn\'t require the levels to be

14 the same for all three endpoints is currently under

15 development in QMR.

16 Other possibilities\--one that occurred to

17 me is you might say, well, before you can conclude

18 that the products are inequivalent with respect to

19 AUC you have to show inequivalence for both AUC to

20 the last time point and also for AUC to infinity

21 but we will look at Cmax separately. But there

22 could be regulation complications in all of these

23 proposals.

24 \[Slide\]

25 So, the main focus of this presentation

195

1 was on power to make the right decision and what we

2 are calling error, which is making the wrong

3 decision and controlling the probability of making

4 a wrong decision. There could be other statistical

5 issues as well. Thank you.

6 DR. KIBBE: We will open it up for

7 questions from the panel. Marvin, go ahead.

8 DR. MEYER: Don, a practical example of

9 what would happen with strategy one if the type 1

10 error were inflated and the three PK endpoints are

11 now highly correlated\--

12 MR. SCHUIRMANN: I apologize, Dr. Meyer, I

13 didn\'t bring those numbers with me\--

14 DR. MEYER: Just conceptually.

15 MR. SCHUIRMANN: Suppose you had a product

16 for which the ratio of the population of the

17 geometric means was something like 124 percent for

18 all three parameters. Then, the chance that you

19 will conclude inequivalence for at least one of

20 them could be something like 15 percent, in that

21 neighborhood, depending on the sample size;

22 depending on how tightly correlated the AUC is with

23 the Cmax. I can\'t give you a very quick answer.

24 In some of the simulations that Dr. Li did about 15

25 percent was the highest I saw. So, it depends on

196

1 whether you are interested in controlling that

2 overall level or whether you are merely interested

3 the level in each individual endpoint.

4 DR. BENET: Since this would be a test to

5 take a drug approved off the market, have you

6 considered that maybe we need more than one study?

7 Have you talked about that? Have you thought about

8 that in your thinking about it?

9 MR. SCHUIRMANN: I can\'t speak for the

10 Center. I have not thought about that much.

11 DR. BENET: Well, I was reacting to

12 Barbara\'s data where you looked at the two

13 different studies with two people running it with

14 significantly different variance in the two

15 different studies, and that could be an issue here.

16 You know, I think it is a statistical issue but it

17 is also a policy issue in terms of, you know, is

18 one study going to be adequate? No matter which of

19 these terrible suggestions you pick, is one study

20 going to be adequate?

21 MR. SCHUIRMANN: On the one hand,

22 requiring two studies would bring it more in line

23 with what we require for Phase III clinical trials

24 where we want a reproducible result so you have to

25 show us more than once. On the other hand, we

197

1 approve generic drugs with only one bioequivalence

2 study. So, what would be the basis for requiring

3 two studies for the opposite claim?

4 DR. VENITZ: But you already have two

5 studies. Don\'t you have a prior study that led to

6 its approval as a bioequivalent generic and now you

7 have a study to disprove it. So, my question is

8 somewhat related to what Les is asking, how do you

9 incorporate the prior information that you have

10 from the fact that your drug got approved based on

11 a bioequivalence study? Because you now have one

12 study done God knows how long ago\--

13 MR. SCHUIRMANN: Yes.

14 DR. VENITZ: \--but it passed

15 bioequivalence. Now you have done a study, no

16 matter what method you use, that shows

17 bioinequivalence. Are you going to pool the

18 studies? Are you going to use Bayesian to

19 incorporate your prior information or are you

20 completely ignoring the fact that in order to get

21 approval it must have passed a bioequivalence

22 study? And this is not a question to you but to

23 everybody.

24 DR. BUEHLER: Usually when we get a

25 challenge study now we will inform the generic

198

1 sponsor of the generic application that their

2 bioequivalence has been challenged, and that they

3 can come back to us with additional data, usually

4 another study, which would refute the study that

5 came in. We usually review the study extensively,

6 the challenge study extensively to make sure that

7 the study was conducted properly. We review it as

8 far as it was powered correctly, etc. Then we give

9 the generic firm that was challenged the

10 opportunity to come back to us with a study or else

11 face being downgraded in the \"Orange Book.\"

12 DR. VENITZ: But if they don\'t come back

13 do you ignore the fact that they must have done a

14 study in the first place that demonstrated

15 bioequivalence?

16 DR. BUEHLER: No, we don\'t ignore that

17 fact. That is why we leave them on the market

18 while they get the additional data to us. I mean,

19 they did submit a study to us that showed their

20 product to be bioequivalent. Now, whatever

21 happened along the way, you know, whatever water

22 flowed under the bridge between then and the time

23 when we have had the challenge study, you know,

24 sometimes it is a long time. Sometimes

25 formulations change or reference listed drugs

199

1 change so we give them the opportunity to come back

2 to us with another study to show that they are

3 still bioequivalent.

4 DR. KIBBE: Go ahead, Marc.

5 DR. SWADENER: Is my intuitive notion that

6 these strategies one, two and three could result in

7 failure to agree on the hypothesis that it turns

8 out that is not inequivalent doesn\'t necessarily

9 say that it is equivalent? Aren\'t there parts

10 where it is really not equivalent?

11 MR. SCHUIRMANN: We are talking about

12 studies here and there is such a thing as an

13 inconclusive study, a study that does not establish

14 that two products are equivalent and the study also

15 does not establish that the two products are not

16 equivalent.

17 DR. SWADENER: Exactly.

18 MR. SCHUIRMANN: That isn\'t to be confused

19 with the actual reality unknown to any human being

20 whether they are or aren\'t equivalent. With these

21 strategies, depending upon how stringent you make

22 it, you could very well have data that, as a

23 clinical, you look at and it worries you\--\"gosh,

24 these products sure differed a lot in this

25 study\"\--but it is not conclusive that they are

200

1 inequivalent. So, yes, you could have that

2 situation very easily.

3 DR. KIBBE: Marvin?

4 DR. MEYER: Gary, did I understand you to

5 say that if in the initial study the generic

6 product came in, let\'s say, at 80-125, just hit the

7 upper and lower limit, and then the challenge study

8 came in at 79-125 the generic would have to redo

9 their study?

10 DR. BUEHLER: No. That is part of the

11 reason for this exercise, that is, we do face

12 situations like that where we will get very

13 marginal challenge studies submitted and where a

14 reasonable person could say, gee, you if threw

15 another six patients or subjects into that study

16 and you probably would have been 80 or 81. So,

17 what we are looking for here is to try to set up

18 some guidelines as to what will be acceptable as a

19 challenge to the bioequivalence.

20 DR. KIBBE: Let me ask Jurgen\'s question,

21 which is when the challenge comes in is there any

22 thought to how clinically significant it is what

23 the challenge study shows? I mean, is it

24 clinically significant relative to the use of the

25 drug itself?

201

1 DR. BUEHLER: The challenge study is

2 reviewed and we make an assessment as to whether

3 the challenge study, as I said, was conducted

4 properly and powered properly. If the condition is

5 that we believe that more subjects, you know, would

6 have thrown it over the line we normally make the

7 generic do another study to prove their

8 bioequivalence. Now, that is a value judgment with

9 respect to what to do. Again, that is one of the

10 reasons we are here right now. We would like to

11 have a little more certainty in making this

12 decision as to when a generic has to repeat their

13 study.

14 DR. KIBBE: Gordon, go ahead.

15 DR. AMIDON: Yes, I think we are, again,

16 treating the BE test just as a simple empirical

17 test, yes or no. I think there have to be other

18 underlying reasons for why there is now a large

19 difference in the performance of the dosage form in

20 vivo, things such as dissolution. I think one

21 should look at other data and have other facts or

22 information supplied by a company saying that it

23 has attempted the bioinequivalence study that they

24 come up with something that suggests that it is

25 bioinequivalent. There should be other facts that

202

1 support that conclusion, in particular dissolution

2 methodology. So, you should look for more

3 information.

4 DR. YU: That is correct. Of course, when

5 we receive such challenge studies we have to make

6 sure that the study is properly designed and

7 conducted and the conclusion is valid. Secondly,

8 we look at the quality of the sample used to

9 conduct the studies. From the cGMP perspective,

10 from the quality perspective we look at the

11 dissolution of the stability and potency, and so

12 on, all the quality standard samples. Certainly,

13 we also look at the process. As I said, in

14 bioinequivalence we want evidence to show

15 inequivalence and we certainly look at many, many

16 other factors. In other words, we want to say that

17 the decision we are making is a systematic decision

18 instead of being based on one parameter.

19 DR. KIBBE: Nozer?

20 DR. SINGPURWALLA: Yes, C, subscript t,

21 and C subscript infinity\--

22 MR. SCHUIRMANN: You mean AUC subscript\--

23 DR. SINGPURWALLA: Yes, is that the time

24 index?

25 MR. SCHUIRMANN: It is not an estimate of

203

1 the time.

2 DR. SINGPURWALLA: It is some index?

3 MR. SCHUIRMANN: When you do these studies

4 you give the products to subjects and then you

5 start taking blood samples from them at specified

6 sampling times, at however many hours, and one of

7 those has to be the last one. Maybe it is 24

8 hours. It would depend on the drug product. So,

9 you can calculate the area under the blood level

10 time curve up to that last blood sampling time for

11 that subject. You have the data for each sampling

12 time and the trapezoidal rule is used to calculate

13 the area. So, that is AUC sub t.

14 Now, there is a way of taking the last

15 several blood concentrations when you are in what

16 is called the terminal elimination phase, and

17 estimate the elimination rate, and to use that

18 estimated elimination rate to extrapolate that

19 calculated area to theoretical infinite time. That

20 is the AUC infinity.

21 DR. SINGPURWALLA: I got the message that

22 AUC infinity is when t goes to infinity.

23 MR. SCHUIRMANN: Yes.

24 DR. SINGPURWALLA: I am not sure that this

25 question is germane, but is there a danger or a

204

1 pleasure, depending on which side of the fence you

2 are, that you may make a certain decision for a

3 certain time t and your decision would be reversed

4 about your hypothesis had t been something else?

5 MR. SCHUIRMANN: That is really not a

6 question that I am qualified to address. I am sure

7 there could be aspects of the profile, the blood

8 concentration over time profile where the action is

9 in a certain time interval, and if that happened to

10 be the last time you sampled\--

11 DR. SINGPURWALLA: In other words, how

12 sensitive is your hypothesis?

13 MR. SCHUIRMANN: I would yield to the

14 pharmacokineticists in the room for that question.

15 DR. KIBBE: Les?

16 DR BENET: There is definitely that

17 possibility. There was a famous brochure that the

18 Upjohn Company\--so that is how long this is\--put

19 out comparing two different drugs and they showed

20 equivalence making that error of picking an early

21 time point so that they actually had very

22 different\--if they had gone to infinity they had

23 very different times. So, that is very critical

24 and usually what the agency will do or what anyone

25 will do, you want to know that the area under the

205

1 curve up to t is a very high percentage of your

2 total area under the curve infinity or you would

3 not qualify this as a reasonable study to make a

4 judgment on.

5 DR. SINGPURWALLA: I have a follow-up, a

6 word of caution, are you familiar with the filer

7 problem?

8 MR. SCHUIRMANN: Yes

9 DR. SINGPURWALLA: Do you think you would

10 be a victim of that particular problem here?

11 MR. SCHUIRMANN: There are in

12 bioequivalence assessments but usually not with

13 pharmacokinetic bioequivalence assessments. We

14 sometimes are not doing the analysis on the log

15 transformed endpoints but, instead, there are other

16 types of bioequivalence studies where we are

17 analyzing the untransformed endpoints and we do,

18 indeed, do two one-sided tests based on linear

19 inequalities like mu-T minus 1.25 times mu-R and

20 you will reject those two one-sided hypotheses if,

21 and only if the 90 percent filer\'s confidence

22 interval falls within the interval. So, we use

23 that method. Which aspect of the problem are you

24 referring to?

25 DR. SINGPURWALLA: Well, the filer\'s

206

1 problem is the following, that when you have two

2 normal distributions with unknown means and when

3 you take the ratio of their means, then it is

4 possible to get confidence limits which are from

5 minus infinity to plus infinity but with the

6 coverage probability less than 1.

7 MR. SCHUIRMANN: I am aware of that. If

8 your data is such that that would happen, then you

9 would not reject the two one-sided tests and you

10 would not reach a conclusion of equivalence.

11 DR. SINGPURWALLA: But then we would have

12 addressed the comment my colleague made that you

13 will have an inconclusive answer.

14 DR. SWADENER: My question really related

15 to rejecting the case that it is non-equivalent.

16 That doesn\'t mean that it is equivalent. Right?

17 Because there are some outliers; there are places

18 between the two.

19 MR. SCHUIRMANN: There are experimental

20 outcomes that are inconclusive. If you reject

21 equivalence, then you conclude inequivalence. If

22 you reject inequivalence, then you conclude

23 equivalence. But there are data sets for which you

24 would not reject either.

25 DR. SWADENER: But I thought you said the

207

1 rationale for trying to define inequivalence,

2 rejecting equivalence doesn\'t mean inequivalence.

3 MR. SCHUIRMANN: No, I said failing to

4 conclude equivalence doesn\'t necessarily mean

5 inequivalence. Perhaps that sounds like word games

6 to you but I assure you it isn\'t.

7 DR. SINGPURWALLA: I think you are facing

8 a statistician.

9 DR. SWADENER: No question about it,

10 right.

11 \[Laughter\]

12 DR. KIBBE: And a frequentist statistician

13 at that.

14 DR. SINGPURWALLA: It pains my heart!

15 MR. SCHUIRMANN: The take-home message of

16 my presentation was it is not reasonable to

17 conclude bioinequivalence if you do a

18 bioequivalence test and don\'t conclude

19 bioequivalence. You have to aim a test

20 specifically at seeing whether you can show

21 bioinequivalence.

22 DR. KIBBE: Thank you, Don. Ajaz wants to

23 say something and I guess, Lawrence, you want to

24 get back to the questions?

25 DR. YU: Yes.

208

1 DR. KIBBE: Good.

2 DR. HUSSAIN: Well, I think this

3 discussion has been focused primarily on the bio

4 topic but the principles, concepts and issues go

5 beyond that and how does this relate to that? Does

6 somebody have any thoughts on that?

7 DR. SINGPURWALLA: Actually, I do.

8 DR. KIBBE: I knew you would!

9 DR. SINGPURWALLA: Again, a problem like

10 this is a problem which should be cast in the

11 framework of decision making or, in other words, it

12 should be cast in the framework of a Bayesian

13 setup, and that is the way you address this kind of

14 a problem where you may have three decisions, three

15 actions\--equivalence, inequivalence or

16 inconclusive. That could be a decision and that

17 provision could be made. Of course, it could also

18 be made in the frequentist framework. But I think

19 this is another example of decision making and it

20 should be cast in the same framework.

21 DR. KIBBE: I think the problem we are

22 facing here is the difference that we have in a

23 court of law between preponderance of evidence and

24 beyond a reasonable doubt, and we accept drugs as

25 equivalent when we have the preponderance of

209

1 evidence. Do we now ask for something beyond a

2 reasonable doubt to reject what we have already

3 accepted, and I think that is interesting. Paul?

4 DR. FACKLER: I just wanted to ask a

5 question, recognizing that there are thousands of

6 generic products on the market and I understand

7 that there have been challenges to those, do you

8 have any idea how many of those have turned out

9 post-approval to be inequivalent to the innovator?

10 DR. KIBBE: He wants a success rate for

11 challenges.

12 DR. BUEHLER: All right. Well, I have to

13 think. I know we have had at least one that I can

14 remember where we had a challenge and when we

15 threatened to downgrade they removed the product

16 from the market. I know that because that was when

17 I was in the Office of Generic Drugs. I am not

18 sure how many more there have been but I do know

19 that there was at least one.

20 DR. YU: I think we just had one right

21 now. In fact, the study is under-powered so it has

22 come back\--

23 DR. BUEHLER: But that wasn\'t removed from

24 the market.

25 DR. YU: It was not removed.

210

1 DR. FACKLER: Could I ask a question then,

2 how important an issue is this?

3 DR. BUEHLER: I think the importance of it

4 depends on the amount of work it generates to the

5 Office of Generic Drugs with each specific

6 challenge that we get because I have the

7 understanding that the challenge studies are sort

8 of bioequivalence studies, sort of masquerading as

9 bioequivalence studies but they are really

10 channeled to show bioinequivalence or showing

11 failed bioequivalence. Therefore, we look at them

12 really with a fine-tooth comb and, as Lawrence

13 said, we look at all aspects of the drug product

14 that was used in the challenge study. We go out

15 and actually make site visits to inspect the CRO

16 that conducted the challenge study to make sure

17 that the study was conducted properly. So, it

18 really involves a significant amount of resource

19 allocation when we get one of these challenge

20 studies because we take them very seriously. If

21 someone challenges the bioequivalence of a product

22 that is currently on the market we, in the Office

23 of Generic Drugs, take that challenge very

24 seriously and we do put a lot of resources into

25 making sure that it is either valid or invalid.

211

1 Because of that, we would like to have a little bit

2 better framework under which to sort of, like,

3 unleash these dogs. You know, if we don\'t have to

4 turn the dogs out we really want to but right now

5 we are.

6 DR. KIBBE: Do you have a comment?

7 DR. DAVIT: Yes, I would like to add to

8 what Gary was just saying. I was directly involved

9 in a challenge several years ago and it was a

10 tremendous amount of work to sort out what was

11 going on. I was a team leader at the time. I

12 pretty much had my entire team working on it. We

13 had project managers working on it. We got the

14 clinical division involved; we had the

15 statisticians involved. We looked at the

16 dissolution. We looked at the RLD. We made many

17 visits to the clinical division to discuss what was

18 going on. We sent an inspection out to the site

19 where the challenge study was conducted. We had

20 meetings with the generic company. So, it was

21 very, very involved. And, the outcome of that

22 particular situation was positive but it took many,

23 many man hours of work from many different people

24 to sort things out.

25 DR. YU: In other words, the effort we put

212

1 in to clarify some of the concept is well worth it.

2 DR. KIBBE: Do you think we should

3 approach it like they do with a challenge flag in a

4 football game? That if they uphold the challenge

5 they still keep their time outs? And, if the

6 challenge hasn\'t been upheld they lose their time

7 outs? So, if a company wants to challenge they

8 have to put a bond up to pay for the expense of FDA

9 adjudicating the challenge?

10 DR. BUEHLER: That would be okay!

11 DR. YU: Basically, in many cases if a

12 study comes back it fails to demonstrate

13 bioequivalence instead of bioinequivalence study.

14 As Don has very clearly pointed out, if you test

15 for bioequivalence you simply fail to show

16 bioinequivalence. So with a guidance, if you do

17 want to show that it is bioinequivalence, here you

18 are, this is how to conduct a study so there is no

19 confusion or ambiguity. It is a very clear

20 definition, clear evidence for agency to take

21 action so we can spend all the time to approve

22 generic applications. We received over 500 this

23 year.

24 DR. KIBBE: Marc?

25 DR. SWADENER: Have you thought about

213

1 whether if, in fact, you clearly defined

2 inequivalence it is going to increase your

3 challenges? Will it, in fact, make your life

4 easier?

5 DR. YU: I think my life would be a lot

6 easier. There is no doubt about it; I am very

7 confident.

8 DR. SWADENER: It may double the number of

9 challenges, or triple.

10 DR. YU: That is certainly a hypothetical

11 question and I am very confident.

12 DR. KIBBE: Jurgen?

13 DR. VENITZ: I am trying to get back to

14 the questions that you want us to answer, Lawrence.

15 I would say you have demonstrated to me that it is

16 different whether you prove equivalence or you

17 prove inequivalence. In other words, they are two

18 different objectives, meaning they require two

19 different studies. So, failing to show

20 bioequivalence is not the same as demonstrating

21 bioinequivalence, which I think is what your first

22 question is all about.

23 DR. YU: Thank you very much\--

24 DR. VENITZ: Well, that is my personal

25 answer; I can\'t speak for the committee. The

214

1 second one, as far as the challenge study is

2 concerned, in order to demonstrate bioinequivalence

3 which, as I said, is not the same as failing to

4 show bioequivalence, you have to have an adequate

5 and well-controlled study to do that, which

6 includes all the characteristics that you are

7 familiar with. From my perspective, in addition to

8 that you have to have preexisting information

9 suggesting that the drug is bioequivalent because

10 that is what is being challenged in the first

11 place. So, in my mind, the burden of proof is upon

12 the challenger t have an adequate and

13 well-controlled study demonstrating beyond any

14 reasonable doubt, to use Dr. Kibbe\'s terminology,

15 that they are truly bioinequivalent. So, among the

16 strategies that you are proposing I would use the

17 most conservative one, which I think is number two,

18 meaning that all three endpoints have to

19 demonstrate bioinequivalence. Only underlying

20 those circumstances would you move to the next step

21 which would be removing, I guess, the generic from

22 the market.

23 DR. YU: And some others too.

24 DR. VENITZ: I am sorry?

25 DR. YU: Assuming the quality\--

215

1 DR. VENITZ: Right, just speaking about

2 the testing procedures. I am sure there are other

3 things that you look at.

4 DR. YU: Yes.

5 DR. VENITZ: So, I would say number one is

6 the difference between showing bioequivalence and

7 showing bioinequivalence. Number two, a study to

8 demonstrate bioinequivalence has to be adequately

9 well-controlled, or the equivalent thereof. Number

10 three, the burden of proof is on the challenge

11 sponsor to demonstrate that, and I would suggest

12 strategy two as the most conservative one.

13 DR. YU: Thank you.

14 DR. KIBBE: Anybody else? Marvin?

15 DR. MEYER: I agree with part one, that

16 this is needed. I think, just from a conceptual

17 point of view, if approval means everything has to

18 be between 80 and 125, then for inequivalence

19 everything needs to be less than 80 percent or

20 above, as you have drawn it on your little diagram.

21 I don\'t know that it is fair to require

22 all three to fail. I think any one should be

23 enough because, after all, it is not fair to expect

24 AUC to always fail along with Cmax. Sometimes AUC

25 is fairly stable and Cmax isn\'t. So, I would say

216

1 number one rather than all three.

2 DR. VENITZ: Can I just give you the

3 reason why I disagree with you on that?

4 DR. KIBBE: Please, go ahead.

5 DR. VENITZ: Because you already have a

6 study that demonstrated bioequivalence in the first

7 place. Otherwise, I would be in agreement with

8 you. But it is not like the study stands on its

9 own. You are basically trying to meta-analyze two

10 studies.

11 DR. MEYER: But I would argue that you are

12 just setting it up for the inequivalence people to

13 fail by requiring all three.

14 DR. VENITZ: But I think there is another

15 study demonstrating that there is bioequivalence.

16 DR. KIBBE: And we are waiting for Les to

17 clarify everything for us\--

18 \[Laughter\]

19 \--but I think the first point is true,

20 that we need to have the study design to show

21 bioinequivalence, not just that you do a

22 bioequivalency study and if it fails that doesn\'t

23 work. That is clear. But the argument over

24 whether you want all three items or not, I think we

25 need to fall back on what is the clinical relevance

217

1 of the thing failing the Cmax component of the

2 bioinequivalency study to a drug that has a large

3 therapeutic index. I think you probably need to

4 put more emphasis in terms of area under the curve

5 if you are going to pick one instead of three. So,

6 I would be inclined to go with my colleague Jurgen

7 and say let me see all three out of whack and then

8 I am ready to get the generic company to do

9 additional studies to balance out what we are

10 doing. Les?

11 DR. BENET: I would like to make a

12 comment\--

13 DR. KIBBE: Good.

14 DR. BENET: \--and it is something I have

15 worried about for a long time, and that is the

16 stability of the innovator\'s product from study to

17 study. It sort of gets to Ajaz\' question. I have

18 always been concerned about the innovator or

19 generic, at the end of the shelf life, is the

20 product equivalent to the product when it was first

21 approved? So, I think in this criteria there needs

22 to be something that is an evaluation of the data

23 of the innovator product, that it is, in fact,

24 representative of what the agency knows. Because I

25 know that there are situations where you could have

218

1 end of the shelf life drugs that would fail versus

2 when they are first manufactured. So, I could see

3 how this could easily be manipulated, if I was a

4 manipulative person which I am not, right\--

5 \[Laughter\]

6 \--to make a failed study. I don\'t think

7 it would be that difficult with some drugs. So, I

8 think there needs to be an additional criteria,

9 again no matter which of these three you pick, that

10 the agency has confidence that the innovator data

11 is, in fact, representative in this study. Maybe

12 that is already true, Gary. I don\'t know.

13 DR. BUEHLER: As part of the review of the

14 study I believe we do look at that parameter.

15 DR. KIBBE: Anybody else?

16 DR. COONEY: Just one point to come back

17 to, in trying to resolve the distinction between

18 one, two or three PK parameters to make the

19 decision on, the issue of clinical relevance that

20 several of you have spoken to strikes me as the

21 most important part of that part of the question.

22 So, my question is it doesn\'t matter what decision

23 is made, whether it is one, two or three of these

24 parameters, how do you factor into the analysis

25 that you are doing that you have chosen the

219

1 parameters that, in fact, are clinically relevant

2 for each individual case?

3 DR. KIBBE: Do you want to give an answer?

4 DR. YU: Instead of giving an answer, I

5 guess we have to make some kind of recommendation

6 that, indeed, when we look at those challenge

7 studies the clinical division is heavily involved.

8 We are working as a team in resolving some of the

9 challenge studies, instead of pharmacologists or

10 chemists acting alone.

11 DR. COONEY: Then the question becomes how

12 do you factor that working into the recommendation

13 that is being made so that it isn\'t just an

14 arbitrary one, two or three or the parameters but

15 that a judgment call is clearly defined in the

16 decision process?

17 DR. YU: That is, indeed, a challenge. We

18 will certainly look at case by case but we do want

19 some kind of clarification so that people know what

20 is going on and what to do.

21 DR. KIBBE: Marvin?

22 DR. MEYER: Two comments. One, I know a

23 body in the street is not a good measure but if the

24 generic product has been out there and has sold

25 five million units, it is probably not that bad if

220

1 your adverse reaction reports aren\'t alarming.

2 Secondly, I think the approach of having

3 the inequivalence confidence limit be totally to

4 the left of the right of 80 or 125 is a fairly

5 rigorous kind of assessment because your point

6 estimate then has to be well to the left or right

7 of the upper limit, in other words, quite a ways

8 away. So, I think one is probably all you need,

9 Cmax or AUC.

10 DR. KIBBE: And, if you are going to go

11 with one I would go with AUC. Gordon?

12 DR. AMIDON: I can readily see how a

13 contention of bioinequivalence could generate an

14 awful lot of work for the agency, and it could be

15 done almost frivolously. Therefore, I would be in

16 favor of requiring that it be all three parameters

17 to be bioinequivalent, plus other supporting data

18 like dissolution data to support that there is

19 something really to go after here and that would

20 merit the action and activity, investigation by the

21 agency. Yes, I am all in favor of having a bond

22 posted. If you don\'t pass, then you lose your

23 money. It is not gambling, is it?

24 \[Laughter\]

25 DR. KIBBE: That is not legal. But this

221

1 is, so it couldn\'t be gambling.

2 DR. BENET: I support that, Art.

3 DR. KIBBE: Les is going to comment. Go

4 ahead.

5 DR. BENET: Thank you. I want to support

6 Marvin\'s position because this is, as is the

7 difficulty of the correction now\--I mean we have

8 very good criteria for approving bioequivalence.

9 The way you have defined bioinequivalence is very

10 difficult criteria that has to be outside the

11 boundary and the confidence interval has to be

12 outside the boundary. For sure, that is going to

13 be so hard to do, and if there is one, then it is

14 real and I think that if one of those three

15 parameters is outside I would go for the one. I

16 think Marvin\'s argument is a very good argument.

17 DR. MEYER: You agreed with me before.

18 DR. KIBBE: I want somebody to make note

19 of the historical events that Marvin and Les have

20 been agreeing everywhere.

21 \[Laughter\]

22 If you and I are going to have to back

23 off, then I suggest you look seriously at the area

24 under the curve, more seriously than Cmax. I

25 think, if anything that might actually meet this

222

1 criteria where the other two wouldn\'t, it would be

2 the Cmax. It is the most open to pushing one way

3 or the other.

4 DR. AMIDON: I think I am still a little

5 confused, Les and Marvin. You want to do one

6 parameter. You want to do a test and if any one

7 parameter falls\--what is the correct statistical

8 language?\--doesn\'t show bioequivalence or shows

9 bioinequivalence as opposed to all three must

10 showit\--it depends on how you word it, all three

11 must show bioinequivalence, that would be tougher,

12 right? That is what I am saying and it is what you

13 are saying. You are saying, Les and Marvin, that

14 is too tough. I am not sure. It makes the agency

15 look like they are trying to sweep everything

16 possible under the rug by having such criteria that

17 it will almost never happen.

18 DR. KIBBE: But the bioequivalence

19 criteria is that way. It requires, you know, both

20 Cmax and AUC to be\--

21 DR. MEYER: But if one fails, it fails;

22 not all three. I mean, if Cmax fails it doesn\'t

23 matter what the AUC was, you failed.

24 DR. KIBBE: We can go around and around on

25 this. One of the nice things about an advisory

223

1 committee is that we give advice and the agency can

2 just ignore us if they want, and they can look at

3 everybody\'s advice and when the committee is split

4 they can take the input of each member of the

5 committee and weigh one against the other and do a

6 Bayesian analysis of it and pick the right

7 decision. All I am saying is that if you are going

8 to accept that the study has shown inequivalence

9 because it has shown inequivalence in one of the

10 three parameters, then I would be careful to make

11 sure it was the area under the curve parameter and

12 not a Cmax. I would have less confidence in that

13 personally and I am sure that is biased.

14 DR. SINGPURWALLA: Mr. Chairman, the point

15 you raise has to have one thing in mind. Are these

16 three criteria interdependent? If they are, it

17 makes a big difference. If they are not, it makes

18 another difference. I suspect they are

19 interdependent and that is what you should keep in

20 mind. So, rejecting one is as good as rejecting

21 all if they are interdependent. If they are not,

22 then the kind of things you mentioned do become

23 serious, or the kind of things that Marvin

24 mentioned do become serious. I am asking the

25 question are they interdependent in your judgment.

224

1 DR. VENITZ: I think they are

2 interdependent and I think the differences between

3 the two strategies are marginal. In other words,

4 if you reject AUC infinity you are likely to reject

5 AUC-t as well. There is a little less

6 interdependence between the Cmax and the area

7 estimates. So, you are really splitting the

8 difference that is very small.

9 DR. SINGPURWALLA: Did I agree with you?

10 DR. KIBBE: I don\'t know. I need a

11 decision tree to find out whether we agree or not.

12 Has anybody got anything else? Lawrence, do you

13 need anything else from us or have we given you

14 enough information to help you go forward?

15 DR. YU: I think so.

16 DR. KIBBE: Then I propose that we take

17 our break. We have two more topics to cover after

18 break. We are breaking right on schedule. We will

19 be back to do topical bioequivalence at a few

20 minutes before 3:00.

21 \[Brier recess\]

22 DR. KIBBE: We have a cadre of taxis

23 waiting at 4:30. We want to be finished. We want

24 to have time for topical bioequivalence, such a

25 wonderful topic and Lawrence again is going to

225

1 start off, only he has no slides.

2 Update\--Topical Bioequivalence

3 DR. YU: The October, 2003 advisory

4 committee meetings report and, in fact, manuscript

5 have reviews and systematic reviews of the

6 challenges in developing pharmaceutical or

7 bioequivalence criteria for topical products. I

8 think we sent it to you one month ago and this is

9 the work that was developed in collaboration with

10 Dr. Jonathan Wilkin. It also further developed the

11 Q3 concept.

12 So, today we want to share with you and

13 seek your feedback. For example, are we on the

14 right track? We will publish this manuscript very

15 soon to initiate a dialogue and then bring back to

16 you the formal proposal. We will have Dr.

17 Lionberger give you an overview of this paper.

18 Rob?

19 Establishing Bioequivalence of Topical

20 Dermatological Products

21 DR. LIONBERGER: Today I am going to give

22 you an update on our current efforts to develop

23 methods to demonstrate bioequivalence of topical

24 dermatological products.

25 \[Slide\]

226

1 The current state of topical

2 bioequivalence is that for almost all products, for

3 almost all locally acting dermatological products

4 clinical trials are necessary to demonstrate

5 bioequivalence. So, I am just going to give you

6 some quick examples of the kind of clinical trials

7 that are actually needed for this demonstration.

8 These are just recent submissions to the Office of

9 Generic Drugs.

10 \[Slide\]

11 As you can see here, the number of

12 subjects used in these comparisons\--these are all

13 for topical antifungals, there were three-arm test

14 references placebo studies in patients. They used

15 700, 400 and 400 subjects. Here is just the

16 percent cure rate for the test and the reference

17 product. The reference product is the RLD. Then,

18 the 90 percent confidence interval on the

19 difference between the test and reference cure

20 rate. The goal for this is to be within minus 20

21 to plus 20.

22 So, you can see that even with these large

23 numbers of subjects these studies still came close

24 to failure. So, if you retrospectively looked at

25 the power of these studies, you would find that

227

1 these studies probably had at least a 50 percent

2 chance to fail even with that large number of

3 subjects.

4 \[Slide\]

5 So, there are consequences to having this

6 cost to demonstrating bioequivalence. It is a

7 barrier to product improvement and also the access

8 of generic products to the market. Innovator

9 products need to use bioequivalence studies after a

10 formulation change. These clinical endpoints have

11 high variability and so, if you think of what the

12 purpose of bioequivalence is, it is to demonstrate

13 formulation similarity and these are just clinical

14 endpoint and there are just not good methods to do

15 that. Also, these lead to possibly unnecessary

16 human testing in these studies that have hundreds

17 of patients to say unapproved products.

18 \[Slide\]

19 So, based on this, some of the goals that

20 we have are to identify when clinical studies are

21 not necessary to demonstrate bioequivalence of

22 topical products and to provide some alternative

23 methods that will still assure product quality.

24 \[Slide\]

25 In this talk I am going to outline and

228

1 give you an update on a strategy to reach these

2 goals. Our bioequivalence strategy starts with a

3 mechanistic understanding of the topical drug

4 absorption process. Then we will identify the key

5 parameters that affect bioavailability. You heard

6 a similar approach in Prof. Amidon\'s talk this

7 morning where he talked about the mechanistic basis

8 for oral absorption and how that led to a

9 biopharmaceutical classification system, and the

10 possibility for bio waivers based on an

11 understanding of the mechanistic processes

12 involved.

13 So, once these key parameters are

14 identified, then we can choose the in vitro and in

15 vivo tests that best measure and detect differences

16 in these key parameters. As part of the selection,

17 we are going to look at classification of

18 formulation similarity. If two formulations have

19 exactly the same components, exactly the same

20 compositions we might focus a different set of

21 tests than if they had different excipients and

22 vastly different formulations.

23 This talk is just giving you the first

24 step to presenting a decision tree that will allow

25 us to decide when we might not need clinical

229

1 studies to demonstrate bioequivalence. This

2 decision tree will be specific for different sites

3 of action. So first we will look today primarily

4 at products that are targeting the very top layer

5 of the skin, the stratum corneum. Finally, I will

6 talk about some of the external research projects

7 that we have under way to support development of

8 this decision tree.

9 \[Slide\]

10 So, the first thing I am going to talk

11 about is just an overview of the topical drug

12 absorption process. Here I have a schematic of the

13 skin showing different layers. If you think about

14 what happens when you apply a topical product,

15 first the vehicle is applied to the skin and then

16 the drug must dissolve in the vehicle, if it is not

17 already dissolved, and fused to the surface of the

18 skin.

19 So, the top layer of the skin is the

20 stratum corneum and this is a very dense layer,

21 about ten microns thick, and it is the primary

22 barrier to keep things outside of the body. There

23 are two paths across the stratum corneum, either

24 the drug can partition from the vehicle which is

25 placed on the surface of the skin into the stratum

230

1 corneum and diffused through the stratum corneum,

2 or there is the possibility that drugs applied to

3 the surface of the skin can travel through the hair

4 follicles and bypass the stratum corneum.

5 If we look at sort of the various areas

6 available for transport by these two mechanisms and

7 we assume that there is no bias in the drug

8 choosing one path over the other, the flux through

9 the stratum corneum will be about 30 times more

10 than the transport through the hair follicles if

11 there is no bias between the two pathways, if the

12 drug is equally likely to go into one path or the

13 other.

14 Once the drug gets across the stratum

15 corneum, then the tissue behind that is much less

16 dense. The drugs can fuse much faster; this is

17 much less of a barrier to the drug finally reaching

18 the systemic circulation.

19 \[Slide\]

20 So, as we think about this process we have

21 to remember that we are looking at bioequivalence

22 and the goal of bioequivalence is to detect

23 differences in the formulations. It is not really

24 about how complicated this absorption process is

25 and how well we can understand that. It is really

231

1 how well we can detect differences in the

2 formulations that have already been demonstrated to

3 contain drugs that work in clinical trials.

4 \[Slide\]

5 Again, as Lawrence has said and we have

6 heard many times today, bioequivalence is defined

7 as no significant difference in the rate and extent

8 of absorption at the site of action. So, if we are

9 looking at products where the site of action is

10 this top layer of the skin, the two sort of rates

11 that can possibly important for determining this

12 are, first the rate at which the drug might leave

13 the formulation and, second, the rate at which the

14 drug might cross this barrier of the stratum

15 corneum. So, if we understand those two rates,

16 then we can understand what rate is actually

17 controlling the rate at which the drug actually

18 reaches the site of action. That is the thing that

19 we are after in bioequivalence, to demonstrate that

20 the two formulations will perform the same.

21 \[Slide\]

22 Usually, in almost all cases, the stratum

23 corneum is the limiting resistance and we

24 characterize this limiting resistance by

25 permeability. The permeability just includes

232

1 contributes from the diffusion of the drug through

2 the stratum corneum, the thickness of this layer

3 and the partition between the vehicle and the

4 stratum corneum. So, we can write an expression.

5 The J is the total flux. That is the sort of rate

6 at which drug is reaching the body and that is what

7 we are interested in when we are making our

8 comparison of bioequivalence. This is related to

9 the permeability times the area that is available

10 times the concentration of the drug that is present

11 in the vehicle. We can sort of do a little bit of

12 manipulation with this partition coefficient here,

13 where S is just the solubility of the drug either

14 in the membrane stratum corneum or the vehicle.

15 So, this is sort of split up into

16 contributions that are just properties of the skin

17 and just properties of the formulation. From this,

18 you can see it is the thermodynamic activity, the

19 ratio of the concentration to the solubility in the

20 vehicle that is the driving force for what the flux

21 is. So, if the membranes were the same between two

22 products and presumably if they were applied to the

23 same person it is the same skin and you would think

24 that these two products would be the same, and it

25 is just essentially this activity in the

233

1 formulation that would determine how fast the drug

2 arrives at the site of action.

3 But the most sort of important

4 complication here and the thing that we are sort of

5 worried about when we are looking at what methods

6 are best to develop, bioequivalence methods, is

7 that properties of the formulation can alter the

8 barrier properties of the skin. So, if by applying

9 the formulation, either the formulation itself or

10 the excipients in it, if they can alter the

11 properties of the skin they will change this flux

12 independent of what is happening in the

13 formulation. There is a whole technology and

14 design in topical formulations to, say, improve

15 bioavailability where there are lots of adjuvants

16 that are known to reduce the barrier and increase

17 the flux. This is not just hypothetical

18 possibility but a known situation that can happen.

19 \[Slide\]

20 Once we recognize tat this is sort of the

21 key mechanism. Then we can sort of identify what

22 are possible causes of bioequivalence for products

23 that have the same drug content. So at different

24 stages in the absorption process we can identify

25 things that possibly can go on.

234

1 First of all, at the application stage if

2 the two products spread differently on the

3 skin\--say, the viscosity or the rheology is

4 different, you could have different outcomes in

5 terms of how they contact the skin, the amount of

6 area each product has if they are applied

7 similarly. If we look into the formulation we can

8 imagine a case where, well, what if a drug doesn\'t

9 leave the formulation at all? Say, the drug is

10 present in the formulation as suspended particles

11 and these particles just don\'t dissolve, the drug

12 never leaves the formulation so, even though you

13 have the same amount of drug in the formulation but

14 it doesn\'t get out of the formulation, the two

15 products might not be equivalent.

16 Again, the thermodynamic activity in the

17 vehicle might be different. In one case the drug

18 might be dissolved into a cream and partitioned

19 between the oil and water phases and you have one

20 concentration of drug, one free concentration of

21 drug in the vehicle. If you had a suspension where

22 the particles were dissolving the dissolution rate

23 might control what the free drug concentration is.

24 And, this could happen if you had the same overall

25 drug content.

235

1 Finally, when you reach stratum corneum,

2 again as I said, formulations might have different

3 effects on the stratum corneum or you might have

4 one formulation preferring the follicular pathway.

5 This is particularly known to happen when you have

6 particles of certain sizes that might bias toward

7 this particular transport pathway. So, that is

8 primarily a concern when you have the drug present

9 in the formulation as a suspension.

10 So, if you we think about the mechanism

11 and possible reasons why products might not be

12 equivalent, that leads us to think about how can,

13 or is it possible that in vivo or in vitro tests of

14 the formulation can measure these differences

15 adequately enough to replace clinical trials.

16 \[Slide\]

17 So, I just quickly want to point out two

18 sort of most important in vitro tests that are

19 relevant to these types of products. The first is

20 diffusion cell. Just a quick description of what

21 that it is, in a diffusion cell it measures the

22 rate at which the drug leaves the formulation and

23 crosses an artificial in vitro membrane into

24 receptor fluids. So, in most implementations of

25 diffusion cells the membrane in the diffusion cell

236

1 is very permeable to the drug so the membrane is

2 not the limiting resistance. In this case, this

3 really measures how fast the drug is actually

4 released from the formulation or diffused from the

5 formulation, and also the rate of release and

6 diffusion is also proportional to the fraction of

7 the free drug. So, it gives you a sense of whether

8 or not the drug is actually bound to the

9 formulation or is free to transport into the skin.

10 Because of this fact that these devices

11 are usually used with highly permeable membranes

12 they are not very predictive of bioavailability in

13 vivo because in vivo bioavailability is usually

14 controlled by the resistance due to the stratum

15 corneum itself. But these tests have been shown to

16 be very sensitive to formulation differences.

17 There is also an important safety role for

18 this test. If you imagine applying a topical

19 product to damaged skin where the barrier function

20 of the stratum corneum has been breached for some

21 reason, perhaps by disease, then the drug release

22 to the patient is going to be determined by how

23 fast it is released in the formulation, which is

24 exactly what is measured in this type of test.

25 The other key in vitro test is a measure

237

1 of the rheology or how the formulation flows. This

2 would determine how vehicle spreads on the skin.

3 This type of characterization is also important to

4 classifying the proper dosage form for the

5 formulation. At the last advisory committee

6 meeting you heard about a decision tree to classify

7 different topical semi-solid dosage forms, and part

8 of that decision tree involved evaluating rheology

9 or how easy it was to make a formulation flow. So,

10 that is part of the testing that is already

11 involved in these products.

12 \[Slide\]

13 If you have a drug present in a suspension

14 form you have additional tests that might be very

15 relevant to apply. It might be direct measurements

16 of particle size in the formulation or measurements

17 of the dissolution rate in the vehicle as well.

18 \[Slide\]

19 There are also in vivo tests that can be

20 used to characterize topical formulations. The two

21 most important ones in this case are a skin

22 stripping method where you apply the formulation to

23 the skin, after a certain amount of time remove it,

24 then remove the layers of skin and assay them for

25 the actual drug content in the skin layers, or

238

1 microdialysis techniques where you insert a

2 capillary under the skin and you measure the

3 concentration that passes through the skin into the

4 lower layers of the dermis.

5 There have been experimental reports in

6 the literature on how they are used. But in this

7 context, please remember that the important role of

8 in vivo tests is to quantify the effect of the

9 formulation on the skin itself. If we didn\'t

10 believe that there is any possibility that the

11 formulation would change the barrier properties of

12 the skin we would be much more confident that just

13 assays of the in vitro performance would be

14 sufficient to determine whether or not two products

15 were bioequivalent. But since we have reason to

16 believe that formulations can affect the skin

17 properties, then we would like to at least have our

18 battery of tests in some way to measure this

19 effect. So, the role of these in vivo tests is

20 sort of very specific.

21 They tell you a lot more information than

22 this. They tell you about the amount of

23 experience, concentration, presence of different

24 aspects of the skin as well. We are specifically

25 here looking formulation effects since we are

239

1 looking to determine bioequivalence.

2 \[Slide\]

3 Now that we have sort of identified the

4 whole list of tests, the question is how do you

5 decide which tests should be relevant to which

6 types of products. So, again, here we are going to

7 be talking specifically about using formulation

8 similarity as part of that classification. So,

9 here we define Q1 similarity as products that have

10 the same components. Q2 similar products have the

11 same components but also present at exactly the

12 same amounts. So, Q3 means we have the same

13 component and the same amount, but they also have

14 the same arrangement of matter or microstructure of

15 the material so that they are sort of identical not

16 just in composition but also in the arrangement of

17 the material. So, based on classification of the

18 formulation difference between test and reference,

19 we want to choose the appropriate in vivo or in

20 vitro test.

21 So, in all the following discussions,

22 since we are talking about bioequivalence we are

23 really talking in the beginning, before we even

24 talk about bioequivalence, about products that are

25 pharmaceutically equivalent and that means they

240

1 have the same active ingredient in the same dosage

2 form so we are comparing a cream versus a cream,

3 not a cream versus an ointment or versus a

4 solution, at the same strength, the same dosage

5 form of the active ingredient and also targeting

6 the stratum corneum. So, again, all those things

7 are sort of prerequisites to determining if the

8 products are bioequivalent.

9 \[Slide\]

10 So, if we start at the sort of highest

11 degree of similarity, if we know the products are

12 Q3 similar and have the same composition, the same

13 structure, you might regard them as identical and,

14 by definition, bioequivalent.

15 One example in sort of a regulatory scheme

16 where this comes up is for topical solutions. If

17 it is a solution it is in thermodynamic

18 equilibrium. If you know that it is Q1 and Q2, has

19 the same composition, then because it is in

20 thermodynamic equilibrium you know it has the same

21 arrangement of matter as well. So, we often give

22 bio waivers for products that are true solutions.

23 Unfortunately, for formulations that are

24 more complex than simple solutions it is harder to

25 directly tell that they are exactly identical in

241

1 their formulation, and possibly manufacturing

2 differences might result in products that have the

3 same composition having different arrangements of

4 matter. A simple example of that might be a case

5 where you have the same composition but in one

6 formulation your particle size is different from

7 the other one. So, that is something that is a

8 non-equilibrium state and usually comes from

9 differences in the manufacturing process of the raw

10 materials. So, those are sort of the origins of

11 cases where products might have the same

12 composition but have differences in their Q3

13 identity.

14 \[Slide\]

15 Now if we step down a little bit and look

16 at products where we just know that they are Q1 and

17 Q2 identical, we want to sort of identify what kind

18 of differences they could possibly have. So, here

19 it is sort of thinking if you deliberately took

20 products with the same composition and you tried to

21 manufacture them in a way where you actually get

22 differences in product formulation, what kind of

23 things would you have to do?

24 So, one of those is that rheology might be

25 different. The flow maybe might be different. If

242

1 you take a cream or some sort of emulsion and you

2 changed the particle size of the droplets you might

3 actually change dramatically how the material

4 flows. It might adhere to the skin differently and

5 you would end up with different performance even

6 though the products have exactly the same

7 composition. By having some non-equilibrium

8 formulation in manufacturing, you might be able to

9 change the solubility of the free drug by

10 increasing the sort of surface area of, say, an oil

11 phase. You know that in these products you have

12 the same excipients. Presumably they should have

13 mostly the same effect on changing the barrier

14 products of the skin. But you might have a case

15 where in one formulation the excipients might be

16 released at a different rate and if you have

17 suspensions, as mentioned before in the particle

18 size example.

19 If we think about these things, these are

20 all sort of manufacturing differences and the

21 question we want to ask is are the in vitro tests

22 that we have able to detect these types of

23 manufacturing differences? So, again, the rheology

24 we can measure directly. In vitro release is a

25 very sensitive measure of are things diffusing

243

1 through the formulation at the same rate; will

2 there be any differences in how sort of excipients

3 or drug reach the skin itself from the formulation.

4 Those two can be directly measured.

5 So, the question that sort of hinges on

6 this is for products where you know that they are

7 pharmaceutically equivalent, you know they have

8 exactly the same composition, in this case are in

9 vitro tests sufficient to ensure bioequivalence?

10 Again, all of these differences, all these possible

11 differences are really due to manufacturing

12 processes. As I said before, in vitro tests are

13 probably the most sensitive and best evaluation

14 methods for detecting manufacturing differences

15 rather than relying on clinical trials, which are

16 very insensitive to those types of differences.

17 \[Slide\]

18 If we sort of step down the level one more

19 time and we look at products that are just Q1

20 identical, they just have the same components but

21 maybe in different amounts, in this case we might

22 be more concerned that the different amounts of,

23 say, excipients in the formulation might have

24 different effects on the skin barrier. They might

25 change the solubility of the drug in the

244

1 formulation. So, in these cases we might be more

2 likely to say that in this category you might want

3 to do some sort of in vitro test to ensure that the

4 change in the formulation does not have a

5 significant effect on the barrier properties.

6 \[Slide\]

7 Finally, if you go down to products that

8 are Q1 different, which means they might have a

9 different excipient between test and reference

10 products, again similar discussion to the previous

11 tests for the in vitro tests, but here it seems

12 that you would always want to do some sort of in

13 vitro test to make sure that the new excipients are

14 not having a different effect on the skin barrier

15 process.

16 \[Slide\]

17 Just summarizing sort of a little bit of

18 our current thinking, we go to the beginning of the

19 process of developing this type of decision tree

20 and looking at classifications based on formulation

21 similarity and the level of in vitro and in vivo

22 testing that you might want to do in those

23 different categories.

24 \[Slide\]

25 So, as we were sort of developing this, we

245

1 sort of identified key problems that we wanted to

2 look at. So, we have sort of two ongoing external

3 research projects, one which with Colorado School

4 of Mines where we are looking at the in vivo skin

5 stripping method, specifically looking to reduce

6 variability and also accuracy of the method to

7 measure both the diffusion coefficient and the

8 partition into the formulation, so measuring

9 effects of the formulation on the stratum corneum

10 and its partition in it. The key aspect there is

11 as you are doing the skin stripping, measuring the

12 thickness of skin removed via transepidermal water

13 loss.

14 We also have another project going on.

15 So, we have emphasized sort of in vitro

16 characterization and its ability to detect

17 manufacturing differences. We have a project with

18 the University of Kentucky where they are

19 manufacturing different formulations that are Q1

20 and Q2 identical, so exactly the same composition

21 but using different manufacturing processes,

22 primarily for cream formulations so oil and water

23 emulsions, and then looking at these known

24 differences and seeing how much difference can we

25 manufacture looking at the ability of the

246

1 rheological and in vitro release tests to detect

2 these manufacturing differences.

3 With that, I would like to thank you for

4 your attention and answer any questions that you

5 might have.

6 DR. KIBBE: Anybody have any questions?

7 DR. FACKLER: I have one.

8 DR. KIBBE: Good.

9 DR. FACKLER: Looking at the decision tree

10 and then at the examples that you gave at the very

11 beginning, the three examples, to me, showed

12 products that were similarly efficacious and I am

13 wondering if in your decision tree you are

14 suggesting that\--I don\'t know if those products are

15 Q1 and Q2 or Q3\--but being that they are similarly

16 efficacious, is it important whether or not the in

17 vitro tests for those products pass?

18 DR. LIONBERGER: Well, I think we are

19 trying to provide an alternative framework so the

20 idea is that, certainly, you can have products that

21 will give similar efficacy and they won\'t match at

22 all the in vitro tests. It is certainly possible

23 to come up with products that have different

24 viscosities, different in vitro release rates,

25 especially since that is not a limiting step, and

247

1 still be bioequivalent in a clinical study. So, we

2 are trying to provide sort of an alternative

3 pathway. It is not that sort of this decision tree

4 will determine bioequivalence; it is sort of an

5 alternative pathway to doing a clinical study. So,

6 it is basically up to the sponsor to decide do we

7 want to try to characterize our product very well

8 in vitro or just do some sort of clinical study,

9 and they have to balance the costs to those two

10 different pathways.

11 DR. FACKLER: The only reason I ask is

12 thinking back on the nasal products, there is a

13 requirement for bioequivalence that they pass both

14 the in vitro studies and the clinical study. So, I

15 am wondering if that is the same direction FDA is

16 going in for the topical products.

17 DR. HUSSAIN: I think right now this is

18 simply our current thinking of moving away from ten

19 years on DPT and so forth, and starting fresh.

20 Again, going to a mechanistic basis, here is

21 another highly variable situation and I think the

22 mechanistic basis decision tree up front as an

23 approach to providing all possible alternatives is

24 the direction. But at the same time, I think we

25 need to keep in mind that in many of these cases

248

1 some of these attributes are critical variables and

2 they will need to be controlled during

3 manufacturing lot-to-lot anyway.

4 DR. KIBBE: Judy?

5 DR. BOEHLERT: Have you also considered in

6 these studies looking at how creams or ointments,

7 or whatever, age? Because there can be differences

8 that develop that are formulation dependent or not.

9 For example, it comes out a solution; you could get

10 crystal growth if it is not in solution to begin

11 with. So, what seems to be equivalent to start off

12 with may not be as the product ages.

13 DR. LIONBERGER: That would be part of

14 sort of the chemistry manufacturing controls to

15 ensure the stability of the product over its shelf

16 life. Is that what you are talking about?

17 DR. BOEHLERT: Exactly, that is what I am

18 talking about. Over the shelf life of a lot of

19 creams you will get crystal growth and the efficacy

20 of that cream will change because the crystals

21 start to grow and they don\'t have the same

22 transport property that they did.

23 DR. LIONBERGER: You would want to have in

24 vitro tests for stability to evaluate those

25 differences, if they occurred.

249

1 DR. KIBBE: Gordon?

2 DR. AMIDON: Yes, Bob, I would like to

3 commend you. I think you have really brought a

4 good focus to how to apply and rationally go about

5 in vitro testing for topicals, which are more

6 complicated than oral, as you have described. That

7 is why I have stayed away from it. The dilution

8 that you get in the stomach is an enormous

9 advantage to regulating oral products, but I think

10 the enumeration of the factors you are really very

11 much on track with, simplifying or quantitating the

12 differences.

13 I like the idea of starting out by looking

14 at formulations that have qualitative similar

15 components because they are maybe going to have

16 similar effects on the permeability; similar

17 effects on the thermodynamic activity; similar

18 evaporation rates of spreading rates\--start with

19 something that is manageable and then go off into

20 different excipients where it is more complicated

21 and determine how you might characterize that. I

22 think it is a very difficult process and you are

23 not going to be able to simplify everything but you

24 can simplify some things and at least characterize

25 where we feel confident about the in vitro test and

250

1 where in vivo testing is needed. So, I think it is

2 really an excellent start.

3 DR. KIBBE: Ajaz?

4 DR. HUSSAIN: I think this is more focused

5 on understanding the mechanisms first and then

6 deciding what is critical and what is not critical,

7 and how it relates to performance. I totally agree

8 with you, here is a much more complex system from a

9 physical-chemical perspective compared to the

10 tablets and how that happens, and here is a highly

11 variable drug situation also. So, this example

12 relates totally to the previous disease that we had

13 on highly variable drug products.

14 DR. KIBBE: Anybody? No? Good.

15 DR. SELASSIE: In terms of your Q1

16 differences, have you looked at the role of

17 hydrophilicity, especially in terms of the

18 different excipients and what effect they have on

19 follicular transport versus stratum corneum?

20 DR. LIONBERGER: Yes. Certainly the

21 partition between sort of the effect of the

22 formulation on different transport paths would be

23 determined by the partition between the two phases.

24 So, I don\'t think that just sort of changing the

25 excipients will have a big effect on partition

251

1 between the two things since they are both

2 partitioning from the same vehicle phase into

3 either the stratum corneum of the sebaceous fluid.

4 So, it is not going to be sort of different unless

5 you have some sort of mechanism by which it can be

6 biased toward the follicle, things like

7 micro-motions or things like that.

8 DR. KOCH: I just had a question and it is

9 related but not necessarily. We heard that using

10 this topical evaluation is perhaps more complicated

11 than the dissolution one would have in the stomach.

12 But what about another form, a suppository? Are

13 there methods in place\--and obviously it is not

14 exactly what you would call a topical, but are

15 there similar equivalence studies in place that

16 either can be drawn from, particularly as you go

17 into some of the European dosage forms, to validate

18 or add to this particular study?

19 DR. HUSSAIN: I think the key is that we

20 often struggle when the site of action is local.

21 Now, rectal suppositories often are for systemic

22 absorption. If they are for systemic absorption,

23 then our current system handles it fairly nicely.

24 But if they are for local effects, and anything

25 that we have to deal with for localized effects, we

252

1 have challenges, inhalation, topical and so forth,

2 where the site of action is the tissue adjacent to

3 where the delivery is. So, those are sort of the

4 common challenges we face.

5 DR. KIBBE: Pat, do you have something?

6 DR. DELUCA: I just wonder if this is

7 going to extend to the transdermal delivery

8 devices, the patches, and all?

9 DR. LIONBERGER: This is mainly for

10 products that are locally acting so if you can

11 measure concentration in the blood and sort of

12 reduce the standard pharmacokinetic measurements to

13 do bioequivalence.

14 DR. KIBBE: We are clearly talking about

15 drugs that act in the stratum corneum. But the

16 direction that drugs move from the applied product

17 is into the stratum corneum and then out. So, now

18 that begs the question where they go after that,

19 and can we measure it there as a surrogate for it

20 being in the stratum corneum. I will argue that

21 our ability to measure trace amount of things has

22 gotten better. I remember the reason we actually

23 even started doing pharmacokinetics is because the

24 Bratton Marshall was invented and we actually could

25 measure sulfa drugs and therapeutic concentrations

253

1 for the first time. So, has anyone thought about

2 the possibility of looking for trace amounts just

3 to show that it has crossed and penetrated into the

4 capillaries?

5 DR. LIONBERGER: Sometimes there is

6 concern that the site of action really is the

7 stratum corneum. You don\'t know how much is

8 accumulating there versus other parts of the skin.

9 DR. HUSSAIN: I think the discussions have

10 always been in terms of two aspects, safety and

11 efficacy aspects. Now, if the site of action is

12 the stratum corneum or the dermis or the follicles,

13 and so forth, clearly that is important from an

14 efficacy perspective. But where it goes next also

15 is important from a safety perspective and often we

16 will have some coverage of that, and so forth.

17 But I think the challenge we have had for

18 the last ten years is that the localized delivery

19 to site of action is the focal point for discussion

20 and looking at systemic circulation because, after

21 topical application, you could look at urinary

22 excretion or even blood levels but that is

23 generally considered from a safety perspective, not

24 to demonstrate bioequivalence because it has

25 crossed over and it is not the site of action.

254

1 DR. KIBBE: But you recognize that

2 bioequivalence has always been aimed at evaluating

3 the dosage form.

4 DR. HUSSAIN: Yes.

5 DR. KIBBE: So, once it gets into an

6 individual stratum corneum, no matter how long it

7 takes to get out, that is a direct measure of how

8 well it got out of the dosage form, and if you can

9 find it and quantitate it, it is a measure of what

10 happened before. So, I think as we get better with

11 LC, MSMS and we can find them it might even be

12 better for some of these companies rather than

13 doing 728 patients to look at percent cure rate.

14 If you can find it with trace amounts with a lag

15 time of an hour and a half, and look at it for

16 three or four hours, wouldn\'t that be acceptable?

17 DR. HUSSAIN: It has not been acceptable

18 for the last ten years. That has been the debate

19 because, if you recall the debates that we have had

20 it was the localized concentration that the

21 clinicians wanted. I could actually argue that

22 measuring systemic circulation can actually

23 indirectly give you that assessment, but we haven\'t

24 been able to convince the rest of the world on that

25 yet, especially the dermatology community. So, I

255

1 think that is a challenge. But also I was hoping

2 that we can also bring a lot of imaging

3 technologies to bear on this.

4 DR. KOCH: That is exactly the next point

5 I was going to make because a lot of the imaging

6 technologies, as they are now being applied for

7 physical measurements\--I have seen different things

8 showing up that have to do with\--well, just the

9 thing I mentioned yesterday about studying

10 coatings. Using the same technology we are now

11 able to get below some of those levels down to 100

12 microns or increasing all the time, and the

13 sensitivity is improving. So, at least from an in

14 vitro method, I think a series of imaging

15 technologies should be able to begin showing some

16 value there.

17 DR. HUSSAIN: We just started a process to

18 look at terahertz microscopy, a spatial aspect of

19 looking at chemical distribution within membranes,

20 and so forth. The technology is evolving rather

21 quickly so we may see some solutions out there.

22 DR. KIBBE: Anybody else?

23 DR MEYER: Silence is interpreted as

24 negative. I think you have incorporated almost

25 everything we talked about here that we would like

256

1 to do for many things. We would like to know more

2 about the mechanism. We would like simpler, or

3 dissolution tests that meant something, or in vitro

4 tests that mean something. We would like a

5 decision tree. It seems like everything everybody

6 mentioned about some of the other problems you are

7 incorporating. You are testing in vitro, trying to

8 look at manufacturing and effects on topical

9 variability. So, I think you are covering a lot of

10 basis and doing a good job.

11 DR. KIBBE: Yes, I think you are right.

12 The one thing that you need to keep in the back of

13 your mind is that the source of the excipient is

14 going to have a dramatic effect sometimes on their

15 viability and their physical and chemical nature.

16 So, the company ought to have good characterization

17 for all their excipients coming in when you get the

18 chemistry data for the Q1 and Q2 evaluations

19 because they don\'t really characterize the

20 excipients coming in. It is hard for them to be

21 assured that they have gotten a good, consistent

22 product.

23 DR. COONEY: If I can just add one more

24 point, when I think back on studies that I have

25 done where I have made mistakes, the most common

257

1 mistake is not to have looked, really looked at

2 what I am doing. So, using imaging and microscopy

3 to visualize what is there should not be

4 overlooked.

5 DR. YU: Since the imaging technique has

6 been mentioned a couple of times, I just want to

7 update you. In fact, as we are speaking right now

8 the studies being conducted, hopefully, will have

9 some results very soon on topical imaging at the

10 University of Kentucky. Thank you.

11 DR. KIBBE: Is the agency happy with the

12 discussion? Okay? Well, we have our last

13 presentation and then Dr. Hussain will summarize.

14 DR. HUSSAIN: As Nakissa is coming over to

15 talk, all the topics that we have discussed are

16 interconnected, and one of the issues that Nakissa

17 wants to bring to your attention is the issue of

18 nanotechnology-based drug delivery systems.

19 Currently, there are a number of issues\--confusion

20 to a large degree with respect to nomenclature,

21 definition and so forth. So, as she talks about

22 that, I think you will see what we are trying to do

23 to address some of these.

24 Future Topics\--Nanotechnology

25 DR. SADRIEH: Good afternoon.

258

1 \[Slide\]

2 The last presentation at this advisory

3 committee meeting will be on nanotechnology. This

4 is an awareness topic so this is going to be a very

5 short presentation.

6 \[Slide\]

7 Why the interest? Nanotechnology is a

8 rapidly growing area of science. You just have to

9 look at the number of publications with the word

10 nanotechnology in the title. With regards to CDER

11 interests, it is anticipated to lead to the

12 development of novel and sophisticated applications

13 in drug delivery systems. The private sector,

14 academic centers and federal agencies are all

15 developing substantial programs in nanotechnology,

16 and there are significant research dollars being

17 invested in this area. Approximately 3.7 billion

18 dollars have been invested by the U.S. government

19 projected for the next four years. So, this is a

20 major area of research.

21 \[Slide\]

22 This talk will focus on the regulatory

23 considerations of nanotechnology, and specifically

24 as they apply to CDER products. We have identified

25 four areas that we would like to talk about. The

259

1 first one is nomenclature, and quality, safety,

2 facility/environmental issues. I will just go over

3 each one of these things right now briefly.

4 \[Slide\]

5 For nomenclature the National Science

6 Foundation has a definition for nanotechnology

7 presently, which is anything with a dimension less

8 than 100 nanometers is considered nanotechnology.

9 However, for CDER purposes we need to first define

10 what are some of the nomenclature criteria, and

11 then having defined these criteria we will need to

12 develop a definition that will be appropriate for

13 CDER, and then identify the potential

14 nanotechnology applications to CDER.

15 \[Slide\]

16 Regarding quality, for products that are

17 going to be called nanotechnology we need to

18 consider these five elements here. The first one

19 is characterization of the nanomaterials;

20 description of the critical attributes; assurance

21 of stability; manufacturing and controls; and then

22 drug release and bioequivalence testing issues.

23 These all have to be identified and described.

24 \[Slide\]

25 For safety, pharmacology and toxicology

260

1 studies have normally addressed the safety issues.

2 Currently, we believe that the studies that we

3 require for any drug the pharmacology and

4 toxicology are adequate for nanotechnology products

5 also. However, since this is a new area and there

6 might be some unique areas of concern, we might

7 need to think about possibly new testing models and

8 whether they be in vitro or in vivo. So, these

9 issues will have to be discussed and this is purely

10 going to be based on scientific issues.

11 For the environmental aspect the things

12 that we have to consider are facility design and

13 the potential impact of nanotechnology products in

14 the environment, whether they be from an industrial

15 setting or other.

16 \[Slide\]

17 The last few slides just identify some of

18 the challenges that we anticipate having to address

19 regarding nanotechnology. At CDER we have decided

20 to meet this challenge by crating a

21 multidisciplinary working group. This working

22 group will identify the regulatory challenges

23 related to the timely scientific assessment of drug

24 and drug-device combination products. We have to

25 consider the drug-device combination products in

261

1 this area because in nanotechnology this might be a

2 very important consideration. Also, this working

3 group will propose solutions to overcome these

4 challenges.

5 Presently, the members for this group are

6 from the Office of Pharmaceutical Science, Office

7 of New Drugs, Office of New Drug Chemistry, Office

8 of Generic Drugs, Over-the-Counter Drugs and Office

9 of Clinical Pharmacology and Biopharmaceutics. The

10 co-chairs of this group are in the Office of

11 Pharmaceutical Science. There is also one member

12 from the Office of the Commissioner because in the

13 Office of the Commissioner there is an interest

14 group for nanotechnology and we would like to

15 maintain a connection between the CDER working

16 group and that Office of the Commissioner interest

17 group so we have that member there to maintain

18 that.

19 \[Slide\]

20 The goals and objectives basically of this

21 working group are to provide a definition and to

22 craft the terminology; to develop a position paper,

23 a White Paper, possibly in the future; to identify

24 areas of concern and propose suggestions towards

25 the development of regulatory guidance documents;

262

1 to identify training and research needs; and to be

2 involved in the coordination of the above-stated

3 activities and also collaboration for potential

4 research activities in the future.

5 So, having said that, that is the end of

6 the presentation. I said it was a \"nanotalk.\"

7 Thank you.

8 DR. KIBBE: It was a \"nanotalk.\" I like

9 that. Are there any questions or comments? Go

10 ahead.

11 DR. SINGPURWALLA: I am very curious. I

12 have seen nanotechnology operate at Sandia Labs.

13 What I saw was miniature gears and miniature

14 machines that they were making. So, as far as

15 manufacturing is concerned or building things is

16 concerned, I saw the relevance of nanotechnology.

17 Can you tell us how nanotechnology is relevant to

18 the kind of things that you do?

19 DR. SADRIEH: Are you talking about

20 devices?

21 DR. SINGPURWALLA: I saw little gears

22 being made.

23 DR. SADRIEH: That sounds more like a

24 device. We are going to focus mostly on drugs.

25 So, you know, there might be drug-device

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1 combinations with the gears that you are talking

2 about, but we specifically are focusing on drug

3 issues for CDER.

4 DR. SINGPURWALLA: Right. So, I just need

5 to get a sense of what you have in mind.

6 DR. SADRIEH: For example, we have

7 nanoparticulate drugs or, you know, platforms.

8 Sometimes somebody designs a platform and it has

9 several different components in it and there might

10 be an imaging component and a treatment component,

11 a targeting component, and all of this might be

12 within a size that actually would be within the

13 nano range. So, that is more the direction that we

14 are going in.

15 DR. SINGPURWALLA: What advantage do you

16 see in it?

17 DR. HUSSAIN: Before I answer that

18 question direction, I think one of the challenges

19 we face is that we often get calls from higher-ups

20 from everywhere, saying, how many nanotechnology

21 products do you have, and so forth, and it is very

22 difficult to answer that because there are a lot of

23 products which have been in nanoscales for years,

24 and every solid material that goes into solution

25 goes into a nanoscale. So, from one aspect, every

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1 product we have is nanotechnology so the definition

2 out there is not really applicable. So, we want to

3 avoid the confusion of what is nanotechnology.

4 The type of products that we have where

5 nanotechnology is being utilized is to reduce

6 particle size to increase bioavailability, and so

7 forth. That is one but that is simply

8 micromization to a nanoscale, right? But other

9 than that, I think you are looking at design of

10 drug delivery systems. These could be nanosomes.

11 These could be other ones which are more target

12 oriented where you want to distribute the drug

13 differently, and so forth. So, these are mostly

14 drug formulation or drug delivery devices in the

15 nanorange. Then, as Nakissa said, you will have

16 combinations where, you know, you have a drug

17 delivery device which is a device, a machine with

18 drug loaded on to that. So, there are many

19 possible combinations. So.

20 DR. KOCH: I was going to add there

21 because I think this committee or working group

22 that you are talking about needs to just take a

23 step back to put on the list those things which may

24 be obvious present products that may go all the way

25 from aerosols through a number of micromization

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1 products but I think that also then takes you into

2 excipients and things that are related. Then,

3 there are the proactive ones where you would

4 actually be involved with, say, nanotubes, etc.,

5 for sustained release and things like that. So, it

6 seems like you first need to begin putting

7 everything on paper that exists and plan as to

8 proceeding or encouraging.

9 DR. SADRIEH: But that is what we are

10 doing. We are presently preparing a database of

11 what we have already in-house, what we have already

12 approved.

13 DR. KOCH: So, we will hear that when you

14 get to the micron presentation.

15 \[Laughter\]

16 DR. SADRIEH: Sure.

17 DR. KIBBE: Gordon?

18 DR. AMIDON: What I can see in the

19 research area are things like polymerized mice

20 cells. You know there are new technology methods

21 being developed and I can see where there are going

22 to be questions what are the things we should be

23 concerned about\--oral, topical ophthalmic, rapid

24 dissolving, and I don\'t know the answer. I think

25 it is being proactive to look at that and, yet we

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1 have systems to go through the nanoparticle size

2 range today and we are seeing new technologies to

3 do that and direct use in delivery systems.

4 Do you have any products or any product

5 areas that you are initially looking into? Let\'s

6 say nanoparticle polymerized oral delivery system

7 or something like that, to kind of focus on what

8 issues do we have to address if we are presented

9 with one of these as an NDA application, or

10 probably earlier during the process of developing a

11 delivery system? Because likely it would be a new

12 material so then you have the drug master file

13 issues, but maybe not. If it is not, then I think,

14 yes\--if it is a material that is used in humans but

15 processed differently then you have to ask the

16 question what standards are we going to set for

17 that.

18 DR. HUSSAIN: Let me give you an example.

19 The challenges are in a sense same material that we

20 have always used now nano-sized, and what issues do

21 they raise? One of the things we had to look at

22 was, for example, titanium dioxide and zinc oxide

23 in sunscreen preparations. You bring them down to

24 nanoscale, you have translucence in sunscreen

25 preparations.

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1 Traditionally these are USP materials and

2 USP does not have physical attributes as

3 specifications so they are USP. Whether nano or

4 micro it doesn\'t matter, they are USP. That raises

5 the same set of issues in terms of do we have the

6 characterization methods? Are these stable? Are

7 there photocatalytic issues, and so forth? Also, I

8 think we are sort of working with the NCTR, the

9 National Center for Toxicology Research that has

10 started a program on looking at skin penetration

11 and photocatalytic activity leading to some

12 toxicity issues. So, we have a small program

13 looking at all those things.

14 But from a general perspective, what we

15 have seen happening is physics become more

16 important now from a stability perspective.

17 Generally, if anything, we will focus\--because we

18 don\'t do physics well today with current products,

19 we have to do physics much better in nanotechnology

20 products. That is an area of gap that we want to

21 fill from a characterization perspective.

22 Also, you will see a lot of issues in the

23 press. There are a lot of concerns being raised,

24 and so forth, so we just want to make sure we are

25 rational, science-based with our approaches and

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1 proactive in our approaches because, otherwise,

2 this area will get stifled and we don\'t want to do

3 that.

4 DR. SADRIEH: We currently think that we

5 are addressing the issues pretty well with our

6 existing system. We just want to make sure. This

7 working group is going to consider all the issues

8 and just make sure that we really are; is there

9 anything that we might not have thought about

10 because, as Ajaz said, we get asked a lot of

11 questions. So, I think it is primarily to just

12 make sure.

13 DR. AMIDON: You are right, it may bring

14 new technologies for quality control and stuff, and

15 things that we aren\'t familiar with within the

16 typical pharmaceutical manufacturing formulation

17 area. Yes, I think this is a good step to be

18 proactive and think about what we may be faced

19 with. In fact, you will be; it is a matter of

20 when.

21 DR. COONEY: I would also like to add my

22 compliments to taking a very proactive view towards

23 this area. I would also suggest that you look at

24 it as a continuum of the activities you have in

25 place now because it is a continuum from interest

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1 in topical application of drugs. It is a continuum

2 from some of the things that have been looked at in

3 the drug delivery area. So, it doesn\'t stand out

4 by itself but it connects back to so many

5 activities that are in place.

6 One of the things that I find very

7 positive about this is that by anticipation and by

8 taking this proactive approach you will be able to

9 put in place the assets, the people, the mind set

10 to be prepared when things come to you and you are

11 not going to be trying to catch up. You will be

12 right on line if not even ahead of the game.

13 DR. SADRIEH: Right.

14 DR. KOCH: If I could add something to

15 that, I think this would be a good opportunity for

16 the MOUS or NSF where NSF is looking to take a role

17 in nano, but to build on what you have already

18 established if you got involved with

19 characterization of tools that would help take the

20 continuum down. I still feel that there is

21 probably in your particle size distributions or

22 registrations an area, as we have talked about,

23 that is called below 400 mesh. That could be a

24 very critical area to what is actually happening in

25 some of the dissolutions and other things. So, it

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1 is a continuum again, but just to move into

2 perfecting the characterization tools that will

3 allow you to move forward.

4 DR. KIBBE: Let me just add a couple of

5 science fiction items. The rate of technology

6 change is exponential and has been exponential for

7 known recorded history. Right now we are at a rate

8 which is astronomical. There are some people who

9 have written, really knowledgeable people in terms

10 of science who have written about singularity in

11 the year 2014 and you can\'t predict what is

12 possible after that because of the rate, and all

13 that. And, it is really good to see the agency,

14 even if it is gradually getting its feet wet in an

15 area that is potentially spectacular in terms of

16 therapeutics which combine what might be called

17 nano devices with drugs or that kind of thing\--so,

18 I think some of the issues that you will deal with,

19 and this working group might be the busiest working

20 group in the agency in about five years. So, it is

21 really good to see that. Anybody else have any

22 comments? If not, we get to let Ajaz have the

23 final word; it is kind of the rule around here.

24 Conclusions and Summary Remarks

25 DR. HUSSAIN: Well, I think I have

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1 actually thoroughly enjoyed the discussion and I

2 think, especially today, the morning discussion was

3 very useful.

4 But let me go back to day one and try to

5 summarize some of the talks and at least some of my

6 conclusions which I think I was able to reach, and

7 I want to share that internally as we start

8 tomorrow and we get back to our work.

9 On day one we started with the process

10 analytical technology update. We provided you a

11 brief summary that covered history, evolution,

12 current status and next steps. I think the

13 committee was generally satisfied with the progress

14 of this initiative and essentially agreed with the

15 direction in which it is going.

16 I think the suggestions we received from

17 you for this topic were that we need to consider

18 more objective metrics, especially for a training

19 program, to see how effective they are. Look

20 towards international harmonization is another

21 message that we heard and we are pursuing that and

22 will continue to do that. Also, I think Dr. DeLuca

23 pointed out the need to encourage publications and

24 research in this area, and I think this links to

25 nanotechnology. Everything is connected in some

272

1 way or form and we will try to do that as much as

2 possible.

3 The afternoon discussion was PAT provider

4 tech. As I sort of summarize the talks here, the

5 Office of Biotechnology Products is a new office in

6 the Office of Pharmaceutical Science. They were

7 not part of the initial team building and the

8 training and certification program that we had for

9 our CDER staff members. Since the guidance is a

10 framework guidance, the framework is applicable to

11 any manufacturing. The reason the Office of

12 Biotechnology Products was not included within the

13 scope of the guidance was because they were not

14 part of the training.

15 So, the afternoon discussion was to give

16 our Office of Biotechnology Products and CBER

17 colleagues an opportunity to discuss with you

18 challenges of the complexity they are facing in

19 their area, and how PAT might be applicable to

20 biotechnology products. I think we discussed a

21 number of emergent technologies and then potential

22 applications, not only by the members here but also

23 in open session.

24 I think the question focus primarily in

25 particular was on how should the training program

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1 be structured as we go to the next training

2 program. The general discussion and what we heard

3 from the committee was that training needs to be

4 emphasizing more critical thinking problem solving.

5 We did not really get a sense that it has to be a

6 technology focus, and so forth, because we cannot

7 do that. If we focus on general principles, if we

8 focus on the concepts and approach that technology

9 will evolve and we can always gather that

10 information rather quickly.

11 Based on that sort of discussion\--I had a

12 chance to talk to Helen also, I think we have an

13 opportunity to think a bit differently than we

14 were. What I am proposing now is that as we move

15 forward, since we already have a mature PAT process

16 within the Office of Pharmaceutical Science and

17 since we never excluded biotechnology products from

18 the PAT guidance because our Office of New Drug

19 Chemistry probably has more biotechnology products

20 than the Office of Biotechnology Products right

21 now, so I don\'t see a need to exclude our Office of

22 Biotechnology Products from the scope of the

23 guidance that we finalize.

24 The key issue there is that of training

25 and certification. Because of the infrastructure

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1 already in place with our OPS PAT team and others,

2 through consultation, and so forth, we can actually

3 build a bridge to that and get the second training

4 program started but not have to exclude our Office

5 of Biotechnology Products from the guidance. So,

6 that is the thought process that sort of evolved,

7 and I think Helen an I thought this might be a

8 better approach as we finalize to include them.

9 So, the guidance will only exclude CBER products

10 because CBER was not on board from that

11 perspective. So, that is how we think we will

12 proceed with that. So, I think the discussion was

13 very useful to make that sort of a decision and I

14 hope you agree with that. If you don\'t, obviously

15 you will tell us before we leave.

16 I think discussions today were very

17 valuable and I am really pleased with how we sort

18 of came up with a decision with respect to highly

19 variable drug products, at least a sharpened

20 decision. But I do want to sort of emphasize a

21 couple of things. In a sense the discussion was on

22 highly variable drug products because

23 bioequivalence deals with formulation of products;

24 it doesn\'t deal with the drug. If you inject a

25 drug, a very simple solution of drug into a human

275

1 being and you see a lot of variability in the PK

2 parameters, that is a highly variable drug with

3 respect to the disposition characteristics\--you

4 know, metabolism, excretion, elimination and so

5 forth. Now, if you give the same solution orally,

6 then you add on the variability, the physiologic

7 variability of gastric emptying, and so forth. So,

8 that is a highly variable drug by itself. For the

9 sake of assumptions, it is a simple solution; the

10 formulation is not an effect.

11 But then you put that drug in a solid

12 dosage form, or any other dosage form, or a topical

13 dosage form and you have a set of variabilities

14 there. If the variability is the same as what we

15 had after intravenous administration the

16 formulation really did not add or subtract from

17 that variability. So, it is a highly variable drug

18 and the product did not alter that variability.

19 But you can also have scenarios where the

20 product that you design can increase or actually

21 reduce that variability. For example, I think we

22 have seen more recently some drugs, especially

23 Class II drugs, which have significant food effect

24 when you administer them in a conventional dosage

25 form. If you can design a formulation, for example

276

1 a nanoparticle formulation or a cyclodextrin-based

2 formulation you can actually eliminate the food

3 effect so you have reduced the variability. So,

4 here is a formulation design strategy that can

5 actually reduce the variability.

6 So, I think the highly variable discussion

7 really is a focus of discussion of highly variable

8 drug products. The variability is no different

9 from the variability of the innovator. That is not

10 an issue. When the variability is higher then that

11 becomes a decision issue, whether it is acceptable

12 or not.

13 But for the last ten years or so that we

14 have discussed that, all the discussion has focused

15 on the statistical criteria and actually trying to

16 clear the check box exercise. The simple answer, I

17 think it is simply an arbitrary number. I hate

18 those check box exercises and it is easy, we can

19 make a decision. So, I am not comfortable with

20 sort of arbitrary numbers defining that. So, I

21 think that is the gap that will remain.

22 But the scaling approach, if we address

23 the arbitrariness of that and make it more

24 comparative scaling to a reference variability is a

25 way forward, and I think that was the general

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1 conclusion of this committee and I think you gave

2 us the signal to move forward. I will ask Lawrence

3 next month to have that ready for you.

4 \[Laughter\]

5 I think that was a very useful discussion

6 and I think we will move forward very quickly to

7 sort of hone in on that. At the same time, I think

8 the decision tree approach is built in there. It

9 is a logical decision tree that will evolve and I

10 think we will move there and I can be assured how

11 it can be done with the topical discussion that

12 followed, and that is a highly, highly variable

13 scenario right there.

14 So, I think the discussion was very useful

15 and helped us move forward in terms of being more

16 confident about the direction we want to move

17 forward in. With Lawrence and his team I am very

18 confident. Probably, if necessary, we can bring

19 our proposal to you in October. That might be an

20 option. I don\'t want to put pressure on Lawrence

21 but I think we can do it.

22 The topic of bioinequivalence I think is

23 to address currently I think a procedural nightmare

24 that comes from the aspect that our Office of

25 Generic Drugs has to deal with. I think we want a

278

1 solution to use our resources more effectively and

2 so forth. So, Lawrence and the group presented

3 this proposal and I think generally we came to the

4 general understanding that it might be very useful

5 to move forward.

6 But I do want to sort of remind ourselves

7 of a couple of things. This is an important

8 concept. It is not a trivial concept because we

9 have to really think beyond the application that we

10 discussed today and how it applies to the entire

11 regulatory scenario. For example, out of

12 specification results and how do we deal with those

13 is a major issue, and how does this relate to that

14 discussion I think is a very serious discussion

15 that probably needs to be considered more

16 carefully. One can think about misuse of this in

17 some ways. If a product is out of specification

18 and the company does a bioequivalence study and

19 fails to establish bioequivalence, and they come

20 back and say there is no clinical relevance so why

21 do you want to recall the product? So, all those

22 implications are there, which we did not discuss

23 today. So, it is not a simple matter and how it

24 relates to the big picture needs to be looked at

25 very carefully.

279

1 The other positive aspect of this is that

2 I hope it will force people to ask the question

3 why, why is it bioinequivalence? That gets to a

4 road to a mechanism understanding, and I think

5 without that the numbers game and the check box

6 exercise will continue. As Helen pointed out in

7 her opening remarks, we really don\'t like check box

8 exercises\--at least Helen and I don\'t like them,

9 and we want to move away from that and be more

10 science based. But the challenge is when you go

11 towards that without proper training, without a

12 proper quality system for our review staff and

13 review processes, it has the potential of creating

14 more questions and so forth. So, we want to manage

15 that very, very carefully.

16 Now, the other two topics that we

17 discussed, I think topical bioequivalence again is

18 a 10-15 year old saga. We have debated and

19 discussed this, and so forth, and the only solution

20 that we could find was to step away from all that

21 we have done for 15 years and to start fresh.

22 Lawrence and Dr. Lionberger really took the step

23 backwards and said let\'s rethink this and work with

24 Dr. Wilkin to rethink the mechanism perspective.

25 Again, the misgiving, if I have any, is in

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1 the sense as a professor of pharmaceutics we knew

2 this 15 years ago. There is nothing new in that.

3 But it is unfortunate that at FDA we have to now go

4 back to the basics that we have been teaching. So,

5 that is a bit of a frustration but I think we have

6 taken a positive step, in my opinion, in that

7 direction and with the support of our clinicians I

8 think we will move forward very quickly.

9 Now, nanotechnology\--I think it is simply

10 a starting point for discussion and we actually

11 have a number of products which companies want to

12 discuss with us, and PAT is actually very well

13 connected to nanotechnology. If you read the

14 guidance, there is a sentence in that and many of

15 the things that we are looking at\--particle size

16 reduction, for example, particle size analysis, you

17 cannot just take a sample and send it to the lab

18 and do this. Most of the particle size reductions

19 are based on on-line assessment of particle size.

20 So, every discussion topic was

21 interconnected and I was thinking that, in a sense,

22 I was going to apologize for quality by design of

23 our advisory committee agenda because I think the

24 topics were a bit lighter on day one; we had more

25 time left, and a bit heavy on day two. But the

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1 sequence that we had in mind was if you look at the

2 discussion of PAT and biotech, and if you look at

3 the discussion of highly variable drug products,

4 there was a quality control check right there from

5 our speakers that we had invited. Everything was

6 connected. The sequence was there but I think the

7 material should have been more in depth on day one.

8 So, we will work on quality by design for our

9 agenda more. With that, Helen, do you want to say

10 something?

11 MS. WINKLE: I just want to say that I

12 agree with Ajaz. I thought the conversation, both

13 yesterday and today, was excellent. I think that

14 yesterday there was total agreement on the

15 direction we are going with PAT. I think that the

16 committee has been very supportive for what we have

17 been doing in PAT and I think we have moved ahead,

18 and I think it is going to be really a very good

19 undertaking for industry, FDA and the public, and I

20 appreciate the committee\'s support of that

21 initiative.

22 Today\'s discussion was especially valuable

23 to us. I think there are a lot of things in the

24 area of bioequivalence as well as inequivalence

25 that we are still learning and still need to make

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1 changes. It is constantly evolving and I think

2 today\'s conversation will help move us forward in

3 the direction we need to go to in making some of

4 the really necessary changes that can reduce the

5 regulatory burden and really get the products out

6 on the market quicker. So, I appreciate the

7 conversation on that as well.

8 DR. KIBBE: Do I get to say we are

9 adjourned? Good. We are adjourned.

10 \[Whereupon, at 4:10 p.m., the proceedings

11 were adjourned.\]

12 - - -