DEPARTMENT OF HEALTH AND HUMAN SERVICES
FOOD AND DRUG ADMINISTRATION
CENTER FOR DRUG EVALUATION AND RESEARCH
ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE
Advisors and Consultants Staff Conference Room
Arthur H. Kibbe, Ph.D., Chair
Hilda F. Scharen, M.S., Executive Secretary
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.
Gary Buehler, R.Ph.
Ajaz Hussain, Ph.D.
Lawrence Yu, Ph.D.
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,
Why Bioequivalence of Highly Variable Drugs is an
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
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,
Statistical Demonstrations of Bioinequivalence,
Donald Schuirmann, M.S. 182
Establishing Bioequivalence of Topical
Products, Robert Lionberger, Ph.D. 225
Future Topics--Nanotechnology, Nakissa Sadrieh,
Conclusions and Summary Remarks, Ajaz Hussain,
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 . 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
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
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
applicability involve many industrial sponsors and
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
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
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.
With respect to FDA's invited industry
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
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.
24 Science, Office of Generic Drugs, Office of
Pharmaceutical Science, CDER, FDA.
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
10 DR. VENITZ: Jurgen Venitz, Virginia
11 Commonwealth University.
12 DR. SELASSIE: Cynthia Selassie, Pomona
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
19 DR. KIBBE: I am Art Kibbe and I am
20 Professor of Pharmaceutical Sciences at Wilkes
22 DR. MEYER: Marvin Meyer, formerly
DR. SINGPURWALLA: Nozer
2 DR. KOCH: Mel Koch, the Director for the
3 Center for Process Analytical Chemistry at the
5 DR. COONEY: Charles Cooney, Professor of
6 Chemical and Biochemical Engineering at MIT.
7 DR. DELUCA: Pat DeLuca, University of
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
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
guests and distinguished audience, I am
24 Yu. I am Director for Science, Office of Generic
Drugs, Office of Pharmaceutical Science, CDER, FDA.
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
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
scientific literature, for example, expansion of
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?
Why Bioequivalence of Highly Variable Drugs
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.
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
highly variable drugs is those drugs whose
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.
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
here, in the
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.
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
plan on using in a two-way crossover
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.
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
other than 80-125 percent.
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
12 confidence interval criterion, just a point
13 estimate criterion.
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
still the bioequivalence issues do represent some
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.
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.
24 Why aren't the current criteria
appropriate for some highly variable drugs? Well,
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
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.
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
drug could be considered to exhibit a wide
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.
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
Cmax, both at steady state, actually in
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.
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.
24 What I have tried to do in this graph is
get some sense of the magnitude of day-to-day
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
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
percent. So, this could be seen
to represent the
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.
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
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
difference here, is almost not discernible at all
1 to the eye.
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
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
24 I think that the criteria, which are still
plotted here, 80-125 percent, need to be
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.
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
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.
Instead, it could be due to inter-individual
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.
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
this is that each group is then evaluated on its
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.
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.
Current bioequivalence acceptance criteria
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?
21 DR. SINGPURWALLA: Certainly, I do. I
22 have four questions and five comments. Do I have
24 DR. KIBBE: You have until everybody
leaves to go to the airport!
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
9 MR. DILIBERTI: Yes, it is maximum blood
11 DR. SINGPURWALLA: Thank you. What is
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
22 DR. SINGPURWALLA: Log transformation of
23 the whole data or just the maximum?
24 MR. DILIBERTI: You would log transform
each of the individual Cmax's and then follow that
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
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
absorption and the other variable is the rate of
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
criticizing what is there but without an
1 alternative in mind?
2 MR. DILIBERTI: Right, I think many of the
3 later speakers will address the issue of potential
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
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?
MR. DILIBERTI: By the day on
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
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.
MR. DILIBERTI: Thank you.
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.
DR. MEYER: Since my light is on I
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
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
that you may have a bivariate situation.
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.
DR. KIBBE: Shall we move
along? I think,
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.
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.
23 So, I want to show some of the factors.
24 We tend to focus on bioequivalence from a plasma
level point of view over here. We
focus on the
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
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.
24 Some of the processes in the
gastrointestinal tract that can lead to the
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.
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
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.
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.
24 Some of the factors in the
gastrointestinal tract then are gastric emptying,
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.
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
that a glass of water was the
that out, what is a glass of water in
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
because of the different contractual activities
1 in the fasted state, shown here as phase 1, 2, 3
2 and 4.
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.
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,
course, food has a large impact on that as well.
1 So, that is a major factor that can be involved in
2 the variability and dissolution and absorption in
3 the gastrointestinal tract.
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
to be position-dependent permeability and the
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.
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.
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,
then look at their properties and decide what
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.
24 So, I think that the BCS classification
help focus on the source of the high
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
unravel and simplify some--maybe not all. Maybe
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
24 What I won't agree with you, at least not
fully, is that it is all a dissolution issue. I
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
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
DR. AMIDON: Right. So, that is the
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.
I think as we move forward here, I think
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
vivo dissolution and how do we capture that in some
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
transporter in the gut with a substance that has no
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
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
talking about in vivo?
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
prerogative of the chair for half a second and then
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
bioequivalence testing we try to minimize that and
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
understand our processes, if we understand the
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
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.
DR. KIBBE: I would argue that the
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
metabolism, what is the reason for it.
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
would want to take on preselecting subjects because
1 what criteria are you going to use? Normal in what
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
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
because we have reduced the variability in the
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
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
that dissolution predicted any of the results
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
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!
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
We have been discussing at an
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
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
same criteria no matter what the issues are.
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.
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.
bioequivalence issues that ever caused any
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
take cognizance of.
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.
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
although most of them are Class II drugs.
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.
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
variability of the reference product both for
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.
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
scaling could solve this issue.
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
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
that they work and they can't prove that they are
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.
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
carried out in 12 healthy post-menopausal
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.
23 I appeared before this committee three and
24 a half years ago to give the recommendations of the
expert panel on individual bioequivalence, and
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
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
24 Here is something that we recommended that
25 I want to bring up again today because this
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.
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
estimate criteria be added to the criteria because,
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
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
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
down by its own weight.
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.
22 DR. MEYER: I think I agree with
23 everything you have said and it embarrasses me no
24 end to say that!
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
need to do is just try to do the best job that
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
DR. BENET: Well, there is going
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.
DR. MEYER: Les, you put a little
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
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
being conservative enough. Right
now you are
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
November my newest concepts in terms of using BCS
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.
is important to have fresh eyes and fresh views
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,
11 Bioequivalence Methods for Highly Variable Drugs
12 DR. ENDRENYI: Thank you.
14 This presentation was put together with
15 Laszlo Tothfalusi and I would like to acknowledge
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
formulations and the confidence limits for the
1 ratio of geometric means should be between 0.8 and
2 1.25. This has already been stated.
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.
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.
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.
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.
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.
23 Study condition--perhaps I would omit this
24 almost entirely because it is considering single
dosing versus steady state. In
the U.S. this is a
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.
6 This is a study showing that and in the
7 U.S. I think this is largely at the moment
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.
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
that one can have a pooled criterion which could
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
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?
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
have for each subject the sum of the test and
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.
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
Another approach is the scaled average
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.
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
higher variations and 50, 60 percent would still be
1 the cut off.
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.
24 Scaled average bioequivalence is very
similar to the previous one except that the S from
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.
This is a demonstration comparing the
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
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.
Perhaps I should go down here.
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
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
bioequivalence and today we are talking about
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.
So, I would like to raise some caution.
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
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
consideration and further investigation.
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?
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
simulation that the COVs for both test and
1 reference are the same, 40 percent. Is that
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
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
24 DR. VENITZ: So, the answer that you are
using then is the reference variation.
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
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
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
19 DR. SINGPURWALLA: As a possible
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
sense that it converges to the center.
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
committee on the secondary criteria.
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?
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
would be 0.7 and you wouldn't know that; you would
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
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
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.
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
passed the 90 percent confidence interval criteria.
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.
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
two-way crossover studies so the best estimate that
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.
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
studies in which only AUC was highly variable. But
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.
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.
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.
23 One of the questions that we wanted to ask
24 was what is contributing to this variability.
Since for a lot of products we look at
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.
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
variability. Some of these are
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.
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
metabolite. But that is quite
rare. In the vast
1 majority of submissions that we have the
2 bioequivalence is based on the parent.
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.
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
class. The number of subjects that
1 used varied from 26 to 62.
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
drug product four times, on four occasions.
1 So, this was quite an extensive study.
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
immediate release dosage forms and the two
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
product and I noticed that the formulations of
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
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
criteria so these are just the passed studies.