ATDEPARTMENT OF HEALTH AND HUMAN SERVICES

FOOD AND DRUG ADMINISTRATION

CENTER FOR DRUG EVALUATION AND RESEARCH

ANTIVIRAL DRUGS ADVISORY

COMMITTEE MEETING

VOLUME III

Wednesday, November 3, 1999

8:30 a.m.

Holiday Inn Gaithersburg
Two Montgomery Village Avenue
Gaithersburg, Maryland

PARTICIPANTS

Scott M. Hammer, M.D., Acting Chairperson
Rhonda W. Stover, R.Ph., Executive Secretary

MEMBERS

Henry Masur, M.D.
James J. Lipsky, M.D.
Roger J. Pomerantz, M.D.
John D. Hamilton, M.D.
Brian Wong, M.D.

SGE CONSULTANTS

Patricia Charache, M.D.
Roy M. Gulick, M.D.
Princy Kumar, M.D.
Wm. Christopher Mathews, M.D., M.S.P.H.
Sharilyn K. Stanley, M.D.
Robert F. Woolson, Ph.D.
Ram Yogev, M.D.

GUEST EXPERTS

Mark Harrington
Brooks Jackson, M.D.
Jonathan E. Kaplan, M.D.
Douglas L. Mayers, M.D.
Carla Pettinelli, M.D.

FDA

Girish Aras, Ph.D.
Lauren Iacono‑Connors, Ph.D.
Heidi M. Jolson, M.D., M.P.H.
Sandra L. Kweder, M.D.
Katherine A. Laessig, M.D.
Jeffrey S. Murray, M.D.
Joanne L. Rhoads, M.D.

C O N T E N T S

Page No.

Call to Order: Scott Hammer, M.D.                       4
Conflict of Interest Statement: Rhonda Stover, R.Ph.    6

SESSION 3
Practical Considerations for the Use of Resistance Testing
in Antiretroviral Drug Development and Use

Introduction:  Joanne Rhoads, M.D.                      7

Presentations

Transmission and Prevalence of HIV Resistance:
Susan Little, M.D.                            9

Factors Confounding Interpretation of Resistance
Testing:
Richard D'Aquila, M.D.                       34

Questions to the Advisory Committee                    67

SESSION 4
Potential Roles of Resistance Testing
in Drug Development

Introduction: Jeffrey S. Murray, M.D.                  90

Presentations

Historical Perspective from the Antibacterial
Analogy and Contrasts with Virology:
Gary Chikami, M.D.                           91

Use of HIV Resistance Testing in Drug
Development: Regulatory Issues:
Jeffrey Murray, M.D.                        120

Open Public Hearing

Clyde Crumpacker, M.D.                      132
Brendan Larder, M.D.                        136

Presentation of Regulatory Proposals:
Katherine Laessig, M.D.                               142

Questions to the Advisory Committee                   195

Recap and Summation of Meeting                        214

P R O C E E D I N G S
Call to Order
DR. HAMMER:  I would like to call into session the Antiviral Drugs Advisory Committee meeting.  This is day two of our consideration of HIV drug resistance in the setting of drug development.
I would like to begin again by having the members and guests of the committee introduce themselves for the record.  I will begin on my left with Dr. Charache.
DR. CHARACHE:  I am Patricia Charache.  I am Professor of Pathology, Medicine, and Oncology at Johns Hopkins.
DR. WOOLSON:  Robert Woolson.  I am Professor of Biostatistics, University of Iowa.
DR. MATHEWS:  Chris Mathews, Department of Medicine, UC/San Diego.
DR. KUMAR:  Princy Kumar, Infectious Diseases, Georgetown University Medical Center.
DR. GULICK:  Roy Gulick, Infectious Diseases, Cornell University.
DR. STANLEY:  Sharilyn Stanley, Texas Department of Health.
DR. YOGEV:  Ram Yogev, Children's Memorial Hospital, Chicago.
DR. HAMILTON:  John Hamilton, Adult Infectious Diseases, Duke University and the Durham VA Hospital.
DR. WONG:  Brian Wong from the West Haven VA Hospital and Yale University.
DR. HAMMER:  Scott Hammer, Infectious Diseases, Columbia University.
MS. STOVER:  Rhonda Stover, FDA.
DR. POMERANTZ:  Roger Pomerantz, Infectious Diseases, Thomas Jefferson University.
DR. JACKSON:  Brooks Jackson, Pathology, Johns Hopkins University.
DR. PETTINELLI:  Carla Pettinelli, Division of AIDS, National Institutes of Health.
DR. KAPLAN:  Jon Kaplan, Division of HIV AIDS Prevention at the Centers for Disease Control and Prevention.
DR. MAYERS:  Doug Mayers, Henry Ford Hospital, Division of Infectious Diseases.
DR. ARAS:  Girish Aras, Division of Biostatistics III, CDER, FDA.
DR. LAESSIG:  Katherine Laessig, Medical Officer, FDA.
DR. IACONO‑CONNORS:  Lauren Iacono‑Connors, FDA.
DR. RHOADS:  Joanne Rhoads, FDA.
DR. MURRAY:  Jeff Murray, FDA.
DR. JOLSON:  Heidi Jolson, FDA.
DR. HAMMER:  Thank you.  I would like to turn to Rhonda Stover who will read the conflict of interest statement.
Conflict of Interest Statement
MS. STOVER:  The following announcement addresses the issue of conflict of interest with regard to this meeting and is made a part of the record to preclude even the appearance of such at this meeting.
Since the committee's discussions of issues related to testing for development of resistant human immunodefiency virus will not have a unique impact on any particular firm or product, but rather may have widespread implications with respect to an entire class of products, in accordance with 18 United States Code 208, general matters waivers have been granted to each member and consultant participating in the committee's discussions.
A copy of these waiver statements may be obtained by submitting a written request to the FDA's Freedom of Information Office, Room 12A‑30 of the Parklawn Building.
In the event that the discussions involve any products or firms not already on the agenda for which an FDA participant has a financial interest, the participants are aware of the need to exclude themselves from such involvement, and their exclusion will be noted for the record.
With respect to all other participants, we ask in the interest of fairness that they address any current or previous involvement with any firms whose products they may wish to comment upon.
DR. HAMMER:  Thank you.
One quick announcement.  For those who are interested, we will be discussing regulatory scenarios this afternoon, and copies of those scenarios and the slide formats are out on the desk for you to pick up if you would like at the break.
Now I would like to turn to Joanne Rhoads, who will introduce Session 3.
SESSION 3
Practical Considerations for the Use of
Resistance Testing in Antiretroviral
Drug Development and Use
Introduction
DR. RHOADS:  Good morning.  I am Joanne Rhoads from the Division of Antiviral Drug Products, FDA.  I would like to introduce Session 3, which is devoted to practical considerations for the use of resistance testing in antiretroviral drug development.
Yesterday's speakers provided an overview of the genotypic and phenotypic assay technology current available, the performance characteristics and limitations of these assays, and evidence from retrospective and prospective studies supporting their clinical utility.
Today, before we continue to discuss the role of resistance testing in drug development, we will consider the prevalence of drug resistant HIV‑1 in selected U.S. populations and also discuss in more depth some of the factors which may complicate the interpretation of resistance data.
These factors which were touched on many times yesterday include the presence of naturally occurring polymorphism, sampling issues, the complexity introduced by combination therapy, pharmacological properties of a drug, and anatomic and cellular compartmentalization of HIV.
The session objectives are:
1.  To review the prevalence of genotypic variants and/or reduced susceptibility in selected U.S. populations.
2.  To illustrate possible limitations in the practical clinical use or application of resistance assays in clinical investigations.
3.  To examine how cofactors associated with treatment outcome confound interpretation of resistance testing.
Dr. Susan Little will present data addressing the prevalence of drug resistant HIV in selected U.S. populations of newly infected individuals, and Richard D'Aquila will provide a review of factors which may confound interpretation of resistance data.
Once again, we look forward to an interesting and productive discussion.  I will introduce the first speaker and then turn the session over to our chair, Dr. Scott Hammer.
Dr. Susan Little will now present Transmission and Prevalence of Drug Resistant HIV.
Thank you.
Transmission and Prevalence of HIV Resistance
DR. LITTLE:  Thank you.
[Slide.]
The transmission of drug resistant HIV was first reported in 1992 by Erice and Colleagues at the 32nd ICAAC.
In this report, a patient with primary HIV infection had blood samples obtained which showed a reduced susceptibility to AZT and sequence data then showed the presence of a tyrosine at position 215, conferring AZT resistance.
Since this first report, there have been numerous reports of the sexual transmission of drug resistant HIV, first in the setting of single drug and more recently in the setting of multi‑drug resistant HIV.
The initial reports, not surprisingly, documented transmission of single drug AZT resistance and more recently 3 TC resistance, while more recent reports, first in the form of isolated case reports and more recently in larger cohort studies document the transmission of multi‑drug resistant HIV.
[Slide.]
The transmission of drug resistant HIV is not limited, however, to the sexual transmission of HIV.  The transmission of drug resistant HIV has also been documented by several groups in the setting of perinatal or vertical transmission following injection drug use transmission, and between two children following a presumed unrecognized blood contact.
[Slide.]
These studies have clearly shown, however, that the transmission of virus with reduced drug susceptibility is not associated with lower pretreatment viral loads.  As was covered yesterday, it is not known what level of reduced susceptibility is reproducibly associated with virologic failure for each drug.
As a result, completely arbitrary classifications of reduced drug susceptibility have been adopted by most investigators and they are shown here for the two most common assay types that are going to be discussed today, so I won't review them again.
[Slide.]
As an introduction to this kind of interpretation or how to interpret these tests, I am going to present a little bit of data from our own cohort study.  We evaluated antiretroviral susceptibility using the virologic susceptibility assay among 141 subjects with primary HIV infection from five cities across the United States.
The number of patients from each city are shown here ‑ 48 in San Diego, 48 in Los Angeles, 19 in Dallas, 13 in Denver, 13 in Boston.
We estimated the date of HIV infection in these study patients using the date of high risk exposure or symptom onset in symptomatic seroconverters or the date of the first positive HIV test in asymptomatic seroconverters.
[Slide.]
The percent of patients with any level of reduced drug susceptibility to the nucleosides and non‑nucleoside reverse transcriptase inhibitors are shown here.  The dark blue boxes and the dark red boxes indicate the proportion of patients with higher level or greater than 10‑fold reductions in drug susceptibility.
Overall, the proportion of patients with reduced susceptibility to the nucleosides was relatively low with only 3 percent of our study cohorts showing reduced susceptibility to one or more of the nucleoside reverse transcriptase inhibitors.  The lighter boxes, I should say, indicate reduced susceptibility in the 2.5 to 10‑fold range.
In contrast, the proportion of patients with reduced susceptibility to the non‑nucleosides was surprisingly high given that the use of these compounds was not widespread at the these patients were identified.
The level of reduced susceptibility, however, that was identified was generally lower than has been described in patients with genotypic resistance.   Again, only 1 percent of the study cohort had a greater than 10‑fold reduction in susceptibility to the non‑nucleosides.  Similarly, only 1 percent of the study cohort had a greater than 10‑fold reduction in susceptibility to the nucleoside reverse transcriptase inhibitors.
[Slide.]
The percentage of patients with any level of reduced susceptibility to the protease inhibitors varied from 1 percent for saquinavir, 2 percent for indinavir, 5 percent for ritonavir, and 9 percent for nelfinavir, again, darker boxes indicating those patients with greater than 10‑fold reductions in susceptibility.
Overall, 10 percent of our study cohort had some level of reduced susceptibility to the protease inhibitors with only 1 percent again having a greater than 10‑fold reduction in susceptibility.
[Slide.]
We then evaluated reverse transcriptase and protease sequence in the 39 patients in whom we identified some level of reduced drug susceptibility.  In the setting of what is now a very extensive list of amino acid substitutions that have been reported to date in association with in vitro or in vivo drug resistance, we chose to identify or report only those well‑characterized amino acid substitutions which have been clearly associated with in vitro drug resistance according to the 1998 JAMA consensus guidelines on antiretroviral drug resistance published by Hirsch and colleagues.
These guidelines identify a set of so‑called primary drug resistance mutations shown here for reverse transcriptase and protease.  These primary drug resistance mutations are generally selected early in the process of accumulation of drug resistance mutations, tend to have a discernible effect on drug susceptibility, and are often drug specific.
[Slide.]
Those, as Dr. Richman introduced yesterday, are these black and white mutations.  These are the gray.  These guidelines also identified a group of secondary mutations, shown here again for reverse transcriptase and protease.
These mutations tend to accumulate in viral genomes that already contain one or more of the primary drug resistance mutations.  They may have a more limited effect on drug susceptibility and may, in fact, be selected because they improve viral fitness.
[Slide.]
So, using population‑based sequence analysis on those 39 patient isolates, there were three patients in our cohort for whom we identified a major reduction or greater than 10‑fold reduction in drug susceptibility.  Among these three patients, primary drug resistance mutations in reverse transcriptase and protease are shown here, the primary drug resistance mutation shown in yellow and/or bold, and the secondary mutation show in white.
These were observed in the background of numerous other polymorphisms.  There was one of these three patients in our cohort who had an isolated high‑level or greater than 10‑fold reduction in susceptibility just to the non‑nucleosides, and in this patient we did not identify any primary drug resistance mutations.
In contrast, among the 36 patients in whom we identified a moderate reduction in susceptibility, we identified one ‑‑ and this is the merit of Power Point, I changed these slides this morning ‑‑ we identified one well‑characterized drug resistance mutation, the presence of a T215Y mutation in a patient who had an 8.4‑fold reduction in susceptibility.  It didn't make the cut‑off of 10 for the major reduction in susceptibility.
So, in the setting of only one well‑characterized drug resistance mutation, in the background again of numerous polymorphisms, this suggests that these may, in fact, be wild type viruses with reduced susceptibility, which are really of unknown clinical significance.
[Slide.]
We also evaluated the isolates in our study according to the year in which their baseline sample was identified, beginning in 1989 through 1998.
Although 70 percent or nearly 70 percent of our study cohort was identified in 1997 or later after the release of the first really potent protease inhibitors, we did not identify any increase in the proportion of patients identified over time with some level of reduced susceptibility to the protease inhibitors, nor for that matter, for the non‑nucleoside reverse transcriptase inhibitors.
Again, these numbers are really quite small, but we did not see any trends.  Like other of the larger cohort studies, however, the two patients in our study who had high level or greater than 10‑fold reductions in susceptibility to the protease inhibitors were both identified in 1998.
[Slide.]
So, using this kind of interpretation, I have taken great liberties with the data that has been presented and published by numerous other North American, European, and Australian investigators, and re‑analyzed their published and presented data using these guidelines, these criteria for primary and secondary drug resistance mutations and cut‑offs for the established two phenotypic assays to try and determine whether there is any degree of consensus among the many published and presented studies now in terms of the overall prevalence of drug resistance in patients with primary or recent HIV infection.
To talk through this table, these are the North American studies using our study first as an example, again, 141 patients, our population was patients with primary HIV infection of less than 12 months duration.   The mean time from seroconversion, approximately two months.
We identified again 2 out of 141 patients with primary drug resistance mutations, again with the caveat shown down here, we did not sequence all samples or at least as of the date of this table, had not sequenced all samples, but only sequenced those samples in which we identified any level of reduction or reduced drug susceptibility.  So, I think it is unlikely that this is going to be substantially higher when we put in the data from all of those samples that we have now sequenced with wild type susceptibility, but it could change slightly.
So, again, 2 out of 141 or 1 percent of the cohort of the primary drug resistance mutations, 31 out of 141 or 22 percent with secondary drug resistance mutations.
Again, we used the virologic assay using this cut‑off of greater than 10‑fold, 3 out of 141 or 2 percent of the cohort with greater than 10‑fold reductions, 26 percent with 2.5 to 10‑fold reductions in susceptibility.
The Boden study, published the same month in JAMA, 80 patients, their population less than 6 months from seroconversion, again mean time from seroconversion 2 months.
They identified primary drug resistance mutations the same primary that I used, in 8 out of 80 patients or 10 percent, and secondary drug resistance mutations in 48 out of 80 or 60 percent using the same virologic assay, but unfortunately, with a different cut‑off that I haven't yet been able to resolve, of greater than 5‑fold reduction in susceptibility, they found 8 out of 67 or 12 percent with a greater than 5‑fold reduction in susceptibility.
So, again, once this number is adjusted to conform to the greater than 10‑fold cut‑off, this number may actually fall to be more in consensus with the others.
In the 2.5‑ to 5‑fold, they identified 10 out of 67 or 15 percent.
The Wegner study, 114 patients, all with recent HIV infection less than three years duration, mean time from seroconversion approximately 12 months.
They identified primary drug resistance mutations in 6 of 95 or 6 percent, secondary in 22 percent, using the Virco assay with their cut‑offs greater than 10‑fold in 8 percent, greater than 4‑ to 10‑fold in 19 percent.
Bob Grant's study, 89 patients, again primary infection less than 12 months duration, primary drug resistance mutation only for the nucleoside reverse transcriptase inhibitors.  That is the only data I had access to for this.
10 of 89 or 11 percent with primary drug resistance mutations, they only presented data on the lower level or 2.5‑ to 10‑fold reductions in susceptibility to only the non‑nucleosides, but again just to show that they are seeing the same fairly high number in terms of frequency associated with this lower level reduced susceptibility to the non‑nucleosides.
This study by Weinstock, 99 patients, recent infection less than 24 months, 5 percent of their cohort with primary drug resistance mutations, roughly 21 percent with secondary.  1 out of 12 or 8 percent showed greater than 10‑fold reductions in susceptibility, 4 out of 12 or 33 percent showed 2.5‑ to 10‑fold.
Verbiest, 230 patients.  Treatment‑naive patients with chronic infection.  5 out of 199 or 3 percent with primary drug resistance, 15 percent with secondary.  They used the Virco assay.  3 percent with greater than 10‑fold reductions in susceptibility and 11 percent with 4‑ to 10‑fold.
[Slide.]
I also reviewed less completely the European and Australian and a few Canadian data.  The French study looked at 370 treatment‑naive patients with chronic HIV, but I was most interested in this subset of 103 patients within their group that had primary HIV infection or infection of less than 12 months duration.
Again, 8 out of 103 or 8 percent had primary drug resistance mutations, 3 out of 103 had secondary, again, the caveat being that in this cohort, the protease domain was sequenced only if the reverse transcriptase domain showed mutations.  So, again, this may underestimate.  This might move up if they were to sequence more of their isolates.
The Spanish study, 150 patient, treatment‑naive, 18 out of 149 with primary drug resistance mutations, but with the caveat that this is the Mirex line probe assay, which interrogates only primary reverse transcriptase inhibitor mutations.
Yerly, the Swiss study, 82 patients, approximately less than 6 months duration of infection, so primary HIV infection.  Again, 8 out of 82 or 10 percent with primary mutations, 51 percent with secondary, but they only called secondary protease mutations.
She used a different phenotypic assay, an in‑house, home brew assay, but it had the same cut‑offs actually of greater than 4 to 10, and greater than 10‑fold for their different categories.
2 out of 14 or 14 percent had greater than 10‑fold reductions in susceptibility, but again, they assessed susceptibility only if primary or secondary protease mutations were identified.
So, again, this number might increase if they also looked at those patients that had primary or secondary RT mutations.  8 percent with the lower level 4‑ to 10‑fold reduction in susceptibility.
Balotta, the Italian study, 37 patients, less than six months duration, 11 percent of this cohort had primary resistance mutations.  Again, this was only the RT domain that was sequenced.  30 out of 33 had secondary mutations.
The Australian study, 84 patients, very recent infection.  Again, 14 percent had primary reverse transcriptase inhibitor mutations.
Finally, Veronica Miller's data, 46 patients, less than six months duration, 5 percent had primary mutations, 54 percent had secondary mutations.
[Slide.]
So, in an effort to try and summarize all of this, at least the North American data, a major reduction in susceptibility, that is, greater than 10‑fold reduction, has been observed in 2 percent of subjects with primary HIV infection and 3 to 8 percent of treatment‑naive subjects with recent infection.
Primary drug resistance mutations have been observed in 1 to 11 percent of subjects with primary HIV infection and 3 to 6 percent of treatment‑naive subjects with recent infection.
So, if I am permitted to very broadly round, something like 5 percent of patients with primary or recent HIV infection in the United States appear to be infected with clinically relevant drug resistant virus.
[Slide.]
Many of these larger cohort studies have also identified patients infected with multi‑drug resistant virus, that is, virus with resistance, primary drug resistance to greater than one class of antiretroviral drugs.  This has been shown in multiple cities now in the United States and Europe with frequencies in the 1 to 4 percent range.
In many of these cases, there was no exposure history to suggest the transmission of multi‑drug resistant HIV with which to promote or advocate a more selective use of resistance testing.  Thus, strategies are desperately needed to permit rapid identification of these individuals.
[Slide.]
As one such example, in our own cohort study, patient 98‑1186, shown in blue here, resistance was not suspected in this patient.  He was started very rapidly after seroconversion on a regimen of AZT, 3TC, and indinavir, and showed a rather slow virologic response, which is fairly self‑evident when compared to a more typical patient, initiating the exact same regimen who shows a much more rapid viral decay.
Because of his slow virologic response, sequence analysis was performed and showed primary drug resistance mutations to AZT, 3TC, and multiple protease inhibitors.  As a result of this data, his treatment regimen was switched to an entirely new, non‑nucleoside‑based combination regimen, which resulted in complete suppression to viral load less than 50, which I am told has now been sustained for six months even beyond the follow‑up shown in this slide.
[Slide.]
Thus, moderate reductions in drug susceptibility to certain drugs are highly prevalent and frequently not associated with recognized drug resistance mutations.
The presence of moderate reductions in susceptibility may actually, we believe, represent wild type virus that is simply being discerned with greater confidence using these more precise phenotypic assays.
However, we cannot exclude the possibility that subpopulations of more resistant virus are present and not being detected by our population‑based sequence analyses.
Alternatively, there is, in fact, greater, as was shown yesterday, natural variability in the susceptibility of wild type virus to the NNRTIs and some of the PIs, which may explain the variability that we observed.
The treatment implications of infection with virus of moderately reduced susceptibility are currently unknown.
[Slide.]
So, now that we know something about the prevalence of drug resistant HIV in North America, what about the transmissibility of drug resistant HIV?
Several groups have identified an apparent selection against the transmission of resistant virus.  In a study by Wahlberg, they identified 4 patients or subjects with recent HIV infection and their sexual source partners or donors.
Although all 4 source partners were infected with AZT resistant virus, only 1 donor transmitted resistant virus to the sexual partner recipient.
Similarly, in a study by Colgrove, 4 mothers who were infected with mixtures of AZT resistant and sensitive virus, 3 of these transmitted only the wild type to their infected infant or only 1 transmitted drug resistant virus to the infected infant.
[Slide.]
Another issue that is going to be relevant to the issue of transmission of drug resistant virus is that of compartmentalization.  A study by Zhu in which patients, 5 acute seroconverters were identified and again their respective sexual source partners or donors were identified.
They compared gp120 sequences in the seroconverters from the time of seroconversion out to 6 months of follow‑up, and gp120 sequences in both the blood and genital secretions of the donor as close to the transmission event as was possible.
They identified a sequence heterogeneity in the blood and genital secretions of all of the donors and relative homogeneity of the viral population in the recipients from seroconversion out to 6 months of follow‑up.
In all cases, the transmitted virus was a minor variant present at 0.5 to 27 percent within the population and the semen of the transmitter, suggesting that a selection process occurred during transmission.
Somewhat in contrast, a study by Poss showed 6 recently infected Kenyan women in whom they also evaluated envelope sequence evolution over time, again, from the time of seroconversion out to 6 months of follow‑up.
They identified more heterogeneous viral populations that were present in the cervical secretions and blood of these women from shortly after seroconversion out to 6 months of follow‑up, suggesting that either less of a selection process occurred or perhaps there may have been more subtle sampling differences which may have explained these discrepancies.
[Slide.]
The transmission of drug resistant virus is almost certainly going to occur more frequently in patients who are receiving or have received antiretroviral therapy in the absence of complete virologic suppression, however, even in the setting of complete virologic suppression, replication competent virus has been isolated from the seminal cells of 2 of 7 subjects that Zhang studied, who were receiving HAART and had plasma viral loads of less than 400.
Actually, in those 2 in whom they were able to isolate replication competent virus, both had viral loads less than 50 on all of the days that they were evaluated.  So, even with complete virologic suppression, these kind of data suggest that the transmission of virus may at least be theoretically possible although to my knowledge it has not yet been documented in this setting.
Both the study by Zhang and that by Overvaugh demonstrate that the viral strain detected in the genital secretions may represent a minor variant of the genotype in the blood, suggesting that even with all of these advanced methodologies that we have access to, we may have a very difficult time predicting who is going to transmit what to whom.
[Slide.]
So, finally, to summarize, we need additional studies to monitor the prevalence of drug resistance, particularly with an eye toward identifying any possible geographic patterns of variability and differences that may exist among persons who acquire HIV through different risk exposures.
We need to better understand the mechanisms and the rates of transmission of drug resistant virus.  We need to learn more about the clinical significance of primary infection with resistant virus and learn the most efficient manner of identifying these patients.
Finally, we need to understand treatment responses among patients infected with virus showing moderate reductions in drug susceptibility since these virus populations seem to be so widely prevalent among patients with primary and recent HIV infection.
Thank you.
DR. HAMMER:  Thank you very much.
Are there questions for Dr. Little?
Please, Dr. Kaplan
DR. KAPLAN:  That was a beautiful presentation.  Thanks very much.
I wanted to ask you about the very last point, which becomes obviously really important in the data you presented showing the moderate phenotypic resistance particularly to NNRTIs, which appears to be common, and we heard about this quite a bit yesterday.
I wonder if, in your studies in San Diego, you will have any opportunity to look at what the clinical significance of what that moderate resistance is, in other words, are any of those patients being treated with NNRTIs or are you using the results of your testing to get around NNRTIs in your treatment.
DR. LITTLE:  Yes.  I tried to look at this in the San Diego cohort, and it is just too small a number of patients with too diverse treatment regimens.
So, what I have recently proposed is to the NIH Primary Infection Group, and to the group that has participated in this first study, is a study to look at the clinical responses of most of the North American patients with primary HIV infection, which should give us a much larger denominator to look at despite varied treatment regimens.
It will be a retrospective look, but we should be able to look at treatment responses in patients evaluated with the same phenotypic susceptibility assay over time.  So, my hope is yes, I will be able to answer that.
DR. HAMMER:  Dr. Jolson.
DR. JOLSON:  Dr. Little, thank you for your presentation.
Just a quick question.  In terms of interpreting the prevalence of mutations of reduced susceptibility, in your cohort, do you know if any of those patients had received prophylaxis to some sort of occupational or other exposure?
DR. LITTLE:  None in our cohort.  I am not aware in the other cohorts, but in most of the presentations I have heard it has never been mentioned.  All the patients I presented were either treatment‑naive or had had less than seven days of therapy to the best of my knowledge.
DR. HAMMER:  Dr. Pomerantz.
DR. POMERANTZ:  Again, Susan, that was a great talk.
Thank you for re‑analyzing all that data.  That is extremely helpful.  But my question is, getting back to those moderate resistant strains, because that I think is a unique finding if they really are wild type that have differences compared to your controls, have you had a chance ‑‑ you quoted our study in the New England Journal ‑‑ we only looked at genotypic markers, so we could have missed in those seminal samples, and we have actually three more patients that go with those ‑‑ we could have missed the ones that are moderately resistant, and have you had a chance to look for moderate resistance phenotypically in general secretions in either men or women that were treated?
DR. LITTLE:  No, we have not.  We are collecting genital secretions, but have not yet done any susceptibility assays in those samples.
DR. HAMMER:  Dr. Mathews.
DR. MATHEWS:  Susan, could you clarify something for me?  I am quite confused about what is actually meant by polymorphism, because nearly all of the studies that you showed, showed a much higher prevalence of the secondary mutations than primary mutations, so are you saying that if the secondary mutations are present along with the primary mutations, they are resistance mutations, and if not, they are possibly polymorphisms?
DR. LITTLE:  Sort of.  My interpretation, most of the secondary drug resistance mutations do occur as natural polymorphisms or genetic variants in untreated patients.  So, the presence of one or more secondary drug resistance mutations in an untreated patient, to me does not imply drug resistance or moderate reductions in susceptibility that one should expect that in that population.
However, when present in association with primary drug resistance mutations, I think I would more likely predict that they might confer some additional perhaps reduction in susceptibility, but in terms of an actual breakdown of ‑‑ I mean lots of people are trying to do this ‑‑ which secondary drug resistance mutations, how many of them, in what combination, and appearing in what frequency predict reduced drug susceptibility.  I certainly do not know.
I believe that most of the secondary drug resistance mutations that were identified or I should say the amino acid substitutions that were identified in these patient populations are more likely to be natural variants.  I cannot prove that at this point, but that is my estimation based on the range of reduced susceptibility of the samples that we saw.
They were mostly in nelfinavir and the non‑nucleosides, which, as I said, are known to have a much wider range of susceptibility.  So, my bet is that those are natural polymorphisms, but we are going to be looking into that.
DR. HAMMER:  Dr. Hamilton, then Dr. Stanley.
DR. HAMILTON:  Is it possible to determine from your analysis and/or re‑analysis whether there is a correlation between viral load and number of resistance mutations?
DR. LITTLE:  I didn't address that, so, no, I couldn't.  Most of the patients in these groups that were identified with primary infection, their baseline isolate was collected fairly recently after seroconversion on the order of two to sometimes six months out.  So, in general, their viral loads were quite a bit higher than mean viral loads in many of the chronically infected cohorts, but other than saying their viral loads were in general higher, no, I haven't addressed that.
DR. STANLEY:  Just a point of clarification, on your slide on compartmentalization, the study by Zhu with the five pairs, are those all MSM or were there some heterosexual?
DR. LITTLE:  One heterosexual.
DR. HAMMER:  Dr. Pomerantz.
DR. POMERANTZ:  I forgot to ask you one question.
Again, back to those moderately resistant patients, they were all shown by the virologic system, correct, the phenotypic moderate resistance?
DR. LITTLE:  In our study, yes.
DR. POMERANTZ:  Have you confirmed any of those in a nonchimeric backbone doing your typical PBMC resistance?
DR. LITTLE:  No.
DR. POMERANTZ:  So, you don't know whether the switch to chimeric backbone might have affected that.  Okay.
DR. RICHMAN:  When we did the original nevirapine studies 10 years ago using the old‑fashioned assays, these sorts of observations were also seen, but we never bothered to write up the baseline thing because we didn't know what to make of it, and we didn't have the type of precision of the data, but I think this information does exist.  The data with these assays are confirmed.
DR. HAMMER:  Dr. Gulick, and then Dr. Jackson.
DR. GULICK:  Did you get a feel for the demographics of the patients?  We talk about the North American experience, but it is my suspicion that this represents select cities and select groups of patients being characterized.  Do you have a feel for that?
DR. LITTLE:  It is definitely overrepresented as one would predict in this country.  The patients that are most frequently sampled are men who have sex with men, the largest proportion of whom are white.
So, certainly trying to look at all of the North American studies, there is some geographic variability, but even in the other studies, they seem to be more weighted to the West Coast than the East.
So, I think at this point, it is fairly representative of most study cohorts in the sense that they are probably something in the order of 80, 85 percent male, and the majority of them, men who have sex with men is their primary respector.  I didn't break it down, but that is my guess looking at most of the studies having about the same breakdown.
DR. JACKSON:  Could you comment on Dr. Kaplan's point about the treatment response or perhaps the clinical relevance of these sort of moderately reduced susceptible viruses, particularly to the NNRTIs?  I wasn't clear how much reduced it was.
For example, with nevirapine, these levels in vivo are typically 200 to 400 times the IC50, and so it is not clear whether these findings are going to have any relevance to treatment response.
DR. LITTLE:  Agreed.  I mean I think the level of reduced susceptibility was quite a bit lower than has been generally associated with virus that carries genotypic resistance.  For instance, in the one patient in our cohort who had up to 20‑fold reduced susceptibility to multiple NNRTIs, not a single primary drug resistance mutation for the NNRTIs was identified.
So, evaluating treatment responses in patients with moderate reductions in susceptibility to the protease inhibitors, I think will be easier than to the NNRTIs, simply because it is going to be impossible I think to evaluate treatment responses within patients in whom moderate reductions in NNRTI resistance or susceptibility are noted, who are then not treated with a primary NNRTI‑containing regimen.
So, we will have a much smaller subset of those patients, particularly in late '98, '99.  Some patients are initiating NNRTI protease‑sparing based therapies, and we may be able to address that in a smaller group, but I still think that is going to be the most difficult group even retrospectively with a large number of patients to evaluate.
DR. HAMMER:  Thank you very much for a superb presentation.  I think we will move on now.
The next speaker is Dr. Richard D'Aquila, who will discuss factors confounding interpretation of resistance testing.
Factors Confounding Interpretation of
Resistance Testing
DR. D'AQUILA:  I would like to thank the committee for the opportunity to speak today.
[Slide.]
Many of the factors that confound interpretation of resistance testing have been discussed yesterday, and I think a good subtitle for my talk might be why it might be optimal to have an 800 megahertz multitasking processor to keep track of all the different factors, whether that processor is carbon based or silicon based probably doesn't matter as much.
[Slide.]
I think everyone is familiar with the fact that drug resistance may not always be the initiator of treatment failure.  Drug resistant virus can be present, as we have heard, pre‑existing before treatment is started or it can be emerging during treatment, and it can initiate failure, but obviously, some inhibitory drug levels or reduction in the host immune responses against HIV can also lead to persistent viral replication, which allows then subsequent evolution of drug resistance and the final common pathway to drug failure.
[Slide.]
This is a partial list of what I consider the most important factors confounding the interpretation of drug resistance tests.  In addition to the fact that drug resistance need not be the initiator of failure, I think we have to account for many of these factors.
The biology of drug resistant HIV includes very complex interactive effects of the mutations.  The issue of detection of minorities of resistant virus has been much discussed.  We have also heard a bit about linkage of multiple resistance mutations within the same genome, and issues of latency or persistence of the resistant virus.
Another factor is the presence of the selecting drug, which is clearly related to the level of drug or drug concentrations that are present in vivo during therapy, and multiple drug combination regimens, the presence of other drugs in addition to the one to which the virus may be resistant.  All of these also have to be taken into account.
Then, keeping in mind the issue of what initiates drug failure, timing of sampling blood from a patient who is failing a treatment regimen is very important in interpreting the results of a resistance test from that specimen.  The timing relative to viral load rebound when in the process of viral load rebound, the sample is obtained.
Finally, I will touch very briefly on issues related to anatomic compartments and cellular mechanisms of resistance.
[Slide.]
The mutation effects are very complicated, but indeed we do have pretty good correlations between geotype and drug susceptibility of clinical isolates.  These correlations are not perfect and there may be some easy explanations for why they are not perfectly correlated.
The mutation effects that we see in clinical isolates may not be identical to the effects that were noted in site‑directed mutants that have been studied in preclinical laboratory tests.  There are differences in genetic background and other sequences of the virus remote from the mutation of interest that lead to variation in the effect of a given mutation, and we really have not defined these to any great extent.  Only a few of these are really defined.
[Slide.]
I think this is one reason why we see this kind of data where there really is a very good correlation between a genotypic sensitivity score and a phenotypic sensitivity score.  This is data from ACTG 372 with a 0.75 correlation coefficient, but there is some splay in the data.  It is not perfect.  You cannot predict the exact IC50 given a predetermined mutation.
[Slide.]
I think there are several potential reasons for discordances between phenotype and genotype, and those discordances can be in two directions.
The phenotype may indicate drug susceptibility when the genotype indicates resistance.  The two most likely reasons for this are that there are mixtures of wild type and mutant basis at a position conferring resistance, or, the second major point down here, that there are interactive effects of mutations causing resistance reversal.  The most well defined mutation that does this is the effect of the M184V substitution on AZT resistance, but let's go back for a minute to the issue of mixtures of wild type and mutant basis.
A minority of resistant variant may be detectable by genotyping, which generally has a level of detection of at least 20 percent, but that may not be a large enough proportion within the virus population to increase the IC50 in a phenotypic assay.
Now, this may or may not provide an early warning of resistance, and I think we need more data on that.  We need to address the question of whether the resistant minority will become dominant and then demonstrate a concordant effect in a phenotypic test if the same treatment is continued for somewhat longer.
[Slide.]
Discordance can also occur in the opposite direction where the phenotype may be resistant and the genotype may indicate susceptibility.  The most likely reason for that, I think, is that there are previously uncharacterized mutations or combinations of mutations, and indeed we have seen some examples of this just in the past couple of years with the identification of the 69 insertion mutation in the RT and some other, more recently identified mutations, which were identified by screening for samples that would have resistant phenotype without any of the known mutations, and then going ahead and thoroughly characterizing those viruses genotypically to identify the new mutations.
I think that is going to continue to occur particularly as we use drugs in different combinations and as we get more new drugs into our patients.
The other reason why there might be this type of discordance is that there might be mixtures of wild type and mutant at a position conferring resistance.  Again, a minority of a resistant variant may be detected by phenotyping and not by genotyping.
Again, it could be related to relative differences in amplification where a minority variant might be better amplified in the genotypic assay than it could be in the genotypic assay.
Will any detection of resistance with either method minimize false negatives?  I think this is an important question for future research, and one of the rationales for studying for some of the clinical trials that are ongoing or in development that will attempt to use both genotyping and phenotyping.
[Slide.]
Now, the complexity of mutation effects also leads to difference in the rule‑based algorithms that are used by different laboratories to interpret drug susceptibility or resistance from a genotype.  It varies to some extent which mutations are included in the rule for calling resistance to a specific drug, and I think that leads to some of the differences in interpretations that you may see from different laboratories, and different algorithms account for interactive effects, such as resistance reversal, some may account for it, and some may not.
Finally, there may be preclinical site‑directed mutant data that is incomplete or not directly relevant to the specific mutational pattern seen in a clinical specimen.
[Slide.]
This leads to some very difficult interpretations, and these are just some examples of that.  There may be on mutation which does not confer increased resistance by itself, but may contribute to resistance when additional mutations are added.
One situation like that is where you might see in a clinical isolate less than a complete RT 151‑complex multinucleoside resistance genotype.
Now, this has been studied in site directed mutants in one study, where this constellation of mutations was studied separately, as well as together.  The 151M mutation alone conferred some nucleoside resistance in the site directed mutant, but any of the other mutations in  62, 75, 77, or 116, any one of those others alone did not confer a detectable level of phenotypic resistance.
But if you put several of these together, then, you do get very broad cross‑resistance to all of the nucleosides.
So, how do you interpret a finding of 151 alone?  That is pretty easy.  We have some data.  We would probably all agree that that is likely to be a nucleoside resistant virus, resistant to most of the nucleosides.
But what about if you have 62 alone or 77 alone or 116 alone, does that indicate that the virus is still susceptible to the nucleosides, or might there be other genotypes under the surface, below the limit of detection, which have one or two or three additional mutations, and therefore that isolate should be considered as potentially resistant.  We really can't give a definite answer to that problem today.
The same issue occurs with the RT 69 insertion, which together with the characteristic AZT selected mutations confers broad multinucleoside resistance in site directed mutants, but when it is present by itself, doesn't give as much resistance.
The same problem may also occur with many single protease active site resistance mutations.  They may not confer much resistance to some of the protease inhibitors, but one always has to wonder, in all of these situations, whether there are additional mutations under the surface in vivo and whether an isolate with just one of the mutations might be predisposed to more rapidly develop further resistance.
Again, this is a very difficult interpretation, and I don't know in every case that the answer will always be the same.
[Slide.]
Again, the biology of the drug resistant virus is what leads to this complexity.  As you know, it replicates as a genetically heterogeneous swarm of different quasi‑species, and a resistant mutant may be present at any proportion including very, very low proportions in the total population, and the current tests only detect optimally 20 percent minority.
Indeed, the ENVA panels, which were presented at this past summer's resistance workshop suggested that some academic laboratories might miss a 50 percent mixture of mutant and wild type.
There has been speculation in discussion of those data that operator experience is important, and I think we need to investigate that further, but that is clearly another issue in the interpretation ‑ how good is the lab, how low is the actual limit of detection in that lab for that operator on that day.
[Slide.]
Now, there is some potential to improve the level of detection of minorities.  Hybridization‑based analyses of a pool of PCR products with one of several methods that are in development by different manufacturers do have relatively lower limits of detection, maybe down at best to about 1 percent of a population, but the tradeoff there is that these methods don't give you sequence in every codon, so that they are limited to specific codons rather than giving you the entire sequence of protease in RT.
The other approach to improve minority detection might be to evaluate multiple molecular clones of PCR products, and in a study from my laboratory, which was published in the Journal of Clinical Microbiology in September, 60 percent of the 30 specimens we examined with both bulk PCR product sequencing, the current state‑of‑the‑art, and with a clonal analysis looking it up to 15 clones.
Sixty percent of these specimens had a minority mutation detected in the multiple clones, that was not detected in the bulk PCR product sequence.
So, I think this might incrementally improve things, but again it comes at great cost in terms of the amount of labor and resources that are required to do this, and I think it is not really feasible on any large scale at this point in time, but again perhaps technology will advance far enough, so that this will become feasible.
[Slide.]
One issue was raised yesterday that it is important to account for the linkage of multiple mutations, and another issue with using bulk PCR product sequencing is that that is giving you the dominant base at each position in the population, and that need not necessarily indicate genetic linkage.
All those mutations don't necessarily have to be in the same genome.  There could be equal proportions of different genomes with mutations at different positions which will give you that same result in a bulk PCR product analysis.
But to date, clonal analyses do support the assumption that when there are multiple dominant mutations noted, they generally are in the same genome.  This is clearly important when we are dealing with salvage therapy choices, because potentially, if you have mixtures of viruses with different mutational patterns, you might be able to construct a regimen where one drug will get 20 percent of the population and another drug will get a different 30 percent, and thereby try to construct a regimen that is more effective.
But to date with really a very small number of specimens from highly experienced patients being reported in the literature, multiple mutations from these heavily pretreated patients have been linked in the same clones.
[Slide.]
This shows you one example of this from a specimen that we studied, that shows clonal analysis detecting a minority population and the linkage of multiple mutations in this minority population.  Of the 15 clones on this slide, the two on the bottom ‑‑ and I apologize, this really is not very legible ‑‑ but the two on the bottom contain multiple resistance mutations including major active site, major primary protease mutations in codons 48 and 82, and primary RT resistance mutation in codons 184, 215, and several important secondary mutations, as well.
So, it is not a swarm of different mutational patterns, but they are all linked together.
The other important thing about this slide and this specimen ‑‑ and I will ask you to remember this pattern as we go to the next slide, please ‑‑
[Slide.]
‑‑ is that that specimen was obtained several weeks after a combination drug regimen was stopped and the patient was off all drug therapy.
It illustrates the point that within weeks of drug withdrawal, the virus population may shift from a multiply mutated virus to a dominant wild type virus population.
Now, this comes at some risk.  The risk is a decline in CD4 count.  I don't want to discuss this as a treatment strategy right now.  My point today is that resistance testing should always be done when drug selection pressure is present because otherwise it can lead to quite misleading results.
[Slide.]
Those results may be misleading because those resistant mutants may not disappear.  This is a very instructive anecdote from John Condra, which he has presented at a meeting several years ago, that indicates that resistant mutants can remain either latent or persistent, below the level of detection, and rapidly reappear.
This is a patient who started treatment with indinavir as monotherapy, developed an 82 mutant in the protease that was 100 percent of the virus population.  The y axis is the frequency of the mutant in the virus population.
Using sophisticated clonal analyses, after indinavir was stopped, this mutant declined to being 0.04.  That would be 4 in 100 viruses in this plasma RNA population.  But many months later, the clinicians thought, well, why don't we try again to restart indinavir, and very quickly this mutant moved from being 5 in 10,000 up to 80 percent of the virus population, indicating, I think, that these resistant mutants may remain archived in one way or another, but I have shown you an anecdote here which is very instructive, and I think probably very common, but we don't have a lot of very hard data on this phenomenon, and I think we need to look for this phenomenon more as we begin to use resistance testing more commonly, because I really don't know how commonly this will occur.
I suspect it will be common, but I think it is important for us to study how commonly this will occur.
[Slide.]
I want to now move to another factor, and this is moving into the area of drug concentrations.  Not only does the drug have to be present, but its concentration when the drug is being used is also very important, because resistance is a relative, not an absolute, phenomenon.
As you heard yesterday, there is still some effectiveness of these drugs even against resistant virus.  The inactive drugs, I believe it was in the GART study, John Baxter told us that there was some drop in viral load associated with use of inactive drugs.
I think this is also one possible explanation for the lack of immediate decline in CD4 cells when viral load rebounds on triple combination regimens.  There is still some ability to suppress.
[Slide.]
Again, we have said many times at this meeting that the level of resistance needed to overcome the drug concentration that is present in the blood is unknown for any drug.  I arbitrarily call this an IC50 cut‑off.
It is likely that trough blood concentrations are the relevant measure for the protease inhibitors and the non‑nucleoside RT inhibitors, and perhaps it would be useful to adjust for protein binding of those drugs.
For the nucleosides, the situation is much more complicated because we need to evaluate cellular triphosphate levels, and that is just a whole additional layer of complexity.
Given all of this, monitoring individual drug levels, as well as resistance, will undoubtedly improve the prediction of drug effect.
[Slide.]
You saw one example of that presented by Dr. Clevenbergh from the VIRADAPT study yesterday, where patients were categorized into these four groups with suboptimal trough PI concentrations or optimal trough PI concentrations either in the control arm or the arm that had treatment chosen based on genotyping.
[Slide.]
There was this very nice discrimination where the best viral load responses were seen in those who had genotyping to help guide regimen choices, and who had optimal protease inhibitor trough drug concentrations.
I don't want to spend a lot of time on this, but I think clearly, this is where we need to move in the future to combine resistance test results with analyses of drug levels in order to fully interpret the resistance results.
[Slide.]
Can resistance be overcome by higher drug levels?  Some drugs which are now in development do achieve higher concentrations in blood relative to the virus' IC50 against that drug than the current drugs.
For current drugs, as we have heard, the level of drug in the blood is probably at most 5‑ to 10‑fold above the wild type virus IC50.
[Slide.]
But Dale Kempf and his colleagues from Abbott were kind enough to provide me with some very interesting recent information looking a new compound ABT‑378/ritonavir in PI‑experienced patients.  Some of this data has been presented at the resistance workshop, but Dale and his colleagues undertook another analysis of the data from their study M97‑765, using the data analysis plan or the DAP that you saw presented yesterday, and I am going to show you just a couple slides about that.
This compound is coformulated with ritonavir and it produces steady‑state trough levels that exceed the protein binding corrected IC50 for wild type virus by at least 30‑fold.  The activity of this drug plus nevirapine and two nucleosides was studied in PI‑experienced individuals.  The virologic responses at 24 and 48 weeks were analyzed retrospectively according to the DAP.
[Slide.]
This shows you a schema of screening for PI‑experienced patients who changed their PI on study day one to 378 ritonavir at one of two doses.  They made only that change for two weeks, and then at study day 15 added nevirapine and also changed their nucleosides with one new nucleoside being new.  The study was evaluated at weeks 24 and 48.
[Slide.]
There was something there yesterday.  We did look at it yesterday.
Well, what it shows is that there is some resistance to all of the other protease inhibitors, and I can actually see it a little bit.  It is a mean of 8‑fold to 23‑fold resistance for indinavir, nelfinavir, saquinavir, and ritonavir.
[Slide.]
But the point is that the responses were not predicted by the baseline drug susceptibility phenotype to ABT‑378 ritonavir.  The wild type IC50 levels listed here, 4‑fold, the IC50 of wild type virus to 378 is also listed there.  The dots indicate the IC50s of the isolates from these patients at entry to the study, and the patients who failed, the six patients who failed really all cluster down here.  This is at week 24, failing by week 24, dropouts as censored.
You can see that there is a wide range of IC50 for the viral isolates, and what is indicated on top here are the mean drug levels in this study at this dose.  So, really, even the highest IC50s appear to be below the levels of drug that are present in vivo.
[Slide.]
This is similar data at week 48.  Again, the failures do not all cluster at the high end of IC50.
[Slide.]
This is looking at the number of mutations.  Again, there is no relationship between at week 24, the PI mutation score in either success or failure.  The failures are in yellow here.  Some have no mutations.
[Slide.]
This the same analysis at week 48, dropouts as censored.
[Slide.]
So, what Dale and his colleagues concluded from this was that at both week 24 and 48, there were very good responses.  The response was not associated with a 4‑fold change in phenotypic susceptibility or the number of PI mutations at baseline.
In these DAP analyses, only the baseline RNA level was identified as potentially associated with virologic responses in these subjects, and these findings could be consistent with the high sustained plasma concentrations of 378.
Now, I would like to add just some of my own personal opinions about this.  I think it certainly does suggest that we need to evaluate this phenomenon more.  I do not take from this the conclusion that there will be no resistance to this drug, and I think no one should walk away from this thinking that.  I think we need further studies looking at patients who have PI experience than those who were studied here, and potentially looking at different regimens.
I will remind you that here, everybody had nevirapine added to their regimen at two weeks, so that clearly has to be taken into account when looking at these effects, but I think the general point that I would like to make from this is that there may be some drugs which will have high enough levels, so that it will be similar to intermediate resistance of pneumococcus.
One can still use penicillin against a pneumococcus which has intermediate resistance to penicillin.  That does not indicate that there are no pneumococci out there which have higher levels of resistance for which penicillin will not be an adequate treatment.
I think it also suggests to me that it will be very important in drug development to perhaps ‑‑ and this should be discussed later ‑‑ perhaps ask sponsor to design studies where, in fact, the resistance pattern can be identified, where some viruses that are resistant can be selected on study and the resistance mutational pattern identified in those viruses.
[Slide.]
Looking at multi‑drug combination regimens adds further complexity, and you have already heard about genotypic and phenotypic sensitivity scores, and the data from 372 are what suggested this, and that was what was used in the DAP.
[Slide.]
This just shows you those data.  If you look at a genotypic sensitivity score, it very nicely predicts time to failure.
[Slide.]
There is another issue.  That first regimen failure may not give you the same mutational pattern as later failures.  There are several studies listed here showing that early during failure of the first triple combination regimen, you can see RTI resistance without PI  resistance.
[Slide.]
But the timing of sampling during rebound is very important here.  Most commonly early on, only resistance to 3TC is seen, but in these first regimen failures it is possible that PI resistant minorities may be present at a lower level and might eventually become detectable if the failing drug regimen is continued.
That remains a research question, and I think we need trials that either continue or intensify the PIs to address the issue about how to use resistance testing in this specific setting.  Again, it may differ in those failing a second or a later regimen.
[Slide.]
This shows you just one isolate from a patient we studied early in indinavir, AZT, 3TC failure, and this patient had prior AZT treatment, and they showed up with resistance mutations to AZT, 3TC, and depicted on this slide, PI resistance mutations, as well.  Trip Gulick has presented data from the Merck 035 study that also shows pretreated patients presenting with mutations to several of the drugs.
[Slide.]
Anatomic compartments are another complexity.  If resistance testing does not identify a resistant mutant in blood, could it already be present and rapidly emerge from a compartment such as CSF or semen where virus can evolve independently?
To date, resistant virus has rarely, if ever, been present in semen in the absence of detection of resistance in blood.  Only a subset of patients in general, only a subset of patients with PI resistant virus in blood have PI resistant virus in semen, and I have cited here some work that we presented last year, and there are several other groups that have similar data.
I think there are fewer studies of CSF.  Joe Wong has some very interesting studies on this, and I think that merits further study because I think there may be greater potential for resistant virus to be present in CSF, if not in blood.
[Slide.]
Cellular mechanisms of resistance are also necessary to be considered.  There may be no virus resistance and yet patients may be failing.  We know of several mechanisms for this.  PIs are substrates for the P‑glycoprotein multi‑drug resistance gene 1, encoded P‑glycoprotein, and there is another MDR transporter, the MRP4 that is induced by adefovir in vitro and effluxes nucleoside monophosphates out of cells.
Obviously, there are also other enzymatic mechanisms possible for the nucleosides including changes in the cellular enzymes involved in phosphorylation, and this is still largely uncharted territory.
[Slide.]
In conclusion, the current resistance tests are imperfect predictors of drug responses, and one needs to take into consideration, in addition to the resistance test result, many viral and drug‑related biologic factors.
Improved essays may be developed in the future.  Interpretation criteria can be better standardized, but I think perfection shouldn't be the enemy of the good.
[Slide.]
I think these limits shouldn't stop us from using resistance testing.  The IAS‑USA Resistance Testing Consensus Panel, that published recommendations in JAMA in 1998, is updating those recommendations.  That paper is being submitted, this week I believe, and much broader recommendations will be outlined in that update based on the retrospective and prospective studies that you heard summarized at this meeting.
In general, the recommendations will be to consider resistance testing for pretreatment screening for the recently infected patient, not for the patient with long‑term, established infection, and to consider resistance testing at the point of regimen failure if you are about to change drug regimens.  Regimen failure is defined as viral load rebound.  This is recommended either when the first or later regimen is failing.
Thank you very much for your attention.
DR. HAMMER:  Thank you, Rich.  That was a terrific talk.  These first two talks have set quite a standard for our second day, but a great kickoff.
I have a couple of questions to start this off, and I think there is no better person to answer a couple of these questions.
The first is that resistance mutations are not all  created equal.  One of the questions that has come up is the  functional consequences of some of the mutations, particularly some of the work you have done in RT, but also the issue of protease mutations and replicate of capacity and functional consequences.
Can you make some ‑‑ perhaps it is unfair ‑‑ general comments about that and what you think because one of the issues we dealt with yesterday, and we will deal with again later today, is resistance in relation to outcome, and if a virus is impaired, the outcome may be different irrespective of the virus load.
DR. D'AQUILA:  I am very glad you asked that question because it didn't quite fit into my topic, but I want to address it because none of the current resistance tests assess the replicative fitness, replicative capacity of the virus.  I think that may be something we will be able to assess routinely in the future, but my read on it is that it is not predictable in advance to say that mutation A will lead to a less fit virus, and mutation B will not lead to a less fit virus.
I think it is quite complicated and, in part, can vary based on the genetic background that the mutation is present in.  So, I think we can't make assumptions that a particular class of mutants will always be relatively unfit or relatively fit.
I do think again we need much more study.  It has largely been studied only in vitro, but I think that fitness is probably a relevant factor.  It is probably another factor that has to be taken into account in looking at viral load responses to drug regimens.
I think a virus that is less fit and grows less well in the absence of drug may be better able to be inhibited by any drug.  So, I think that is something that again we can't factor in easily right now.
DR. HAMMER:  Let me ask you a different question, and that relates to looking at different points on the virus inhibitory curve, and there are pros and cons to looking at IC50 or IC90 or 95 depending upon how you want to look at the virus population, but this also gets to the issue of sort of uniformity and analyses particularly in drug development issues and what this committee might see.
Do you have opinions about whether it is best to look at an IC50 or an IC90 or 95 in relation to a drug's activity and particularly in relation to looking at the pharmacologic/pharmacodynamic interactions and the results?
DR. D'AQUILA:  I think many of the people in the room will recall when we first started working on this, we chose IC50s for a completely technical reason, because the measurement of an IC50 is a bit more robust.  In the laboratory, you can really have greater confidence in that measurement.
But I think biologically, our goal is to maximally suppress the virus, so that I think a measure, such as an IC90 or IC95, if it could be as reproducible as an IC50 would be a better measure, but that means doing much more expensive testing and many more replicates, and I think for practical considerations, an IC50 is probably a more relevant measure to use for clinical purposes.
DR. HAMMER:  Questions from the committee?
Dr. Pomerantz.
DR. POMERANTZ:  I want to actually continue, Rich, on what Scott brought up because I know you left out some slides on fitness, and clearly, there is data that there may be differences in fitness, at least in vitro, between different viruses, but fitness is really, as you know, reaching a local peak on an evolutionary landscape, and that is associated with replication.  It does not say anything about the pathogenesis or the effects on the host.
So, my questions are twofold.  One is do you think that fitness, as defined or as it is being evaluated, will always correlate with virulence or the destruction of CD4 cells either by direct or indirect methods and mechanisms?
Secondly, isn't it formally possible that a less fit virus without drug is actually quite fit in the presence of drug, a little bit like some bacteria are now?
DR. D'AQUILA:  I will address your second part first.  Absolutely, that is why you see them.  I think we have to be very careful when we use the term.  Resistance mutation allows the virus to have better fitness in the presence of the drug, and it can only lead to decreased fitness in another situation, when another drug is being used or no drugs are being used.
DR. POMERANTZ:  I don't want to go too far on that, but don't you, though, have to measure the replicative capacity of that resistant virus in the presence of drug and compare it to the wild type without drug, not both without drug?
DR. D'AQUILA:  That would be a more direct reflection of the in vivo situation, but it is much harder to do, yes.
Just to address your other question about virulence, I don't think there is an absolute correlation.  I think what we are looking at here, particularly for protease and RT, are viral replicative enzymes.
So, what we call fitness really relates only to replication.  There clearly can be effects of other genes that cause more or less CD4 depletion or other virulence effects.
DR. HAMMER:  Dr. Wong.
DR. WONG:  I have two questions.  I think one is pretty easy, and one might be a little harder.
The easier one is you showed the proportion of protease inhibitor resistant virus that reappeared when a protease inhibitor was reinstituted in one case, but you didn't give the absolute number.  So, I wonder if you could give that in addition to the proportions.
DR. D'AQUILA:  I am sorry, I don't understand.
DR. WONG:  You showed that it went from less than 1 percent to 80 percent when a new drug was instituted, but it was only the proportional data you gave as opposed to the number of ‑‑
DR. D'AQUILA:  I don't have access to that data, but my recollection of John's presentation ‑‑ I don't know if John wants to comment on this, I think he is here ‑‑ is that that variant was dominant in the plasma RNA at the point that was shown on that slide.
DR. HAMMER:  John, please come to the microphone and identify yourself for the transcriptionist.
DR. CONDRA:  John Condra, Merck Research Labs.
Unfortunately, we don't really know the absolute.  We can't really interpret the absolute viral load in that circumstance because the patient was on and off RT inhibitors during the time that the protease inhibitors were used, and withdrawn and reinstituted.
So, there is really no simple way to interpret the relationship between the viral load in that patient and the proportions of that mutants in that particular situation.
DR. WONG:  Thank you.
The second question I guess goes to the data you showed about analysis of individual clones in people receiving or not receiving drugs.  I guess it worries me that ‑‑ as I am sure it worries you ‑‑ that looking at bulk genotypes or even bulk phenotypes may not accurately represent what is happening.
I understand that it is going to be a tremendous increment in the amount of work that would be required, but should we, at this point, really focus on trying to introduce the idea of clonal analysis and resistance testing and characterizing the individual members of populations as opposed to bulk populations as a goal down the line.  I mean is this something that we really should be focusing technology development on?
DR. D'AQUILA:  Well, I would say yes, but in follow up, I would say we have spent more than a year trying to do that, and to date, have not found viruses that look tremendously different in a clonal analysis than they look in a population analysis.
Yes, you get more information, but the take‑home message, the important bottom‑line message is the same.  That is based on study of a dozen or so viruses in this kind of detail, and this is our unpublished ongoing work.  I still think it is important to develop that area, but I don't think ‑‑ I mean the take‑home message that I would like everyone to leave with is that so far the bulk assays do appear adequate.  They do not seem to be missing major minor species.
DR. HAMMER:  Dr. Yogev.
DR. YOGEV:  A comment and a question.  The comment is about the IC50.  Both in the study you showed us, for example, the 378, and non‑anecdotal data, the IC50 or the wild type is almost the lowest level of the virus tested during the study.  You also said the IC90 and 95 are very difficult to get, but maybe a factor of 4 of 10 of IC50 should be added now, especially for the experienced patient, because every time we use the IC50, upfront giving 50 percent of population sensitivity.
The question I have this is the second time I see on a study of experienced patient that those who failed have much lower mutation than one would expect.  For example, the study you present was more than 50 percent had no mutation, and yet they failed.
Are there factors of those which you mentioned as a reason for the failure should be now included when a drug is developed, and not only the genotype, phenotype presentation?
DR. D'AQUILA:  I certainly would think that information on levels of the drug in the blood are relevant to the drug development process.  I am not sure that one would need to go very far beyond that.  Obviously, tolerability, toxicity is a concern, and that is relevant for adherence.  I think the major issue in my mind would be again merging the pharmacology and the resistance data.
DR. YOGEV:  You pulled the rug under my foot when you showed the data of the 378, because that is exactly what I thought, but all the patients were below the level unless the individuals were not, but the level you showed versus the failures, they were all below.  About half of them have IC50.
DR. D'AQUILA:  That is correct.
DR. YOGEV:  So, you would expect that those would not fail.
DR. HAMMER:  Rich was meant to confound us.  That was the point of his talk.
DR. D'AQUILA:  I can't explain why they failed.
DR. HAMMER:  Dr. Pettinelli.
DR. PETTINELLI:  You mentioned that the new IAS Resistance Consensus Panel is recommending pre‑therapy screening for recently infected patients, but not for patient who has been infected for a longer period of time.
Can you explain the rationale for that?
DR. D'AQUILA:  The rationale is based on a theoretic concern that in a patient with established infection where the virus has had a long time to replicate in the absence of any drug selection pressure, it may be possible for a more wild type virus population to become dominant.
The committee was concerned that we didn't have a lot of data in that setting and we did have good data in recently infected patients, as you heard from Susan's presentation, so that we went there.
I think the issue is are the tests currently sensitive enough to detect the mutants if they have declined over time to be now a minor proportion of the total population, and I think we just need to study that before we recommend very broad screening.
I mean the other point that I would say is something that came out in the case that Susan showed, that one doesn't have to always screen before starting if one is concerned that the viral load is not dropping, that is a very appropriate point at which to get resistance testing.
DR. HAMMER:  I would also just make the important point related to the recommendations of the consensus panel, that they haven't been ‑‑ they are about to be submitted, they haven't been peer‑reviewed yet, so I don't think they should be widely, publicly disseminated as clear cut.
The other thing is that I don't think we should infer a black or white recommended or not recommended.  Each population is dealt with independently in the draft paper, and there are different levels of "consideration and recommendation" that are being put forward by the panel.
So, I think we need to say it is not recommended or not recommended for particular populations.  Each one is being handled with the level of data that the panel thought was available.
DR. D'AQUILA:  Thank you, Scott.
DR. HAMMER:  I think we need to move on.  Thank you very much, Rich.
Questions to the Advisory Committee
DR. HAMMER:  There are three questions for the committee in this session, which I think we can deal with relatively efficiently, hopefully.  We have a long agenda today, I just would remind the committee members, so we need to stay focused and on time, and some of the members need to leave early.
The first question is:  Please comment on the types of patient populations in which HIV resistance testing might be useful in drug development.
Again, let's focus on the drug development issue.  That is why are here.
Who would like to comment?
Dr. Pettinelli.  Thank you.
DR. PETTINELLI:  My opinion, you know, there are several point of drug development.  One aspect is that patient experienced a first failure, because I think at this point, we are dealing with more understandable pattern of mutation, so that is definitely a patient population that will be interesting to study.
DR. HAMMER:  Dr. Yogev.
DR. YOGEV:  I think it is a difficult question if you want to exclude any population because now we are getting to the point that you can get an infection with a resistant strain although it is only 25 percent of your patients, but in the pediatric population, for example, we start seeing patients where they are resistant to the drug, we want to use like AZT, so I think when a drug is developed, we should basically go to any population where they use initial therapy and later they will be different, no question, but I think we need to have some data how the drug is really going to do, which is going to increase percentage of resistance in so‑called naive patients.
DR. HAMMER:  Dr. Mayers.
DR. MAYERS:  One patient population that I have been very concerned about is the heavily pre‑exposed population, and currently at Ford, 48 percent of our patients have failed two PIs and a non‑nuke and have detectable virus.
The problem that you see is that most of the pharmaceutical development is focused on naives and first failures, and so what you have is the drugs are coming out with data on very early treated populations, but are immediately then going to be placed into extended access into the very sickest, latest stage patients we have with almost no data.
There has to be some mechanism early on in the process to get some data on what the impact of these drugs are going to be on heavily pretreated patients even though that may not be on a direct line to the early accelerated approval because that is where these drugs go almost immediately in the clinical arena.
DR. HAMMER:  Dr. Wong and then Dr. Hamilton.
DR. WONG:  I guess I have been very impressed by the data that we have seen over the past couple of days, and although it is not the case that ‑‑ I mean Dr. D'Aquila is perfectly correct that no one has demonstrated precise correlations between resistance testing results and the results of therapy of any drug.
It would be my opinion for any new drug that is coming along, I would want to see these sorts of data on any new drug in all populations that are studied for registration purposes at least, at least in patients in whom we are going to be asked to assess was the drug efficacious or not.
DR. HAMILTON:  I think the sponsor is going to want to know what the various reasons are for success or non‑success in the conduct of their clinical trials, and this is but one variable.  So, I think they are going to want to use these parameters in virtually all patient populations.
DR. HAMMER:  Dr. Kumar.
DR. KUMAR:  Again, we know very little on the resistance profile in the inner city patient population because everything that has been published so far, and even as Dr. Little said,  has all been in certain areas and among white men and whose main factor has been different from what we are seeing in the inner city.
So, I think it should be done for any newly developed trial.  That is the only way we are going to be able to see whether it is going to be effective in all patient populations.
DR. HAMMER:  Dr. Kaplan may wish to comment, but there are studies underway to look at, as was mentioned yesterday, naive populations in at least some of the inner cities around the country.
DR. KAPLAN:  Yes, I would agree. I think it became apparent this morning that most of the data that we have so far are mostly in white men who have sex with men, and I think we would like to see more demographically diverse data.
Since I have got the microphone on, I guess I would just concur with what we have been hearing from others, that I think we want to see data in all populations in which the drug that is being proposed would be used, and that would include not only experienced patients, but like Dr. Mayers said, people who have been heavily pretreated with other drugs or people with acute retroviral infection or other recently acquired infection.
DR. HAMMER:  Dr. Pomerantz.
DR. POMERANTZ:  Just to join the band, I think Brian said it very well in that you have to ‑‑ and just to reiterate ‑‑ you have to do a different analysis if you are thinking about drug development versus how you are treating patients in the clinic when it comes to resistance testing, and we have had trouble even on this committee separating the two things.
I think Brian is right.  For drug development now, it would be very important to test virtually everyone in the drug studies, all the patient populations, especially up‑front, because unlike some patients in the clinic, where up‑front testing is still somewhat controversial, this will be a unique way to not only develop the drugs, but give the information for other drugs that are still out there, especially looking at these moderately resistant strains that may be wild type out there.  Studying during drug development may be key to getting an answer to those and will help with the development of the particular drug.
DR. HAMMER:  Dr. Jolson.
DR. JOLSON:  I am going to ask the committee to take this question just one step further.  It sounds like there are potentially many populations, if not everyone, in whom it would be useful to have baseline genotypic or phenotypic information for purposes of future data analysis.
My next question to you would be for which patient populations would you recommend either prescreening and eligibility based on knowledge of genotype or phenotype and consequently exclusion because of failure to have susceptibility at baseline.
DR. HAMMER:  I might start since no hands are raising immediately.
I think it partly depends, of course, on the drug you have got and its target populations are and where you are studying the drug.  For example, if it is a drug that is not specifically with a high profile to go after drug resistance, and so it is a standard sort of drug development, you are looking at, let's say, a naive population for activity, then, I think the epidemiologic data ‑‑ that we are accumulating, and we saw this morning ‑‑ should help drive the need to test all patients before those patients enter the trial.
We should also recognize that some drug development is going on outside of the United States with trials that are going to be evaluated here in populations that are truly naive, so I think we have to be careful about recommending drug susceptibility testing in every patient in every trial.
So, I think one has to factor in what the likelihood is of resistance and then determine whether it is a subset or the entire population that needs to be evaluated for resistance testing in a "standard" drug development process.
If, however, it is a drug that has a particular profile against drug resistant virus, and is to be tested in that fashion, then, I think we should move to prospective testing, in which case testing should be done at baseline with the knowledge of that going into the trial, and either randomizing on the basis of that or stratifying for analysis on the basis of that or whatever, but that is a situation in which the entire population, I think, should have resistance testing if, in fact, you are going after the indication that is going to be safe and effective in a treatment drug resistant population.
Did that raise questions?  Good.
Dr. Mathews.
DR. MATHEWS:  This was something we sort of touched on yesterday.  My feeling is that if you know, for example, somebody has K103, and there is a trial that could randomize them to an NRTI, it would be absurd to put somebody in that if you had that information.
On the other hand, if the question is does a particular mutational pattern enhance the response to the drug, then, you ‑‑ I mean to handle it up‑front by stratifying the randomization, so that you could actually examine it with adequate numbers of patients rather than to have to stratify retrospectively where you wouldn't necessarily have balance.
DR. HAMMER:  I agree about the 103, but I think it gets very complicated as we have talked about this morning with data that Rich showed from the ABT‑378 trial, that it may be that pharmacologic factors will overcome a particular level of what was previously thought to be resistant, and we have also seen some data where a measure of susceptibility of the regimen and response was statistically significant and correlated, but individual drug susceptibilities phenotypically or genotypes were not.
So, we need more data in that regard, but it is very complicated.
Mr. Harrington.
MR. HARRINGTON:  I just think that we have seen a number of mutations and their complexity.  There is going to be huge sample size issues in trying to get statistical significance out of things that might be biologically very significant, but for a relatively small number of people, so you want to know the answer, but I don't really know if you would want to exclude people on the basis of certain mutations except for in the cases where they were extremely common and extremely well characterized like, you know, a drug active against people with 184 or something like that.
But I would think in many cases, you would be denying yourself information about how the drug really works in the clinic if you excluded people, but you in some cases would want to gather the information at baseline.
DR. HAMMER:  Dr. Woolson, did you have a comment?
DR. WOOLSON:  I think this is a follow up to a comment that you made earlier, Dr. Hammer. I think we really need to remember the importance of sampling in particular in clinical trials.  It is not going to be necessary for us to do this in every single patient entering a trial, but perhaps subsets of them.
I guess I am particularly concerned that if we only sequence individuals who are treatment failures, that it is going to be very difficult to sort a lot of the data out, so I think we do need to have a mechanism where we are actually getting a broader base sampling, but it doesn't have to be again on every individual.
DR. HAMMER:  Although I would take Dr. Pettinelli's statement, take it a little more broadly.  Basically, the populations need to be well defined and they can be naive by history, but we need to document that now I think by testing.
First failures are the clearest picture in many ways as to what is happening and it taught us a lot about what patients are failing, whether virologically, and those have to be separated from multiple failures, because that level of complexity is more than one order of magnitude higher.
Dr. Gulick.
DR. GULICK:  It just occurred to me during the discussion that one unique population to look at would be the expanded access programs, perhaps requiring that resistance be part of any expanded access program given that that is the clinical situation where you have the most advanced patients and that may be a patient group where the clinicians are willing to take the chance of just using one drug at a time, which we all think would be a problem, but here you sort of get out of the ethical dilemma because the primary care doctor and the physician have made that decision.
That would be a unique opportunity, I think, to really assess what the resistance pattern is in that population.
DR. HAMMER:  That raises a huge question, (a) financially, where is that burden going to fall, and it also raises a question that this committee has dealt with historically, and that is, trying to turn expanded access data into some sort of experiential data that can be analyzed in a quasi‑clinical trial sort of fashion, but we probably can't go up on that right now.
Dr. Stanley, did you have a comment?
DR. STANLEY:  Well, it was really to reiterate something that I think you already said, which is we have asked for preclinical in vitro data on resistance patterns and where a drug might have an advantage, and so you are clearly going to want to validate that in clinical studies where you target patients that appear to have the resistance pattern that you think your drug is going to overcome.
DR. HAMMER:  Dr. Wong, and then we will move on to the next question.
DR. WONG:  I just want to echo what Mr. Harrington said.  I would be very careful about trying to mandate that certain patients be excluded from trials unless it has been demonstrated in advance that the drug being studies would be ineffective in that patient, and it is unlikely that that is going to be known before it is tried.
So, I wouldn't exclude people, but I would expect that the sponsors collect the data and analyze the results in light of the pre‑randomization susceptibility results.
I can well envision a situation in which the overall results of a drug might not be that great, but within a population, for example, that has susceptible virus, that the drug would be effective, and in those circumstances, I could see voting for approval even though the drug overall was less impressive.
DR. HAMMER:  Dr. Mayers.
DR. MAYERS:  I think the one thing that has really changed in the last few years is the fact that you used to have to go to PBMCs and grow them up, and it was very expensive to expect a sponsor to sock cells down in every patient at baseline.
I think things have changed.  Now that we are sequencing plasma virus and we are doing RVA assays out of plasma virus, I think it is not unreasonable to expect the sponsors to put plasma down on every patient going into their studies including expanded access potentially, so that they can go back and look at these issues, and then for the failure patients, pare that baseline sample of a failure sample.
This is a very inexpensive requirement.  They are getting HIV‑RNAs on these patients, and this I think would allow them to do the studies that need to be done if it was just simply collected.
DR. HAMMER:  I think it is fair to state that for the most part ‑‑ and I may be wrong ‑‑ that, in fact, that is being done.  Plasma storage is going on in almost every Phase I, II, and III trial that I am aware of, and I think that is appropriate.
You can prospectively test whatever sample you wish.  It allows you to retrospectively test the remainder if you need to, so I would echo that.  I just wanted to make it clear that I don't think we should glibly state every patient in every study for drug development should have resistance testing at baseline.
I think it basically needs to be evaluated on a population, drug, and study basis.  We have to be careful what we say because sometimes these statements are overinterpreted.
Just to summarize this question, I think we have answered it.  On the types of patient populations in which drug resistance testing might be useful in development, essentially, it is very population that may be studied.
I think for the most part, you know, drug resistance testing is going to be useful in primary infection, established infection, first failure, multiple failure, and in pregnancy.  For the most part, as far as drug development, we are talking about established infection and failure patients.
I think the committee's consensus is that all of those are relevant populations in which drug resistance testing is useful.  You need to factor in again the activity of the drug, its target population, its target indication, and the epidemiologic data that tells you about your population at that time.
So, I don't think we really have excluded any population from resistance testing, and I think that is probably the answer.  I wouldn't do it on every patient in every trial at this point necessarily.
For example, if you had a 700‑patient Phase III trial in a drug that wasn't going to be particularly active necessarily drug resistant virus, and you selected your population for a very low risk prevalence in the community, or, in fact, it was an international trial where there were no drugs, I don't think you have to test everybody.
2.  Please comment on the timing of HIV resistance testing in the setting of a clinical trial.
I would just ask that we make our responses targeted here, so we can stay on time.
Dr. Mayers.
DR. MAYERS:  I think that probably the most efficient way to do it is to get the baseline and when they fail, get a failure sample and test it against the paired baseline sample, and for naives, that would take care of most of your requirements.  For the experienced, you are going to have to do some of the baselines, as well, but I think that pairing your failure sample with your baseline gives you most of what you need and allows you to find out what mutations are being selected by the drug.
DR. HAMMER:  Dr. Mathews.
DR. MATHEWS:  I was just wondering, do you think there is a role for systematically testing at least a subset of the population irrespective of failure, so that the patterns of evolution of these resistance mutations and their correlation with phenotypic resistance could be better characterized.
DR. MAYERS:  Can I answer that, Scott?
DR. HAMMER:  Absolutely.  I think it was directed at you, so please do.
DR. MAYERS:  The problem becomes how because if you are defining failure as a viral load coming up to 500, and people were defined as successful when they are below 50, you are not going to be able to sample with existing technologies the patients who are doing well, so I think it makes sense in a statistical way, but I am just not sure how to do it technically.
So, I think the answer is in most patients, you are going to have to assume that if they are below limits of detection of your ultra‑sensitive assay, they are probably still drug susceptible.
DR. HAMMER:  I may have missed this, but I think testing, if you are going to do baseline and follow up on the failures, you need some proportion of the successes at baseline to compare in a case cohort or some other design.
Dr. Yogev.
DR. YOGEV:  I just need to add to a similar situation, for example, if a company come around and said that drug X is working in resistant strain, what combination of drug do they want to use, for example, addition of a drug similar to DDI or the ABT, that a test has to be done prospectively to identify this population as a subset to work with, and make sure that it does work, because, for example, into the study introduce people who are not involved in the study at week 12, for example, the contention is really working to that specific group is not, which was identified as resistant is not suffering from the study if it goes for 24, 48 weeks, so I think there are some studies that you have to do it prospectively as part of the inclusion criteria.
DR. HAMMER:  I think there is consensus about that.
Dr. Stanley.
DR. STANLEY:  And then just to state the obvious, that we were told this morning the example we were given that if you are testing at the time of drug failure, they need to still be on the drug when you collect the sample.
DR. HAMMER:  Right.  I think actually, there were maybe two parts to this question, the timing in the setting of the clinical trial.  I think one is the broader question whether it is prospective testing or retrospective testing, and I think the sense is that increasingly we are moving towards prospective testing and clearly baselines, depending upon the relevance of the population and whether you are going to randomize on the basis of that or not, and follow‑up specimens at the time of failure, I think have all been recommended by the committee.  I think this is fairly obvious based on the discussions over the last day.
The third question.  I am going to take the Chair's prerogative here.  The question is:  Please comment on factors that may confound the interpretation of resistance testing in the setting of clinical trials and what may be done to reduce these effects.
At least the first part of that question, I personally think Dr. D'Aquila answered better than any of us can answer, but I would state that just personally and let the rest of the committee comment, but also what may be done to reduce these confounders is potentially where we might contribute something.
So, Question 3.  Does anyone disagree with my premise?  Dr. Charache.
DR. CHARACHE:  I agree with your premise.  I think in terms of reducing confusion, high on my suggestion list, as has been pointed out by Dr. Jackson, as well, is that the IC50 has clearly proven very useful as an interpretative point, but I think with new drug development, it would be very helpful to also relate the interpretations to the pharmacology of the sponsor's drug.
So, I think it would be very important to provide this information, not only as it pertains to comparison with the lab strain of virus, but also in terms of achievable blood levels and trough levels and area under the curve, and I think that data should be provided.
I think there should be similar information for all drugs a patient is on, since they are going to be on a scrap basket of drugs, and that might help make it easier to interpret the results that you are looking at in terms of clinical failure or success.
Finally, I think it would be very important for the sponsor to be able to validate the competency of any laboratory that is used to derive this information.  I think the Virco and virologic models are outstanding, but I think also they may want to, if they use other units or even those to have testing done between laboratories because this is going to be absolutely critical to make sense out of this very difficult area.
DR. HAMMER:  Thank you.  Well stated.
Dr. Hamilton.
DR. HAMILTON:  It seems to me to evaluate the results of genetic and/or phenotypic testing in the context of a clinical outcome, one needs the simultaneous collection of additional data that can easily or alternatively explain the clinical event, namely, drug exposure, drug adherence, and I would strongly recommend that if genetic and phenotypic testing is to be done, and it appears that there is a move that it should be in virtually all drug populations, I think there should be simultaneous collection of those other parameters.
DR. HAMMER:  Dr. Kaplan.
DR. KAPLAN:  This is part of Question 3, and I guess it also relates back to 2 a little bit.
If a company is going to propose use of a drug for recently infected persons, then, an interesting issue that has come for discussion is whether the best timing of an initial specimen is before treatment or maybe after the person has been on drug for a couple of weeks, because then perhaps anything that has partially reverted to wild type will have a chance to pop up.
That may be less of a factor for recently acquired infections where there hasn't been that much of an opportunity to revert to wild type than for people who are farther along, but I would be interested in what others think about the timing of that initial specimen if the drug is going to be proposed for those conditions.
DR. HAMMER:  I think it is quite difficult although in some trials that have been designed have specifically stated that eligibility criteria include having remained on the failing regimen until a specimen is obtained, at least a plasma specimen is in the freezer, and I think if one wants to ensure that, that it is the best way to ensure the selective pressure, then, that is the way the inclusion criteria should be.
However, it excludes populations that come on and off drugs, and I think one thing that is difficult, at least for me, I will just state it, is a patient has come off a failing regimen, asking a patient to go back on a failing regimen to reselect in order to make them eligible for a trial, I personally find difficult to do, and have not done that in trials I have been involved in where the question has come up.
So, I think the point is well taken, and it is an issue of again carefully defining your population for the trial, and so that you can analyze it in the best way possible, and then what the broader applicability is to populations who had come off.
I think that what that is going to be is a compendium of trial results that will tell us, again with all the interest in salvage therapies after treatment interruptions, and there will be a flurry of data over the next year and plus that will tell us more about whether that is a reasonable strategy or not, I think we will be able to put this stuff together, but I think the selective pressure question can be handled by the eligibility criteria.
Dr. Yogev.
DR. YOGEV:  I think you might consider around three months or 16 weeks, if you are not less than 400, those should be also tested because even if you have the wild type, and you got the reduction, did you get a reduction of the wild type alone or in combination of the resistant one, you can pick it up at that point in time, so I would not wait until the end of the study.  I would do it at that point, but I would not exclude patients for entering the study.
DR. HAMMER:  Dr. Mayers.
DR. MAYERS:  I think that as the issues of PK come up and potentially adherence come up, that it is clear the companies aren't going to do PK on each patient in the study, but I think that what you can do now is get a time on your drug levels, your last dose at each visit, and with the new technologies of plasma, you can, with 2 mL of plasma get a genotype, a phenotype, and drug levels for all the drugs that your patient is on.
So, if the effort is made to attain the time of the last dose of each drug at the visit, they can then go back and actually map out whether the patient had a low exposure, no exposure by that level or not, and if you don't get that data, you simply can't do it.
So, I think if you we are going to try and relate resistance and drug levels to failure, there is going to have to be an attempt to be able to at least pair the timing of the last dose with the plasma sample.
DR. HAMMER:  Let me just summarize.  The confounding factors I think as far as trying to reduce them, I would reiterate that what they are was well stated in Richard's talk.  To reduce them, as one suggests, the simultaneous obtaining of particularly pharmacologic information and drug adherence information, that may vary trial to trial, but they are the two major factors in trying to determine what failure is all about, and since resistance testing is related to failure on study in at least one circumstance, I think that is the critical issue.
Up front, I think it is the issue of defining the population that you are studying and then deciding whether you are going to do resistance testing at baseline, which we have talked about several times.
Anything else?  Dr. Mathews.
DR. MATHEWS:  A couple of other things just having to do with conduct of the trials, and it is not a problem right now, but one of the scenarios related to this, that people could start using resistance testing during the conduct of a trial and drop out on the basis of resistance testing.
Now, you could say, well, they wouldn't get it if they were nondetectable, but it is easy to just withdraw from drug for several days, rebound, measure it.  I don't know whether these things will happen, but they are certainly within the range of possibility.
So, selective dropout for that basis could affect the interpretation of the resistance assay.
Another issue relates to what is the appropriate control for interpreting the response to a particular mutational pattern.  It may not be the particular site or codon that is the issue, and if the trial wasn't randomized on the basis of the mutational pattern, then, there will be a fair amount of heterogeneity.
So, mutation A in the setting of several other mutations may not have the same effect as in another context, and so I think it will require quite a bit of thought to figure out what is the appropriate comparison group in a trial.
DR. HAMMER:  That is a very important point, and it brings up a corollary point, and that is the interpretation of resistance testing, how that factors in to any clinical trial design, and trying to create balance in interpretive skill and recommendations for regimens that may be as a result of some strategic intervention with the results of resistance testing since we have seen that imbalance in certain trials so far, and we have also seen the complexity of interpreting these tests.
So, I think that is a factor we haven't talked about in trial design, but it certainly confounds the interpretation of how resistance testing is applied in trials because it is no better than who interprets it.
We are running 15 minutes behind, but I think we should stop for a break and reconvene at 10:45.
Thank you.
[Break.]
DR. HAMMER:  I would like to officially reconvene and turn to Dr. Jeffrey Murray, who will introduce Session 4.
SESSION 4
Potential Roles of Resistance Testing
in Drug Development
Introduction
DR. MURRAY:  Session 4 is entitled Potential Roles of Resistance Testing in Drug Development.  From our perspective, this is the mother of all sessions because this is where we are expecting the most lush feedback from the committee.  That is why we also wanted to get this session started, get the presentations out of the way in the morning, so there would be a lot of chance to talk on the regulatory scenarios which are coming up.
The objectives for this session are:  to obtain committee recommendations on the amount and type of in vitro resistance data sufficient to initiate and to support a clinical development program;
To obtain committee recommendations on the amount and type of in vitro and clinical resistance data sufficient to characterize the clinical activity of an antiretroviral drug against resistant viral isolates;
To obtain committee recommendations on the amount and type of clinical resistance data appropriate to determine an antiretroviral drug's potential to induce resistance and cross‑resistance;
To obtain committee recommendations on testing can be optimally incorporated into Phase II/III clinical trial design.
Our two presentations, the first one is Dr. Gary Chikami, who is Division Director of Anti‑Infective Drug Products.  He will talking on Susceptibility Testing in Drug Development from an Anti‑Infectives Perspective.  I think this will give us good groundwork to then talk about how it would be applied in the HIV arena.
Historical Perspective from the Antibacterial
Analogy and Contrasts with Virology
DR. CHIKAMI:  Thanks, Jeff.
On the original agenda, my sort of charge was to sort of talk about the historical perspective the development of resistance testing, and I think that is sort of a daunting task, so what I want to do in the few minutes that I am going to be talking, about 20 minutes, is to provide sort of the framework that has been developed over the years for susceptibility testing and how it is used for the development of antibacterials, and touch on two regulatory issues with regard to how susceptibility information is included in produce labeling.
A related concept which has really come to the fore recently with the rise of the importance of antibiotic resistance, the development of products specifically for resistant indications, and how we have attempted to deal with those in terms of drug development and how those would be included in package labeling.
[Slide.]
With respect to antibacterials, the goal of susceptibility testing is to predict the likely outcome of treating a patient's infection with a particular antimicrobial agent.  This would be useful and important for organisms that are not particularly predictably susceptible to drugs of choice either because of an acquired resistance.
These tests should do a couple of things.  One is detect frank resistance.  Moreover, they can also be useful for the quantitative measurement of susceptibility to antimicrobial agents with some species where there may be direct therapeutic relevance.
For example, the magnitude of penicillin, cephalosporin MICs for Strep pneumoniae, and that was referred to by one of the talks this morning where, in certain circumstances, an intermediate Strep pneumoniae in fact may be treatable with higher doses of penicillin.
[Slide.]
Over the years, a framework for the standardization of susceptibility test methods have been developed by numerous organizations.  The NCCLS, for example, which provides standard protocols, and also reviews data to send interpretative criteria.
Those sorts of activities are also important at the FDA as we review drug development and approve drugs for marketing.
[Slide.]
In regard to the regulatory responsibility for antibacterials, in vitro diagnostic tests are reviewed and approved in the Center for Radiologic Health, Medical Devices and Radiologic Health or CDRH, with consultation of CDER, the drug review divisions.
Sort of the two steps involved in standardization of the test methods are defining optimal assay methods and development of the interpretive criteria.
Later in my talk I will touch on again these two regulatory issues, that is, the inclusion of susceptibility information in product labeling and then specific claims of effectiveness for the treatment of infections due to resistant bacteria.
[Slide.]
In regard to the development of test methods, the goals are to develop reproducibility and reliability in the test method.  These include a number of sort of physical, chemical, and other specifications including standard assay conditions, sort of a caveat in this or sort of the driving force is measurement of a minimal inhibitory concept is not a physical or chemical measurement.
It is a measurement of the interaction of the test drug with the test organism that may be affected by a number of sort of conditions of the test, temperature, ion concentration, inoculum effects.  All of these things, in fact, may affect the observed test results, and it is critically important that all of these be optimized and specified in a standardized way, so that the measured results again are reproducible and reliable.
Secondly, specification of quality control parameters are very important.  Again, this relates to standardization of the assay conditions, but also a standard battery of test microorganisms and their expected results.
Again, these tests are done in multiple laboratory conditions, different labs, different quality of laboratories.  So, having built into the system an ability to assess the performance of the test, not just with the target organisms, but standardized organisms is very important in being able to develop interpretable results.
The third point in terms of developing reproducible and reliable test methods is correlations of different methods that may be used under different clinical conditions for bacterial susceptibility testing, common sort of methods that are used include dilution methods.  They may be macro or micro dilutions or disc diffusion methods.
In the course of developing these assays, and standardizing them, correlations of these two different methodologies are very important in being able again to develop interpretable results that can then be used in the next step of the process.
[Slide.]
And that is the development of interpretive criteria.  Interpretive criteria relate the quantitative results of susceptibility testing to again that overall goal of the testing methodology, which is to predict the likely outcome of therapy.
There are a couple of caveats here.  Susceptibility in vitro does not necessarily predict successful therapy.  Host factors are often more important in terms of eventual clinical outcome.
Secondly, we would like to have methods where resistance in vitro should predict therapeutic failure, and again in terms of understanding why a patient may not respond to therapy or being able to guide therapy in patients in which it may not be obvious what is the optimal choice of therapy.
[Slide.]
From that follows these three definitions which are commonly used or which are used for development of interpretive criteria for bacterial susceptibility testing.  A strain is called susceptible to the test drug if it may be appropriately treated with a dosage of the antimicrobial agent recommended for that type of infection.
An intermediate classification are strains with MICs that approach usually obtainable blood levels or tissue levels and for which response rates may be lower than that for susceptible isolates.
There are several factors built in or other considerations built into this intermediate category.  There are also conditions, for example, where an intermediate MIC may be appropriate or use of a drug with an intermediate MIC may be appropriate at a tissue site where the drug is concentrated, for example, the use of quinilones in the treatment of urinary tract infection.
Some other considerations are also for drugs with a narrow therapeutic index where, in fact, inaccuracies in the assay may, in fact, have great therapeutic import.
Finally, the third category is resistant, and these are strains which are not inhibited by the usual achievable concentration of the agent with normal dosage schedules and/or or fall in the range where specific microbial resistance mechanisms are likely and clinical efficacy has not been reliable in treatment studies.  An example of that is, for example, an organism and beta‑lactamase.
[Slide.]
With these definitions in mind that have been applied to anti‑infectives, what are the sorts of information that are considered in setting these interpretive criteria?
Just parenthetically, breakpoints as they are called, or interpretive criteria, are developed early on in the course of development of an antibiotic.  Much of the information, for example, the in vitro activity, animal model data, and early pharmacokinetic information which would allow the setting of tentative breakpoints, say, at Phase I or Phase II within a drug development process.
Those tentative breakpoints are then used in the design and conduct of Phase III clinical trials which are meant to support safety and efficacy, and it is in the context of those Phase III clinical trials which collects more extensive clinical efficacy data that the correlation between clinical outcome and these tentative breakpoints are developed.
That activity is done within the review process of a new drug application at the FDA, so, in fact, during the review of an antibiotic, we review not only the clinical efficacy information, the basic pharmacology and toxicology, but the microbiology package includes, in fact, the proposed standardized assays, quality control information, as well as the proposed breakpoints and all of the information that is shown here to support setting those interpretive criteria.
The NCCLS is also an organization which reviews similar sorts of information, often the same package of information, and also develops interpretive criteria for new drugs and organisms.
So, the information that is considered in developing the interpretive criteria, setting these categories of susceptible, intermediate, and resistant, include the assay characteristics, population distributions of MICs for the important pathogenic organisms.
This is usually based on in vitro information that is collected on a large number of organisms, the example that is given or proposed is 500 organisms, which represent both geographic diversity around the country.  They should be recent clinical isolates relevant to the infections that are being sought, and should represent not only susceptible organisms, but if there is evidence on relevant resistant mechanisms, those should also be represented in this sample of microorganisms for which the distribution of MICs is produced.
This is often presented in tabular form or most conveniently in histograms, which it is very easy to then determine what the overall population distribution of these MICs are.
The third part of the equation are pharmacokinetic and pharmacodynamic information.  That includes not only strict or simply blood levels or tissue levels, but also an attempt to look at what are the important pharmacodynamic correlates with a particular class of drug and organism.
For example, parameters such as time above MIC or AUC to MIC ratio.  Many of these are peak concentration to MIC ratio, may, in fact, be important in predicting efficacy of certain drugs.  These have been worked out for different classes of antibiotics.  These factors are also considered in determining what are likely achievable, not only blood levels, but pharmacodynamic parameters, which are associated with predicted efficacy.
Finally, correlation of test results with clinical outcomes.  As I stated, proposed interpretive criteria are developed in the course of drug development for antibiotics, and these are incorporated into the Phase III clinical trials, so that within an application, a sponsor may then analyze baseline MICs for the particular isolated organisms and then correlate that with clinical outcomes observed in the Phase III trials.
These are usually broken down by organism and site of infection to really get a sense of how not only the drug is performing at a specific site of infection, but what the correlation with the particular MIC and the site of infection and the etiologic organism are.
[Slide.]
To finish up this section of my talk, I just put up a list of organisms for which interpretive criteria have been established, and this is from the most recent NCCLS publication actually.
You can see that there are, in fact, a large number of bacteria for which interpretive criteria have been established.  There are also clearly important pathogenic organisms not on this list, for example, chlamydia pneumonia, mycobacterium tuberculosis, to name just a few.
I think that the challenges in developing sort of standardized test methodology and interpretive criteria really harken back to some of the issues I have talked about and some of the issues that this committee has been grappling with in terms of developing standardized methodology for HIV susceptibility testing.
[Slide.]
What I would like to move on to now is to touch on a couple of these regulatory issues, and that is how is this susceptibility information, once it is obtained in the course of drug development, now included in the product labeling, which is the source really of how we communicate all of this information about the utility of this information to the practitioner in produce labeling.
Importantly, it is also sort of the basis for a product promotion, and in that sense, these two important goals of providing accurate important information and accurate information for product promotion are very important, and need to be sort of integrated into how the approach is taken.
[Slide.]
In 1992, the Division of Anti‑Infective Drug Products published a clinical points to consider document, which was titled, "Clinical Development and Labeling of Anti‑Infective Drug Products."
It was quite a comprehensive document that dealt with not only a number of general issues in the development of antibiotics, but also had a number of sections that dealt specifically with clinical trial design within specific indications, and the level of evidence to support approval of specific indications.
It provided a regulatory framework for the development and product labeling of anti‑infective drugs, and while there are certain parts of that document that we have more recently started to re‑review, that is, specific guidance in terms of clinical trial design, I think there are some general parts of the document which are still important to consider.
The overall goal of this document was specifically related to reporting of susceptibility testing, and the format of the Microbiology section was to eliminate advertising or other promotion that implied greater effectiveness of one compound versus other compounds based solely on in vitro microbiologic data.
To that end, one of the sections recommended a format for the Microbiology subsection.  In it, it included a description of testing methodology and interpretive criteria and also a format for the listing of susceptible microorganisms.
[Slide.]
In regard to the inclusion of susceptibility information, the reason I am sort of laying this out is because susceptibility is the converse of resistance, and the approach in antibacterial labeling historically has always been to label products for those infections due to susceptible microorganisms.
As I said, at the end of my talk I will speak briefly about the whole issue of specific resistance claims and how that has come to the fore given recent sort of clinical and scientific development.
Susceptibility information is included in two parts.  There is what is often called the first list or list of organisms found specifically in the Indications and Usage section.  Specific wording in the label is that the drug has been shown to be active against most strains of microorganisms, both in vitro and in clinical infections as described in the Indications and Usage section, and I will speak more about this in a minute.
The second list of organisms are organisms for which only in vitro data are available, and the specific wording for their inclusion is drug X exhibits in vitro MIC concentrations ‑‑ and this would be of a clinically relevant susceptible breakpoint based on the interpretive criteria set during the review ‑‑ or less against most greater than equal to 90 percent strains of the following microorganism, however, the safety and effectiveness of the drug in treating infections due to these microorganisms has not been established in adequate and well‑controlled clinical trials.
[Slide.]
In regard to the first list, the first lists again are those organisms which are listed in the Indications and Usage section, and for the purposes for this document, an indication was defined as the treatment and/or prevention of infections at a specific body site due to a specified susceptible microorganism.
I think the important point I want to make here is that the indication is supported by substantial evidence of effectiveness from adequate and well controlled clinical studies.  So, there are clinical data contained within the application which support not only the use of the activity of the drug at the body site, but also for the specific listed microorganism.
In general, organisms considered to be etiologic agents in at least 10 percent of the specific infections successfully treated within any particular indication may be included in that list.
Now, clearly, there are review issues that relate to this, but this is the general guideline that is provided in the points to consider document.  So, again, these are organisms for which there are clinical data to support effectiveness and safety.
[Slide.]
The second list or the in vitro list, a list of criteria were developed to support their inclusion in that list, again with the caveat that there were no clinical data submitted in the application to support safety and effectiveness.
At least 100 isolates of each microorganism would be tested ‑‑ again, this is a general guideline ‑‑ that they be geographically representative throughout the U.S., that they be recent clinical isolates, represent clinically relevant susceptible or resistant mechanisms.
They should be pathogens at body sites of infections for which clinical effectiveness have been established, and this links it to the approved clinical indications for the product, and the mean MIC90 should be equal to or less than the clinically susceptible breakpoint.
[Slide.]
Finally, the point to consider document in the Micro subsection also lays out the format for the description of the test methodology, again describing diffusion or dilution techniques, describe quality control measures, provides the interpretive criteria, which came from the FDA review, and also provide reference of NCCLS methodology, if appropriate.
[Slide.]
That sort of lays out how within product labeling's claims or descriptions of activity against susceptible organisms are described.  Now, more recently, we have been reviewing or asked to review specific claims of effectiveness for the treatment of infections due to resistant organisms, and they may come in two forms ‑ either those based purely on in vitro information or that information which may be collected in the course of the clinical development of the product.
There are examples of labels which carry information on in vitro activity for resistant organisms, for example, penicillin resistant Strep pneumo or methicillin resistant Staph aureus, which are included in the second list or the in vitro list that I have described within some product labeling.
Those will have had to fulfill the criteria that I have already laid out in terms of inclusion in that list.
[Slide.]
The second are claims of clinical effectiveness and where are we in terms of the quantity or the quality of evidence and how much evidence do we require to support claims of clinical evidence for the treatment of resistant infections.
This is an issue that we have had two recent advisory committee meetings on, and I think it is quite a complex issue, and what I will do is sort of lay out some of the considerations that have been discussed.
[Slide.]
This again just gets back to the idea that the indication, that is, the treatment of infection at a specified body site due to a specified microorganism, either susceptible or resistant, say, PRSP, would be supported by substantial evidence of effectiveness from adequate and well controlled clinical studies, and that in general the organisms to be considered in at least 10 percent of the specific infections successfully treated may be included in the list.
[Slide.]
I think the general framework that we have developed in terms of thinking about the types of evidence we would like to see to support claims of effectiveness in these situations are laid out in the next two slides.
That is, there should be data on activity in vitro against both susceptible and resistant strains of the organism, and that there be an exploration of the relevance of the mechanism of resistance to the mechanism of action of the drug.
For example, if a sponsor is developing a product for penicillin resistant Strep pneumo, and the drug is a quinilone, in fact, the relationship of the resistance to penicillin may or may not be relevant to the site of activity of the drug.  Whether or not that bears on the observed clinical activity of the product is something that needs to be explored and developed throughout the product development of that product, and that can be done in a number of ways.
One is to look at activity in animal model systems, again, developing data on activity against susceptible and resistant strains in vitro, and also something that was alluded to in the discussion of HIV resistant strains is that information on whether resistant strains behaved differently than susceptible strains, that is, are resistant strains more or less virulent, and this could either be in animal models or is there any clinical evidence that they either are more virulent or respond differently to therapy.
Finally, the clinical information that provides a framework for assessing whether or not a product has demonstrated clinical effectiveness for the treatment of a resistant organism includes effectiveness of the product for the treatment of infections at a particular body site, for example, if you are developing a product for the treatment of penicillin resistant Strep pneumo, we need to know, in fact, that the product is effective for the treatment of pneumonia, that is, it fulfills all the pharmacokinetic/pharmacodynamic characteristics that are requisite of such an agent.
Secondly, it is effective for the treatment of susceptible strain to the organism. That tells us something about in general the activity of that product against that genera and species of microorganism.
Thirdly, that there is some clinical data that speaks to the effectiveness of the treatment for the resistant organism in question.
[Slide.]
One of the questions, sort of a central question is how much clinical information is necessary for us to draw a conclusion, in fact, that effectiveness has been demonstrated.
Again, these considerations relate to the type of evidence that I have spelled out in the previous slide, that we would like to see a sponsor develop in the course of seeking one of these indications, that is, how related is the mechanism of resistance and the mechanism of action of the drug, what do the in vitro and in vivo data tell us on the relative activity of the drug against susceptible and resistant strains.
Finally, this issue of biologic behavior, is there any evidence that the resistant strains are more or less pathogenic than susceptible strains or are they more or less likely to respond to therapy.
All of that speaks to how readily we can extrapolate from the large body of evidence that may be available on activity against susceptible strains to the more difficult to collect activity on resistant strains.
That is my last slide.  I will stop there.
DR. HAMMER:  Thank you very much.
Are there questions from the committee?
I have one or two questions.  The data and the history of bacterial infections and the in vitro activity and clinical effectiveness are in a very important framework, and we have had the greatest history for that, but much of that clinical testing except for diseases like enterococcal and endocarditis and other special circumstances can be a single identified organism and a single identified antibiotic, so the clinical outcome issues are often easier as far as clinical outcome and microbiologic outcome.
I think one of the things we have been wrestling with, of course, is we can potentially deal with the in vitro aspects, but the combination therapy aspects and the clinical outcome issues are more problematic.
Do you have any comments on that as far as labeling issues?  I can actually foresee that there may be for anti‑HIV drugs now a resistance section within the label that gives an in vitro description, not too dissimilar from what we see for antibacterial agents, and then there can be the clinical data, whatever it might be, in the clinical description aspect of it.
But do you have any comments on that, because this leap is a big leap as far as the clinical effectiveness side of it?
DR. CHIKAMI:  Yes, I guess in general in the development of most antibiotics, we don't run into that problem.  In the area in which there is most experience is an area that I really don't know well, and that is anti‑tubercular therapy.
That is where I would look for most of the analogies, so I can't answer that question.
DR. HAMMER:  The other question I would have ‑‑ and maybe Dr. Wong wants to comment on this ‑‑ when you think about other microbiologic categories, susceptibility testing, a lot of research, and a lot off activity, attempts at standardization, but still at least for the non‑mycologist, still confusion is the area of antifungal therapy, and what progress is being made there.
Does that give us hope that other groups of organisms besides bacteria that we will be able to bring rational issues of susceptibility testing and to standards to testing and labeling?  Maybe Dr. Wong wants to comment.
DR. WONG:  Well, I mean antifungal susceptibility testing has been standardized, but the extent to which the standardized results have been related to clinical outcomes is really minimal.
So, in some respects, you know, the situation is similar to what we see with HIV and that we now have techniques that appear to give reproducible results, but the relationship between the results of those susceptibility tests and clinical outcomes is just in its very early development of analysis.
So, I don't know that we can draw any conclusions or analogies from the antifungal field at present.
DR. HAMMER:  But we can take comfort.
DR. WONG:  Right, we are not alone in not knowing exactly how to interpret the results.
DR. HAMMER:  Thank you.  Dr. Yogev.
DR. YOGEV:  I think if you want to stay in the bacterial area, we need to look into the enterococcus as an example of a combination with so‑called resistant glycoside and yet the combination with ampicillin wouldn't work, so maybe you have to look in the other one which I never saw any agency regulation is the immunocompromise, because there we have a lot of other committees, they will work together to try to decide which triple combination is better or dual combination in this population, and I think that is where the bacteria can help us, if at all.
DR. HAMMER:  Dr. Masur.
DR. MASUR:  One of the things that I am not clear about is what the relationship of the FDA is to a group like NCCLS.  If you are talking about establishing breakpoints or other laboratory parameters, I guess it is desirable to have an independent respected group which is establishing that for you, but is the proposal that the FDA would determine what appropriate breakpoints and techniques would be or would they defer this to some other organization, and if so, what other standard would you accept, would you accept what the commercial assays are telling you, because obviously, we spend a lot of time talking about whether those are based in solid data or not.
In other words, if you are saying you want to compare something to a breakpoint, whose breakpoint are you going to use?
DR. CHIKAMI:  In regard to antibacterials, the current status is that the FDA, in the course of their review of a new drug application, will review the proposed methodology, which may have also undergone NCCLS review independently, and we will review the data to support setting the interpretive criteria.
Again, the NCCLS ‑‑ and we have representatives on the committee of the NCCLS that does set those interpretive criteria ‑‑ will again do that independently.
DR. MASUR:  Does that mean that the NCCLS might consider taking this project up?  This would be quite an undertaking.
DR. HAMMER:  I think Dr. Charache might be able to help us here.
DR. CHARACHE:  I refuse to speak to the NCCLS, but I can perhaps add some information.
DR. HAMMER:  That doesn't stop us from inferring, so please go ahead.
DR. CHARACHE:  I have noticed that.  It is wonderful.  We can also give comfort to it is no only fungi that has standardized assay, which has to be interpreted in terms of what it means clinically, but also mycobacteria, as well, and the viral group is well underway now in terms of getting into the same pickle.
But I think the key concept here has been to develop an assay in which the answers always mean the same thing.  This is like you have got a swamp, you drop piles down, you build a platform, and then you go from there to see what the relationship is between a given number and what happens clinically.
I do think that in terms of who sets the breakpoints now, the manufacturers come to the NCCLS before they start their clinical trials, and at that point, present data which is based primarily on pharmacology and microbiology, which proposes what the breakpoints will be for that clinical trial, just as a starting place, so they can have a legitimate clinical trial based on some information on susceptibility and resistance.
The NCCLS approves or modifies the proposed breakpoints at that stage.  The clinical trials are run, and then they come back to the NCCLS, and the final breakpoints are decided based in part on the clinical information that is produced, as well as the advanced pharmacological data.
The original breakpoints are set by having no fewer than five laboratories do the testing on the same clinical isolates, as well as different isolates to determine reproducibility and standardization.
So, it is not done in a single laboratory, and that is a very important point.  It is done in multiple labs.
After the breakpoints are selected, every effort is made to have concordance between the FDA breakpoints and the NCCLS breakpoints.  Increasingly, this has been accomplished.  There are a few of the older drugs in which they are not the same, which presents major problems to drug manufacturers and equipment manufacturers, if they have two different numbers they have to worry about.
So, I think the overall process is one which it probably is a good idea to think about right now for the viral testing and how these are set up in terms of considering your breakpoints, which address pharmacologic, as well as performance standards, and in which the reference viruses that are used to determine your IC50s are the same in different laboratories.
So, the IC50 numbers mean the same thing.  Right now they can mean very different things as a function of how the method has been set up.
I think the NCCLS could be very helpful in helping to work out some of these things because they put a lot of thought into it over time.  I should mention that that group is made up, one‑third of industry, one‑third academics, and one‑third government, and the government does include the FDA and sometimes other groups that classify as government according to their scheme.
So, it has developed competency in a number of areas.  Now, I think in terms of what we are seeing here, step one is to get some standardization.  It doesn't necessarily mean that every company would have to use the same standard approach.  It means they have to be able to translate it from one to another and say this is number means something that will have some basis in which you can talk the same language.
DR. HAMMER:  Great.  Thank you very much.
Mr. Harrington.
MR. HARRINGTON:  I had a question about the FDA.  How many patients you require in the resistance dataset as compared to the approval dataset?  Is there a standard amount of clinical data on numbers of patients with resistant organism treated with the agent in order to get approval, and do you need statistical significance in that group or do you just sort of use a subjective judgment that you have seen enough patients to put it in?
DR. CHIKAMI:  I think it depends on the context, which are some of the considerations that I laid out in my last slide.  It depends on the context of the overall drug development.  For some of the development programs that have been targeted specifically at resistant organisms, in fact, clinical trials have been targeted toward again enrolling patients with resistant pathogens.
Those are, of course, adequate and well controlled statistically powered studies.  In other settings where you are looking at collecting efficacy data on resistant isolates in the course of a broader clinical trial, say, for example, community acquired pneumonia, in fact, the evidence may be much smaller.
There again, you would have to make a judgment based on again the overall context of the product in terms of its activity both at the site of infection and its activity demonstrated against susceptible organisms which, by and large, are much more common as etiologic agents.
DR. HAMMER:  Thank you.
Any other questions or comments?
Dr. Yogev.
DR. YOGEV:  I think one of the dangers to accept the bacteriology as a model for what we are doing is we are dealing with a different disease, which is systemic, and not organ oriented, which most bacteria area.
One of the problems I had in the past with this issue of pharmacological versus MIC, they are related to what is achievable in the blood, and the best example is Keflex for years was thought to be very good for otitis media, to find out that it hardly penetrated over there to get that ratio.
So, while we are considering what parameters to use, we should not forget the CNS and other tissue what this virus is, and most of the bacteria are not.
DR. HAMMER:  Agreed.  I think this example is just the area that has been most well standardized for us to see how far off we are, and the pathogenesis of the disease puts us even further off than just the technical aspects of things
Thank you very much.
I am sorry, Dr. Jackson.
DR. JACKSON:  Just one question.  In terms of just practically speaking, for a given bacteria, say, pneumococcus or any other one, in terms of calculating the mean MIC90s, is there a standardized panel of isolates that is used in this area that is given to the drug companies?
DR. CHIKAMI:  You mean as quality control?
DR. JACKSON:  If you wanted, like Dr. Charache was talking about, if you want to talk about this is the MIC90, whether it is between laboratories or looking at different drugs, is there some sort of standard panel that has been used to define what the MIC90 is?
DR. CHIKAMI:  There are quality controlled organisms set that with known susceptibility MICs to answer that specific question.  In the course of development of a product, of course, we would also expect to see its activity or MICs calculated or tested against clinically relevant isolates, as well.  So, it is the combination of two that allows us to look at the activity of a new product.
DR. HAMMER:  I think we do need to move on.  Thank you very much.
Dr. Jeff Murray will now talk to us about the regulatory issues related to HIV drug resistance testing and drug development.
Use of HIV Resistance Testing in
Drug Development:  Regulatory Issues
Jeffrey Murray, M.D.
DR. MURRAY:  I hope to just set the stage really for this afternoon's discussion and the regulatory scenarios.
[Slide.]
I want to briefly comment on what I think the role of HIV resistance testing is from our Division's perspective, again reiterate what our objectives are for Session 4, briefly discuss what I think the limitations of resistance testing are so far based on what we have heard in this meeting, what the regulatory use of HIV resistance testing has been in the past and how it is currently being used, talk about some proposed uses of resistance testing in clinical trials, and then introduce the regulatory scenarios.
[Slide.]
First, some possible products of this meeting I think would be hopefully publication of the proceedings, and I think the Resistance Collaborative Group have talked about doing this, and this will be very good to disseminate what we have heard here beyond Holiday Inn in Gaithersburg.
Also, if possible, to develop or start to develop an FDA guidance document or something written that can be disseminated for public comment on the use of HIV resistance testing in drug development, and then also I think that there are maybe some possible research initiatives, so that we will be able to share from this meeting with other agencies, maybe relating to quality control studies that could be accomplished through NIH through DAIDS Virology grant or also, as was just referred to, a committee or some mechanism for developing mutational algorithms for setting breakpoints, and I think we have already got some leads on that, and then also some surveillance initiatives that CDC may have interest in.
[Slide.]
So, the role of resistance testing I think, from the Division's perspective is really to provide useful information to clinics, clinicians and patients, and have scientifically sound labeling, which would then mean scientifically sound promotion, labeling being the basis for promotion.
To potentially improve clinical trial designs both in treatment experienced patients and salvage, I think is where resistance testing could be helpful, possibly to enrich study populations for patients likely to respond, and we are just very keenly interested in surveillance issues to characterize a drug's activity postmarketing, and of course monitoring the transmission of drug resistant HIV.
[Slide.]
What we don't think of resistance testing doing is serving as ‑‑ not being a primary efficacy endpoint, and I think HIV‑RNA together with CD4 or clinical progression are the accepted efficacy endpoints, but they may help to establish a niche support for accelerated approval and indication, and they are likely to influence drug development strategies in all phases of drug development.
[Slide.]
Some considerations is that compared to HIV‑RNA testing ‑‑ the Resistance Collaborative Group was kind of modeled after the Surrogate Marker Working Group ‑‑ HIV resistance testing is more drug‑dependent, much like concentration monitoring, monitoring of therapeutic drug concentrations, and so mutational algorithms and breakpoints will need to be revised for each new drug approval and updated postmarketing, and, you know, one size doesn't fit all, one breakpoint is not going to fit all drugs.  It is going to be very drug‑dependent.
Efficient use of a way of developing this would be to do it, of course, during drug development, and to characterize the clinical relevance of genotypic and phenotypic susceptibility throughout all stages.
I think if we could do this as each drug comes out, much like anti‑infectives do, it would be a more efficient way of doing it.  Right now I think we have to play some catch‑up with the 12 or 13 drugs that are already out there.
[Slide.]
So, the objectives for this afternoon really are to obtain guidance on the amount and type of resistance data that should be expected of sponsors during drug development, for an NDA, and postmarketing.
How this data can be used in product labeling and indications, and how we can start using resistance testing in clinical trial designs or considering the impact of resistance testing if it is done independently by investigators.
[Slide.]
It looks like the limitations of resistance testing for HIV so far.  There is still some question about the reliability of the assays, there is assay factors, clinical factors as we discussed earlier this morning, difficulty in defining resistance, and then other confounding factors in interpreting the analysis, and also the feasibility of real‑time use of resistance testing in clinical trial will need to be discussed.
[Slide.]
So far, how have we used resistance testing, HIV resistance testing?  It does appear in the labeling, and it appears prominently in some labels, in Warning sections, box warnings even in the Microbiology section, even in the Clinical section in at least one label.
There have been proposals to use it for treatment indications, and I think the example from Monday for adefovir was a current proposal for that.  There has been at least one case, been an approvability issue, and that for saquinavir and the Invirase formulation.
Then, it is currently, of course, being used to support clinical development.
[Slide.]
In labeling so far, I think the emphasis of the Division has been to include things in the label that emphasize high level resistance that leads to class cross‑resistance, that jeopardizes the use of other drugs, drugs of the same class.
The Division has been somewhat reluctant to include statements describing lack of resistance, and that is because of recognizing the current technological limitations, the presence of resistance is more useful information than the absence of detectable resistance.
So, it seems like a double standard, but I think it is really the limitations of the data.  In all of the non‑nucleoside reverse transcriptase inhibitors, there is a warning regarding resistance, and nevirapine, not to pick on any one drug, but since it was the first approved, I will read what its warning is on the label regard resistance.
"Resistant virus emerges rapidly and uniformly when Viramune is administered as monotherapy.  Therefore Viramune should always be administered in combination with antiretroviral agents."
[Slide.]
This was based on really pooled data from Phase I/II studies using nevirapine as monotherapy.  The database was about 24 patients in which 100 percent had a greater than 100‑fold decrease in phenotypic susceptibility at 8 weeks.  All of these patients had one or more predicted RT mutations, 80 percent of them had it at 181.  It was biologically plausible because this mutation was near the RT binding site.
[Slide.]
Resistance labeling has been used to describe really appropriate use of the drug, so as far as things that have been in the label regarding what the frequency of resistance would be, it has been described to encourage proper dosing and use of combination therapy as would occur in the Crixivan label, and it has not been used to date to emphasize that resistance develops more slowly with one drug compared to another.
[Slide.]
In the current indinavir label, there is a table that describes the frequency of resistance at 24 weeks ‑‑this data is pulled from two studies ‑‑ showing that the frequency of resistance to indinavir is less if you take the appropriate dose, 2.4 grams per day versus less than 2.4 grams per day, or if it is used in combination with AZT.
We included this in the label because it hopefully promotes good use of the drug.
[Slide.]
Treatment indications.  The most recent example, of course, probably the only true example is adefovir where the sponsor has actually requested an indication.  In this case, it was in a treatment experienced, nuke‑experienced patients.  It was based on the sponsor's interpretation of resistant subgroup analysis in patients with high level AZT and 3TC resistance.
Currently, there is no such indication in the label.  Previous labels, you might note that there have been labels where there has been indication for treatment, experienced individuals, but it really wasn't based on resistance testing.  It is sort of by default, like the previous labels for Zerit and Hivid.
It was more so because the data didn't support first‑line treatment, not because of the study result suggested exceptional activity in experienced patients or patients who were necessarily identified as having resistance to another drug of the same class, and the studies in these cases were done in treatment experienced to fulfill the intent of accelerated approval.
[Slide.]
As far as approvability issues, I think with saquinavir, the issue of resistance came up as a possible approvability issues.  As you know, Invirase was the first protease inhibitor on the market, but suffers from poor bioavailability of about 4 percent, and a concern at the time of approval was what the effect of Invirase would be on the use of subsequent protease inhibitors, that is, after virologic failure, with Invirase, would patients still be able to derive benefit from subsequent PIs, and this question was the impetus for conducting study AZTG‑333, which you heard about yesterday, where Invirase was followed by indinavir.
I think at the time of the approval, we were somewhat comforted by something that really didn't translate clinically.  The resistance data that was in the NDA suggested that saquinavir selected for mutations 90, NR‑48 in both in vitro and clinical isolates, and it looked like there was incomplete overlap with mutations selected by other protease inhibitors being developed at the time.  That would be ritonavir and indinavir, which were selected for mutations, such as 82 and 84 and 54.
In the back of I think a lot of people's minds was a concern regarding other polymorphisms, so that kind of based on this kind of data, you know, Invirase was approved with a label precaution, which is also now included in other protease inhibitor labels.
[Slide.]
It states, under Precautions, Resistance/Cross‑resistance, "The potential for HIV cross‑resistance between protease inhibitors has not been fully explored.  Therefore, it is unknown what effect (drug name) will have on the activity of subsequent protease inhibitors."
[Slide.]
I think maybe what we learned from this example is that non‑overlap of mutations selected by a particular drug does not always predict lack of clinical cross‑resistance.  In fact, the L90 mutation appears to decrease clinical responsiveness to most all of the approved protease inhibitors including ritonavir, indinavir, and nelfinavir, and the L90M mutation was included in the DAP algorithm for the approved protease inhibitors.
[Slide.]
As far as use in clinical trial design, preclinically, the sponsors have sometimes considered resistance testing to possibly support a duration of monotherapy.
As you are aware, HIV drugs are sometimes studied for short periods as monotherapy, maybe up from 1 to 3 weeks, and perhaps if your preclinical program showed a high level resistance to a single mutation, maybe a study as monotherapy would not be a wise thing to do.
So, resistance testing might even influence the design of the first clinical trial in HIV infected patients.
It could also be used for inclusion and exclusion criteria, and I think there is some thorny issues here, which has already been brought up by the committee.  You could exclude patients with mutations to enrich patients who are likely to respond.
You could include patients with mutations to evaluate drug against resistant virus, and then you could also use resistance testing for protocol management criteria, choice of drugs or concomitant medications after virologic failures.
[Slide.]
Some other problems with resistance testing in trials, some possible limitations.  Can they be conducted in real time?  Are the assays ‑‑ there is a consensus that the assays are reliable enough to start using them as inclusion and exclusion criteria.  To exclude patients from participation would be probably a big step.
Then, would ad lib use by investigators, clinicians of resistance testing in open‑label trials, could this lead to bias in that investigators might use resistance testing to choose part of the regimen, and this could be applied differentially across treatment arms.
We already seen differences in GART, VIRADEP, and other 3001 studies, so how resistance testing is applied across treatment arms, whether we choose to include it in trials or not, it is still, I think, a force that we are going to have to deal with.
[Slide.]
There are four basic questions to this session, and I think after lunch, we are going to try to address these questions using examples, regulatory scenarios which sometimes a specific example I think can help to tease out the issues better, but the basic questions are we want the committee to comment on the amount and type of data needed to support a clinical development program, support and initiate a clinical drug development program, the amount of data needed to claim activity against resistance isolates, and to profile a drug's potential for inducing resistance or cross‑resistance within a class, and also then to comment on the amount and type of data that sponsors should be collecting postmarketing since, as new drugs are released to the market, will have to be continuously updated.
[Slide.]
We have four regulatory scenarios that you will hear after lunch.  Each has its own set of questions, and we hope that they will be able to help the committee to address the rather tough questions we have under Session 4.
DR. HAMMER:  Thank you very much.
Are there questions for Dr. Murray or comments?
The only comment I would make is to emphasize ‑‑ and I will get to this in the afternoon discussion ‑‑ that refraining from any label indication about a diminished potential to engender cross‑resistance, that caution should remain because I think for all the reasons you stated and as we develop more knowledge, there are direct and indirect mechanisms for cross‑resistance, and would be potentially quite harmful to take a step, giving a label indication for lack of cross‑resistance engenderment without a huge dataset to support that.
Questions?
Okay.  This was an introduction to this afternoon.  We are on time.  We will break and return at 1 o'clock.  Thank you.
[Whereupon, at 11:59 a.m., the proceedings were recessed, to be resumed at 1:00 p.m.]

AFTERNOON PROCEEDINGS
[1:00 p.m.]
DR. HAMMER:  I would like to call the session back to order.
Open Public Hearing
DR. HAMMER:  The first order of business this afternoon is the open public hearing.  There are three individuals who have signed up in advance.  I would ask that those individuals who come up to speak, please identify themselves, their organization for the record.  Also, if you have not signed up, but have a statement you wish to make, you will be permitted to do so.
I would also ask anyone who speaks to limit their comments to under five minutes if at all possible.  We have a lot to do this afternoon and need to accelerate the schedule a bit.
With that, the first individual signed up for the open public hearing is Dr. Clyde Crumpacker from Harvard Medical School.
Clyde.
DR. CRUMPACKER:  Thank you, Scott.
My name is Clyde Crumpacker.  I am at Harvard Medical School and Beth Israel Deaconess Medical Center.  I paid my own way to this meeting.  I am not being sponsored by anybody.
I just wanted to make just some very brief comments about what I think is where we are with trying to get accurate ways of measuring resistance and sort of where we have been.
I think the whole effort to develop measures of resistance to antivirals has been a very difficult one.  There are only two viruses and two drugs that we have resistance data on that I think is clinically significant.  That is HSV with acyclovir and AZT with HIV.
I think the clinical significance of resistance of HIV to AZT, I think was established through a collaborative working effort of six AZTG labs and the Department of Defense to use a PBMC‑based assay, which we struggled a long time to develop, because we could then measure every virus that we could grow out of a patient.
I think we are unlikely to ever be able to repeat this with that same degree of rigor because of the onset of combination therapy complicating everything we do, but I think we were able to show that a high level of resistance to AZT, measured by 1 micromolar or more, did predict more rapid acceleration to death in a statistically significant way, a moderate level of resistance like 0.2 was not associated with progression.
So, I think it was a useful exercise and established for the first time that resistance of AZT and HIV was bad.
I think that some of the things we learned may still be useful for this current era. I think standardization of a panel of clinical isolates is essential, and we have heard about this several times during this meeting, and I would just like to repeat it, that a baseline and follow‑up data on a new drug is really I think key, and just to point out historically that Larder and Kemp, with only six pairs, were able to identify 80 percent of the AZT mutations ‑ 67, 72, 9, and 15, and with one more, 41, they eventually described 90 percent of them.
So, pairs are crucial in patients who are taking multiple drugs.  I think the new recombinant viral assays being so attractive because they can be done rapidly, I think that it is going to be still important to try to compare some standard panels.
I think the current dilemma about NNRTI resistance being measured by Virologic and Virco might be able to be clarified by using a standardized or a different PBMC‑based assay perhaps and comparing that to the data that they are getting, as was suggested by Roger Pomerantz this morning.
The other thing I would like to address is the question of viral fitness, and I think we have heard about this in several contexts this morning, but I think we are still at a very early stage in trying to define what fitness means, how to measure it.
I think a definition of fitness as a measure of the ability of a virus resistant mutant to result in a virus which replicates less efficiently might be a useful one in this context, and I think John Coffin pointed out, I think very elegantly, that resistant mutations may confer a difficulty or a less replication advantage to a virus compared to wild type.
Our lab in Boston has recently shown that the 74V mutations selected by ddI and the 184V mutation selected by 3TC will result in a virus which has a less processive reverse transcriptase.  So, the decreased growth that occurs with these mutations, I think now has as biochemical confirmation, that the altered enzyme is biochemically different, and I think this could provide a way then to understand how drugs that are going to be developed to work on resistance virus might definitely be generated.
I think companies can help their case by trying to define biochemical alterations associated with resistance mutations to their drugs, because I think, as we heard earlier from the FDA, understanding mechanisms of resistance is a very powerful argument that a drug might be useful.
I think the best example that we have of this so far, as we have heard earlier, as well, is the case of adefovir producing a greater decrease in viral load on those viruses that have a 184 mutation.
I think the work of Michael Miller and others to define this is the best example we have so far of a new drug that might have a niche really against resistant viruses.
So, I think the more we can understand what the virologic consequences on the replicative enzymes that viruses need to replicate are of drug mutations, I think the faster this process can go forward.
Thank you.
DR. HAMMER:  Thank you very much, Clyde.
The next speaker is Francois Houyez.  Is he here?
[No response.]
DR. HAMMER:  The third speaker then signed up is Brendan Larder.
DR. LARDER:  I am Brendan Larder from Virco.
This is kind of a last‑minute thing.  I just put a few slides together to really talk about drug profiling, preclinical drug profiling, because some things have been discussed about profiling so far, but a bit like resistance testing, it is already here, we are already doing it, and I thought I would like to just go through a few issues and show a bit of data just to try and put it into context, and then address some questions in a few minutes time.
[Slide.]
The background, I think we all know about the term  new, new antiretroviral is now being developed in the backdrop of extensive resistance.  In some cases we are seeing, when we survey thousands or tens of thousands of samples, 50 percent of those or nearly 50 percent of it, 215 mutation or 184 mutation, et cetera.
So, it is very obvious, and I think it was obvious from the discussions on Monday amongst the panel, that drugs that are being developed now are needed in salvage therapy to inhibit resistant strains.  So, it is very clear that the determination of cross‑resistance profiles as new inhibitors prior to clinical evaluation is very important.
[Slide.]
There are obviously a number of issues, and these are all the issues that we are grappling with in the middle of actually doing these studies.  So, firstly, how should we choose samples to actually study and from which pool should we derive those samples?  How many samples should we analyze, how are they selected?  Which assay should be used to actually analyze them?  How should the data be analyzed, presented, and interpreted?
[Slide.]
Just as an example of some of the work that we have done with profiling, we have profiled really quite a lot of drugs now to a greater and lesser extent.  I think a primary example is this good one, and maybe can serve as some sort of model for how these things could be done in the future.
I guess, as many people know, tipranavir is a protease inhibitor that had a suspected novel resistance profile.  [Inaudible] came to us to try and see if we would profile using our database to look at a large number of isolates to see if this suspicion was borne out, the activity to tipranavir against a diverse selection of PI resistant clinical isolates was warranted.
[Slide.]
This is how we went about sample selection.  We looked for recent samples from our pheno/geno database, which currently has in excess of 35,000 samples and with a whole gamut of phenotypes.
So, we made selection actually based on the phenotype, but not on the genotype, which I think is quite important.  We picked out the first, I think 100 or so samples that were broadly PI cross‑resistant, and we defined that.  This was just before tipranavir was approved, so we didn't include tipranavir, we didn't have the data.  So, broadly resistant, cross‑resistant, was resistant to 304 of the current MPIs at that time.
We also wanted to look at resistance to samples that were resistant only to one of the PIs, saquinavir, nelfinavir, ritonavir.  We couldn't find any samples that were resistant only to indinavir.
Obviously, all of the recombinant viruses, because we used the antiviral method, was sequenced, ABI Technologies confirmed the genotype.
[Slide.]
This is how we presented the data.  First, the population data.  I am just going to concentrate with the two data slides on the highly cross‑resistant sample, that sample of 105.
The top part of this panel shows the prevalence of mutations in the protease, and it shows really what you would expect from protease inhibitor resistant sample, and shows kind of a balance.  It was about 40 percent, 82, 84, something like 70, 80 percent, 90, some 48, and then a whole range of different polymorphisms or secondary compensation mutations.
If you look at the composite or the mean fold resistance of all those isolates to the drugs, this is what we found, so this confirms what we pulled out of the database, indinavir, ritonavir, nelfinavir, saquinavir, the mean fold resistance or increased IC50 was at least 40‑fold, it was somewhere between 80 and 90‑fold for ritonavir.
The mean fold resistance for tipranavir was 2‑fold.  So, this is really quite impressive and was quite nice to see in terms of trying to develop a drug that was active against these viruses.
[Slide.]
What I show is really all of the data.  This is an easy way of looking at it.  So, what we did here was to line up the fold increase of IC50 going from the most resistant, tipranavir isolate to the most sensitive, and in this case, some hypersensitivity, again, the fold increase in resistance to indinavir, ritonavir, saquinavir, sample by sample.  These boxes have got numbers in them, you don't see them, but if you just look at the colors, then, the color coding is the kind of coding we have been talking about over the past couple of days, green being less than 4‑fold increase, in IC50, the yellow, between 4 and 10, and the red, greater than 10.
So, obviously, the first thing that is striking is that most of these isolates that we picked out are resistant to most of the PIs at a high level greater than 10‑fold.  I can see some of the numbers from here.  This sample is 105‑fold for indinavir, 90 for ritonavir, et cetera.
The other striking thing, by lining up the samples like this, you can see there is a large degree of lack of cross‑resistance, tipranavir to the other drugs.
So, we can immediately now quantitate this with a fair sample size, say 95 of the samples are sensitive 8, and this is where the percentage is now, intermediates, and only two showed any sort of resistance.
So, we have some quantifiable number that we can put on it with a panel of isolates that we have now defined by genotype.
[Slide.]
To summarize that, I think that this study in particular highlights the utility or the potential utility of in vitro susceptibility profiling of new antiretrovirals, and maybe can be taken as some sort of model.  One might think about doing this in the future.
But there are issues.  The sample choice and the size of the sample is an issue, and these are really quite important issues to avoid selection bias and underrepresentation, and so we can't just hand‑pick a few samples.
Finally, and this is something that we have been trying to develop ourselves, but this is in somewhat of a vacuum, standardization of data analysis on presentation we think would be useful to enable comparisons between inhibitors and between studies.
If we are talking about regulations and what companies or pharmaceutical companies are hoping to expected to do when you bring packages forward to the FDA, then, these sort of packages would be useful, but we need to be able to have some standardization, so we can make comparisons between the different drugs.
That is all I wanted to say.  Thank you.
DR. HAMMER:  Thank you, Brendan.  That is very helpful.
Brendan is the third and last person who signed up in advance for the open public hearing.
Is there anyone who wishes to step forward and make any additional comments?
[No response.]
DR. HAMMER:  If not, the open public hearing is formally closed, and we will move on to the continuation of Session 4.  The committee has been given five questions to discuss and also four clinical regulatory scenarios, I should say, not clinical, but regulatory scenarios, and what we are going to do is discuss the regulatory scenarios first and hopefully, fairly completely.
What we don't address in the five questions in our discussion of the regulatory scenarios, we will then attempt to cover briefly at the end, because it is quite an agenda and we want to try to hit this efficiently.
To kick off the regulatory scenario presentation, I would like to welcome Dr. Katherine Laessig from the FDA.
Presentation of Regulatory Proposals
Katherine Laessig, M.D.
DR. LAESSIG:  Good afternoon.  For the next 30 minutes or so, I will be presenting four regulatory scenarios designed to highlight some of the issues of HIV resistance testing and drug development.
The information I will present includes hypothetical claims and indications, potentially involve sections of the drug labeling, and examples of supportive evidence.
After I present each scenario, please take five minutes or so to respond to the questions posed.
So, let's get started.
[Slide.]
Scenario No. 1 involves a claim of lack of development of resistance.
[Slide.]
The affected sections of the labeling include Microbiology, specifically, antiviral activity in vitro and resistance.
[Slide.]
Drug R is marketed for the treatment of HIV in combination with other antiretroviral agents.  Investigation of resistance to Drug R has included in vitro selection studies, involving passaging of HIV strains in the presence of Drug R.  A few clinical and laboratory strains with reduced phenotypic susceptibility have been isolated, however, no consistently identified genotypic mutation has been seen in either the lab or the clinical isolates.
[Slide.]
The Scenario No. 1 questions are:  Is the failure to identify genetic mutations in the presence of reduced phenotypic susceptibility sufficient to support a claim of a lack of development of resistance?
Second.  What can be concluded if there is neither reduced phenotypic susceptibility nor evidence of genotypic mutations?
Third.  What type of evidence is needed to support a claim of infrequent resistance or slow emergence of resistance?
DR. HAMMER:  Thank you.  Five minutes to discuss this is a challenge for a single individual, let alone a committee, but what I would suggest is that committee members who wish to respond, rather than going through each question around the table, why don't you respond to one or more of the questions placed, although start with the first.
Who would like to leap in?  Mr. Harrington.
MR. HARRINGTON:  In answer to Question 1, no, it is not enough to failure to identify the genetic mutations, and the third question, I think it is really important to distinguish between in vitro and in vivo, and the question doesn't really do that, but I would just say you want clinical evidence of activity to support a claim of infrequent resistance.
DR. HAMMER:  Dr. Mayers.
DR. MAYERS:  I think I have talked to Company X a few times.
I think that it is really important when you do these types of studies that you get the right samples collected at the time point, and there are certain drugs for which patients feel about phenotype, but I believe there actually is genetics.
One example.  A company that came to this committee had 1,000 samples they looked at, but they weren't collected right, and they didn't look in the right reservoir, and Stanford looked at 25 patients who had good baseline plasma and good failure samples after they had failed with rising viral load, and with 25 samples, identified 6 mutations that were clearly selected for by that drug in failure.
So, I think that it is going to be critically important for these types of claims that there be a very good collection of samples at baseline, collection of samples of virologic failure, and a careful look at what is selected out of the genetic background by that drug, and if you saw absolutely nothing, my suspicion would be that the drug wasn't doing anything virologically, and wasn't pressing the virus at all.
DR. HAMMER:  Other comments?
Dr. Pettinelli.
DR. PETTINELLI:  In this kind of scenario, would the standardization of the assay per se, for example, in terms of inoculum or other parameter, would be half‑full because we don't really know the reason why, there is concern what are the conditions in which the assay was conducted, and is there any way we can standardize that at the list and give some guidance.
DR. HAMMER:  Are you framing a question, Carla?
DR. PETTINELLI:  For the virologist.  In a certain sense, you know, could that have an interpretation on this kind of scenario, is that possible.
DR. HAMMER:  I think depending on the assay, certainly the inoculum size can be important.  It depends I think on the potency of the agent, the target, and the virus inoculum that you are putting in, but there are certainly assays in which you can drive the IC50 one way or another by the amount of virus that you put in, so I think it is a good point about standardization.
I think the last two points raise issues about the technical background in which the data come forward, and I think that needs to be the first point of departure.
I might just comment.  If we accept that, then, how do you interpret these scenarios and these questions or how do you answer them?
I agree with Mr. Harrington.  I think probably the consensus of the committee is the failure to identify genetic mutations in the presence of reduced phenotypic susceptibility does not support a claim of lack of development of resistance.
We know that patients fail on drugs, that there are resistance mechanisms that are there that you can measure fold changes in susceptibility to at least one other approved agent, and there is no controversy ‑‑ I am talking about D4T right now ‑‑ for which there is no controversy about the genetic basis of that.
I think the other aspect that comes out, and it has come out this morning, and I think we will be hearing more about it, is the issue of cellular mechanisms of resistance, particularly for drugs that are intracellularly phosphorylated anabolically, and also that can be subject to pump mechanisms.
So, I think the answer to No. 1 is no.
What can be concluded if there is neither reduced phenotypic susceptibility nor evidence of genotypic mutation?  This is a slight different question, but I guess the question is deriving from if you isolate a virus in the presence of failure, is that what that question is driving at?  Okay.
Then, I think you have to ask the question are there other factors which we have talked about, pharmacokinetic factors, as the drug is absorbed, is there adherence, et cetera.  Assuming that the drug is absorbed and reaches a level, what can you assume by this if there is failure?
If there is not resistance by what we are classically defining, I think you would also again have to ask the technical question of how the assays were done and what they looked for.
I think, for example, has the entire reverse transcriptase, if an RT inhibitor, been sequenced, because the more we look at the right end of the molecule, the more interesting mutations show up there, and many assays don't classically, although we have extended out beyond codon 240 to codon 400 routinely, we discovered a lot there, we may discover more.
So, the issue is again what the dataset is and how good the data are, but I think the major question there may also be assuming that the drug is absorbed and that adherence is not a problem, is the drug potent enough, and it gets to the issue of lack of selective pressure.
A claim of lack of resistance relates to the first question.  It may mean that you are not just putting enough pressure on the virus.  So, if you have a modestly potent drug that then claims lack of a resistance emergence, the first thing I would worry about is that you are not putting enough pressure on the drug to actually select resistance.
The other thing that should be mentioned in this, and it comes up with a later scenario, is the issue of cross‑resistance.  You have to be very careful.  It is another reason to test isolates, not only against the agent itself, but against other agents in the same class, is that a number of agents may not select for resistance to itself, but may select permutations to other agents and the thing that has now been reported by a number of laboratories about D4T or D4T and ddI in combination that I can select for.
Associated resistance mutations and the 151 multi‑nucleoside complex means that you need to look beyond the drug itself and susceptibility change to the drug.
And what type of evidence is needed to support a claim of infrequent resistance or slow emergence of resistance?  I think the latter is easier probably than the former.  I think the issues of slow emergence of resistance really relate ‑‑ we have had a number of examples of that ‑‑ it relates to the genetic barrier for the particular drug and the numbers of mutations that are really required and the frequency with which they go in and the facility with which they go in to develop resistance.
So, if you have single‑step, high level resistance, it is fast, and if you need multiple mutations to develop high level or higher level resistance, like with zidovudine or the protease inhibitors it is slow.
Developing that dataset in vitro and potentially in vivo is reasonable, I think, and can be done, and is important because genetic barriers and putting combinations together need to be thought about by physicians and patients.
The frequency with which this all happens, I think requires a fairly large dataset and is complicated by the concomitant drugs that are administered, and all the other confounding factors we have talked about.
So, in order to answer the former first part of the question, you need a pretty large dataset, probably hundreds of patients treated with different combinations studied very carefully.  I don't think I could be more specific than that, but a 20‑patient study would not do it.
Other comments on the first scenario?  You were told five minutes.  We have already gone over, but we are going to be efficient on the other five questions.
Any other comments on Scenario 1?  Did we respond to a reasonable extent to Scenario 1?  Okay.
DR. LAESSIG:  Scenario No. 2 involves a claim of a lack of cross‑resistance within a drug class.
[Slide.]
The affected section of the labeling is Microbiology: Cross‑resistance, Indications and Usage, and Description of Clinical Studies.
[Slide.]
A sponsor has conducted an uncontrolled rollover study of 30 patients who met protocol definitions of virologic failure in earlier clinical trials, and failed protease inhibitor A.  Patients were treated in the rollover study with a combination regimen including protease inhibitor B.  Genotyping was done prior to therapy, initially with protease inhibitor A, and at the time of virologic failure to protease inhibitor A, and that revealed a typical mutation.  Results of the rollover study revealed that greater than 50 percent of the patients had a viral load below the limit of quantitation at week 24 of the rollover study.
The questions are:  What type of evidence is needed to support a claim of lack of cross‑resistance between two drugs in the same class?  Please consider each class individually.
Would analysis of a statistically significant difference in responder rates of test drug versus control regimens be needed, or would predetermined percentage response rates in a minimum number of patients be suitable?
How should additional studies be addressed postmarketing as additional drugs of the same class enter the market?
DR. HAMMER:  Another straightforward scenario.
Who would like to start?
DR. JOLSON:  Scott, the one thing I will mention for the committee to consider ‑‑ I know these sound like these are very, very hard scenarios, but even though they are hypothetical, they are somewhat typical of the sorts of issues that sponsors are asking the agency to think about in terms of where we might be heading.
So, as difficult as they may seem, they are not too far removed from the sorts of things that sponsors are starting to consider or have already asked us to consider with regard to labeling their product.
DR. HAMMER:  Thank you.  Also, some of these scenarios do look familiar actually, even though the names are changed to ‑‑
DR. JOLSON:  To protect the innocent.
DR. HAMMER:  I will start and maybe others can contribute, hopefully.
The evidence to support a claim of lack of cross‑resistance between two drugs in the same class, first of all, has to start with the in vitro data that we have talked about many times, that any new drug in development needs to be tested against a panel of well‑characterized isolates.
That should include, of course, isolates resistant to other members of the same class for sure.  If you don't have that, you are starting from a very difficult position to make this claim, but assuming you then do have this ability ‑‑ and we have certainly seen some cases where it looked like certain mutational patterns and in vitro susceptibilities didn't look like there would be class cross‑resistance, it turned out there would be.
If one wants to draw the conclusion from the clinical trial scenario or the open‑label trial scenario that is listed here, what would be needed?  Well, the thing is a 30‑patient study in which you have a 50 percent response rate, no doubt, and a rollover study with other combination treatment makes it difficult to know without, of course, the level of resistance that was determined, the pharmacokinetic variability, interpatient variability, that may have been determined.
So, this particular study that is outlined doesn't  prove it simply because there are too many other factors involved that could give you a 50 percent, i.e., a flip of the coin response rate.  Also, longer term data would be needed.
But one thing that one could do is you would need to know the characterization and that you do have it genotypically, but, for example, one would need to know whether, in fact, a 50 percent response rate is, in fact, a blunted response rate for the new drug and the particular combination in this patient population.
One could do that by looking at a comparator group, not in this particular study, but if you actually had a comparator group that had wild type virus treated with the same identical combination and controlled for basically, and both groups were controlled for their RT component, then, you would see whether in a naive population where the only differences between them were the PI‑associated mutations, if you will, if you could control for that, and you got a 90 percent response rate, then, a 50 percent response rate, and here it would actually show that it is blunted.
Actually, this is familiar territory for sequential PI studies that we have seen in the past, 333 and other studies, and some of the important studies out of the Stanford group showing blunted responses to certain PIs following previous PIs.
I think the only way you can know that is either by some comparator where, in fact, the same drug in a PI mute wild type patient group gave you similar or a different response.
So, I think if it were identical to a wild type patient population, it would be evidence or at least suggestive, but even then a 50 percent response rate would be tough.
So, I think you need the appropriate comparator group and you need to control for the mutations in the other genes and the other parts of the combination regimen.  The outline that is put here does not prove it to me.
The second question.  Would analysis of a statistically significant difference in responder rates of test drug versus control regimens be needed, or would predetermined percentage response rates in a minimum number of patients be suitable?
My own feeling, I think I just answered that.  I don't think you can say, well, a priori, if we got a 50 percent, you know, that is good enough.  I think what you need is the comparator and actually to look for whether there is a difference or not, with the right comparison group.
The postmarketing issues I think are very difficult, and I don't have a really good answer for that.  The same drugs of the same class enter the market, how should additional studies be addressed postmarketing.  I think without surveillance issues of (a) to know what is happening with the drug resistance in the population that are ongoing and uniformly reported, it is difficult.
I think one could ask, although I don't know how strong the commitment can be, to be able to continue similar studies where you see response rates over time with good characterizations of baseline and follow‑up isolates.
I honestly do not have a good answer for the postmarketing because the studies, because unless there is a strong commitment, standardization of this is going to be very difficult, particularly the regimens, the patient populations, et cetera.
So, one could try to initiate a series of Phase IV trials that are specifically designed to look at response rates over time with relatively standardized regimens and well‑characterized populations, but that is easier said than done.
Comments on this?  Dr. Yogev.
DR. YOGEV:  From the scenario it seems like there is no testing of those who did not get the below level of quantitation.  I think this is the most important population to approach, those who did not respond to the change that you assigned to, to see what changes are there, because both affecting other drugs and itself bringing out what you are looking for.
I, for one, think that 30 is not sufficient.  I think there was a very nice example just shown to us, one of the people who just spoke recently, that out of 100 samples, you got two in a drug which supposedly would make such a claim, and it is good to know the ratio.  I would be surprised if we are going to find one who never had it, but what is important is we need to decide how many are okay and how many you cannot make that claim.
This is an important issue that needs to be addressed, and maybe part of this study which presented can give you some clue if the n will be increased and then you check all those who did not respond.
Also important is I don't see here when that happened.  I think if we look in data 12 week or 24 week, it might be a little bit too early, so we need to ask for longer follow‑up to see what happened, especially when you are looking at the minority of resistance, it might take time to come out that when the data changes the philosophy at 24 weeks in this era with multiple combination drug is enough.
DR. HAMMER:  That is a very good point about looking at the evolutionary pattern of resistance mutations and the failures, (a) are they occurring, and (b) what are they, because we have learned that with the sequential rollover PI studies, sometimes the evolution of resistance mutations is not to the second drug, but promotes the continued pattern of evolution of the first drug, and it is a way to sort of come out with the subtle issues of cross‑resistance, and that has also been shown by the Stanford group and others, so it is a very good point about the analysis of what the mutational pattern is in the viruses that come out under treatment with Drug B.
Dr. Mayers.
DR. MAYERS:  I think this points out one of the real limitations of 24‑week studies.  I think it is going to be critical as we study these drugs to go to randomizations that go to second round to failure, and so you look at durability of first round followed by salvageability into the second round protected by randomization, because the only way you are going to find out how good is this as a first strategy and then how salvageable are you off of that strategy.
I was trying to figure out how you could do this, and I think for this particular scenario, you could have the manufacturer have people coming out of the study and then give them two nukes or a background of nukes and your protease B versus a background of nukes and the non‑nuke, a drug from a different class that is potent to try and run a comparator arm, because your problem is you are going to have the background drugs are going to be factoring in, so you have to somehow control them and then randomize.
The other issue that I think has got to be very carefully addressed is the fact that there is no standardization for resistance data.
I have seen two companies recently have a fight over the data in which one company would have called all the isolates resistant, and the other company was calling them sensitive because they had a low number with a zero in front of it, and so I think, you know, it is really important that if they are going to get into these types of discussions, that there be baselines established for wild type, for those drugs and comparisons for your in vitro data, because it can get very confusing when two companies can have a poster up next to each other, and one company has resistance determined to be X number, and the other one is calling it sensitive in the same session, right next‑door to each other, and it is just because there is no standardization of what the expected range for these drugs is with the expected reference isolates.
I think the proof in the pudding is durability of virologic response, and the issue is going to be finding a way to do a randomized comparison to a virologic response that is convincing if they think there truly is not resistance.
The way to do this up‑front, if you believe it is true, is to have a randomized second round in your study.
DR. HAMMER:  I would agree, of course, that we interested in durability of response, but in asking the short term virologic response question of resistance to Drug A and response to Drug B, the majority of that answer can come in the first 24 weeks.
I think we know from several published anecdotes that the trajectory of the RNA is not going to be what you want it if there is drug resistance, and if you control for the other components of ‑‑ a single arm study, you can't do it, but if you control for other elements in the regimen, and you control for the rest of the genome besides the gene of interest, I think you can know or have a fair idea in the first 24 weeks for sure that you have got something that is active against drug resistant virus.
I think we have to have a practical look at ‑‑ we can't have every study of every aspect of new drug development and particularly some of the important virologic characteristics being 48‑week studies or it will be years before we have the information.
So, you need a combination of early virologic parameters for what the question is, and then durability of response based on the other characteristics of the drug and the regimen that it is being studied in.
So, I certainly completely agree that we need long‑term data, but I think that some of the virology questions can be answered quickly because of the pathogenesis of this infection.
Dr. Mayers.
DR. MAYERS:  I agree, but I still think that the true issue is how long do you go in the first round and can you salvage to the second round, and that requires a trial infrastructure at some point.  Maybe it's a postmarketing look in which you look at durability and salvageability in a randomized way.
DR. HAMMER:  I agree, but I just would say strategies of antiretroviral therapy over time for a patient are important to think about with drug development, but it is a distinct issue from drug development, not totally distinct, but they are overlapping Venn diagrams so that we can't ‑‑ every new drug has to be thought of strategically, but can't be studied in an independent strategic fashion over the next five years.
Mr. Harrington.
MR. HARRINGTON:  Two things on the sort of the short term or the clue or the nonclinical proof that I think could be useful.  One would be if they think the drug has a reasonably high genetic barrier, you are not going to bing through to resistance in two weeks.
Like the adefovir that we saw on Monday, versus placebo, you might want to actually look at a two‑ to four‑week study in wild type versus in people that were going to take a strategic structured treatment interruption, and if the people who had the SGI had been people that were resistant to that class, you might get ‑‑ if you saw no activity, then, you would think maybe you have some cross‑resistance.
The other idea was that you might ‑‑ and we really haven't talked about this and don't have the expertise at the table ‑‑ but I don't know how feasible it is to use an animal model like the shiv monkey in some of these cases, and use sequential monotherapy to detect in vivo genetic barriers to resistance and how long they take.
You make them cross‑resistant to earlier agents and then put in your new agent.  You might do it both ways.  I don't know how either feasible that is or how expensive it is, but it might be worth considering in cases of some classes of drugs.
DR. HAMMER:  Thank you.  Other comments on Scenario 2 before we move to Scenario 3?  Dr. Gulick.
DR. GULICK:  One of the things I think that is challenged here is even if you say okay, Scenario 2, we have shown activity of Drug B, is what is the next step, what is the control arm to compare Drug B to.
Doug Mayers suggested that you switch to another class, a non‑nuke perhaps, but can you do that?  I think it is going to be a challenge to figure out how you design a control arm for a true salvage study.
Most of the salvage studies that we have available right now have really inferior control arms.  So, what is the optimal control arm for a study like this?
DR. HAMMER:  Best judgment.
Let's move on to Scenario 3.
DR. MURRAY:  Before you go to Scenario 3, Dr. Hammer, one comment that you made was that you didn't think maybe a 30‑patient uncontrolled study would make it, but some criticism that we hear is that we don't include enough resistance use information in the label.
Some clinicians think that such information might be useful and like to see it reviewed is what it is by FDA and put in labeling.  So, I mean we have to kind of walk a balance.  If it is not in the label, it is going to be presented, it is going to be out there anyway.
So, in considering that you might have five or six PIs to test, you know, 30 times 5, you know, it adds up as far as numbers of patients.  What would you think about, is there anything about this scenario that would be appropriate in anyplace in the label?
DR. WONG:  Without controls, I personally don't believe it belongs.
DR. HAMMER:  My general response ‑‑ and that is why I was hesitant ‑‑ I would agree.  I think more and more, and you indicated in your own talk, that we are going to have resistance data in the label, and it needs to be there.  Whether we can somewhat standardize a little bit how those data are presented, as labels have evolved, and whether we have an in vitro section and then a somewhat more variable clinical section until we get further along the line, I am not sure, but I would hope that that is where we get to.
A 30‑patient study with a 50 percent response rate, 24 weeks, doesn't say a whole lot.  I think the only way this could ever make it is if there is a very sophisticated virologic analysis of these patients, fully characterized at baseline, fully characterized at failure, potentially including quasi‑species analysis, and that sort of thing.
But a gross 50 percent rate, I would say by itself doesn't make it, but I think a sophisticated virology study in association with it might.  So, I would be slightly different from Brian, but basically, I think we are saying the same thing, you need more data.
DR. MURRAY:  I guess the other thing, too, is that the comment I have heard is that people who fail one regimen, they fail for multiple reasons, and they might be a biased population.  They may be less likely to respond to a second regimen.
So, if you compare them to a naive person, you might expect a low response rate because you might have selected for a group who are more likely not to be compliant with the regimen or have metabolism that would handle the drug maybe differently than a responder.  I guess a controlled study would help, but ‑‑
DR. HAMMER:  A controlled study would help.  I agree that that is the case.  There are these clear‑cut issues of cross‑resistance that we can define, and there are patient characteristics that also make it problematic about sequential regimens.
There is also an unquantifiable issue.  The drug exposure itself seems to be presage drug failure on other regimens, and whether that is subtle issues of cross‑resistance, we don't fully understand yet.  Cellular mechanisms, et cetera, I agree are there.
For small studies, though, there are enough populations, if well screened, that I think one could potentially ‑‑ if you took, for example, if it was a new PI for this case, and you had a nucleoside‑experienced population, you could try to at least control for the nucleoside experience, control for the genetic background and the RT, concentrate on the RT inhibitor with a standard dual nuke or other combination regimen along with the PI, and then see what your response rates are.
It is difficult because of the combination regimens, but you have to now try to control, if you have small numbers, for as many of the confounders as possible, which would mean at least the RT inhibitor background and the genetic background outside of the region of initial interest, but it is difficult.  I think controlled trials are difficult, but they are still better than an uncontrolled, 30‑patient observation with a 50 percent response rate.
It is no better than what we do just by ‑‑ with the current agents, we can get a 30, 40 percent response rate at 24 weeks with our current agents, as well, and so you are not really necessarily proving anything as far as superiority over where we are.
Dr. Mayers.
DR. MAYERS:  It would certainly have to have been a trial that you designed with them up‑front, having decided that there was no other way to look at the issue, because I think if you open this Pandora's box up, you are going to have a "Let the buyer beware" section at the end of your product label, and it will be quite long.
DR. HAMMER:  I don't know that we really answered your question because it is very difficult.  I think we all recognize that and don't have the answer.  In other sessions of this committee over the years, we have never been very good at study design proposals.  We can raise the issues, but we can't always answer the questions.
Next scenario.
DR. LAESSIG:  The claim for the third scenario is that of efficacy for a subpopulation with specific phenotypes or genotypes.
[Slide.]
The affected section of the labeling is again Microbiology: Cross‑resistance, Indications and Usage, and Description of Clinical Studies.
[Slide.]
Nucleoside reverse transcriptase inhibitor C appears to retain activity against zidovudine resistance based on preclinical studies.
The sponsor has proposed a study to evaluate Drug C as a part of a HAART regimen in patients screened for high level AZT resistance at baseline by a single mutation at codon 215.
[Slide.]
The questions are:  Considering there are both resistance‑associated mutations and polymorphisms present in HIV genes, how should mutations be grouped and analyzed in prospective studies?
No. 2.  Would post facto analysis of stored specimens in treatment‑experienced patients be acceptable?
No. 3.  What type of evidence is sufficient to support a claim of efficacy and receive an indication for Drug C for the treatment of drug resistant virus (in this case zidovudine resistance is identified by a single mutation at 215)?
DR. HAMMER:  Another straightforward scenario.
Comments?  Dr. Pettinelli.
DR. PETTINELLI:  Kind of general comments.
DR. HAMMER:  We will take anything to start.  Thank you.
DR. PETTINELLI:  Why I think it will be important to such a study to have as inclusion criteria, patient to have a 215 mutation, however, think that is not sufficient, because patients are going to be treated with combination therapy and probably having the 215 mutation, they will have also other mutations to other class of drugs.
So, my recommendation is that, first, the patients should be screened, so we should really know the genotype and the phenotype of those patients at baseline even if 215 can be used as inclusion criteria.
I don't think that retrospective study or analysis of storage sample will be sufficient in this case, and I think that there are always problems with interpretation at the end.  It seems to me it will be much more straightforward to do that at the beginning.
However, it will be interesting to see analysis of this data.  I mean I don't know if sponsor wants to go directly into such a target study or might want to have just general information from looking at storage sample and then decide to go into the study, because you are going directly from preclinical to clinical, so there might be some more information before doing that.
DR. HAMMER:  Dr. Mathews.
DR. MATHEWS:  Well, fortunately, we had a very good example of this earlier in the week where a claim was made for the product, that it was active in a certain mutational setting.
What struck me was that was a subset analysis.  The sponsor selected a certain group of specimens to analyze.  The agency had different criteria for selecting samples, a different definition of the response was selected.  I think one was a 24‑week, another was DABG, whatever.  Different methods of analysis were used.
Obviously, the cleanest thing to do is to do a prospective study, and I think that is one of the points that the majority of the committee made, to examine that in an adequately powered setting where you would randomize, stratify on that mutation in that setting.
I think that these retrospective studies can be done, but the problem is that the methodology has to be stated up‑front how it is going to be done, what are the inclusion/exclusion criteria for samples, the methodology of analysis, and all that stuff.
If the effect is small, as it was in the case that was being looked at, it is going to be even more problematic.
DR. HAMMER:  Dr. Wong.
DR. WONG:  I agree that the best way to answer the question is prospectively, but I can well envision a set of data in which a retrospective analysis might be convincing.  I thought we did not see it earlier this week, but, you know, if it is obvious and robust, then, I think it is conceivably believable.  It depends on how good the effects are.
I wouldn't rule out accepting retrospective data, but they would have to be very convincing.
DR. HAMMER:  Dr. Masur.
DR. MASUR:  Actually, I don't have an answer, I have a question.  What isn't clear to me ‑‑ and this is the problem we were dealing with Monday ‑‑ is when you are dealing with combination therapy in which this drug, which has purported activity as part of the multiple drug regimen, how do you prove that this drug had any role in suppressing in the virologic response.
You can look at genotype and phenotype of viruses down the road, but how do you prove that this one drug had activity when you hopefully have multiple drugs with activity against this isolate.
I guess I would look for clarification from someone else.
DR. HAMMER:  Dr. Mayers has his hand up, and then I think Brooks Jackson had a comment.
DR. MAYERS:  I am much more comfortable with this if the company is doing it as a safety parameter, i.e., you don't get an effect.  When we look at our experienced patients, you don't see a good antiviral effect, maybe you should stay away from these types of patients of our drug.  I think that is a little more convincing.  When nothing happens when you give the drug, that is fairly easy for people to see.
I think this does argue for the fact that the companies need to collect baseline samples on all their patients and all their studies, because I think the thing that might be compelling would be if they saw this observation, to come back to the agency and say we would like to look at a carefully selected randomized sample of patients that was not included in this analysis to see if it confirms independently of a sample size that your statisticians believe will be convincing.
Then, I think I might be compelled.  On the other hand, if it is 65 patients that somebody has pulled out and done once, I am not nearly as compelled as if they went back in an took 200 patients who were different from the same study and reconfirmed it, and independent, of a sample size that was big enough that the treatment effect was believable.
DR. HAMMER:  Brooks, did you have a comment?
DR. JACKSON:  Just to reiterate one of the points by Carla that clearly, a single mutation, you would clearly want to verify that with phenotypic resistance data.  I think Brendan was showing or implying that that is probably the ‑‑ if you really want to show that there is resistance there, I think you have to really show reduced drug susceptibility, not just a single mutation there, which would be very important.
But Henry's point, this becomes tougher.  Even if you do that and show, yes, there is phenotypic resistance there, and you do this study, how do you really ‑‑ and you do see a decrease in viral load ‑‑ how do you really know it is Drug C, I mean it is part of a HAART regimen, and then claim that it's ‑‑ and because, of course, the AZT is no longer there, and the drug pressure is off.
So, I am not sure how ‑‑ you still can say that, in fact, your drug is good against resistant AZT virus.  It would probably be unethical to leave another arm with AZT on there in the presence of failure.  So, it is difficult.
DR. HAMMER:  I agree it is fairly complex, and I think some of the information would be potentially inferential however you define it, but if you could have an idealized study where you are really looking just at a single key mutation.
I agree that having some phenotypic change is important, however, there have been studies to show that certain individual genotypic changes can be associated with an inflection point in the RNA response without much measurable change in the virus.
That was shown with the case 70R for zidovudine, and you can see inflections with the 82 mutation in some of protease inhibitors without much in the way of ‑‑ certainly before you got to a 4‑fold change in phenotype.  So, there can be subtle changes to even single mutations.
One thing to think about, though, if you could have an idealized study where you randomize on the 215, for example, or some similar mutation, is to have a regimen, have two populations, one with a 215, one without, give the same regimen, look very carefully at the slope of decline.
If that is the same and everything is identical through 24 weeks, then, I think what you are looking for is rates of escape and what those escape mutants look like later, and it gets to the longer term issue that Doug Mayers raised earlier.
But it is not clean, but if you don't put an ethical control arm in, and don't control for the viruses in the control population, you won't be able to tell.
Also, look at the Merck 035 study, highly zidovudine experienced.  They did fine on ZDV, 3TC, and indinavir.  So, it is also further complicated.
Henry.
DR. MASUR:  In the problem we were dealing with Monday, when you have a drug which has a relatively modest effect compared to something else, how do you protect yourself from ‑‑ you could have a drug that in vitro looks great, that is hydrolyzed to an inactive compound in vivo, how do you know that your drug is really having any effect at all in vivo by doing that kind of experiment?
You are expecting a lot from a relatively weak drug, to expect to see a change in the slope of the curve.
DR. HAMMER:  If you are dealing with a relatively weak drug, I agree with you it makes everything harder. I agree.  I think it also gets back to ‑‑ you know, without being able to do monotherapy studies over anything more than a few days or a couple of weeks, it makes it extremely difficult.
I think the comment that Mark Harrington made about animal models and some of the possibilities there may help us.  It certainly will help us with some of the mutational issues.  I don't have a good answer for that because I think, you know, weak drugs are problematic from the start, and it just compounds as you go further down the developmental process.
Dr. Mathews.
DR. MATHEWS:  A thought on Henry's point is to take advantage of this notion of enrichment, which Jeff was talking about, because if you were able to characterize how many drugs in the patient's regimen were actually active, which in the studies that we were looking at the other day was impossible to tell, so that if you could either enrich up‑front by having the concomitant therapies having a reasonable likelihood of activity, or analyze the mutational patterns of all the drugs, not just the agent that you are analyzing, you would be able, at least in a subset analysis, a retrospective analysis, break out the different prognostic groups a little bit more efficiently than what you could do in aggregate, just looking at the single drug.
DR. HAMMER:  Dr. Yogev.
DR. YOGEV:  Well, first of all, I would like to sit on your right side for next time.  You are always looking to the right first.  I am teasing you.
DR. HAMMER:  I am fully balanced because I am always accused of looking to the left from the people on the right, because of the screen.
[Laughter.]
DR. YOGEV:  I think one possibility, you mentioned to take a mutation of 215.  I was wondering of a possibility of taking a HAART with, let's say, three drugs, and then add the new drug as a fourth one to see if there is anything different in the dynamics of reducing the virus and the amount of the response.
And that necessitates you have to go to a patient with a much higher viral load, so we cannot get into this unfortunate situation that with 1 log or 1.1, you are already undetectable.  So, a study can be devised that if you start with a very high number, look at the dynamic.
We have anecdotal data that if you increase the dose of certain drugs, the rate in the first week of reduction is faster, and that might be one thing that should be pursued that can give you an idea on a potent drug or less potent just because you have a control group now on the same population who has a high viral load, which can bring you this, if there is any effect, out.
DR. HAMMER:  Other comments on Scenario 3?
I can't quite summarize this.  We actually didn't really answer the first question ‑ Considering there are both resistance‑associated mutations and polymorphisms present in HIV genes, how should mutations be grouped and analyzed in prospective studies?
I think that is a very difficult question.  It took a lot of the conversation, for example, among the best experts in the country on the RCG group to put it together for the DAP analysis, so I think it is hard for the committee to do that.
It comes through basically consensus panels about what proven specific drug associated mutations there are that have had history by clinical isolates and site‑directed mutagenesis studies and also class‑relate mutations.
I think if you are going to base a trial on this, there is no perfect way to do this, but there are reasonable consensus.  I mean studies have been done based on trying to look at these and make interpretations.  So, as long as one is willing to disagree about some of the fine points of interpretation, you need some basis to move forward prospectively with what your hypotheses are.
So, I think there are enough consensus tables around and they will be evolving every year, and they are on the web, et cetera, that as long as this is decided upon up‑front, it is okay for a study, and as long as you balance expert advice across the arms.
We answered or tried to answer the post facto analysis of stored specimens question.  Probably the consensus of the committee is that if you are looking for a specific indication that it is active against viruses with X mutation, in this example, the 215, then, a prospectively designed trial with prospectively obtained specimens and tested is the right way to do it, but that retrospective studies are not completely excluded from this although it is perhaps better to generate the hypothesis from a retrospective study and then test it, as Dr. Pettinelli said, prospectively.
Then, what type of evidence is sufficient to support a claim of efficacy and receive an indication for Drug C for the treatment of drug resistant virus (in this case zidovudine resistance is identified by a single mutation at 215)?
The answer is efficacy in the population studied, but I think the question is how you get there, and I don't think the committee really is able to really come up with a study design that everyone would agree with because of the complexities of treatment.
I think the bottom line is careful looks at virologic response and escape early on and later on are the ways to do it, and to try your best to have a comparator arm that serves as the best control you can get.
Scenario 4.
DR. LAESSIG:  The final scenario is use of resistance testing in a clinical trial to enrich a patient population.
[Slide.]
Protease Inhibitor X has a somewhat unfavorable safety profile.  However, based on preclinical data, it may be useful as salvage because it appears to retain activity against protease inhibitor resistant virus.  A study is proposed to look at Drug X in treatment‑experienced patients.
The FDA recommends inclusion criteria based on resistance testing to demonstrate likelihood of failure to other regimens.
[Slide.]
The questions are:  Please comment on the appropriateness and feasibility of incorporation of resistance testing in the inclusion criteria.
Can efficacy in the enriched study population be extrapolated for use in populations where resistance testing is unavailable?
In studies that don't incorporate baseline resistance testing to choose optimal regimens, what are the implications of independently obtained genotyping or phenotyping?
Since this is the last scenario, we got to have an extra question.
[Slide.]
If after starting the initial regimen, genotypic or phenotypic information becomes available that indicates the chosen open label regimen is not optimal, could one or more of the drugs be changed without considering the patient a treatment failure if the patient had not met other criteria for treatment failure?
DR. HAMMER:  Can I just ask a clarification on the third question?  Do you mean that someone in a clinical trial obtains genotyping or phenotyping outside of the clinical trial?
DR. LAESSIG:  Correct.
DR. HAMMER:  And he or she, and his or her physician have that information and want to use it?
DR. LAESSIG:  Exactly.
DR. HAMMER:  Just like the viral load era.
Dr. Mathews, do you want to start?  You just mentioned issues of enrichment in populations.  Not to put you on the spot, but ‑‑
DR. MATHEWS:  The use of resistance testing in real‑time, one thing I feel reasonably confident about is that whatever the evolving guidelines are for use of resistance testing in clinical practice have to match what is being done in clinical trials.
Right now, resistance testing, I mean it is just a moving target, but once these new guidelines are coming out that are saying people should have resistance testing after failing a regimen, then, you are going to have problems trying to withhold that kind of information in the clinical trial setting.
DR. HAMMER:  I think the question is really what are your thoughts about up‑front resistance testing as part of the study design and stratification and randomization.  Is that right?
DR. MATHEWS:  I think it is going to be very important to do that.  The dilemma is that a lot of the resistance patterns that we have talked about and seen in the last couple of days are not well characterized, and so what decisions would you make on observing a certain pattern of uncertain prognosis.
Where I think it is most useful is what I was trying to get at earlier, was that you can reduce the heterogeneity of response and improve your chances of detecting the main effect that you are looking for by doing resistance testing and selecting the concomitant agents which are likely to be active in the regimen, and therefore the drug or drugs that you might be looking at in a factorial design, superimposed on background therapy, you would be more likely to detect an effect.
So, I think it is very prudent to incorporate baseline resistance testing at least for those circumstances where the interpretation of the results is reasonably clear.
DR. GULICK:  I would agree.  The heterogeneity of virologic failure that we have been talking about for the last two days, the demonstration of appropriate resistance mutations would actually sort of narrow the failure right down to the level of actually knowing that the patient was resistant to the prior drug as opposed to non‑adherent or perhaps with other PK issues.
So, it seems very appropriate to take this next step.  As an observation, many of the salvage studies which will now be looked upon as the old kind of salvage studies tried to get at this point by requiring a certain amount of months of therapy and certain patterns of breakthrough.
This cuts through all that and just gets right to the demonstration of resistance.  So, that would seem the most appropriate way to go.
DR. HAMMER:  Other comments on Scenario 4?  Dr. Wong.
DR. WONG:  That is true insofar as the tests that we have really demonstrated resistance.  I mean that is, you know, that is what it is, right?
DR. HAMMER:  Mr. Harrington.
MR. HARRINGTON:  Yes, and the other key statement was if it is clear up‑front what that resistance test means, and I think for most of the resistance patterns, it is not at all clear, and it is especially not going to be clear for a new agent where you are making a guess, extrapolating from old data.
The other point I want to make is ‑‑ a couple points.  I mean supposedly we are trying to treat a virus, not a point mutation.  So, resistance has an explanatory value for explaining why treatments may or may not work, but in reality, we want to know, I would think the sponsor would want to know how the drug works in a pretty heterogeneous population.
A lot of these designs disturb me because they seem to really focus on trying to make really tiny trials prove more than they can possibly prove, and I think that is a real danger.
There is a clinical need to prove utility in salvage populations, but we also ‑‑ I wouldn't think a sponsor would make very much money off of only treating these tiny niches.  So, I am wondering whether they are just trying to save money by proposing all these ridiculously small, one‑arm, open label studies to the FDA.
I think we need to get back to the bigger picture, which is what is the most useful information that can be generated for across the spectrum of HIV disease and how do you use resistance testing in that.
I am not sure that means you would use it in a regulatory framework all the time although that is why we are here.
DR. HAMMER:  That is a very good point, and we focused in, in our discussions here, but I don't think the scenario is meant to mean to the exclusion of having tested the drug or testing a drug in parallel in broader populations.  I think that we are being focused ‑‑ and correct me if I am wrong ‑‑ on a sponsor trying to apply for a specific indication, not necessarily the only indication, but a specific indication of use in a targeted treatment‑experienced population with a particular treatment experience, because the drug may have some particular advantage there.
I don't think that is to the exclusion of trials in broader populations, and I think that point is very important for us to sort of take a step back from the focus of these discussions.
Dr. Kumar.
DR. KUMAR:  I think the inclusion of resistance testing and inclusion criteria will really help many of us and our patients in the salvage protocols.  Many times there is a limit to how many months patients can be on a salvage protocol or on a salvage regimen before they are eligible for the second salvage regimen.
So, having this kind of resistance testing and inclusion criteria may allow us better to end all these patients instead of having patients remain on the salvage protocol for that period of time.
DR. HAMMER:  Thank you.
Dr. Mayers.
DR. MAYERS:  As someone who incorporates this into trials every day, you can clearly put patients on the studies with resistance testing as part of the randomization criteria.  We have a 500‑patient study that is doing both phenotyping and genotyping on the way on starting in December.
We have got three studies which do it on‑line every day, so it can clearly be done if you set up the mechanisms to handle real‑time, on‑the‑fly data.
The other issue I would like to bring up is if you don't do resistance testing, you can be sure that your doctors and patients are doing resistance testing if there is any indication your drug doesn't work against a certain type of virus, and if you are worried about resistance testing in the middle of your study, you probably have an ethical problem in your study design.
DR. HAMMER:  I would just take exception to that last statement at this moment in time.  I mean there isn't uniform access, and many patients are being managed without the availability of drug to resistance data, and there are other ways to manage patients based on their RNA change and other things, that failure can be handled clinically in some says when you don't have the finances to order a resistance test.
DR. MAYERS:  The point I was making, Scott, the point I was making was that if there is a certain pattern that they can find on a resistance test, that will get them to bail out of your study, there may not be equipoise in both arms of the study for that category of patient, and maybe there is a problem.
DR. HAMMER:  I would agree if there is uniform interpretation of what that meant, what those results meant.
Dr. Mathews.
DR. MATHEWS:  One other point on that, if resistance testing is done up‑front and as a basis for randomization, there is the possibility of misclassification error depending on when the resistance testing is done, and these are issues that we talked about already.
If it is done on drug for a certain period of time, that means one thing.  If there has been a washout period, that is another thing.  We see numerous examples where this can create misinterpretation of what the patterns are.
DR. HAMMER:  Other comments on Scenario 4?
Dr. Charache.
DR. CHARACHE:  Just on bullet 3, which asks what are the implications of independently obtained genotyping and phenotyping, and I would suggest that unless it is done in the same laboratory in which the basic studies are done, it could not be merged.
DR. HAMMER:  It wouldn't be merged, but it will be done in a different laboratory.  It may or may not be done in a different laboratory.  We face this all the time with viral load tests when they were not uniformly done and being handed back to patients in real time in the clinical trials, and it is a problem, but it can be handled basically because of the nature of the disease process and the clinical trial process in HIV.  It is not easy, but it can be done.
Just to summarize, the appropriateness of resistance testing and incorporation in inclusion criteria, I think there is a general consensus that it is quite appropriate depending upon the objective of the study, and if there is request indication for a specific target population, the one proviso of the committee was made by Mark Harrington, I think, that we are not interested in a series of small, tiny, little indications.
We are interested in a larger indication with a clarification as to which populations the drug would work best in.
Can efficacy in the enriched study population be extrapolated for use in populations where resistance testing is unavailable?
A very hypothetical question.  I think it depends really on the epidemiology of drug resistance in the population, and that is again a moving target.  It is worsening in different parts of the country.  You really have to know your own regional epidemiology and the individual epidemiology of the person you are treating and the potential requisitions over resistant strain and the drug history to know whether it is applicable.
So, I think extrapolating from a small study to a larger population is difficult without the kind of larger study I think that Mark Harrington was referring to.
In studies that don't incorporate baseline resistance testing to choose optimal regimens, what are the implications of independently obtained genotyping or phenotyping?
I think it means a greater off‑treatment rate.   That is what it means.  You have to basically power your study to incorporate that.  I would agree with Doug Mayers that you need to be ready to revise your study along the way and make sure that it is ethical ‑‑ and I wouldn't disagree with that point ‑‑ if the field is changing in relation to that.
It also depends on when that resistance test is done and whether it is post of virologic endpoint, because it could be not terribly meaningful if, in fact, the endpoint is virologic failure.  That endpoint is met and determined and set in the database, and then a resistance test is done, it is not going to hurt the trial endpoints per se.
It depends what you are doing on treatment as far as continued, issues of availability of resistance testing, but I don't think that is an unmanageable issue.  We have seen it many times over in HIV disease, and I don't think that is a big deal, honestly.
If after starting the initial regimen, genotypic or phenotypic information becomes available that indicates the chosen open label regimen is not optimal, could one or more of the drugs be changed without considering the patient a treatment failure if the patient had not met other criteria for treatment failure?
This is a completely unanswerable question because it really depends on the study design and the rigor with which ‑‑ and how things are defined, because if it is up‑front defined that any treatment change is failure, it is failure no matter what, if is a virologic endpoint failure, which is more likely, and this is a protocol amendment in a patient who is suppressed, then, I think it is reasonable that that protocol amendment could take place and the data could still be interpretable.
Without a full study design and basic assumptions and a stat section to review, I find it impossible to answer this question.
DR. MURRAY:  I think it was, let's say, of a protocol design, and not knowing the feasibility and the turn‑around time for the results, you get the results back after you have chosen your testing drug X, and you add it with two other drugs, and you get the results back as the patient is undergoing a viral reduction, you get results back that say oh, I wish I would have picked a different concomitant regimen.
Would this be reasonable?  I mean would you look askew at the data if, at that point, the concomitant regimens were changed, you know, while the viral load trajectory was still going down?
DR. HAMMER:  You mean not the test ‑‑
DR. MURRAY:  The background.
DR. HAMMER:  The background regimens?
DR. MURRAY:  Yes, like the nukes you would be combining with the PI.
DR. HAMMER:  And you are in the specifics of the downward trajectory, that is what you are saying?  That is what you said.
DR. MURRAY:  Well, I would think that it would refer to maybe the time it would take to get the results back, so it would be something early on, you know, typically before the failure endpoint is looked at, like before 24 weeks, something like that.
DR. HAMMER:  I would like to hear what other people think.  If that issue is not balanced between the arms or among the arms, I think you are in deep trouble.
DR. WONG:  I think the protocol needs to be written to take this eventuality into account. If you are really going to bail out and change everybody, obviously, that is going to have an effect unless it is specified in advance that this can be done.
DR. HAMMER:  Also, I think if you are doing baseline resistance testing and then choosing, you know, randomizing on the basis of plus or minus a certain pattern, but then that information is there.  It should be there before you initiate the regimens, and you should have enough flexibility in your initial regimens to adapt the background therapies to the resistance profile.
So, I would think, for example, if you used a line probe assay to look for a specific mutation, and then you also sent out full‑length sequencing and randomized on the basis of a line probe, and got the full‑length sequence back which caused you pause about the background therapy, you have painted yourself into a corner, and it is then better to have all of that resistance information up‑front if, in fact, you think you are going to allow modifications in the background therapy and try to optimize treatment to start with.
I agree with Dr. Wong you have got to have this in the protocol up‑front and try to anticipate these torpedoes coming below the water line.
DR. YOGEV:  It also depend when you are getting those results, because I think this is a unique opportunity if you get it within 12 weeks, and there are no failures, to see if the phenotyping/genotyping is really meaningful to what you are testing.  As of today, we don't know that genotyping is 100 percent.
It might be a unique suggestion by our committee or whatever that this pertaining to resistance, and in this specific combination, it doesn't work.  So, if I get a phenotyping/genotyping down the road, I would continue the study because it is very important information for us to learn, that we don't know what phenotyping/genotyping really means.
DR. HAMMER:  I agree with getting the information.  The issue is really whether you would act on it and change treatments, and I think one of the caveats in thinking about this is, in fact, the results of the ABT378 first failure study, where, in fact, if they operated on that sort of information, they might not have come up with some of the interesting results we have that resistance profiling doesn't necessarily correlate with response, and it may be the pharmacologic profile of the drug is more important, or we have to think about a different breakpoint.
So, I think it is quite complex and the best thing is to try to avoid that as much as you can.
DR. HAMMER:  Dr. Mayers.
DR. MAYERS:  I think, though, it is important to realize that as genotyping and phenotyping get more available and get more rapid turn‑arounds, that you almost should assume that your patient coming into your study has a genotype and a phenotype, and use that data to decide whether they liked your randomization to get into your study in the first place.
It would almost be better to say would you give us your resistance results as you come in, so you would know what they had had, than to assume that people are coming in blind, because I think less and less people are coming in blind over time, and if the recommendations to the IAS that were shown this morning come in, you can assume that probably almost no one is going to come in to your trial blind.
MR. HARRINGTON:  That is just not true at this time though, Doug.  I mean in an ideal world, that would be the case, and I think maybe in a couple years, more people will have access, but the majority of people I know, and many of whom have private insurance, don't have that information right now, some of them do.  But it is not as easy to get as you may think.
DR. MAYERS:  I realize it is not easy to get, but I am just saying that there is a fair number of people who are doing it now, and I think that fair number of people, if the new recommendations come out, that you should consider doing it, will increase dramatically.
I think that the assumption that patients are coming in blind into a study, especially anything beyond the first round, is probably flawed to begin with.  You aren't getting a random population coming into those studies.
DR. HAMMER:  I agree it is changing rapidly.  It will change, and we may not be able to do some of the studies in a year that we could do now, but that I think has been true.
We have come to the end of the regulatory scenarios unless there is some further clarification on these that you want us to attempt.
I think what we will do is turn to the committee questions.  There are five questions.  As I mentioned before, particularly in light of the fact that a number of committee members have left, and others have to catch other transportation soon, we will run through these and, honestly, just try to hit the highlights of what we haven't touched on.  I have also been asked to give a recap of this meeting, which we might or might not do, but in any case, we are prepared if we have to.
Some of these questions, honestly, I think we have touched on before in the last two days, and we can move through quickly as a group.
Questions to the Advisory Committee
DR. HAMMER:  The first question ‑‑ and just for the record ‑‑ is Please comment on the amount and type of preclinical resistance data sufficient to support a clinical development program.
We talked about the type of preclinical resistance data.  I don't know if anybody wants to comment on the amount.  I think that really relates to, for example, what size panel should be tested.  That is the way I would interpret this.  As far as the type of resistance data, we have talked about that repeatedly.
Does anybody want to tackle that?  I don't think we really have a clear notion.
Dr. Wong says enough.
I would venture, though, that, you know, we put some numbers on other things.  I think if we are talking about really characterizing a new drug, the panel of isolates, you know, it has to be laboratory and clinical isolates and well characterized on their mutational basis and their phenotype.
You are talking in the range of 50 to 100 isolates at least ‑‑ I don't think you are talking 1,000, I don't think you are talking 10 ‑‑ on the order of magnitude, we can probably say to develop a profile for the drug.
I think it also depends a little bit on whether it is a drug that is being developed as a better drug with a good PK profile, that is more a first generation drug, and is not going after drug resistance, or if it is a drug that really is a second or third generation drug that is going after a drug resistant virus.
If it is, the ladder of the panel has to be larger.
Does anyone disagree?  Dr. Charache.
DR. CHARACHE:  I would just add I think that it is time for the FDA to help the various drug manufacturers by stipulating what the basics of the testing should include, and I think that can be done with the panel agreement.  I don't think it is complicated.
DR. HAMMER:  Dr. Mayers.
DR. MAYERS:  I think the one thing that has become clear from the bacteriology field is that if they can use a standardized panel of strains and a standardized assay, especially if you are going to try and relate resistance to some pharmacokinetic parameter in the future, that it would be really useful if we would establish some reference resistance test and some reference panel of strains to look at, because currently, every company has a different in‑house assay.
They all have a different batch of 15 isolates that they pull out of their freezer, and it is almost impossible to relate the way they test the drugs against a clinical assay that will be done or their isolates against anyone else's isolates for any other drug.
DR. HAMMER:  Other comments?
Question 2, which we have also touched on in the last day and a half.
What type of in vitro and clinical data should be provided by drug sponsors to characterize the clinical activity of an antiretroviral drug against "resistant" virus?  Should different standards be required to support a labeled indication (for treatment of resistant subpopulations) as compared to that to support descriptive statements in the Microbiology section for use in resistant patients?
In your discussion, please include details such as:  methods, patient subsets, number of patients, number of isolates, duration of treatment, number of drugs, definitions for assessing treatment response, etc.
I am glad there is laughter from the agency representative.
We have touched upon this a little bit and to a fair extent as to again if the indication is going after drug treatment experience, drug resistant population, the bar is a lot higher than if you are doing a general "population" and then perhaps retrospectively looking at subgroups.
Would anyone like to sort of describe this further?  I am not sure we really can.
Dr. Charache.
DR. CHARACHE:  I would say it depends.
DR. HAMMER:  And we all concur with that, I think.
I think we have given you guidance on this.  If we can move on.
Question 3.  What type of in vitro and clinical data should be provided by drug sponsors to characterize an antiretroviral's ability to induce resistance and cross‑resistance?
In your discussion, please include details such as methods, patient subsets, number of patients, number of isolates, duration of treatment, number of drugs, definitions for assessing treatment response, etc.
We also touched upon this I think in the scenarios about resistance and cross‑resistance.
Would anyone like to add to this, because we have really talked about this.  Certainly, I think everyone is in agreement that the lack of ability to induce resistance or cross‑resistance, the bar for that claim is very, very high.  The issue of a little more slowly developing resistance, I think can be made based on genetic barriers and the mutations that come in, in vitro and in clinical isolates.
The clinical trials have to be well designed to really assess failures on treatment when they are in salvage regimens to really start looking at the issue of cross‑resistance to other agents in the class.
I am not sure we can be more specific.
Dr. Pettinelli.
DR. PETTINELLI:  Just as a general statement, however, I think it will be important to be able to evaluate all the drug available in the specific class.  The recommendation will be really to look in terms of cross‑resistance all the available agent.
DR. HAMMER:  I was looking at Dr. Stanley's comments to me to see if there was something to add.
Did anyone else want to add to Question 3?
Dr. Mayers.
DR. MAYERS:  I think that the in vitro data can clearly can tell you drugs you don't want to randomize to the patient the next time around, but I don't think in vitro data necessarily helps you for activity, so that I think that it would be very useful if there could be some mechanism developed for the companies, where there appears not to be cross‑resistance, to arrange to randomize the next round off their large Phase II's into those classes of drugs to get that answer, because right now we are really not getting the randomization until the next round of the patients, and the next round cleanly for these drugs, so that there is a lot of claims based on in vitro data, and that there is very little true efficacy data, which I think is the gold standard here.
DR. HAMMER:  Although there is sequential selection bias in such studies.
Dr. Mathews.
DR. MATHEWS:  One thing that we haven't really emphasized as much, although Trip made a comment that I think is important, is that the resistance and cross‑resistance information I don't think can be a substitute for the treatment history, the previous treatment history, because of all of the problems, and cross‑sectional looks at resistance.
So, collecting and analyzing the treatment history in conjunction with the resistance data, I think until we get further information validating the resistance data, it is going to be very important.
DR. HAMMER:  Although I would mention one.  It is only one study, and it was a relatively small study of Mike Saag at ICAC, 71 patients ‑‑ and I think the resistance testing was done retrospectively, but in a multi‑variant analysis, phenotypic testing, when that was included, antiretroviral treatment history was no longer a significant predictor.  However, it was just a single study, and it was only 71 subjects.  I think the point is very well taken.
The next question, we really should try to spend some time on, because this is important.  It has come up, and it is really where many of the issues of resistance and cross‑resistance have come out in academic and company studies.
The fourth question is:  What types of postmarketing evaluations regarding drug resistance/cross resistance should be conducted?
This, we really haven't wrestled with, and it is an important issue because that is where the larger numbers are treated, and these issues come out.
Dr. Pettinelli.
DR. PETTINELLI:  Just one comment.  After accelerated approval, I think that some of the patients are being followed up for a longer term, and I believe it will be essential to also evaluate those patients in terms of their genotypic/phenotypic profile.
DR. HAMMER:  Also, I would like to read into the record Dr. Stanley's comment on Question 4.
"There should be a plan up‑front to continue to monitor certain subsets of patients for development of resistance or cross‑resistance.  For instance, a sponsor could establish an agreement with certain clinicians to perform genotyping and phenotyping on any of their patients who go on the drug and who then fail."
I think there are a number of research groups, practice research groups and academic research groups and others who would be even more than happy to participate in such studies.
That last comment is my editorial comment.
Dr. Charache.
DR. CHARACHE:  It would probably be important to also test a subset of those who did not fail, so you can better interpret some of the changes.
DR. HAMMER:  You mean at baseline, because at least with the current techniques, we can't amplify the virus in those who succeed by our most critical definitions of success.
DR. CHARACHE:  You are absolutely right.  Take it back.
DR. MURRAY:  I have a question regarding maybe ‑‑ well, I don't know, since Dr. Stanley made it, I don't know if anybody can answer this ‑‑ so, a company does some postmarketing surveillance, it is not going to be controlled, what do we do with the information?  What can they say about it?
DR. HAMMER:  I don't have Dr. Stanley's cell phone number.  In Phase IV data, I think what you can try to do, and I think what she has indicating, is to really set up a number of cohorts of reasonable sizes where although it is uncontrolled information, you have got data on treatment experience, treatment history, follow‑up information, and marker data, and try to make thoughts about really, and then be able to grab the isolates and study them.
So, I think that the cohort information that one could develop through these sorts of things, it wouldn't take more than perhaps several hundreds of patients really, can be quite valuable.
So, it is uncontrolled, but it is really the length the follow‑up and the completeness of the dataset and the virology analyses that can correlate, that would be of greatest use.
DR. MURRAY:  And label‑worthy?
DR. HAMMER:  Potentially label‑worthy, but we would already be in Phase IV, so this would be an amendment to the label rather than a primary indication.  I would think that that kind of data would be supportive information, and, yes, could be label‑worthy because it may be supportive of the smaller dataset that says a drug is helpful in a certain population, or it may actually modify the label to back off.
Dr. Yogev.
DR. YOGEV:  I was just wondering if this is not the time to think about the clearinghouse, like we have immunization, an agency, you said.  Because we are going to do more and more often, as clinician, to identify for ourself why the patient failed, we should have a place where we can send those results to be verified or whatever, and that is how you can collect them there.
A company now, for example, are following pregnant women who are receiving their drug to see if there are any side effects, and that is how they will change or at least inform the rest of us.  I think because it is so complicated, and because we are individually doing it, we need a place maybe to think about it, it will be part of the postmarketing, to have a place to send those, which we identify resistant as the reason for the failure, to send over the accumulated data.
DR. HAMMER:  It is a good idea, but it just seems like an overwhelming undertaking if we have 30, 40, sometimes, in some populations, 50 percent failures  of treated patients.  The sheer numbers are extraordinary, and if one doesn't control for the data that is given, and it's passive acquisition, I would be concerned about really what conclusions could be drawn outside of the kinds of cohort populations that are being described now.  The numbers are just overwhelming as far as failures in this country on antiretroviral therapy.
DR. YOGEV:  I was not talking about the failure per se, I was talking about an investigator tested, found out they are resistant, to have a place that will collect them, because then you find out suddenly that there are 300 cases, nobody knows how much, because the cohort might be very difficult to follow, because over there they investigate all the failures.  All I am talking about is collect the resistance, verify them, and then you know what you have.
DR. HAMMER:  That is a good point.  I think we have actually a little bit of handle on that from the two commercial groups that are doing phenotyping, have put out some very impressive information that have helped evaluate, that is selected by obviously what they receive, but it gives you an indication at least in patients who are failing and getting resistance testing what it looks like.  So, that could be taken further in a more independent fashion I think is what you are suggesting.
Other comments on Question 4?
Question 5, which I think we can deal with very quickly, is:  Please comment on the feasibility and limitations of incorporating "real‑time" HIV resistance testing in clinical protocols.
I will ask Dr. Mayers to answer this in three words.
DR. MAYERS:  It is doable.  I think that actually real‑time resistance testing costs the same amount as post‑hoc resistance testing, if you define what you want to test, and I think that actually there is a real utility to the industry to collect baseline samples to look at the resistance in their failure patients in their early studies to understand whether their drug is working and how it fails to tailor and design their Phase III studies.
So, I think it is of great utility to the company, and it is a reason for the patient to stay in the study because they are going to get some data back that is potentially of use to them.
So, I think that this has real utility in understanding your drug's success and failure and designing your Phase III studies and helping to manage your patients when they fail the study.  I personally don't see a drawback to introducing this if you have a plan set up of evaluations for your drug.  Doing it real‑time just gives real‑time data rather than data a year down the road.
DR. HAMMER:  Dr. Mathews.
DR. MATHEWS:  Several times this issue has come up about doing resistance testing in people that are succeeding on therapy and how it is not feasible.  I just wonder what your thoughts are on looking at the idea of periodically withdrawing therapy even on trials to sample rebound virus, and see what the mutational patterns are.
DR. HAMMER:  It certainly is feasible to do.  I think there are issues of safety.  One has to decide what you are doing that for, and to me, there are issues still of safety.  Within a clinical protocol, it is not unreasonable, but given the fact that you are re‑seeding targets and you may drop your CD4 count, it is a bit problematic to me, although obviously, it needs to be studied further.
Mr. Harrington.
MR. HARRINGTON:  TAG co‑sponsored a workshop on this topic in the summer, and Dr. Miller was there.  I think there is going to be plenty of protocols that are going to be looking at structured treatment interruptions in the suppressed and unsuppressed, and first line and salvage population.
I think we might get some answers out of that, and I would think it probably wouldn't be clinically ready for prime time other than in the context of a study that had a good hypothesis behind it.
DR. HAMMER:  I think Chris's point is it is a good way to get an isolate approximate to the regimen to see what it looks like.  I think we will get information about this at the Retrovirus Conference and subsequently.
Other comments?
Dr. Gulick.
DR. GULICK:  One thing that has been said before should be restated is that the use of resistance testing on study should, at the very least, be up to where the level of where clinical care is, as we have learned from viral load and the use of real‑time viral load testing, which suddenly became really the only way to do it, because that is what was being done in the community.  The same principle probably needs to be applied here.
DR. HAMMER:  Does anyone on the committee see that there is a real block to the feasibility of real‑time testing in the setting of drug development and clinical protocols?
Dr. Jackson.
DR. JACKSON:  Maybe Doug could comment, but I agree the utility is clearly there for a number of reasons, but in terms of the feasibility, obviously, you need to get a viral load, as well, at the same time before you waste your time trying to amplify something that isn't there, but what has sort of been the range of turn‑around times that you have been able to achieve in real‑time for these protocols?
DR. MAYERS:  We are turning them around in about 10 days right now on average.
DR. JACKSON:  On average.
DR. MAYERS:  On average.  The problem is if everything amplifies right for you the first time, it's a three to five day procedure.
DR. JACKSON:  But when they don't ‑‑
DR. MAYERS:  It can be ugly, and we do go to back‑up primers and back‑up to get sequence.
I think that the way we are doing it in the CPCRA, which I think is reasonable, is we bring the sample in, we do a viral load.  As soon as the viral load hits the cut point, you send the sample in for sequencing.  Since it is the same labs, that is a fairly rapid process, and it can be done out of the same vial of plasma.
It is feasible if you are set up to do it.  I think that the new technologies, especially the rapidly improving ability to put it all together quickly and check it, what used to be eight hours of somebody's day is now significantly shortened.
But the turn‑around times, I think in the next year are going to be 96 hours, you know, five to seven days is going to be expectation, five to seven working days will be the expectation of getting this data back.  It is getting faster and faster.  The technology is getting better.
DR. JACKSON:  Although obviously, there are always problems that take sometimes longer, two to three weeks even, and, of course, that is in the laboratory.  Our experience with viral load testing, of course, the real delay is getting the specimens from the sites with the correct labeling and everything else.
So, I think it is important for sponsors or investigators, in designing trials with real‑time testing, that if there are randomization points, that we have to be very realistic in terms of how much time is really necessary to do real‑time testing, taking into account all these factors.
DR. MAYERS:  If you are asking from when the blood is drawn to when the result goes out to the field, right now it is taking us ‑‑ because we ship weekly and there is an average of six days between the blood draw and actually getting it to the lab ‑‑ it is a 13‑day turn‑around time from the shipment of the sample coming in to it going back out to the field, and it is probably on the order of 21 to 24 days between the sample being drawn, the viral load and the genotype getting back in the current system.
I think that you may not, people may not be willing to wait that long, you are right.  On the other hand, I think that if the issue is, is it worth the company getting the data on a real‑time basis, I think it actually has pragmatic utility to the companies.
I have a little more concern actually about the issue of doing genotyping on the way in for a drug where I don't know what its responses are going to be, because you are presuming you know how the drug is going to work before you have ever put it into people, and that is a limitation.
If you assume that you know what is going to happen before you do the study, you can limit yourself from finding out data that is very useful.  So, I think I am a little less comfortable unless there is 100 percent resistance between the two drugs.
In my early, first‑time failure studies I am going to limit the patients until I get some data to support that knowledge.  I think you can prejudge yourself out of utility unless you at least do a little clinical data to make your decisions.
DR. HAMMER:  I think we can rely on the sponsors not to do that.  We will have their drugs before they have adequately tested them.
Dr. Jolson.
DR. JOLSON:  I had another feasibility question.  Maybe Doug could answer it.
We are often criticized that our requirements are burdensome, financially burdensome, particularly for smaller companies.  What we are talking about I think at this meeting involves more testing than is currently being done in clinical trials.
What would you ‑‑ and this is just an estimate ‑‑ the incremental cost is when you consider doing at least baseline resistance testing on most trial participants, and then resampling again at the time of failure, how much additional cost should we be anticipating that will be incurred?
DR. MAYERS:  Phenotype and genotype together?
DR. JOLSON:  Well, maybe you can tell me for each one.
DR. MAYERS:  It is running about $385 to 400 ‑‑ around $400 a sample, a little bit less, for genotyping of the protease and RT gene.  The companies are just establishing pricings for phenotyping, but my understanding is it is going to be in $750 to $900 range.
So, you are probably adding about $2,500 to that company for that patient who fails.  For the ones who don't fail, obviously, not as much money.  On the other hand, my counter to that would be that if you don't do this testing, and you go into your Phase III program, you are going into your Phase III program blind, and what is the cost of doing 1,000‑patient trial in the wrong patient group because you didn't have the data to select the right patients for your large studies.
By investing up‑front in your Phase I/II program, and looking at where does my drug work best in experienced patients, how does my drug fail in inexperienced patients, that allows you to design a Phase II/III program that efficiently targets your drug to the right patient groups.
DR. YOGEV:  Do you really need both of them?  We just thought that it's so nicely correlated, it would be enough, the genotype only, for example, in Phase I/II.
DR. MAYERS:  I think in Phase I/II, you are going to have to get both data, because it's a new drug.  You don't know what the relationships are.
I think that you can then, once you have enough Phase I/II data, make an efficient strategy as to how you are going to go beyond that, but I really do think, in Phase I and II, it pays off to invest the money in those patients.
DR. HAMMER:  I would agree.
Dr. Jackson.
DR. JACKSON:  Getting back, if a company is going to make a claim about resistance, and efficacy against resistance, I think you have to have phenotypic resistance data.
DR. HAMMER:  That makes sense, internally logical.
Are there other comments on Question 5?
Since we are running ahead, I have been asked to recap this meeting, which I think is just a slightly daunting request, but at lunchtime I put a few slides together and I will try quickly to do that.  I think this is intended really for the potential sponsors in the audience.  I would say that, in advance, that any comment I am going to make has not been vetted with my colleagues here, it is really personal sense of this, and is somewhat generic.  The details need to be worked out, I think, as each drug comes through development, but maybe it will create a summation for this meeting.
Recap and Summation of the Meeting
[Slide.]
DR. HAMMER:  First, I think the first important thing at the recap is I think thanks should go to Heidi Jolson and Jeff Murray and their colleagues for putting this meeting together.
I think I can speak for the committee that it has been a privilege to participate and particularly to hear the wealth of data that has been presented, some of it, and a fair amount of it, for the first time in this venue.
I think and I hope that we can look back at this as a turning point, if you will, in the resistance field in HIV drug development, much as the RNA Symposium was the turning point for drug development in clinical trials, as well.
[Slide.]
So, I started the meeting recap with a non‑controversial question and answer, which I think is at least where we should start.
Does resistance testing have a role in drug development?
I think the consistent message from the group subject to disagreement here is an emphatic yes.  It not only does, but should have a role.
[Slide.]
I think there is also general agreement that both genotypic and phenotypic testing are important.  I think it is also well to remember that we get wound up in specific assays, but that no single assay, genotypic or phenotypic, has been individually discussed or should or can be recommended.
I think there are pros and cons to each of the assay and the formats.  The technical and interpretive limitations have been well outlined, particularly in Doug Richman's talk, and that, as everything else in science and development, you need to choose which assay is most appropriate to answer the question that is at hand.
I think whether you are doing genotyping or phenotyping and which platform you are using and which assay you are using, you need to think about what question you are asking and what answers you want.
I also think in relation to NDAs that come in, and certainly data that is seen by this committee, that validation data for any assay that is used needs to be provided.  This can be more easily done, obviously, if there is a well validated commercial assay, but I think for in‑house assays, we need to know really what their performance characteristics are.
I think the agency will demand that, and as we have talked about, standardization is going to be increasing important.  It may be externally imposed, as well.
[Slide.]
So, yes, I think NDAs should include resistance data.  They already do essentially, at least the ones that this committee has seen in the more recent times, and that is not going to change.
There is an issue of what level, I think, and it is termed at sort of the minimal level, and again I say this as personal.  This is not committee‑wide, this is not the agency, this is just me from this meeting.
At the preclinical level, I think we have talked about this, that at the minimum, we want to see drug passage studies where passage in the presence of drug with genotypic and phenotypic characterization of mutants is performed, and then the testing of the significance of those mutations, there would be a site‑directed mutagenesis.
For companies that have been well involved in this, this is old hat.  For companies that are thinking about getting into this area, this may not be so old.  But I think it is important, and again, in the era of non‑monotherapy, the in vitro data and trying to characterize mutations and what they do in vitro, and potentially, as we talked about in some animal studies, but certainly in vitro, it is going to be important because the clinical data will be increasingly complex.
The second minimum characteristic is to test the drug against a panel of well‑characterized isolates.  We talked about this a lot.  What we haven't really wrestled with is what the size of that panel should be.  I threw out a number, I think somewhere between 50 and 100 isolates is what we should think about, laboratory and clinical strains, and certainly if you are looking at an agent against drug resistant mutants, you are looking at a panel closer to 100 or more.
I think in the open public hearing, the data on tipranavir is sort of an illustration of sort of what a panel of 100 or 100 isolates can tell you of highly resistant isolates against a new drug.
[Slide.]
On the clinical side, minimally, one needs to characterize the escape mutants on therapy, and the group lacked a lot of consensus on this, it is the issue that resistance is not the sole reason for drug failure, and to make sure that the confounders of failure are taken into account when looking at the escape mutants on therapy.
So, PK substudies and adherent substudies within larger trials are important to try to pin down what is the cause of the failure, and it may not be just one issue.  It may be more than one issue.
The other issue, at the minimum, is depending upon the prevalence of drug resistance in the population, and this drives to Susan Little's presentation.  You may need baseline characterization of the population that you are studying even if you are not particularly going after a drug resistant population as an indication, if you are testing that drug in New York, San Diego, San Francisco, et cetera, you may in the next few years need to really at least sample that population to know what you are doing.
Retrospective resistant studies, I think are still acceptable, but obviously, our notions are changing, and there will be an increasing emphasis on pressure to look at prospectively designed studies that bring in resistance testing even in drug development that it not going after a specific indication for an experienced population, because even in naive populations, this will have, in areas where there is a lot of drug penetration, will have an impact on response.
[Slide.]
At the next level, not the minimalistic level, but the next level, for drugs with potential activity versus drug resistant virus and for which a primary indication for treatment of patients with resistant virus is sought, characterization of those patient up‑front, genotypically and/or phenotypically, is important, and then to test for activity in combination regimens.
This slide was made at lunchtime, before the afternoon discussion, so this was already covered.  This is a question mark because no one really has the answers for what the right comparator regimens are.  You can just do your best.
I think you do need comparator regimens, and you need to try to control for as many factors as you can virologically and otherwise and regimenwise to try to come up with what those treatment responses are in those patient populations.
Again, the confounding issues need to be taken into account.  One needs to think about not just up‑front, randomizations based on drug resistance, but also strategic trials, i.e., looking at patients who fail on regimen A, and then using a new drug and a new combination to rescue those patients strategically.  So, that is the use of resistance testing in the midst of a trial in more of a strategic fashion.
Then, the RCG analysis has brought up with us the issue of uniformity of analyses, and this is not for me to really state one way or another, but I think the committee over time is helped by sort of looking at data that is analyzed perhaps primarily in one way, but more uniformly in another way, and it was very helpful to see the RCG analysis, and I imagine discussions will go on with the agency and sponsors about how to sort of put forward their clinical resistance information and drug activity information in the packages that are submitted in much the same way that endpoint issues were discussed when RNA became the primary endpoint, both for accelerated approval and for most traditional approvals.
So, I think this needs to be discussed more at the agency sponsor level, but the committee looks at this with interest.
[Slide.]
There are a number of secondary gains from this meeting, which I hope, in fact, will occur.  The first and most important is despite what happened at the beginning of this week, I would hope that there is strong encouragement for the further drug development and treatment‑experienced patients, and the presence of drug resistance testing and its prevalence in trials and in clinical practice is going to drive this, and I hope this meeting helps that.
We can hopefully facilitate more collaborations among industry, academia, and government.  It is the only way we are going to develop the data.  We need the drugs, we need the assays, we need the clinical trial design, we need the patient populations that bring all these groups together.
The RCG effort, I think was a beautiful example of the way these three groups came together to analyze their data in a record‑setting time frame.
We talked about standardizations, and we need to move toward that.  We are recognizing the issues that are involved in that.  It is not antibacterial therapy.  I think that helps us strongly to say what might be a reasonable Holy Grail to go after, but we are far from that, but we can standardize in certain areas as far as panels of isolates, the assays that are used, validation criteria, et cetera.
Obviously, through all this, we are going to get smarter, and most importantly, this is going to increase the pressure to improve access to resistance testing for populations in this country and elsewhere, a very important side effect.
[Slide.]
I thought the way to end really was to talk about ‑‑ you know, we talked a lot about what we don't know and what we need to know, so I thought it was important actually to try to organize or at least put some thoughts forward about where current research and future research should be going in the resistance field, which directly drive to drug development, as well, although not specifically or uniquely.
There are four areas to consider.  One is the epidemiologic.  I think it is clear from the presentation this morning that we need increasing data and ongoing studies about the prevalence and transmission of resistant virus.  There are some, but we need better studies about the cost‑benefit of resistance testing to prove to third‑party payers that if you stop wasting drugs or expose patients to toxic drugs that they won't succeed with, that, in fact, resistance testing can be cost effective.
[Slide.]
The long list of current and future areas of research is on the clinical research side, and we saw some of the prospective studies that are currently going on.  We need to really think about whether it is phenotypic testing, genotypic testing, or both, the most expensive option, and it won't be this, but whether it be one or the other, or virtual phenotype, so called, or whatever, we will know in the course of the next year, year and a half.
The role of resistant testing in primary and established infection and, really, how widespread that should be in our population needs to be determined, and how it improves outcome.
The further definition of the role of testing and treatment failure.  I mean I think there is still debate despite what recommendations may be forthcoming about whether a physician's best guess and treatment history and RNA trajectories may be just as valuable as an expensive resistance test.
We have talked about assessing relative contributions of resistance, adherence, and the pharmacokinetics to treatment failure.  The best thing about the attack on resistance is to prevent it in the first place, and this means good drugs, good regimens, intelligent treatment choices, which we don't talk about too much, we assume this probably too often.
We talked about this, and this is very important ‑ the long term benefit of resistance testing.  Those are data we did not see in the last two days, and hopefully we will see over the next couple of years.
We also talked about the fact that we don't know what the relationship of an IC50 or an IC90 or 95 is to the in vivo activity of the drugs, and one clearinghouse we do need is the knowledge base of achievable drug levels and the determination of whether peak trough, area under the curve, or whatever, is the best measurement, the pharmacologic correlate of response.
This is going to be helpful particularly if we get into therapeutic drug level monitoring.
It has been brought up a couple of times that individual drug susceptibility may not be as helpful as looking at a regimen score of susceptibility at least in clinical trial assessments of success.  That is a little bit more difficult on an individual patient basis.
[Slide.]
The pathogenetic side we didn't talk about too much, but this is going to drive the rest of the field.  The dynamics and the relative fitness of viral subpopulations in relation to strategic treatment interruptions and drug recycling will help us, I think, in knowing what to do in salvage therapy, and will also help us in trying to think about interesting trials with new agents for salvage.
Rich D'Aquila touched on this, the importance of resistance mutation interactions.  We have a number of them.  Some have been tried to be exploited in therapeutic interventions, but we really haven't exploited this to the full degree that we can.
There is a very important issue about the evolution in latent reservoirs and compartments.  The compartment issue was talked about.  We know that there is virus in latent reservoirs.  Some individuals have evolution in their envelope.  To date, there has been very little evidence of evolution in RT and protease, but the numbers of overall patients that have been studied is relatively small, and if you are evolving an envelope, it is I think only a matter of time before there is evolution in RT and protease even in these very low level infected reservoirs.
The cellular mechanisms of resistant and particularly the most recently described MRP4 with PMEA and some of the nucleosides is important.
The role of protease cleavage site mutations, Doug Mayers brought up, and we have talked about natural polymorphisms and what is a polymorphism and what is a drug resistance mutation, and that debate has been going on for a couple of years, but I think one of the key issues now are these low level changes in susceptibility in recently infected individuals, again Susan Little's data, and really what that means clinically is a 4‑fold change or a 6‑fold change or an 8‑fold change, and susceptibility to an NNRTI in a naive population important, and we honestly don't know the answer to that, and these are recently accrued data that we need to investigate through treatment response studies.
[Slide.]
Lastly, there are technical issues, and we talked about this, I think on the first day.
We need improved detection of mixtures because of the fear that a low level mixture, a low level subpopulation will arise quickly when we start a new regimen.  The amplification issues I think are going to take care of themselves.  I think we will see technical improvements that will be very consistent at 1,000, increasingly consistent at 500, and potentially down to full levels of detection.
The different subtype issues has been raised with certain types of assays, for example, the chip technology has not performed particularly well with subtype issues, so this always has to be looked at, particularly as subtypes circulate around the world and as drug hopefully circulate around the world.
Then, the data analysis interpretation both at the clinical trial level, cohort study level, and individual patient level is a huge issue in really handling these data, not only in a physician's office, but in a clinical trial's operation, as well, and for the agency.
Some of the issues that are going on with relational database development are going to be very helpful there.  Again, we talked about we are only as good as the assays.  The information that is produced is only as good as the assays that produced them, so the quality assurance issues need to be tackled, and this can be done in a number of ways ‑ through external groups, through quality assurance programs like the BQA program that was mentioned through DAIDS, and others, but that needs to be also brought to play.
So, that is sort of the future, and I think a lot of this relates to drug development, so these issues of research are relevant.
On that note, those are my recap comments.  I would like to actually give the committee a chance to add anything they would like to since I didn't have a chance to vet this with my colleagues except that I think they knew by inference that I was going to make this mercifully brief.
Dr. Jolson, is there anything else that the committee needs to tackle?
DR. JOLSON:  Well, I certainly wouldn't dare ask you any more questions.  In fact, the committee has just been incredibly diligent to work through all these questions, and when we started off the meeting, we called it a workshop, and hopefully, you understand why the emphasis was on the work, not on the shopping part of it.
I just really want to one more time thank everybody for their thoughtful input.  I want to thank the speakers on both days who just did a wonderful job providing really important background information for the committee's deliberation.
Yesterday, I acknowledged the RCG, but I would also like to reiterate our commitment to an effort like that, that brings together industry and academia with the government and other interested groups.
I want to acknowledge everybody on our side who just did a tremendous job putting this workshop together.  We really do see it as a milestone, as you were mentioning, that it really is a very public statement for us that we are seriously concerned about the issue of development of resistance to current and future antivirals, and we do want to see the field move forward, so we can provide better information and product labeling.
We saw these two days as an initial step towards those goals.  So, I just again want to thank everybody for their hard work and their contribution towards that effort.
And thank you for chairing a meeting that ended on time.  That really met our goals.
DR. HAMMER:  Thank you.  I would just like to thank my committee members and guests, and thank you and also once again thanks to the audience participation and particularly to the speakers, who I think did a fabulous job in updating us on a state‑of‑the‑art fashion.
With that, the meeting is adjourned.  Thank you.
[Whereupon, at 3:15 p.m., the meeting was adjourned.]
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