ATDEPARTMENT OF HEALTH AND HUMAN SERVICES

FOOD AND DRUG ADMINISTRATION

CENTER FOR DRUG EVALUATION AND RESEARCH

ANTIVIRAL DRUGS ADVISORY

COMMITTEE MEETING

VOLUME II

Tuesday, November 2, 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.
Courtney V. Fletcher, Pharm.D. (Guest Consumer 	                Representative)
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.
Jeffrey S. Murray, M.D.
Joanne L. Rhoads, M.D.

C O N T E N T S

Call to Order: Scott Hammer, M.D.	5
Conflict of Interest Statement: Rhonda Stover, R.Ph.	8

FDA Introduction: Heidi Jolson, M.D., M.P.H.	10

SESSION 1
Performance Characteristics and Limitations
of Currently Available Genotypic and Phenotypic Assays

Introduction: Lauren Iacono‑Connors, Ph.D.	22

Presentations:

Principles of HIV Resistance Testing and Overview
of Assay Performance Characteristics:
Douglas Richman, M.D.	25

CBER's Policies on Assay Regulation: Definitions
of Assay Performance Characteristics:
Andrew Dayton, M.D., Ph.D.	66

Questions to the Advisory Committee 	87

SESSION 2
Evaluation of Relationships Between Genotype, Phenotype
and Treatment Outcome

Introduction: Girish Aras, Ph.D.	132

Presentations: Prospective Studies

VIRADAPT Study: Phillipe Clevenbergh, M.D.	134
GART Study: John Baxter, M.D.	150

Presentations: Retrospective Studies

Introduction to Resistance Collaborative Group's
(RCG) Reanalysis of Selected Studies Using the
RCG Data Analysis Plan:	
John Mellors, M.D.	165

DAP Statistical Methodology:
Victor DeGruttola, Sc.D,	169

Overview of Retrospective and Prospective Studies
Reanalyzed Using the DAP:
John Mellors, M.D.	176

Key Retrospective Studies Analyzed in a 	Standardized Fashion:
  ACTG 333: Michael Para, M.D.	187
  CNAA2007: Mounir Ait Khaled, Ph.D.	191

C O N T E N T S

  Franfurg Cohort: Veronica Miller, Ph.D.      	198

Summary of Key Points: John Mellors, M.D.	202

Summary of Studies Analyzed by DAP: FDA	
 	Statistical Comments:
Girish Aras, Ph.D.	206

Questions to the Advisory Committee 	228

P R O C E E D I N G S
Call to Order
DR. HAMMER:  Good morning.  I would like to open this session of the Antiviral Drugs Advisory Committee Meeting.  We are here today to discuss the application of resistance testing and its utility in the drug‑development process.  But this symposium over these next two days will cover a wide range of topics related to resistance testing and, really, tell us what the state of the art in the field is.
I am hoping this will have a fair impact on the field as previous meetings, for example, with respect to RNA did in the past.  So I look forward to an interesting two days and commend the agency for bringing this meeting together.
I would like to start with introductions.  Dr. Charache, please introduce yourself and your institution.
DR. CHARACHE:  Patricia Charache.  I am Professor of Pathology, Medicine and Oncology and, in Pathology, my primary appointment, I am the Program Director for Quality Assurance and Outcomes Research.  In areas related to this meeting, I am Chair of the Microbiology Panel in the Center for Medical Devices.  I am a member of CLIAC, the Clinical Laboratory Improvement Advisory Committee at CDC.  And I am a member of the Secretary's Committee on Genetic Testing.
DR. FLETCHER:  Courtney Fletcher from the University of Minnesota.  I am Professor in the Department of Clinical Pharmacology at the College of Pharmacy.
DR. WOOLSON:  Robert Woolson.  I am Professor of Biostatistics at the University of Iowa.
DR. MATHEWS:  Chris Mathews, Department of Medicine, U.C., San Diego.
DR. KUMAR:  Princy Kumar, Georgetown University Medical Center.
DR. GULICK:  Roy Gulick, Infectious Diseases, Cornell University.
DR. STANLEY:  Sharilyn Stanley, Associate Commissioner of Communicable Diseases, Texas Department of Health.
DR. YOGEV:  Ram Yogev, Children's Memorial Hospital, Chicago.
DR. HAMILTON:  John Hamilton, Adult Infectious Diseases, Duke University.
DR. MASUR:  Henry Masur, Critical Center, NIH.
DR. HAMMER:  Scott Hammer, Infectious Diseases, Columbia University.
MS. STOVER:  Rhonda Stover, FDA.
DR. POMERANTZ:  Roger Pomerantz, Infectious Diseases, Thomas Jefferson University.
DR. WONG:  Brian Wong, Veteran's Hospital in West Haven Connecticut and Yale University School of Medicine.
DR. JACKSON:  Brooks Jackson, Professor of Pathology, Johns Hopkins University.
DR. PETTINELLI:  Carla Pettinelli, Division of Aids, National Institute of Health.
DR. KAPLAN:  John Kaplan.  I am from the Center of Disease Control and Prevention where some things that I do there‑‑one hat I wear is to try to coordinate activities we have related to HIV resistance and, specifically, an interest in surveillance in antiretrovirally naive persons.
DR. MAYERS:  Doug Mayers, Infectious Diseases, Henry Ford Hospital.
DR. ARAS:  Girish Aras, Team Leader, Division of Biometrics, CDER, FDA.
DR. IACONO‑CONNORS:  Lauren Iacono‑Connors, FDA.
DR. RHOADS:  Joanne Rhoads, Medical Officer, FDA.
DR. MURRAY:  Jeff Murray, FDA.
DR. JOLSON:  Heidi Jolson, FDA.
DR. HAMMER:  Thank you.
I will now turn the meeting over 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 these issues relating to testing for development of resistance human immunodeficiency 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 12A30 of the Parklawn Building.  In the event that the discussions involve any products or firms not already on the agenda in which a 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 participants, we ask, in the interest of fairness, that they address any current or previous involvement with any firm whose products they may wish to comment upon.
DR. HAMMER:  Thank you.
I would also like to extend my welcome to all the guest speakers that we will hear over the next two days and now I would like to turn to Heidi Jolson who will give the FDA introduction.
FDA Introduction
DR. JOLSON:  Good morning.
[Slide.]
I would like to welcome everyone here today to the first of a two‑day meeting that we are really quite pleased to be sponsoring on the use of HIV resistance testing in drug development.
Before I even get into anything specific, I just want to mention, by way of housekeeping, that we have two overflow rooms so, for comfort, if people want to spread out, there are two other rooms that are down the hall and to the right that have monitors that folks can go to as well.
These were set up because we would hope that this would be the sort of meeting that would have a lot of broad interest through the community and through industry.
Our division is extremely excited to be sponsoring this meeting.  The goal is to stimulate further development of the science of HIV‑resistance testing with a focus on how resistance testing should be optimally incorporated throughout drug development.
Our ultimate goal in bringing forward this issue is to work towards and improve our understanding of how to better use therapeutics for HIV and how to translate this information into product labeling for clinicians.  Toward this objective, we are extremely pleased to welcome our guest consultants and also representatives from the NIH and CDC and all of our invited speakers the this week's meeting.
[Slide.]
In the next few moments, I would like to just set the stage for the two days of scientific discussion.  In my comments, I would first like to contrast the implications of HIV drug‑resistance development from the somewhat different perspective of individual patient management and overall drug development because the latter is really the focus of today's meeting.
I would also like to elaborate somewhat on our goals for this meeting and our expectations and provide a little background regarding how this meeting evolved.
This is a very complex subject.  Everyone in this audience and on the committee is aware of that.  You will notice that the agenda actually covers four pages.  I think that that is probably a record for one of our advisory committee meetings.
Therefore, in my remarks, at least I will provide a road map to the logic of the meeting which is organized into modules or sessions.  That is a somewhat different format than our typical advisory‑committee meetings.
[Slide.]
From the perspective of individual patient management, the implications of HIV drug resistance are profound.  Develop of resistance is a critical factor that undermines the safe and effective use of therapeutics for HIV.  Resistance is one of several factors that may be the cause of treatment failure and, in the setting of resistance and cross‑resistance, selection of new regimens becomes increasingly limited.
From a risk‑benefit perspective, patients who continue medication to which their virus has developed resistance are at risk for drug‑induced toxicity without a potential therapeutic benefit to appropriately balance this risk.
The potential for transmission of resistant virus raises further complicated patient‑management questions regarding optimal strategies, the treatment of newly infected persons and prevention strategies in perinatal and occupational exposure settings particularly when contact with drug‑resistant virus is suspected.
[Slide.]
From the drug‑resistance and regulatory perspectives, additional implications are raised.  Our lack of a more complete understanding of HIV drug resistance complicates and impairs our ability to interpret clinical‑trial results and limits our understanding of why patients respond or fail combination therapy in clinical trials.
Further, our ability to test new drugs in patients with the greatest need for new options is limited until we can make predictions regarding who is most likely to respond to an investigational agent on the basis of virus susceptibility.
Last, at the present time, we have a limited ability to provide advice on the optimal use of a new drug in product labeling because of our incomplete understanding of viral resistance development.
[Slide.]
Part of the agenda today is to develop a common understanding of the current constraints in our ability to utilize the emerging technologies.  Some of the limitations are listed on the slide and include factors such as the diversity of genotypic and phenotypic testing methodologies and the uncertain relationships between different types of tests, the lack of approved, well‑validated assays, the lack of uniform requirements for resistance characterization throughout drug development and into the postmarketing period, the lack of consensus regarding the clinical utility of prospective testing and questions about the interpretation of test results and definitions of resistance, and, last, how resistance testing may be impacted by a host of other biological factors.
[Slide.]
Although we acknowledge that the preceding lists an almost intimidating spectrum of hurdles, we strongly believe that this meeting, as a forum, may facilitate progress towards resolution of at least some of these issues.  The first step towards these goals is the identification of what needs to be addressed and to make certain that we understand the limitations that are currently preventing the wider use of resistance testing and drug development.
On this slide, we sort of summarize some of the overall meeting goals which I will just briefly go through.  But, first, we would like to define what is known or what needs further study regarding actual technologies.  That would include the reliability of assays, how to interpret the results and what their strength is for predicting treatment outcome.
We would additionally like to discuss different approaches for defining resistance including mutational algorithms and break points that would be applicable now and in the future as new drugs are developed.
We would like to discuss approaches to standardized methods for analyzing data that the agency can use when it considers new drug applications.  We would like to obtain the committee's guidance on use of resistance testing and drug development and we would like to discuss what future initiatives should be undertaken to encourage progress.
It is really our expectation that this meeting today is the first step towards achieving those goals.
[Slide.]
I would like to say a few words about the evolution of this meeting.  Because of the undeniable public‑health importance of this issue, our Chair, Dr. Hammer, at least a year ago, encouraged the division to have this meeting as a proactive approach from the agency towards moving the field forward.
At a similar time that we began to consider the feasibility of this meeting and tackling these issues, we were also approached by a newly formed industry collaborative group that you will hear about today which was addressed to answer some of the issues that are in common with today's meeting and also some issues that are beyond the scope of today's meeting.
You will hear more about this group on the next slide.  The agenda for this meeting was developed collaboratively within our division, with our chair, with the Resistance Collaborative Group and from feedback that was solicited by a Federal Register Notice that I will describe in a moment, because planning the agenda required, first, identifying the important issues.
Completing the agenda took many months and was a goal, in itself.  In fact, the agenda for this meeting changed so many times, it was described within my division as a living document.  But, hopefully, the agenda that is before you is the last iteration.
[Slide.]
When the division was initially approached about participating in the Resistance Collaborative Group, we stated that our participation was contingent upon well‑diversified participation by many pharmaceutical companies and assay manufacturers.  We believe that the general composition of this group reflects this diversity.  Dr. Douglas Richman, who is Chairperson of the Research Collaborative Group and our first invited speaker, will speak more specifically on the composition of this group and its goals and other purposes.
[Slide.]
In addition to working with the Resistance Collaborative Group, we published a Federal Register Notice on August 23 both to announce this meeting and, also, to make a broader request for information and perspectives that would be relevant to designing today's meeting.
In this notice, specifically, we requested interested persons to provide to the agency data on the relationship of HIV mutation development and changes in susceptibility either prospective or retrospective data on the relationship between genotype or phenotype and clinical outcome, proposals for the use of resistance testing in clinical trials and, also, proposals for how this information could be incorporated in product labeling to support labeling claims.
[Slide.]
Just a word about the format for the next two days.  As noted, this format is a departure from our usual structure of our advisory‑committee meetings.  We have planned this meeting more as a scientific workshop and, specifically, we do not anticipate that there will be focussed discussions on issues that are relevant only for specific products.
Additionally, there will be no issues that require voting from the committee.  Because of the overwhelmingly broad nature of the topic, the meeting's design is modular.  Each session has specific objectives.  Each session has invited presentations from other academicians or from FDA reviewers that have been selected to provide relevant background to the committee's discussion of the questions that will be posed.
The common theme for each session is issue identification and how to achieve progress within that given area.
[Slide.]
I will just briefly let you know about the sessions that we will be going through and a little bit of logic of how they are organized.  The first session relates, really, to developing and understanding of the currently available technology to assess resistance.  In the presentations this morning, we will cover general principles of resistance testing and an exploration of the performance characteristics of currently available genotypic and phenotypic assays with a commentary on their limitations, on quality‑control issues that need to be addressed and on existing data to link genotype and phenotype.
We will also have discussion of the committee of potential roles of assays in drug development and we will hear an update from the Center of Biologics Evaluation and Research on their proposed approach to assay regulation based on an advisory committee meeting that was held in September of this year.
[Slide.]
This afternoon's session relates to the clinical validation of these assays and will explore the predictive value of knowing baseline genotype or phenotype in treatment outcome.  Towards this effort, the Resistance Collaborative Group has assembled a spectrum of studies that has been reanalyzed using a common data‑analysis plan.  This has been really a major undertaking from this academic group and we want to thank them for their efforts in terms of reanalyzing the data for the committee's consideration today.
Additionally, you will hear the results of three prospective trials that have evaluated whether knowing genotype or phenotype at baseline is correlated with ultimate treatment outcome.
We will also discuss approaches for characterizing and categorizing and analyzing resistance patterns and we will identify additional clinical research to further define the clinical utility of resistance testing.
[Slide.]
Tomorrow morning, we are going to devote some time to the practical considerations and feasibility issues of resistance testing.  First, we will identify patient populations for whom resistance testing and drug development is most important and most useful and we will explore other factors to consider when resistance testing is incorporated into clinical trials.
To give you a preview, just a few of the factors that will be for discussion will be the complex biology of drug‑resistant HIV, the issue of drug concentration, the timing of sampling of specimens, the issue of anatomical compartments and a host of other issues that the committee will be asked to consider as they relate to resistance testing.
[Slide.]
Tomorrow afternoon, we will spend time devoted to discussing potential roles of resistance testing in drug development and we will specifically ask for guidance from the committee, both on in vitro and clinical data necessary to characterize resistance and cross‑resistance potential as drugs come to market.	
We will also obtain guidance on postmarketing evaluation of resistance and then we will ask you, in an informal, more interactive session for feedback on potential scenarios for the use of resistance testing to support regulatory claims.
I will just refer the committee, in the slide series, those scenarios are actually at the very last slide packet.  We would ask you to take a look at them this evening so that you will be familiar with the scenarios which are just very generic.  They are not product specific but they represent scenarios that either we have received through the docket or that sponsors have posed to us.
Tomorrow, we will ask you, in an informal session, some feedback on what sorts of evidence would be necessary to support those scenarios.  That is our homework assignment for tonight.
[Slide.]
Last, I want to acknowledge many folks who worked on this meeting.  This has been a very complex meeting to develop.  First, I want to acknowledge the expertise on the Resistance Collaborative Group.  You will be hearing more about this group in a few moments, but there is no question that the agency alone could not have put forward this meeting because of the degree of technical expertise that was required, in terms of formulating the agenda and gathering the relevant data.
I would also like to specifically acknowledge Glaxo Wellcome who has been responsible for coordinating the logistics of when the Resistance Collaborative Group meets.  I want to thank all of our invited speakers who are listed in the order of their presentation starting with Dr. Douglas Richman, the first speaker for this morning.
I would like to acknowledge the many folks within our division and outside of our division at FDA who worked on planning this meeting.  Specifically, I would like to acknowledge Dr. Jeff Murray who was really the leader within our division and planner of this meeting.
This is one of those no‑win slides because I am certain that I have left off other folks' names or, even worse, have misspelled them.  But this should just give you a general idea of the effort of many folks in terms of developing this meeting and the importance that our division has for this issue.
So I will close with that.  I would like to next introduce Dr. Lauren Iacono‑Connors who is our Microbiology Team Leader in the division and who will introduce Session 1.
SESSION 1
Performance Characteristics and Limitations of
Currently Available Genotypic and Phenotypic Assays
Introduction
DR. IACONO‑CONNORS:  Good morning.  I am Lauren Iacono‑Connors from the Division of Antiviral Drug Products in FDA.  Before we begin each session of this two‑day workshop, a member of our division will take a few minute to provide a brief introduction to each session.
I am going to introduce Session 1 which is devoted to the performance characteristics and limitations of currently available genotypic and phenotypic assays.  As you are all aware, the majority of this workshop is devoted to the critical evaluation of resistance‑testing data generated in the clinical setting and how these data could or should be used to support the regulatory development of the drug.
It is clear that the reliability and quality of the resistance testing data are directly dependent upon the reliability and quality of the assays used to generate it.  Therefore, it as felt that this two‑day workshop should open with a session devoted to summarizing the types of assays that are currently in use along with the presentation of data demonstrating the performance characteristics of selected assays; that is, to literally define the important features of an assay such as an assay's precision, accuracy, specificity and sensitivity.
The session objectives are: to describe the methods and accuracy of genotypic assays in discriminating among wild type and viral virulence; to describe the methods and accuracy of phenotypic assays in discriminating susceptibility profiles; to obtain scientific input on issues related to sensitivity and specificity,  reproducibility and quality control, of genotypic and phenotypic assays across a range of HIV RNA levels; to review criteria used for the analytical interpretation of assay results; to explore data that describes comparability between genotypic assays; and, finally, to explore data evaluating the correlation between genotype and in vitro phenotype.
Due to the magnitude of assay information and performance data, the majority of the session will be devoted to a comprehensive overview of the diverse assay technology and the performance characteristics of all predominant assays including data quality and comparability within an assay class.
Since there are a number of diverse genotypic and phenotypic assays currently in investigational use, it was difficult to select a subset of speakers that could complete our session goal.  Therefore, it was felt that a single speaker could best provide an appropriate and comprehensive presentation on the subject.
Dr. Douglas Richman from the University of California, San Diego, agreed to tackle this very difficult task.
Since these assays were intended to be used to assess the resistance status of HIV‑infected patients, a presentation and discussion of the current policies followed by the agency on the regulation of these types of assays give an important balance to this section.  Currently, there are no FDA‑approved assays for HIV resistance testing.
The Division of Emerging and Transfusion Transmitted Disease from Center for Biologics, FDA, is the group within the agency who is responsible for the regulation of HIV resistance‑testing assays.
A member of this group, Dr. Andrew Dayton, will complete this first session with a presentation on historical perspectives of the FDA's requirements for HIV resistance assay marketing followed by the current regulatory policies which were born out of a recently held advisory committee meeting on assay regulation in September of this year.
In addition, Dr. Dayton will take a few minutes to provide us with the general assay characterization principles and what we can expect in the near future on resistance‑testing regulations.
We look forward to an interesting and productive discussion.  I will introduce the first speaker and then turn the session back to our Committee Chair, Dr. Scott Hammer.
Dr. Douglas Richman will now present an overview of performance characteristics of genotypic and phenotypic assays.
Thank you.
Principles of HIV Resistance Testing and Overview
of Assay Performance Characteristics
DR. RICHMAN:  Good morning and thank you, Lauren.  I have, as Lauren mentioned, a complicated task and, hopefully, the areas I have selected to present and to overlook will be considered fair and balanced.
First a few introductory remarks.
[Slide.]
Those of us who care for patients, especially in infectious disease, are familiar with drug‑resistance testing.  Testing for drug resistance serves to guide the treatment of many infectious diseases.  It predicts what drugs will not work and why they don't work and what drugs might be useful.  Everybody is familiar with this principle and it is now being introduced into HIV.
[Slide.]
Before I proceed, I would like to provide a few definitions that I think will help clarify people's perspective.  First, the term "susceptibility assay."  Susceptibility; we measure the phenotype of replication or its inhibition by various concentrations of drug.  It is important to appreciate that resistance is a phenotype.  So the terms phenotype and genotype resistance are really somewhat confusing.  Resistance is a phenotype and various mutations, the genotype, are what confer the phenotype.
Sensitivity and resistance, to my mind, are value judgments.  Sensitivity is a value judgment hopefully based upon data that a drug is likely to work based upon the results of the susceptibility assay.
Similarly, resistance is based upon data that a drug is not likely to work based on the susceptibility result.  There are certain susceptibility values that may be sensitive for one drug and resistant for another.  Once again, the issues of pharmacology come up so that an IC50 for indinavir, for example, may be resistant for indinavir monotherapy and conceivably could be sensitive for indinavir enhanced by ritonavir.
That is theoretical.  There are no solid data at this point but the point is that these various cutoffs and definitions are also impacted by pharmacology and other issues.
[Slide.]
With regard to genotype, wild type is a strain of virus that has not been selected by drug treatment.  We obviously need to know the history but that is how we define wild type.  A natural polymorphism is a genetic variant present in wild type populations.  With HIV, there is an incredible amount of genetic variation and there are a lot of amino‑acid usages in various HIV isolates that are also seen in drug‑resistant isolates.
It is important for us to appreciate whether it is a natural polymorphism or a drug‑resistance mutation.  A drug‑resistance mutation is an amino‑acid change conferring reduced susceptibility selected by drug treatment.  I make this distinction because there are lot of changes that occur with drug treatment that are also natural polymorphisms that are seen more frequently following drug treatment.
Their presence, however, cannot be used to make a claim that drug resistance is being transmitted.  So these are important distinctions.  A drug‑resistance is a mutation, or a collection of mutations, associated with the phenotype of drug resistance and best, it is what accounts for it.
[Slide.]
So drug‑resistance assays for HIV, just as with other microorganisms, cannot be expected to guarantee efficacy‑‑ that is, drug susceptibility is not the only predictor of treatment response and there are other factors; drug potency, pharmacokinetics, plasma‑protein binding, adherence and all the other things that we all know about, the point being that if one has an isolate that resistance is a better predictor of failure than sensitivity is of success for these reasons.
[Slide.]
So, before I proceeding with the discussion, I just want to make a few remarks about this Resistance Collaborative Group.  The background is that phenotypic and genotypic HIV drug‑resistance assays are now being used to evaluate drugs.  They are now being used to manage patients.  There are lots of them.  They are multiple in number with limited standardization and their clinical utility has not been well established.
We are all aware of this and we are also aware of what happened with HIV RNA assays where the transition from clinical endpoints to the use of RNA for drug development and its approval for patient management was a little bit of a slow and painful process.
I think HIV drug‑resistance testing is a technology that fits into those categories and I think what the various people in the field wanted to do was to make this transition a little more smooth.  So there was an existing model.  There was a Surrogate Marker Working Group that was established that involved representatives of various groups to help put together the data regarding the use of HIV RNA in the documentation of drug efficacy.
That helped to make the transition for HIV RNA.  What we wanted to do was to be a little more proactive with regard to drug‑resistance testing.
[Slide.]
So we put together a group that included academic investigators, both from the U.S. and Europe, clinical virologists and statisticians, representatives from industry and virtually every company we were aware of who had an interest was invited, and this included those in pharmaceutical development and the companies involved in development of diagnostic assays.
We invited representatives from the agency and from various centers because the impact involves them all, the representatives here from CDER, the representatives from CBER who have oversight for the devices in HIV.  I am sure Dr. Dayton will get into this more, but devices outside of HIV are under the Devices Center and so we invited them as well even though they didn't have the oversight for HIV because of their expertise and experience with antiinfective devices in general and also because I believe that any precedence that we set for HIV will, by definition, I think, have implications for hepatitis B and hepatitis C and all of the principles of virology dynamics and resistance in HIV are going to apply there.
So I think the decisions made here are going to have other implications.  We invited European health authorities and members of the community.
[Slide.]
The activities of the group which was formed just over a year ago‑‑at our first meeting, we decided we needed to focus on two issues; one was to set up a Resistance Technology Standardization Subcommittee of which Tom Gingeras was the chair to try to address criteria for assay performance and validation and then we set up a Clinical Validation Subcommittee under John Mellors chairmanship to identify studies characterizing the utility of drug‑resistance testing; that is, correlating phenotype of genotype with outcome and to develop a data‑analysis plan to standardize definitions of resistance and outcome.
You will hear about this second part later today from Victor DeGruttola and John Mellors.  What I will try to do is summarize some of the issues regarding technology standardization.
[Slide.]
The issues that I want to consider fit under the issue of assay performance characteristics.  But, first, I want to mention that we have to think of these in terms of their applications.  There are really two different applications and we should keep these, I think, separate.
The first is drug development and evaluation with subcategories of the preclinical development and evaluation or putting together the investigation of a new drug package, and then the clinical evaluation of drugs, the NDA and product labeling indications.
Those should be considered separate from the issues of patient management.  Drug‑resistance testing needs to be used for both but we may be using different tests in different situations and some tests may be better applied to some than to others.
[Slide.]
So, before getting to the characteristics, I want to remind you of the specific challenge of HIV in terms of developing these tests.  No two strains of HIV are identical.  Within each individual, an HIV infection represents a mixture of genetic variance or a quasispecies; that is, each individual's virus which is different from any other individual's represents a complex swarm of genetic variance.
This quasispecies is constantly evolving.  Drug treatment selects for additional variation and the drug treatment practices in this country and the world are rapidly changing.  What we do with our patients often precedes what is in the published peer‑reviewed literature and what we do with patients often includes investigational drugs and this is, obviously, having an impact on the quasispecies in the various patients.
So the drug assays that are being developed have to be useful and have to be valid in the context of this incredible complexity.
[Slide.]
So, in terms of the assays that we are going to use, the first consideration is what is the intended use of each assay.  They may not all be the same.  First, the specimen; is it useful for plasma or for other specimens like semen, peripheral blood cells or whatever, and, in terms of these issues, one might sort of think, by analogy, to HIV RNA assays.
For example, the extraction methodology for the Organon Technika assay for HIV RNA made that assay useful for semen while inhibitors in semen made the assays developed by Roche and Chiron nonapplicable to semen unless a different extraction method is applied.
What is the target pathogen?  Analogous to HIV RNA, the initial assays only work for clade B.  They are expanded.  It is obviously clear that we want applications to work for other clades circulating especially outside of North America.  So, does it work for all of HIV I?  Does it work for HIV II?  Which agents does the assay work for and has it been validated for?
What information is provided?  For a genotype assay, for example, it could state that it is going to provide the sequence of 1500 nucleotides of pol or it might provide just twenty specific codons, a codon‑specific assay.  Or it might provide some other information.
What is the application?  Is it useful for drug evaluation or useful for patient management.
[Slide.]
In terms of validating these assays, and this is the responsibility of each diagnostic company, there is a whole series of issues‑‑and I am sure Dr. Dayton will get into these in more detail‑‑that one needs to consider and one needs to know this information to decide whether that assay is useful for any of the particular applications we have been talking about.
Sensitivity; we want to know, with regard to genotypic assays, what is its sensitivity for specific nucleotides or codons and, because of the sequence context around each codon, one may have a different ability to pick up different codons with different assays.
Also, what is its sensitivity for plasma HIV RNA levels; in other words, can this assay be applied to plasma from someone who has got a viral load of 5,000 or 500 or 50.  We want to know those data.
What is its specificity?  Does it get the right answer for specific codons?  Does it get complicated by the presence of other agents?  The precision.  The reproducibility.  I will show you examples of each of these types of information.	
We also want to have information about clinical conditions.  We want know whether the assay is going to provide the same results if a person has HTLV circulating, CMV, hepatitis B, hepatitis C, rheumatoid factor, renal failure, liver failure, hyperlipidemia and various drugs on board, heparin and so on.
So these are all very practical issues but we want to know whether that assay is going to perform under those conditions.
What we do know, with the various genotype assays, is that these characteristics vary for different codons.  With the genotype assays, they do have some variability for different drugs for the phenotype assays and the type of answers you get vary with different mixtures and I will show you some of the data.
For genotyping, a major variable is the performer, and I will discuss this a bit.  It is important, I think, with HIV‑‑and I describe some of the issues regarding the challenge of HIV‑‑to not get too discouraged by the complexity.  If somebody wanted sort of a fixed, platonic situation in which the validation of a test worked and it worked in perpetuity and there were going to be no changes, they really should be going to another field in HIV.
What we have to do is decide what criteria, what sort of validation, what sort of level of security we feel comfortable with and then realize that we are facing an opponent that is going to be constantly changing and a step or two ahead of us all the time.
We just have to accept what are set‑up standards and then proceed.  We can't let excellence be the enemy of good.
[Slide.]
So, in terms of the assay performance characteristics, other issues that are important are what reagents are we going to use for control and standardization.  With this complex agent that we have to deal with, we have to make certain arbitrary and finite decisions about which codons we are going to use with regard to genotype assays are going to be tested as the standards for comparison.  We can't test all 500 or whatever codons we are interested in as controls for every assay.
Which concentrations of virus are we going to be testing at 1000 copies or 50,000?  Which mixtures are we interested in; 50:50, 80:20, and so on?  Pure samples?  And which samples?  Are we going to look at purified nucleic acid, purified virions?  Are we going to look at plasma spiked with these?
These are practical issues for setting up standards and controls.
[Slide.]
What I am not going to do is go through the painful details of the technology of each assay and go through their relative merits and demerits.  I will try to go over some general principles.  It is really, I think, the obligation of each of the diagnostic companies to document their assay and its validation separately.  So I will just try to give some general principles.
[Slide.]
With regard to genotype assays, there are sort of two general approaches to genotyping.  One is sequencing.  Usually, but not always reverse‑transcriptase PCR of products from patient plasma and then that is assayed by either chain termination or some other variant of the Sanger sequencing method.  Examples of this are the Perkin Elmer and Visible Genetics type of hardware approaches.
Or one can use a hybridization‑based system to ascertain the sequence using the microchip technology a la Affymetrix.  Then there are point‑mutation assays that are just interrogating for whether you have a wild type or mutant at specific codons that are known to be important for drug resistance.  Examples of this are differential hybridization developed by the Chiron group or the line probe assay developed by Innogenetics.
[Slide.]
There are advantages and disadvantages of everything.  With regard to sequencing, the advantages are it interrogates the complete sequence of the amplified gene that you are looking at; thus, it detects the unknown.  So, for development of a new drug, this is clearly important.  We already know what causes AZT resistance but, if one has a new nucleoside that one is developing, one can't assume that that is going to be what one is looking for.  So one has to try to detect the unknown.
The limitations of sequencing are the magnitude of the data.  If one gets 1500 nucleotide sequences and one starts getting them from dozens or hundreds of patients, then one has a lot of data to digest and handle.  A software to deal with this is critical.
Once one has that all put together, one still has the incredible difficulty of interpretation of the contributions of each genetic variation.  With the natural polymorphisms and the mixtures, this is a challenge as well.
[Slide.]
The differential hybridization assays, the codon‑specific ones, have certain advantages.  They are usually more sensitive in detecting minority species.  The sequencing methodologies will often pick up mutant populations if they are in the 20 to 50 percent range, at best.  These assays will often be tenfold better, perhaps two to five‑fold.
So picking up minority species could be an advantage in mixtures.  They are simpler to perform and usually simpler to interpret.  Their limitations are they only detect what they look for.  So each of these applications may have its benefits and limitations in different situations.
[Slide.]
What I am going to show you now are some data from various studies regarding the comparability of sequence determinations in different laboratories.  I will give you sort of the more complicated issue first.  Rob Shuurman and his colleagues in Utrecht have sort of put together standard panels and distributed them to interested parties who perform sequencing to see how they compare.
The first panel was published in the Journal of Clinical Microbiology this summer.  This is the result of the second panel.
[Slide.]
What they did is they took plasma samples that had been spiked with infectious HIV that contained genotypically characterized compositions of clonal origin.  Each of these had mutations both in the reverse transcriptase and the protease.  They looked at homogenous and heterogenous samples; that is, pure wild type, pure mutant and various mixtures.  They did this, once again, an arbitrary decision, at a viral load of 50,000 copies.  One will get different results with different viral loads.
[Slide.]
So this is what they put in there.  They put in two different viruses in various combinations that contained either wild type or mutant protease and reverse transcriptase.  The mixtures that they selected were 0, 25, 50, 75 and 100 percent of each of the components.
[Slide.]
They let each of the sites perform the assay according to whichever of the many methods that they happened to have and they reported them as differences from a standard reference sequence and entered that electronically into a database.
[Slide.]
Fifty‑six laboratories worldwide participated.  This is important, I think, to point out that same of these laboratories are academic or commercial labs that do service and have validated their methodology for reporting results.  There are some industry laboratories that do drug‑resistance sequencing for their own purposes and there are some academic labs that are quite small that are doing this to see how they are just doing.
So we have a wide range of different types of performers.  What they looked at was interlaboratory variation looking at five resistance mutations in protease, five in reverse transcriptase and they looked at the various comparisons.
[Slide.]
There were various approaches using home‑brew reagencies in Perkin Elmer using the kits of Perkin Elmer, using the Visible Genetics system and using various other types of systems.
[Slide.]
The results; what this shows on the left side are the results in which we have 100 percent wild type.  You can see almost all the labs got almost all the answers right.  On the right side, you have 100 percent mutant and you can see most of the labs got all the answers right.  The rest of the labs, all but two, got nine out of ten right.
As you get various sorts of mixtures, you get various sorts of mixed answers which is part of the reality of this world as well.
[Slide.]
Looking at the number of correct calls with various mixtures, you can see that some labs get all the answers right all the time.  Some get most of the answers right all the time and some need to work on their answers.  This is just part of learning how to do all this.
[Slide.]
You can see, here, the frequency of mutation.  One thing that is important, both in the ENVA‑1 and the ENVA‑2 panel, is that the success or failure rate of various laboratories appears to be independent of which of the kits or assays they used.  Those of us who have done sequencing know that having a good, experienced person doing it gets the best results.
Just as if you give someone a recipe and some food, and give ten people the kitchen to do it in, some are going to come out with a better product even given the same materials and the same recipe that others will.  This is one of the practical issues that performance characteristics is an important issue here.  It is just something that needs to be considered.
[Slide.]
This shows the variation of the resistant ones.
[Slide.]
In terms of more experienced laboratories, the results do seem to be quite comparable.  These are the data provided to me by Brendan Larder from the Virco group in which they have looked at reproducibility of their own quality assurance.
They looked at 16 samples on ten different occasions, repeated on ten different occasions.  This shows the reproducibility of nucleotide variation and amino‑acid variation and we are well over 99 percent.  In addition, those patients who had viral loads over 1000, 95 percent of the time, they got a successful result.
This is reassuring that an experienced laboratory gets more reproducible results.
[Slide.]
In a study presented at the Drug Resistance meeting in San Diego last June, a blinded comparison of specimens in two experienced labs, one a commercial laboratory and one an academic laboratory.
[Slide.]
What they tried to do is determine if the sequence analysis of complex clinical isolates yielded comparable results from the two different experienced laboratories.  These materials were aliquoted, distributed blinded and the sequences were generated on 44 isolates from heavily pretreated individuals.
The laboratories used different in‑house validated ABI‑based sequence‑analysis protocols.
[Slide.]
Once again, using the sequence of over 4000 protease and 11,000 RT amino‑acid residues, the concordance was quite impressive.  So I think that this is, once again, an example of experienced laboratories getting relatively reproducible results.
[Slide.]
The issues of genotype, the various of kits and methodologies, are under the purview of CBER.  The performance of the various laboratories is an issue that we are all interested in.  There are a lot of different assays and different approaches.
The phenotype situation is quite distinctive.  Those of us who started looking at HIV susceptibilities as long as ten and twelve years ago, developed and used assays that can fairly be described as expensive, cumbersome, labor‑intensive, slow and relatively imprecise.
That type of methodology that we used which helped to identify the phenomenon of drug resistance are really not satisfactory for patient management or for high through‑put data generation.	
So two companies have now come up with assays that fulfill those criteria and have pretty much taken over the field with regard to drug‑resistance assays.  What I will do is try to describe what they are doing.  Once again, as with the genotype assays, I will not try to contrast them but describe what they have in common and what they can do and their limitations.
There are two assays that exist, Virco and ViroLogic.  What they have in common is they amplify a segment of the gag/pol gene that incorporates protease, much of RT and some of gag from the patient plasma HIV RNA.  They put this amplified material into a recombinant virus construct that, then, they use in a standardized high‑throughput assay.
Right away, one sees one potential limitation here in that these assays are potentially useful for reverse‑transcriptase inhibitors and protease inhibitors which represent all of the approved drugs now but, obviously, there are people trying to develop drugs against fusion inhibitors, chemokine receptors, integrase and so on.
These particular assays will not be applicable to those.
These are rapid high‑throughput automated and they are in‑house assays.  They are done at each of their sites and they cannot be put in a little plastic container and just add water at each site and then run it.  The material has to be shipped to them.
So, right now, they are not regulated by the FDA and their accreditation for their performance is overseen by various laboratory‑accrediting agencies like CLIA, College of American Pathologists, various states and European agencies.  Both of them have fulfilled this accreditation.
[Slide.]
These are data provided by Brendan Larder regarding Virco quality‑assurance data.  Looking at between 1700 and over 2200 determinations per compound‑‑we are talking about twelve to fifteen drugs per assay‑‑the variation was less than three‑fold for each of the assays over a period of a year looking at a wild‑type reference strain.
[Slide.]
Looking at inter‑assay reproducibility, looking at sixteen samples on ten different occasions, the variation in IC50, 1.2‑ to 2.5‑fold.  The sensitivity; 95 percent of the samples with a viral load over 1000 copies/ml were successfully amplified and assayed.  Their subtypes; they get amplification and successful genotyping that has been demonstrated in all the groups and subtypes of HIV‑1.
[Slide.]
This is an example of the type of report of the data that are generated by Virco.  It doesn't project ideally but it shows each of the approved drugs.  The susceptibility that is generated is then shown with this blue dot here and the data on this scale are the fold difference from a laboratory reference strain.  So one would be the same as the reference strain.
They are reporting things as not significantly different from the reference strain as anything less than four‑fold different.  That is in this green area.  Then everything greater than ten‑fold difference is in this red area.  Then there is also a yellow area between four‑ and ten‑fold.
It is important to appreciate for this assay and, also, for the other one I will describe that this cutoff of four‑fold here is what they are confident is statistically not different, that they can reliably say is not different from the wild type.
That does not tell you what is clinically important.  Clinically important is the susceptibility that makes the drug work or makes the drug not work.  What we don't have with this assay or with the other phenotypic assays or with a lot of genotypic assays is‑‑what we would like optimally is systematic, prospective controlled data saying what level of susceptibility would predict success or failure with monotherapy with that drug.
Those data would be unethical to obtain and so it is going to be real challenge now for us to generate data that will help us to say what cutoff is clinically important.  Those data have been, I think, best generated with abacavir now with retrospective studies and you will start to see some data from the group this afternoon that will give some suggestions.
But the precise cutoff is a challenge.  From the data that Dr. Little will show you, there are wild‑type isolates out there that have eight‑fold reduced susceptibility to certain drugs.  The question is they are not resistant transmitted virus.  That is the wild‑type variation and we don't know whether that is clinically important or not.
[Slide.]
The other assay is the one from ViroLogic.  I mention there is amplification of segment from the plasma of the patient containing protease and RT, then put into a test vector which is transvected into cells and the generation of the test vector which, then, infects a cell line that expresses luciferase is what is measured and what is inhibited by the presence of various concentrations of drug.
[Slide.]
Once again, the type of validation done with this assay, repeated testing of multiple patient plasma specimens, twenty replicates per sample, looking at multiple operators, assay runs, reagent lots, repeated testing of drug‑sensitive and drug‑resistant viruses and, once again, the multiple‑conditions runs and so on.
[Slide.]
This is the fold variability that all those repeats generated.  You see the fold change is less than two‑fold for all of the approved drugs with one exception here, of course, with AZT now showing a little bit more variability with multiple patient samples in terms of reproducibility.
It is on the basis of this that their reports say that if it is greater than 2.5‑fold from the control, it is different, once again, that being a laboratory definition of reproducibility and not a definition of clinically significant difference.
[Slide.]
The assay variability on repeated testing of reference samples; this is a controlled sensitive virus and these are two resistant viruses.  Once again, you see the variability.
[Slide.]
None of 80 seronegative samples produced a result and testing of HIV samples containing multiple interfering substances under interfering conditions had no impact.  These included high levels of triglycerides, hemoglobin, bilirubin, bacteria, fungi, other viruses and so on.
Then, looking at various virus concentrations and processing times and so on‑‑so these are important variables that need to be defined.
[Slide.]
This shows a point from patients who are known to be treatment naive.  Many of these samples were obtained in the '80's before the drugs were available so we are confidence they were naive.  You see the variation from the control virus.  The mean here is pretty tightly around 1, but the 5th and 95th percentiles, you see three drugs here in which over 5 percent of the isolates are greater than three‑fold different than the control virus.
That is shown, I think, better on the next slide.
[Slide.]
So these are the means and the 25th, 75th percentiles and then the 95 and 5 percent variation.  You see a large number of samples fit within a type range for wild‑type virus but there are a few drugs for which there are wild‑type viruses circulating that are almost always less then ten‑fold resistant.
But the questions are is something between four and ten‑fold less susceptible.  Is wild type going to respond or not?  That is an interesting question and that will be discussed more by Dr. Little.  The genetics that account for these natural variations are now being defined.
[Slide.]
These are data showing the level of plasma RNA that generates a result.  90 percent of patients will generate a result with 540 copies or more, 95 percent with 700 or more.  So these are important issues more for patient management, really, than drug development but they are important pieces of information to know.
[Slide.]
The issue of mixtures, putting artificial combinations of mixtures with various‑‑10 percent, 20Êpercent and so on‑‑of highly resistant virus to each of these drugs up here with a wild‑type virus, you can see the fold change here.  This is the 2.5‑fold cutoff.
You can see with the various protease inhibitors mixtures of highly resistant virus and wild‑type virus.  At least if 20 percent of the composition is resistant, it is going to be picked up as significantly different‑‑in some cases, 10 percent‑‑with the protease inhibitors.
With 3TC, the wild type has much better growth characteristics and one has to have the majority of the virus being 3TC resistant before one can see a phenotypic change in the assay.
So whether it reads out as resistant or sensitive will depend on how much sensitive or resistant is in the composition and also whether the resistant virus is highly resistant or moderately resistant.  This is just part of the reality.  This sort of issue applies to genotyping as well.
[Slide.]
This is the type or report from the other assay as well.  You can see that one gets, with a virologic assay, a result showing that it fits in the same as the reference, decreasing susceptibility.  Also, here is an example of another phenomenon, the significance of which needs to be defined.
There are a number of mutations, especially in reverse transcriptase, but it has also been shown in protease, that confer resistance to one drug and make the virus hypersensitive to others.  Here, 3TC abacavir resistance has been associated with a shift to greater susceptibility to NNRTIs.  The question is whether this is going to have any impact on treatment effects.  The answer is we don't know.
[Slide.]
So correlation of phenotype and genotype; the phenotype of drug susceptibility is conferred by the viral genotype.  Thus, they must correlate.  But how well do they correlate.  Basically, the virus has it all figured out.  It correlates 100 percent for the virus.
The issue is what our assays do, how well our assays correlate with each other because we are only approximating what the virus is up to.  The challenge here is that some correlations of black and white‑‑that is, if you have a mutation at 184, you are absolutely resistant to 3TC and if you are a wild type of 184, you are sensitive to 3TC unless we discover some new drug‑resistance mutations which, in fact, some people have‑‑the Virco group described at the last drug‑resistance meeting.
But, at least with that mutation, we know that the drug will not work.  That is true with a number of drugs, especially the non‑nucleoside RT inhibitors and other drugs.  If we see certain mutations, we know that drug won't work.
But, unfortunately, there are various shades of gray because, for many drugs, especially the protease inhibitors, multiple mutations are usually needed to generate high‑level resistance and there are very complex interactions which can be additive or suppressive and have various effects on each other.  Some of these, we haven't figured out yet.
So this is part of the complexity of analyzing genotype data.  Dr. D'Aquila, tomorrow, will discuss this in more detail.
[Slide.]
But with the various drug‑resistance assays, they have all shown that, in general, if you have the right mutations, you are going to show the right answer; that is, if you have various mutations known to cause resistance to non‑nucleoside RT inhibitors, you are going to have reduced susceptibility to those drugs.
There are some exceptions in that here is one mutation that doesn't confer resistance to delavirdine, another one that has a very small effect on efavirenz.  What we need to know is what happens when these drugs are used in patients who have developed these mutations.  Can you then use a second NNRTI inhibitor if you happen to have the right mutation or the right susceptibility.
[Slide.]
Similar data with protease inhibitors, but these are even more complicated.  These are defined viruses with given mutations.  You can see here that there is a significant amount of cross‑resistance but there are some exceptions.  Most notably, the first mutations seen with nelfinavir do not cause cross‑resistance to the others.
You add some more mutations and it may or may not cause cross‑resistance.  You get various combinations and permutations.  So when one looks at a genotype report, it is often very difficult for a non‑experienced caregiver‑‑and I can tell you for some of us who think we are experienced, it is still very difficult sometimes to make predictions based on the complex data.
But one does get reproducible answers with the same virus in these sorts of assays.
[Slide.]
Here is an example of a type of approach from the ACTG 372 data in which a list of drug‑resistance mutations known to cause resistance were listed.  Then the susceptibility was obtained.  There was excellent correlation for almost all the drugs and then this set of drugs for which the genotype resistant situation is a little more complicated, the prediction wasn't quite as good.
[Slide.]
This shows a phenotype sensitivity score was arbitrarily generated and a genotype sensitivity score based on the number of mutations that were considered bad.  Then you can see a clear correlation.  The size of these dots are the number of specimens in each of those categories.
You can see it doesn't fit perfectly on a straight line but it is clearly statistically significant.  The type of endpoint data that these approaches have generated, you will hear more about to reassure you that, in fact, this is meaningful data.
[Slide.]
So how do we use genotype and phenotype?  In closing, what I will do is give you my personal opinion.  I haven't run this by the rest of the Resistance Collaborative Group so you can take it or leave it, as you will.
For drug development, I think we clearly need both.  For patient management, the most useful test‑‑that is, the test that gives the best predictive value for treatment efficacy considering cost, turnaround time, et cetera, requires data.  We don't have the information to say which test to use in which situation and, in fact, it may vary in different clinical situations.  It may be different in naive patients and in highly experienced patients.
But our job today is to concentrate on drug development.
[Slide.]
So I will give you my personal opinions about what I think the center and this committee should expect to see at the very least.  In terms of preclinical studies, for an IND, I would think that for a new drug, one would want to assay for that drug's activity‑‑that is, get the phenotype‑‑against a series of laboratory strains, both wild type and with well‑characterized resistance, well characterized by both phenotype and genotype to other approved drugs, and then look at various clinical isolates, both wild type and well‑characterized with regard to resistance to the approved drugs.
Then one would want to ask to see what happens when one selects for resistance in vitro, characterize the phenotype of that resistant virus against approved drugs so we know what kind of cross‑resistance it causes and then sequence the resistant virus that has been selected and identified in mutations that have been generated and characterize these mutations to see which ones are important by in situ directed mutagenesis for the resistance to that drug and to the other drugs.
[Slide.]
Clinical studies for new drugs; what do we want to see?  What I would like to see is when a new drug is used, both as monotherapy or in combination, for patients who fail treatment‑‑that is, don't become suppressed‑‑we want to look at the post‑treatment isolates and look at their phenotype against that drug and other drugs and the genotype.
Also, importantly, we need a lot more data to test the activity of this drug that we are interested in in patients with resistant isolates that have been well characterized at baseline for genotype and phenotype against this drug and against other drugs.
This is a very important and practical problem.  We know that a significant proportion of patients have detectable virus with resistance in them and these patients need help with new drugs.  We would like to know how to use these new drugs most intelligently.
This is the type of data that we need.  Presumably, this type of data will help in the new product labeling.
[Slide.]
I would like to close and acknowledge the people who have helped me put the talk together.  The drug guidelines for validation, the subcommittee that wrote, really, beautiful reports were Tom Gingeras from Affymetrix and Chris Petropoulous from ViroLogic, and then some folks who lent me some slides; Brendan Larger at Virco, Nick Hellman at ViroLogic, Rob Shuurman from Utrecht and John Mellors from the 372 team.
Thank you for your attention.
DR. HAMMER:  Thank you very much, Doug.
We have a few minutes, I think, before the next presentation if there are immediate questions from the committee.
DR. POMERANTZ:  Are you, or someone afterwards, going to discuss the development of computer programs or algorithmic analysis for this because it looks like maybe some of us need that help as we look at patients.
DR. RICHMAN:  Right.  There are two types of computer programs.  The various sequencing companies have developed and are developing software just to align sequences and identify those sequences that are known to be associated with drug resistance.
A number of the companies that generated genotype reports provide those lists.  For many drugs, it is straightforward; 184, 3TC resistance; 215, 41, AZT resistance.  But for other drugs, especially the protease inhibitors, you see two or three mutations and you could say, "Well, this person was on indinavir, developed two or three mutations resistant to indinavir and ritonavir."  But the average expert can't predict, "Is it going to work against amprenavir or nelfinavir for whatever, or drug X?"
Sometimes, there isn't the database to do that.  Other times, there may be but it is very difficult.  There are proprietary databases.  Virco has correlated its genotypes and its phenotypes and comes up with a prediction that is usually right, or often right, but, once again, not always right because the virus is always a step or two ahead of us and there are mutations we don't always know about.
But that is proprietary.  So this is an area that, I think, is in a lot of flux.  Just for the fun of it, I will venture an opinion.  My own opinion is that, for "salvage" patients, highly experienced patients who have failed many times, it is going to be easier to manage patients with phenotype data than genotype data.
For other situations, that is something else but, for developing a new drug, I think it is complicated.  You probably want both types of data.  But the perfect analysis of a genotype?  We know this virus is too fast to know everything ahead of time.
DR. MASUR:  Doug, one of the things we have wrestled with is how to determine activity of drugs and determine that the activity is really due to one drug rather than a drug cocktail.  On your next‑to‑last slide, you talked about testing activity of drugs in patients with resistant isolates but, since there is so much reluctance to add one drug to an existing regimen or to use monotherapy, if you are going to be looking to add at least two active drugs, how do you actually get any positive data?
You can get negative data if the patient fails to respond, but then you get drug interactions, antagonisms.  How do you actually accomplish that second bullet on your next‑to‑last slide?
DR. RICHMAN:  That is a very important question.  It is a critical question.  You are going to see from John Mellors the type of data that would suggest that these data are predictive of success and failure in combinations but, in fact, from the point of view of developing a new drug, it is a fundamentally important‑‑and I think we do have a dilemma.
I think knowledgeable patients and caregivers are unwilling to participate in studies that use prolonged monotherapy, are unwilling to do add‑on studies if there are other alternatives and are unwilling to participate in study designs to meet "regulatory" purposes in which there is a suboptimal arm.
It has clearly been done in the past and I, personally, don't think that type of approach is acceptable anymore.  So, now, if we design‑‑the only right type of study to do is one in which patients are randomized to arms that are acceptable standard of practice.
If that is the case, we have automatically precluded the opportunity to define the activity of any of the components.  So, what I have done is I have reiterated your question.  I haven't given you an answer.
DR. MASUR:  That is a very effective technique.
DR. HAMMER:  You are also in the right part of the country to do that, near Washington.
DR. RICHMAN:  But I think this is a dilemma that is important for people who are trying to conduct studies, people who are trying to develop drugs and people who are trying to regulate it.  So all of us, the investigators, industry and the agency really have to figure out what is acceptable given those constraints without compromising proper patient care.
DR. HAMILTON:  Doug, you indicated that certain technologies were applicable, perhaps exclusively, in different populations, different body fluids.  Is there reason to think that different strains exist in one body fluid versus the other?
DR. RICHMAN:  There is not only reason, there is data.  Yes; there is no question that there is discordance.  We showed two or three years ago that both the quasispecies and the drug‑resistance mutations could be discordant in the brain and central nervous system.
There are three or four such studies that have shown similar data in genital secretions, primarily semen, which is important for transmission as well.  So there is some discordance.  I think Rich D'Aquila is going to discuss that to some extent tomorrow.
DR. YOGEV:  You talked about how you predict success.  Do we talk also length of time?  For example, if you define certain drugs are sensitive by phenotype and genotype, then we have a pressure on the quasispecies who are in the minority that we cannot pick up by the sensitivity of the test and are not coming out.
So should we define a success by how long that specific therapy is effective, six months, a year, or‑‑
DR. RICHMAN:  Oh, absolutely.  It has to be long enough to be clinically significant because we have a chronic disease and, even though we are interested in what is going to happen in five or ten years, we can't wait for a five‑ or ten‑year study before making decisions.  So we have to figure out what duration is acceptable, and whether that is six months or more is‑‑
DR. MAYERS:  Doug, some of the most difficult drugs we have to deal with are the drugs that lose their virologic activity without a clear phenotype, the drugs you listed, ddI, ddC and d4T.  How should we work on trying to define for the clinic to determine success and failure when you don't have a clean resistance phenotype?
DR. RICHMAN:  There are mutations that do confer significant resistance to those agents but we also know that an experienced patient with AZT is not going to have, necessarily, the same response even though they have got wild type virus as an inexperienced patient.
That always raises questions about pharmacologic effects.  There are data regarding p‑glycoprotein efflux pumps for protease inhibitors.  There was a very nice paper in last month's Nature and Medicine from St. Jude showing that MDR‑related gene will pump out triphosphates and can be selected for and amplified in cells.
So there may be other drug‑resistance mechanisms analogous to those in oncology that may be applying to patients here.  They will only add to the complexity.  They won't supersede the issue of drug resistance to the virus.
DR. CHARACHE:  Is there data for drug development or would you suggest data on drug combinations in vitro to see if they may be inhibitory or synergistic, particularly if they have the same phenotypic target?
DR. RICHMAN:  I didn't put that on the list, and maybe I could have, of things to recommend in the preclinical package.  Clearly, I am a believer in looking at combination studies primarily to show antagonism.  I think that is worth doing to provide an indication why some drugs might not be combinable.
So that type of information is useful and I guess it is also useful for looking at resistant virus and whether combinations are useful.  There are some practical technical problems that I could get into if you want about problems with doing such assays.  They have to be done at concentrations of drugs which are subtherapeutic or they can't be analyzed.
There are some practical issues, but I think those sorts of assays are useful in a package; yes.
DR. POMERANTZ:  Doug, you touched on a real difficult question in your talk and that is the group of viruses that are phenotypically mildly resistant, four‑ to eight‑ or ten‑fold but have no genotypic correlate.  It has come out in a few papers, one recently from your group.
Now, one of the things that you can think of is that we just haven't found those subtle genotypic markers for that phenotypic resistance or it might be something more complex.  Clearly, we don't know what they mean clinically.  Do you have comments on what may be causing that?
DR. RICHMAN:  Yes; there is some preliminary data that Andy Leigh Brown has generated with the data that Susan will show you that there are some sequence explanations with natural variability that account for much of this.  So I think that the variability of wild‑type strains will account for susceptibility to certain drugs.
DR. POMERANTZ:  I'm sorry; so you think that that is natural variability in the wild type and not subtle changes due to pressure from the drug?
DR. RICHMAN:  Yes.
DR. KAPLAN:  Doug, in your comments about correlation of phenotype and genotype, you mentioned there are some blacks and whites, like, for example, the 184 mutation, but there are a lot that are in the gray zone.  I guess there are some data being developed by the drug companies.  You mentioned a lot of it is proprietary, but I wonder what the prognosis is for more publicly available information in the future on correlation of genotype and phenotype such that people using genotype assays may have to resort to phenotypic testing less frequently.
I recognize, in some situations, you are always going to have to do it but where are we headed in that regard?
DR. RICHMAN:  This is a moving field and there are usually are not many secrets too long.  So my guess is there are going to be lots of data generated by various companies, by various investigators and more sophisticated ways to analyses and correlations.  I suspect we will know a lot more in the future than we know now.  I certainly hope so.
But I am optimistic that we will learn more.  Obviously, looking at the unknown, what the phenotype does is measure the susceptibility while the genotypes are a current approximation of that.  But which situations each of those assays will be most useful will‑‑data is what drives it.
DR. GULICK:  Doug, you made a big point about the experience of the labs doing the genotypic assays.  Could you comment on what is the definition of an experienced lab and how we evaluate the experience of a lab?
DR. RICHMAN:  That is a very good question.  Experience doesn't necessarily mean size or name recognition.  It means performance.  So there are some academic labs that are small that do good work and there are some very large labs that may not.  But I certainly would not name names.
So this is a practical problem.  This may be better addressed by Dr. Dayton, but there are two issues.  One is the kits and reagents I think the agency can oversee.  But performance of laboratories is more lab practices, accreditation type thing.  I am not really ideally qualified to discuss that.
DR. HAMMER:  Thank you very much, Doug.
With that segue, I would like to introduce Dr. Andrew Dayton from the Center for Biologics and Evaluation.
CBER'S Policies on Assay Regulation: Definition
of Assay Performance Characteristics
DR. DAYTON:  Good morning.  That last talk of Dr. Richman's is going to be tough act to follow but, on the other hand, since he has covered most of what I want to say, you can be forgiven if you begin to lapse into a little bit of late‑morning sleepiness.
[Slide.]
I am Andrew Dayton.  I am in the Division of Emerging and Transfusion‑Transmitted Diseases at CBER.  I am going to discuss our policies on assay regulation primarily focussing on definitions of assay performance characteristics.
[Slide.]
The theme of my talk today is the regulation of in vitro diagnostics, or IVDs.  It is not rocket science; that is, the regulatory handling of it is not rocket science.  I think coming up with the data maybe does qualify as rocket science but there is an inherent logic to how we regulate these IVDs.
There are two centers at the FDA which regulate in vitro diagnostics, the CDRH and CBER.
[Slide.]
In CBER, we currently have IVDs on the market which are basically blood‑borne pathogen tests for hepatitis B, HIV and HTLV, hepatitis C and syphilis.
[Slide.]
The statutes that cover IVD regulation are the Public Health Service Act, Biologics Regulation, 24 CFR 600 to 699, the Food Drug and Cosmetic Act, the Medical Device Amendments of 1976 and intercenter agreements.
[Slide.]
The intercenter agreements are critical to how the IVDs get divvied up between CDRH and CBER.  The Intercenter Agreement of 1991 basically established that CBER would get the in vitro diagnostics for all blood‑donation screening and for all HIV diagnostic applications.  CDRH gets the IVDs for almost all other applications.
[Slide.]
The current CBER or FDA‑licensed or approved HIV tests include tests for the following: HIV antibodies detected by ELISA or immunofluorescence analysis for screening or diagnosis‑‑screening meaning screening for blood donors or plasma donors; HIV antibodies by Western blot of immunofluorescence analysis for confirmation of screening results; HIV p24 antigen screening for screening and for prognosis; and HIV‑1 RNA for prognosis and management.
[Slide.]
One of the general regulatory requirements for HIV tests‑‑basically, they are what you would expect and, actually, Dr. Richman did a very excellent job of introducing you to them and covering them in considerable detail.  The key areas that you have to pay attention to are manufacturing consistency; can some of the manufacturers' tests give us some kind of guarantee that they are going to be able to continue to manufacture at the same level of assay performance that they used to test it or to develop it with.
Obviously, sensitivity is of concern, specificity, reproducibility, and clinical utility.
[Slide.]
Manufacturing consistency, as I said, means basically can you continue to produce an assay that performs the same as the one you developed.  For us to be assured of this, a sponsor needs to give a full description of the manufacturing process.  The sponsor needs to give a demonstration that the manufacturing process is adequately controlled.
This basically means quality control tests and specifications with supporting data.  We need such quality control tests and specifications, for instance, for acceptance criteria for reagents that come into the manufacturing organization.  In other words, the manufacturer has to put limits on what condition he will and will not accept reagents under and the sponsor has to test those limits to show that they are reasonable.
We need quality control for critical manufacturing steps and we also need quality control for lot release.  It is important to remember that quality control for lot release is necessary but it is not sufficient.  Someone who is making an assay can't just introduce one quality control step at the very end‑‑in other words, measuring how the assay works when you are finished manufacturing it and then say, "Hey; it works.  It's good."
That is not enough.  In order to be assured of manufacturing consistency, we need to know that all the critical steps are also adequately controlled.  We also need data on consistency of the assay over time.  This includes obvious issues such as reagent and overall assay stability on storage and shipment.
We also need data on lot‑to‑lot variability.  No assay is perfect.  It is going to vary from lot to lot and we certainly are not going to just take a look at the results from one single lot and approve a product based on that.  We also need insurance of long‑term availability.
[Slide.]
Sensitivity, in general, basically is the question of showing that assay measures the analyte.  This can, obviously, take different forms and, in this case, with different codons, different codons may have different sensitivities.
We are very interested in knowing the range of the measurable analyte and also the effects on sensitivity of interfering substances and conditions.
[Slide.]
Specificity, in general, is the issue of false positives.  We want to know how often do you get false positives under ideal circumstances and, also, under situations where you have interfering substances or conditions.
[Slide.]
Reproducibility; this was very well handled in the previous talk.  I think all I will say here is that we are interested in run‑to‑run variability, in site‑to‑site variability and, again, we see that is a very major issue; operator‑to‑operator variability.  Again, that has already been described as being a major issue; and day‑to‑day variability.
Finally, in general terms, clinical utility; I don't have a separate slide for that, but, basically, does it do clinical good.  Let me discuss some of these general terms with specific relevance to HIV drug‑resistance genotype tests.
[Slide.]
Again, as I mentioned, we need to know the sensitivity; how often do you get a mutant or a codon that is there?  How does it vary from codon to codon and, in particular, how sensitive is the assay at various levels of viral burden?  Does it always pick up 20,000 copies per ml, or 5,000 or 2,000 or 200?
We need to know that.  We need to know how sensitivity varies across a range of expected viral burdens.  The same is true for the range of mutant proportions.  Do you pick up a mutant that is there 25 percent of the time, 10 percent of the time?  We need to know that.
Reproducibility has been extensively discussed this morning.  As I said, lot‑to‑lot, site‑to‑site; all very important.  We particularly want to know how the reproducibility varies under challenging conditions such as low mutant proportion or low viral burden.
Finally, clinical utility, which is the most complicated issue of all; does the assay benefit the patient?  We are currently wrestling with just how to handle this as a regulatory agency.  Basically, the two principles I want to introduce are the predicate device and special controls.
We are seeking to regulate these genotype drug‑resistance assays at a Class II level.  This usually involves reference to a predicate device, something that is already out there and approved.  But it doesn't have to.  In lieu of a predicate device, if none exists, we can still regulate it at a Class II level or the 510(k) level by reference to what we call special controls.
[Slide.]
For Class II devices, special controls can obviate the need for a predicate device if the knowledge of the field is such that adherence to the special controls can adequately insure that a device is safe and effective.
[Slide.]
One type of special control can be a guidance document or, actually, not the guidance document but criteria specified in a guidance document.  As many of you know, the Blood Products Advisory Committee has recently recommended that HIV drug‑resistance genotype assays be regulated as Class II medical devices.
In this case, the special control would be the criteria laid down in the guidance document which is currently under development.
[Slide.]
I only have time to very briefly go into the guidance document that is under development and I really can only highlight a couple of the concepts that are being entertained by us for putting this guidance document together.
We may list several well‑documented loci, resistance loci, with the expectation that the intended use would be limited to the listed mutations.  So intended use could be limited to certain codons, certain drugs, as well as being limited to certain body fluids.
Extension to other mutations may require additional data.  This can, perhaps, be cited in the literature or, perhaps, be provided as new data by a sponsor.  This is all up in the air.  This is something I want to emphasize.  And extension to other mutations may be handled at a later time by amendments to the original application.
At the bottom, I have listed examples of some of the loci which are probably the most well accepted as having definable phenotype, well‑defined phenotypes.
[Slide.]
Clinical utility can be looked at in several ways.  One way might be to demonstrate premarket by correlating virus responses to assay predictions during trials.  This would involve going to archive samples or prospective samples from clinical trials correlating the disappearance of certain susceptibility loci, amino acids of susceptibility loci, correlating that with drug therapies and institutions that do drug therapies.
This may be sufficient but it also may not be necessary.  Again, I am not giving you the full answer because we don't have the full answer.  As an alternative, clinical utility may be established premarket by certain in vitro studies.  For instance, a mutation that confers greater than, let's say, an eight‑ or ten‑fold increase in the IC50 or 90 contractions might be considered validated.
[Slide.]
What I want to emphasize is that the draft guidance document that I referred to HIV drug‑resistance genotype assays is currently under development and will be released as a draft for public comment.
At that time, we expect a significant input from the entire scientific community and we encourage a vigorous debate and, at that point, we will intend to incorporate the results of the debate into the document.
So when we get this out as a draft document, don't think we are sitting on high and saying, "This is the way it is," particularly for an incredibly complicated field like this.  We expect a vigorous debate and, at that point, we will have to see what we can do to best incorporate comments from the scientific and medical community into the document.
[Slide.]
So, if I could summarize, CBER regulates all HIV tests.  HIV drug‑resistance assays pose unique interpretations of some regulatory criteria but these interpretations are not counterintuitive and, absent a predicate device, HIV drug‑resistance tests may be allowed to go to market with identifiable special controls instead of extensive Class III or PMA‑level clinical trials.
Thank you very much.
DR. HAMMER:  Thank you.
Let me start with one question or a comment to see if I understood this correctly.  In one of your slides, and I understand this is all a draft issue, but you indicated that one of the possible scenarios is regulation or approval based on known loci or determined loci or drug specificity.
For certain techniques of genotyping, those, for example, that don't interrogate just certain regions but, as Doug outlined, give you lengthed sequencing over a defined region, it just seems to me that making something loci‑specific or drug‑specific means it will be out of date and need to be continually updated on a nearly quarterly, if not monthly, basis.
Did I understand that correctly or am I misinterpreting?
DR. DAYTON:  This is a difficult issue.  We would like that it weren't that way but this is a continually evolving field.  From a regulatory standpoint, we are always going to be behind the cutting edge.  So this is an issue we are dealing with.
If you look at the situation that you have now, they are out there and being used and, yet, they are not really FDA approved and cleared.  We are always kind of behind the times.  There is no other way to do it in such a complex and rapidly evolving field.
That is one of the concepts, what I put up there, possibly starting out with a certain set of defined loci and approving, or clearly submissions on that basis.  That is a concept we are considering.  Again, I expect that to feed into the debate.  We don't know yet whether that is going to make it into the final guidance document.
DR. HAMMER:  I don't know if you wish to comment on this, but I would bring up for discussion because it is, I think, important in the larger context that this committee deals with, not just the specifics of development or approval or recommending approval for a specific drug.
But Class II approval, while deregulating to some extent these kits, it then does not require, if I understand, a clinical validation study in some fashion, or application of that particular kit or whatever it is to an outcome or whatever it might be.  What is, then, the implication, and if you don't want to comment on this, I would understand, for access as far as federal funding for that kit for application in the clinical context?
As we saw, for example, with RNA assays, it was very important to have FDA approval in order to gain reimbursement for populations with limited access.  I think the same holds true and we are on the threshold of that for resistance testing.	
Does Class II or Class III approval generally, if you want to answer this in a generic fashion, have an implication for whether federal assistance will be forthcoming to improve access for patients?
DR. DAYTON:  I am close enough to be inside the beltway even though I am technically outside it that I could say, "No comment."  It is certainly a valid question.  Really, all I can say at this point is that the government has to be petitioned for that.  I can't make policy here.
DR. HAMMER:  Okay; at least I got it on the record.
Let me open this up.
DR. KWEDER:  I have a question.  Do you review any applications for these‑‑do you consider them priority application reviews or standard?  I think that speaks to the issue of always feeling like we are a step behind.
DR. DAYTON:  These applications get put through, at least they are done in a fairly quick time scale.  They are not the PLA time scale which takes a year, or which takes six months expedited.  These are much faster than that.  They are a high priority for us.
DR. STANLEY:  You talk about reproducibility, operator‑to‑operator, site‑to‑site.  You are talking about licensing devices, basically.  What kind of authority do you all have for a certification of the actual lab or the actual performers?
DR. DAYTON:  In something like this, the sponsor usually will come up with certain training protocols and proficiency panels.  It is up to them to institute.  That becomes part and parcel with using the assay.  You use the assay when you have been trained and demonstrated to be proficient.
That becomes a part of the assay.  The sponsor says, "Here is the panel we are going to test the lab on.  Here is the training we give them.  Here is how well they have to do."  When they pass this, then they are allowed to use the kit.
DR. STANLEY:  So that is part of the QC package that you would have to approve.
DR. DAYTON:  Yes; we want to see training and proficiency studies.
DR. JACKSON:  From a manufacturer's point of view, will you be requiring that sensitivity, specificity, precision and clinical utility be demonstrated for different subtypes besides subtype B that may have different drug susceptibility patterns or mutation‑dependent pathways?
DR. DAYTON:  We certainly want to see it out there for as many subtypes as possible, but the way that can be handled is in the intended use or the limitations for us.  We will only clear you or approve your application based on what data you submit.  So, whatever subtype data you submit, we can clear you for use with those particular subtypes.
If certain subtypes are not covered in the data you submit, then it has to be very clear in the product labeling that use of this kit for those subtypes has not been validated.  Really, your biggest worry there is market forces rather than FDA.
DR. JACKSON:  But, in the past, for example, with blood screening, FDA CBER‑‑
DR. DAYTON:  Blood screening is a different story.
DR. JACKSON:  I understand.  But, in that instance, CBER has required that antibody assays, for example, must be able to detect HIV 2 and group O‑‑
DR. DAYTON:  Right.  This is a very different story, but you may be faced with a predicate device which does do those so you have to be careful.
DR. FLETCHER:  Dr. Dayton, on the question of reproducibility, if the candidate assay, in the best of all possible worlds, had a between‑day reproducibility of two‑fold, does that meet‑‑do you have regulations or standards for reproducibility?  Would that kind of variability meet it?  Second, would it be consistent with other assays that are presently on the market?
DR. DAYTON:  We would have to make that decision on an ad hoc basis but, from the data Dr. Richman supplied today, it looks like two‑fold is not an unreasonable amount of variability from application of the assay to application of the assay.
DR. HAMMER:  Perhaps I could just interject, though.  I think what was discussed this morning, at least the state of the art as of today, is that the phenotyping, which measures two‑fold change, is not coming under these regulations because they are in‑house commercial assays and what we are discussing are genotyping methodologies that would come to the FDA for approval and which fold changes are not really obviously measured but the sensitivity and specificity and reproducibility of picking up the codon alterations is really what we are discussing.
DR. DAYTON:  Yes; that is a good comment.  I am glad you made that.  We are currently trying to, or hoping that we can, regulate the genotype assays as Class II.  The phenotype assays may eventually go that pathway of regulation but currently we are regarding them as basically home‑brew situations at the moment.
DR. CHARACHE:  I would like to pick up, Dr. Dayton, on the question of clinical utility.  As that term is currently used and defined, it refers to how a given drug or product works in the patient.
If we are to use a different concept and say that an in vitro assay equals what is going to happen in the patient, would this not set very difficult precedence for all susceptibility testing including bacteria, fungi and other viruses in which we do not, at this time, accept an in vitro test as meaning it will work in the patient?
DR. DAYTON:  This was examined at length by the Blood Products Advisory Committee who did actually recommend that we regulate this as Class II.  In the regs, for a Class II regulation, if the knowledge of the field is sufficient or if you have a predicate device, you can go ahead and do it this way.
So it is not without precedent and it is well established in law.
DR. PETTINELLI:  Regarding the issue of sensitivity, I was wondering if there has been internal discussion in the agency regarding what would be an acceptable level of sensitivity when you are dealing with a mixture of wild and mutant virus.
DR. DAYTON:  There is lots of discussion and no answer.  We really have to get applications submitted to us and make decisions on what we see.
Let me just interject.  FDA has, in some ways, an easier task and, in some ways, a harder task than practicing physicians.  We need to show that something is safe and effective.  It needs to be proven to us that something is safe and effective but you don't have to prove to us it is perfect.
A physician in the field, I think, is really striving for perfection but, to get past us, we just have to know that it works.  After all, nothing works perfectly.
DR. HAMMER:  Physicians are not perfect either, I think we all recognize.
Other questions?
DR. MAYERS:  Dr. Dayton, it was clear in front of the Blood Products Advisory Committee that there were significant concerns about the fact that four companies could get the same genetic data and give four different interpretations back out to the field.
My understanding is that the current regulations would lead to, potentially, standardization of kits that were submitted for licensure but would not address any commercial laboratories that use an in‑house assay and did not apply for kits.  So is there any intention to try and get standardization across the whole field or is this just going to be for licensure?
DR. DAYTON:  Don't forget home‑brew assays actually are subject to FDA regulation should we decide‑‑based on the analyte‑specific reagent concept, a home‑brew assay needs to be ready for an inspection and they need to have data that is adequate for us, if we decide to go and inspect them.
At the moment, we are waiting for sponsors to come in and apply to us.  Those who stay on as home brews, we can't make any representation to this point.
DR. HAMMER:  Can I ask a follow‑up question to that, then?  Would the regulations include‑‑I was assuming it would be the technical performance of the assay, but does it also include an applicant's interpretation of the results, because that is a very slippery slope in that question.
DR. DAYTON:  I am not really sure what you are asking.
DR. HAMMER:  I am asking, for example, two different applicants could get the same results and show the same performance characteristics on defining X number of codon alterations but the interpretation of that can vary substantially depending upon resistance mutational interactions, expertise, et cetera.
The Class II regulations really are the technical performance of these kits, not necessarily the interpretation of what a particular incorporation of mutations means.
DR. DAYTON:  I think the answer that is going to come to that is we will probably‑‑and this is just probably‑‑we will probably approve or clear these assays for a limited set of loci in which the data seems fairly clear.  Then, to extend that to more complicated loci, we would need additional information and additional clarification.
It would seem to me that this kind of situation you are talking about would involve a controversy in the field as to how to interpret something.  We would hope that we would know enough about the resistance testing that if a sponsor does claim an interpretation that is controversial in the field, we would know it is controversial.
Certainly, if a sponsor wants to claim a controversial mutation, or any mutation, they have to back it up even if it is just literature citations.  At that point, we will review the data.  If it is controversial, then that would not part of the intended use.
DR. HAMMER:  Thank you.
I think we will take a fifteen‑minute break and return at 10:50.
[Break.]
DR. HAMMER:  I would like to call the committee back into session.  The committee has three questions for it to consider.  Before we do that, I just would like to ask Dr. Dayton for one clarification quickly just for everyone's sake and that is if you could quickly define the difference between Class II and Class III and what the level of clinical data is that is required for a Class II approval.  It is less but is there still some.  I think this may help us understand some of the issues related to codon‑specific approvals, et cetera.
Could you just quickly define that for us so we know what we are talking about?
DR. DAYTON:  To put it very simply, a little bit over‑simply, the Class II regulations generally refer to situations where there is a predicate device, there is already something on the market which does that and all you have to do is show that you are like that.
Class III, or PMAs, have been, typically, a more stringent level of regulation.  It is generally considered that there is not enough information in the field out there to approve a Class III device in the absence of clinical trials.
So, typically, for Class III, you have to run clinical trials.  For Class II, you don't.  But the regulations are not quite that simple.  Under Class II, we have the right to require clinical trials if we feel that there is a gap in the knowledge.
Also, instead of using a predicate device, we can use special controls which we identify.  Basically, those would be typically what you would think of as preclinical studies.  If we feel the knowledge is so far along that we can guarantee a device would be safe and effective based on general knowledge in the field, we can establish a criteria that the device has to meet such as the ability to perform at a certain level on certain analytes in a certain panel or something like that.
That is the basic division.
DR. HAMMER:  Thank you.
DR. HAMILTON:  Dr. Dayton, could you clarify for me‑‑
DR. DAYTON:  I thought you said one question.
DR. HAMMER:  I did, but I take the Chair's prerogative to recognize Dr. Hamilton.
DR. HAMILTON:  Could you clarify for me, please, when and how the authority is transferred from the FDA to a subsequent regulatory body that monitors and evaluates on an ongoing basis the adequacy of performance of a test such as I think occurs in microbiology where a clinical lab‑‑I don't know the name of the group but there is ongoing monitoring of that.
How does that happen in this case?
DR. DAYTON:  What we envision happening in this case is we will probably require certain postmarket studies or postmarket surveillance and the results of that would have to be delivered to us and then we would have to pass on it.
We haven't started to examine other possible mechanisms but I'm sure‑‑we definitely plan to follow these up postmarket.
DR. HAMMER:  Thank you very much.  That is the last question.
Questions to the Committee
DR. HAMMER:  There are three questions posed for discussion by the advisory committee for this morning's first session.  We will take them in order.  The first is, what are the relative strengths and limitations of genotypic versus phenotypic drugs in assessing resistance to antiretroviral drugs through the stages of drug development.  Please comment on the potential roles of these two types of assays throughout drug development.
I think, rather than going one‑by‑one around the table, I will open this up for people who want to comment in an ad hoc fashion.  Who would like to start?  Not everyone at once.
DR. PETTINELLI:  Actually, I would probably more like to comment on the second part of the question which is the use of the two assays in drug development.  I think that, really, both of the assays, when we are talking in terms of preclinical drug development and try to determine the genotypic and phenotypic resistance to the compound, that they both should be used in preclinical‑trial development as well as we should evaluate the clinical isolates.
In terms of, now, clinical development, I think that probably we could target the use of such an assay.  For example, from the preclinical, we are now developing a pattern in which the new compound appears to really be sensitive to all other resistant viruses, and then maybe we can do some target study and try, really, to look at a patient who now has the genotypic mutation and how to respond to drug.
I am not sure‑‑it would be more like complementary to what we have as a rationale development until now, but just target this specific aspect.
DR. MATHEWS:  I want to just comment on a specific aspect of their use in clinical trials.  It seems to me that, in the circumstances where it is known with a high degree of confidence that a particular mutational pattern confers resistance, or if you are going to use a phenotypic assay with a certain fold change with a high degree of confidence, that we should not be conducting clinical trials that expose patients to agents for which there is a high degree of confidence up front that they will not respond.
I think it is a very different situation where the significance of the mutational patterns is uncertain.  I know some trials are already being formulated in which results of resistance assays are made available up front for the selection of certain concomitant drugs but not for others.
I don't know if everybody agrees with that or maybe they don't, but that would be my opinion.
DR. STANLEY:  I guess if I were going to try to simplify things or break it down, if you can, I would think you would start with genotypic assays to try to understand if there are specific correlates of resistance with specific mutations.  But then you have to show whether that is relevant with phenotypic assays.
The other use for phenotypic assays, I think, is going to be in drug combinations.  We don't do monotherapy.  So you are going to have to try to somehow understand that even if there is a mutant genotype, does that still confer resistance in a specific drug combination.  I think you are going to need phenotypic assays for those.
DR. HAMMER:  I think one thing we might do, to frame the discussion, is think about this in the preclinical development and the clinical development and, just to put something forward and an extension of what Dr. Richman said, certainly in the preclinical development, what this committee would like to see‑‑I can't speak for the agency but can infer‑‑would be assays that determine genotypes of viruses that are passaged in vitro and then to follow that up with what the phenotypic pattern is, also in vitro studies that then look at the activity of this drug, of the particular drug, against viruses with known resistance patterns.
That would be sort of the minimum issue.  I think the issue of looking at what combinations might do in vitro is something that would be a nice addition to what we have seen thus far.  Maybe we should sort of talk a little bit about that stage and then quickly frame the discussion for the clinical development issues which Dr. Mathews introduced.  I think going back and forth becomes a little heterogeneous in the discussion.
DR. YOGEV:  I would contend that the genotypes would be at the Phase I portion of the development of a drug.  I was quite impressed by the correlation between the genotype and phenotype.  I think we understand better the genotype today in certain mutations that can be defined.
We don't understand the phenotype well enough to really accept certain data coming to us that this is in this range, what really are the clinical implications.  So I would suggest that, in the drug development, I would rely on the genotype which would suggest the strains, as you suggested, into the drug and see what change in genotype is happening because I am very concerned about the quasi‑species.
I think a major problem in those two methods is the variant which is already there, but in a lower percentage.  I didn't hear anything‑‑hopefully, it will be clarified later‑‑what does that mean 20 percent?  20 percent of a million is 200,000.  20 percent of 1000 is 200.
There must be sensitivity of how low you can go when you bring it up that we can define in the genotype.  So, for me, the genotype is much more scientific at this point to understand that I would like to understand that I would like to see the drug developed on that level and then we move to the clinical, move into the phenotype also.
DR. HAMMER:  I would just add that I think genotype and phenotype are important both at the preclinical and at the clinical level but maybe we can define that better later.
DR. MASUR:  One of the issues that it would be actually interesting to get some clarification from some of the people who do the phenotypic assays is what the practicality would be of looking at combinations because certainly‑‑and Pat, before, was talking about other susceptibility testing that we do.
In most areas of infectious disease, we have gotten away from asking the laboratory to do synergy or antagonism studies and we take, on faith, the fact that if we use several active drugs, that is better than using one active drug alone although there is often not good information.
It would be nice to know that if the four drugs that you are using are really better than some combination of three of those drugs or two of those drugs, but that becomes extraordinarily complicated.
Is that technically and financially feasible?  As we look at these assays, no one has yet mentioned what the prospect is that these could be done at an affordable price even in Montgomery County.  Is that something we could get some comments about?
DR. HAMMER:  Would anybody like to comment from the audience?  Dr. Larder?
DR. LARDER:  Brendan Larder from Virco.  I get asked this question all the time.  It comes up.  I guess the guys from ViroLogic do as well.  The short answer is no, it is just not feasible to do it.  But we have done some studies where we have looked at just two drug combinations.  In those studies, we didn't really see any advantage of adding two drugs into wells over and above deriving separate phenotypic IC50 or resistance levels for individual drugs.
We have looked at it but, as you said, it is not two drugs we are interested in; it is three or four.  Actually, to derive any meaningful synergistic data from that would be just impossible, a three‑D checkerboard.  If people think assays are fairly expensive now, then, to do that, would be just prohibitive and I don't think it would add very much to the information.
DR. MASUR:  Then, just to follow up, Brendan, are you suggesting that, as in other areas of infectious disease, we are just going to have to take on faith that multiple drugs are likely to be better than a single drug?  Obviously, you can do viral‑load studies but it is getting harder and harder to break that down into segments.
DR. LARDER:  I think that is right.  I think you would want to look at the reports and try and pick out the most susceptible drugs in that report to build a three‑drug regimen that you can try and use.
My understanding is, in the antibody field, that people have been struggling in that field for years and years with combinations and it is not really done as well.
DR. HAMMER:  I think one thing that could be said here is that although in the clinical application of looking at all these two‑, three‑ and four‑drug combinations and requesting from a commercial phenotyping firm those results on a fee‑for‑service basis is impractical.
What is not impractical, I think, is to request, as a drug goes through development, that certain in vitro studies be done to look for synergy and/or antagonism in two and three and possibly four drug combinations within limits.  I think, as Dr. Richman said, there would have been certain antagonisms in vitro that have been confirmed in in vivo studies.
So I think that there are issues on the commercial side and from physician or the physician/investigator side that make it impractical.  But there are ways to look at some of this in a more limited fashion as a particular drug goes through the development.
DR. LARDER:  I think you are right.  But getting to three drugs is going to be difficult.  One of the cases that Doug was probably alluding to is AZT and d4T.  But, actually, going back to the preclinical in vitro data, there was controversy about whether there are additive effects or antagonistic.
It was only really until it got to the clinical trials that it showed up pretty antagonistic.
DR. HAMMER:  Dr. Richman would like to comment.
DR. RICHMAN:  I agree that it would be potentially dangerous to require too much in terms of these combinations for several reasons.  One, the practicality of combinations is, in reality, much more dictated by issues of pharmacologic interactions than antagonism.
To my mind, we haven't had enough pharmacologic interaction data when we have been using these drugs in people which, to me, would be a much more important issue in terms of safety and intelligent use than these theoretical issues on antagonism because we really only want to not use a combination if it is antagonistic.
That is the only question because additivity or synergy isn't really that important.  We clearly need combinations and we don't want to use them unintelligently or dangerously.
DR. HAMMER:  Thank you, although‑‑just a last comment‑‑some focused studies are helpful because sometimes they reopen our horizons.  For a long time, people were nay‑sayers about dual NRTI combinations and maybe that won't evolve.  But, in fact, now they are being investigated clinically.  There had been in vitro data to suggest we would think about them from the past and not just assume, because they bind to the same region on the RT, that it would not be worth looking at them together.
So focussed developmental approaches may help expand the horizon.
DR. POMERANTZ:  I would like to continue on what Dr. Masur said or what was the discussion between him and Dr. Larder because I think it is important to look back at some of the knowledge we do have from other infectious diseases, to put on my ID hat.  We have, at times, flown by the seat of our pants on a number of different treatment regimens and have not had as much data in other classical infectious diseases that we have even now for HIV.
But, that being said, it is important to realize that there are organisms, we have used the pneumococcus as one, where you have partial resistance in vitro and yet you can still use that drug in vivo to treat the infection.
I think that comes to bear on HIV in the group that I had asked Doug about before, and that is the development of understanding phenotypic resistance in the modest range.  Being close to eight‑fold or ten‑fold resistant, it is not clear to me that, as Doug was saying, those are all truly resistant, both in vitro nor in vivo.
So it is important that when we get these studies together that we realize that modest in vitro resistance may still not obviate you to not use that drug especially in certain combinations.
So I agree with Doug.  I think that, up front, you are going to do genotypic and phenotypic analysis in both preclinical and clinical drug design.  But then it is going to get far more complicated unless it is a cut‑and‑dry issue.  If you look at some of the resistance data that has been coming out, there are a number of different phenotypic analyses that are not clear how they are going to fall in vivo.
DR. MAYERS:  I think one area that Doug showed in his slides earlier is the issue of what is sensitive.  Previously, we would take a group of untreated patient isolates and define the sensitive range for a drug.  Now the companies are taking the standard of one reference strain and that reference strain could fall anywhere in the high or low or intermediate range of susceptibility so that there is an hundred‑fold difference in AZT resistance in the range of susceptible.
If your isolate references lands high, you are likely to call many things resistant that are probably in the normal range.  If it is low, you are likely to call things sensitive that‑‑so I think it is really important that there be some standard of what is the normal range for a drug and, perhaps, a panel like Doug has put out where you have isolates before 1985 in untreated patients is the type of panel you need to get to define "normal" for a drug.
I am very concerned that the phenotyping companies are calling resistance because it is two‑fold greater than their reference strain for isolates that are probably in the normal range of untreated patients.
DR. HAMMER:  I think this discussion will also come up in Dr. Little's presentation.
Dr. Mellors?
DR. MELLORS:  Doug mentioned this but it needs to be restated.  We are talking about a couple here, and we are only talking about the husband and not what is wrong with the wife.  Whenever we discuss drug susceptibility, we have to discuss‑‑
DR. HAMMER:  Are we in the right committee session?
DR. MELLORS:  It is a byproduct of counseling here.  Whenever we talk about drug susceptibility, we have to talk about drug exposure.  To think that we can predict clinical response from the fold variation over reference strains is ludicrous.
So these two days are devoted to resistance testing but we have to keep in mind that susceptibility is determined by many factors in vivo.  One important quantifiable factor is the concentration of drug, free drug, active drug, that can be achieved and maintained.
DR. HAMMER:  Very important point.
DR. WONG:  Just with respect to this question, I actually like Dr. Richman's list very much about what should be expected of a sponsor, particularly in the preclinical develop package.  I think both phenotype and genotype results should be shown.  I might also say, for the sake of perspective sponsors, that, over the past year, we have seen a few presentations in which the virology data were provided only in kind of gross summary form.
I, personally, would prefer to see the populations of viral isolates tested and see the results so that we can evaluate for ourselves what the range of susceptibility to various drugs was for the new drug.
So I would like to see a thorough preclinical evaluation of drug‑resistance testing both at the phenotypic and genotypic level in susceptible and resistant strains with a full presentation of those data.
DR. MAYERS:  The only difference I would have between the list that Doug gave in clinical studies and my own personal bias is that I think we need to define the genotypes and phenotypes associated with loss of activity of a drug, and genotypes and phenotypes in experienced patients that predict a lack of response on the next round of therapy.
A concern that I have, if that is the standard you move to, is that, for many of the drugs that we currently have in practice, this has never been done.  The problem the company is going to have, if we have never defined d4T and ddI resistance cleanly and you use that in a combination, how do you define the component that is related to the new drug.	
I think there has got to be an association between genotype and phenotype and loss of activity in naive patients and genotype and phenotype and lack of activity in pretreated patients because that is the data you would need clinically to use these drugs.
DR. MASUR:  Although it is getting a little bit ahead of where we are right now, one of the things in other infectious diseases, to put a couple of people on the panel on the spot, it is very useful when governmental agencies do surveillance and provide information as to what the activity of various drugs is against certain isolates.
When you get data about a particular drug, it is very hard to put that into context with other drugs as to whether this is active.  It is active, but it is active compared to what, if you accept what John Mellors said that this is only one aspect of treating a patient.
Is it conceivable that, at some point, the CDC or some other agency is going to do surveillance so that we can put all these drugs into some kind of regional or national or international context?
DR. KAPLAN:  Actually, as I mentioned when we went around the table at the beginning with our introductions, we have a great interest at CDC in doing surveillance of resistance.  But the population, I think, we are mainly interested in is antiretroviral‑naive persons because I guess it is our perception that there is a lot of this work going on in experienced patients and other networks, particularly NIH‑funded studies such as an ACTG.
We do have a system now where we are funding various state and city health departments to recruit antiretroviral‑naive persons.  We are playing around the idea of using the detuned assay to try to further define which people are recently infected.
In fact, one of the reasons I am here at the meeting is to look at possibilities for expanding our system.  So this is a priority for us.  I think our focus right now is in naive persons.
DR. GULICK:  Just to take a step back from a clinician's point of view on question No. 1.  It needs to be stated that probably one of the great needs in drug development right now is to develop new agents which have activity against viruses which are resistant to the agents that we have.
It seems, with these new tools, that we have really turned a corner in sort the encouragement and the development of new drugs given that we have fifteen approved agents.  New drugs in development‑‑they should be encouraged if they show activity against resistant virus so, on the preclinical level, just to reinforce what you said, characterization of resistance at baseline and, importantly, activity against clinical isolates that already have resistance would be important to demonstrate.
DR. FLETCHER:  Just a comment on the clinical drug‑development side, at least as you being Phase I studies, there it would seem to me that the opportunities for use of phenotypic assays could become very important in terms of trying to integrate, as Dr. Mellors and others said, that information on how susceptible the virus is with what concentrations are actually going to be achievable in the body.
To state the obvious, you can have the most sensitive virus but, if you can't reach effective concentrations, it is not really going to matter.  I think a converse could be plausible as well‑‑that is, you could have an intermediately susceptible virus but the concentrations that you can achieve in the body are so several‑fold higher that you could have activity in that setting.
So I think there, in the Phase I environment, that integration between phenotype and pharmacology could be a real role for that.  Just lastly, what will need to be sorted out is what is really important quantitatively about the phenotype.  Is it the IC50?  Is it IC95?  Is it multiples of the IC95 that one needs to use there?
DR. HAMILTON:  A nuts‑and‑bolts question that I think has some implications for preclinical‑trials design and that has to do with several points that I think Doug Richman raised having to do with what seemed pretty sensitive levels of detection of mutants for both the genotypic and phenotypic assay.
If I understood him correctly, it was between 95 and 99 percent for phenotypic versus genotypic assays.  Does that mean, actually, that above a thousand copies, let's call it, that the level of detection is the same at all levels or would one need, actually, to stratify based upon viral load when using this methodology?
DR. RICHMAN:  If I could clarify that a bit.  The assays that are based on amplification of RNA from plasma, and this applies both to the genotype and the phenotype assays‑‑what they are doing is analyzing the population of the amplified material so that, at most concentrations of plasma RNA, it really doesn't make a difference if you are starting with 10,000 or 100,000; you are looking at sort of the consensus or majority population.
The issues; it depends upon the specificity of the assay.  I showed examples of both phenotype and genotype, that maybe some situations for a particular drug or a particular codon, 20 percent of resistant is enough to show the difference.  Sometimes it maybe 50 or 60.  That depends on the drug and the codon.
That is really independent of the virus load.  Where an exception occurs is at the very lowest concentrations.  There you can have what we call a founder effect.  If you happen to get a positive amplification from someone with a hundred copies, the source for that amplification could, just by chance, be a single RNA molecule.
So, at the very lowest concentrations of RNA, you can get founder effects and stochastic‑effect data.  But, in general, from a practical point of view, what we do is we do RNA assays to define whether someone has succeeded or failed the treatment and then someone who has failed is someone who then gets a drug‑resistance assay for either evaluating a drug or for managing a patient.
For most of those patients, we don't start doing these assays when they have got 200 copies.  But that is a potential risk.
DR. HAMILTON:  Doesn't that, then, make quantification difficult over time when you are evaluating longitudinally the emergence of resistance if, in fact, what you are looking at is an amplicon and not a total load of resistant virus?
DR. RICHMAN:  I think what I tried to say is once you are up in the thousands or more, what you are looking at is reflective of this complex mixed population.  Now, that doesn't deal with the fact that that complex mixed population is changing over time, too.  That is true as well.
So you may get a different result when someone is at 2,000.  If you keep treating them and they go up to 200,000, the result may change because the population has shifted as well.
DR. STANLEY:  I want to go back and build a little bit on what Dr. Gulick said and break this again, as you suggested, Dr. Hammer, into the preclinical and the clinical.  Clearly, resistance testing and drug interactions are not going to predict in the individual patient but I think we need to decide, preclinically, what do we want to know, in vitro, about these drugs before we then go to PhaseÊI trials and can go better pharmacokinetic studies and look at interactions.
As Dr. Gulick said, if, in your in vitro studies, your early preclinical development, you are able to pinpoint some advantages that this agent may have over certain resistant strains or whatever, then, as you move into PhaseÊI, you can start to do the pharmacokinetics to understand the drug interactions and also any resistance advantage.
So I think I still would advocate for some drug studies on panels and resistance and combinations in the preclinical in vitro setting.
DR. HAMMER:  We need to move on to the second question.  Are there any other comments?  Just for the agency's sake, maybe just to help summarize as we go question‑by‑question, first I think, as was stated earlier, two of the last slides that Dr. Richman showed really answer the first question pretty much as was summarized by the committee.
When asked about the relative strengths and limitations of genotype versus phenotype, I think we saw them‑‑for the purposes of drug development, both are important.  The genotypic profile of each drug that comes through, in vitro needs to be classified.
We need to know whether it is multiple step resistance or whether single‑step high‑level resistance and what the phenotypic correlations in vitro are.  We already talked about looking at a new drug that has activity against a defined panel of isolates in vitro.
On the clinical side, one is interested, of course, in what the emergence of resistance will be to that drug, recognizing, again, that this will be in combination with other agents and so there is a challenge there because of what may be effect of other agents on the combination.  But at least one can try to determine the pattern and also, as Dr. Mathews said, I think, on some of the clinical‑development side, and this subsumes Dr. Gulick's comments too, an agent that looks good against resistant isolates should be potentially tested prospectively in populations where you define, up front, those populations.
I think, to echo Dr. Mathews, when one knows that isolates are resistant to a particular agent and that will threaten a combination doing prospective studies that eliminate patients who would be potentially harmed by that combination being suboptimal, should be more and more a part of the developmental process.
So I think, basically, on the clinical side, it is resistance emergence which, I think, is part of a lot of profiles but prospective resistance at baseline, et cetera, is important.  Then, also, one can think about management and strategic studies along the way but I think that may be best discussed tomorrow.
Anything else you want on this question?
DR. HAMILTON:
DR. MURRAY:  I wonder if anybody could comment on, perhaps, using one type of genotypic assay over another early in clinical development when you are screening virologic failures for the presence of mutations.  Would you want to use, like, a hybridization technique where you might detect lower numbers, or a lower percentage of mutant mixture, to have a more sensitive screen for resistance popping up with that drug on what you have predicted preclinically?  Or could that data be, actually, misleading?
DR. HAMMER:  I think I will tackle that because I think when you are dealing with a new drug in a new combination, as I think Dr. Richman outlined, you don't know what the outcome is going to be and, therefore, I think you need a dideoxy method that sequences the region of interest because, if you are not smart enough to know in advance what to look for, you will miss it with an assay that interrogates certain regions.
My personal feeling is that, in looking for emergency of resistance in vivo, once a drug hits the clinical‑research side early and in different combinations, that you really need to look at the regions of interest‑‑RT and protease is what we are interested in today‑‑and make assessments as to the relevance of those mutations.
Then one can go back to the laboratory with what one sees and prove, or disprove, what those mutations may do to an isolate in vitro.  However, I think a point in favor of the assays that interrogate certain regions, once you have defined, for example, perhaps a key mutation, those are very helpful assays to look at baseline to see whether somebody has or has not that particular mutation and then randomize on the basis of that.
Therefore, a more sensitive assay even helps you with your randomization scheme.  So I think it depends, really, on the nature of the study design as to which assay is better.  But I think when you don't know what the answer is going to be, you should not be doing an assay that goes just after codons of known, previously determined, resistance.
Anybody want to disagree?  Dr. Mayers?
DR. MAYERS:  I wouldn't disagree.  I would just extend that even further to the point that the isolates that have been selected in the preclinical development programs for resistance have, not infrequently, not been the isolates that you actually see in the clinic so that I think you have to look very carefully in your Phase I program to see what the failure mutations are in your patients before you could even consider doing that.
The problem is going to be, as you start combining these drugs into regimens, you can shift to new patterns that you didn't anticipate before.  So I think you are sort of stuck, at least in the understanding of what is happening in your trials, in doing something that screens the whole genomic interest.
DR. HAMMER:  We will let Dr. Pomerantz have the last word on this first question.
DR. POMERANTZ:  Thank you.  I just wanted to point out something that I had talked about before and that is, if you are going to look at phenotypic resistance and correlate it with genotypic assay as you are asking which assay, I do agree that you should sequence it up front.  But I think it is going to be even more complicated than that and that there are groups of viruses that have their resistance that may vary based on context dependency, which is a term that John Condra taught me a while back.
It is not only within a gene but there are a variety of other genes in HIV that affect protease and, in particular, affect reverse transcriptase.  So, if you are not going to sequence that gene, you may actually need to look at other genes besides that.  Nucleocapsid is important for template switching and reverse transcription.
There are differences.  There are differences.  There are integrase effects.  The integrase gene affects reverse transcriptase.  So it is going to get somewhat even more complicated and I think you are going to need the baseline values of sequencing at least that gene of interest.
Protease not only has mutations in protease but, as you know, there are PR cleavage sites that there are mutations in that may become more and more important.  So I would definitely agree with sequencing them up front as a genotypic assay because you are going to need that as you move to the more complex molecular interactions.
I see Dr. Mayers shaking his head, so I would be very strong on that.
DR. HAMMER:  Thank you.
We will move on to the second question which we have already sort of broached a bit with Dr. Murray's question; what studies are needed to further define performance characteristics of available assays in order for them to be useful in drug development.
I think no one disagrees with the fact that they are useful in drug development.  It should be there.  I think this is the issue of where we need to move from the current state of the art as was described this morning.
DR. KUMAR:  I have a practical issue to raise regarding this.  Most assays currently available need to have a viral load greater than 1,000.  So, for clinical trials, right now, the problem that they are facing is as soon as a patient fails, we withdraw the patient from the clinical trial.  That is usually when we define failure; in most clinical trials, it is greater then 400 copies.
And then we move them on to another regimen.  And so we really lose a window of what to do with these patients because we have withdrawn them and we have given them another antiretroviral regimen.  So that is something that I would like to raise here to see what will we do with this information that will clearly be lost.
DR. HAMMER:  I would just say that this is, a), a moving target and already the manufacturers are moving down and have some data to suggest that they can get down to 500 copies.  It is not as reproducible as a thousand or more copies, but, like everything else in assay development in HIV, this is moving and will be different in six months and twelve months than it is now.
MR. HARRINGTON:  That raises something that I wanted to mention before which is there is a difference between fitness and virulence.  That is particularly clear in studies like Steven Deeks where those patients are only partially suppressed but there is some virus around their CD4, there is a disconnect between their CD4 and their virus.
So if the clinical trials are just taking everyone off who fails, then we are not really looking at what is happening out in the real world.
One other thing that is, I think, very interesting clinically is what is the relative fitness and virulence of the resistant phenotype because, if the 3TC‑resistant virus is replicating very well in people but it doesn't replicate very well in the lab assay that Doug showed, you have to actually have a lot more 3TC‑resistant virus than protease‑resistant virus.
So that would something else that might be very useful clinically to know which is the disconnect or what is the relative fitness versus the relative virulence of the resistant phenotype of the given virus.
DR. KUMAR:  I want to come back to this issue because this is an issue that we continue to face while we do clinical trials.  Invariably, written within these protocols, as it is written right now, for most protocols, is if the viral load goes about 400, we withdraw patients from these protocols.	
So I really want to raise that issue here to see what to do with this disconnect that we are seeing.
DR. HAMMER:  I think that is a slightly separate topic about the strategic issues of clinical‑trial endpoints but I would make a general comment that, although the failure endpoint, whether one chooses 400 copies or whatever, is quite a valid endpoint depending upon the study design.  Many studies, although they call that an endpoint, don't necessarily mandate a switch at that point.  It is up to a physician and patient to determine whether one moves to a second regimen or not.
It varies study to study, but the mandatory switch is not necessarily a part of every regimen.  I think the point that you are raising for this morning's discussion is the window of knowing, irrespective of what you do strategically at the next step, is defining what the genotype or phenotype of the virus is at the time of the virologic endpoint.
So I think, if I may interpret, the point you are raising is what we need, and what you would like to see, is greater sensitivity at the levels of 400 copies and even above 50 copies to be able, actually, to define the profile of the viruses that patients are failing with and correlating that with the endpoint, whether that is first failure or multiple failure.
We know that one can be surprised, if not intrigued, by what the results are when one looks in those failure populations as to whether it is adherence or drug resistance or whatever.
So I think, if I may infer, what you are saying is we need greater sensitivity at lower copy numbers to eliminate the disconnect between the virologic endpoints and the threshold for amplication that we currently see today.
DR. STANLEY:  When I try to answer this question when it comes to the genotypic studies, aside from needing more sensitivity, as you just discussed, I guess if I understand Dr. Richman's data, I have a little bit of difficulty because, when he showed the comparison of the laboratories and how much they got things right, he made the statement that the success or failure was independent of the kit that was used, that it really depending on the quality of the lab.
So I think that is going to be critical and who is doing the preclinical testing or who is doing the drug development, how do you assess the quality of the lab as opposed to the individual kit, if I am understanding the data.
DR. HAMMER:  You can hold that for question No. 3, what quality‑control data are needed.  You are leading us right into the next question.
DR. MAYERS:  An issue that I think is going to come up with the committee is going to be how to interpret results done by different companies for different drugs.  There has got to be some effort made to get a comparison of what the expected range of normal would be and what, for certain type isolates, the expected range for an assay done by one company versus an assay done by a second company versus, perhaps, a PBMC‑based assay would show you because it isn't clear that the ten‑fold difference for one company would be a ten‑fold difference for another company or that that would relate in any way to a PBMC assay.
So I think there is going to have to be some effort to define what those ranges are for the different assays.  It may be drug‑specific.
DR. HAMMER:  One thing to ask in that regard is, certainly when you only have a single isolate from an individual in a trial, you have nothing to relate it to except the control that the assay uses in a phenotypic assay.  But one thing to also think about is having sequential assays from the same patient which provides not only more real baseline information than what is available in the clinical trial but what the sequential changes are related to an individual's isolate.
More and more, that information could be, and perhaps should be, part of packages that one sees where it is definable and would help answer that question that you have got.
DR. YOGEV:  Maybe one would like to see resistant in vitro really mean in vivo.  We define resistance and in a certain situation, we try to use the genotype getting nice phenotype‑‑those blue marks and everything‑‑and it didn't work in real life.
So I was wondering how to define what we call resistance; for example, a phenotype of eight‑fold.  What does that mean in a certain situation?  Just to reflect of what was allowed by Dr. Masur before, it is into microbiology.  We know for a fact that what is called resistant for a certain number of bacteria in the blood is completely irrelevant to what is happening in the CNS let alone the penetration of the drug.
But you need much more of the drug when you have much more of the virus in certain areas.  I don't think that we understand what resistance really means defined in vitro.  So I would like to see, in the performance, in vivo correlates to that specific definition so that we can accept it.
I think the best example is AZT.  If you have one mutation, it is two‑fold.  If you have two mutations, it is four‑fold.  This is the gray area that one should define a little better by the range of pharmacological data in a certain area versus the correlate of the in vitro.  So, basically, I am asking for an in vivo definition.
DR. HAMMER:  That raises the question, again, and I think, also, as Dr. Mellors mentioned, that the drug exposure is the key issue and we need to be able to interpret, at some point, IC50s and IC90s in relation to achievable drug concentrations in vivo and whether it is Cmin, Cmax, area under the curve, whatever, that is really the exposure characteristic that is important.
That may be drug‑class specific and even drug specific.  But one would hope, I would think, as this process moves forward, that a database will develop of what achievable drug concentrations are and what is important.  We already have some data about trough protease inhibitor levels being predicted, for example, and trying to relate certain achievable drug concentrations to a particular fold change in drug.
Whether this also means therapeutic drug‑level monitoring is going to be important for certain drugs and should be available is the topic of another symposium.
DR. YOGEV:  I agree with your statement.  I just am raising also the issue of the amount of the virus.  We all are taught that what you have in the blood is 100 or 1,000 or what is in the lymph node or in other issues.  I would like to see a correlation of resistant to the amount of the virus.
So I know, for example, that drug is called resistant when I tested it at 10,000 as well as at 2Êmillion.  So, I think, on the top of the pharmacokinetic, I am talking about the virus, itself; the test done in a defined number of viruses is appropriate to the in vivo just on the virus amount.
DR. HAMMER:  I think Dr. Richman answered this before and he may want to comment again that at levels of, certainly, in the several thousand and above range, that the amplification techniques from plasma are relatively representative of the population, at least the lymph‑node population that is most immediately reflected in the plasma and that, as you get to the very low copy numbers, then you are at risk of the population.
DR. MAYERS:  I would extend the previous comments even further.  The cut points, and that is the real challenge to the industry and the phenotyping companies right now, is what is the upper limit of normal for your drug and where is the break point for your drug where you lack of clinical responsiveness.
They have got to define those break points by drug, not with an arbitrary relation to a one‑virus standard.  So I think that is going to be the real challenge is what is normal, what is the break point that defines lack of activity in the clinic.  And then you have your three ranges.  We don't have those for any drug at this point.
DR. HAMMER:  Are you suggesting that we need an NCCLS‑like apparatus for HIV drug‑resistance testing?
DR. MAYERS:  I think we need a thought process like NCCLS in this.  There has to be a relationship between your break points and the clinic.  Right now, those arbitrary break points are not that standard and we don't have the data.  The only way we are going to get the data is if the companies acquire it during the drug‑development process.
DR. HAMMER:  Other comments on question 2?
DR. MATHEWS:  Again, dealing with clinical trials and experienced patients, it seems like there needs to be some standardization of the background therapies in relationship to when the baseline resistance assays are done; in other words, a cross‑sectional estimation of what the resistance is conditioned on, whether the patient has been unstable or antiretroviral therapy for eight weeks, four weeks, has had a washout period, all those kinds of things.
So if you are trying to correlate response based on a given mutational pattern, it may be completely uninterpretable if there is a great deal of heterogeneity across trials trying to evaluate the same drug or the same regimen and those factors.
DR. GULICK:  Just to add on to that, it is a problematic situation when you are in drug development at an early stage and you want to characterize in vivo what resistance mutations are developing with a new drug.  However, directly opposed to that is our wish that we can come up with a new regimen that will actually suppress viral load.
How do you come to terms with wanting to do both those things at the same time?  Just to echo something Dr. Mathews said before, we don't want to design unethical trials in order to gain resistance information.  It is a bit problematic.
DR. HAMMER:  It is very problematic.  I think one thing it raises, which, I think, is a little bit beyond the scope of today is what the role of certain animal models might be in allowing you to investigate regimens and drugs that would be unethical to use in humans and where animal models may or may not help you define, at least generate hypotheses and give you some look at what may happen in an in vivo situation‑‑Skid‑U mice, for example, have been used, primate models, et cetera.
DR. MELLORS:  Just to follow up on what Chris and Trip said, we are not always going to be successful in our salvage regimens.  Hopefully, we will be more successful in the future but it points to the critical need to not let failures escape or follow up and our detailed evaluation.
There has to be a shift from presenting the proportion that are successful to the proportion that failed and why and what resistance occurred as a consequence.  Right now, we are in an era where dropouts are treated as failure and they are kind of swept under the rug.
But the agency and the research community has to really focus on characterizing well the failures.  That is the way we will build a database.  It won't be a comprehensive database.  If we do our job well, there will be few failures.  But we really have to hang on to them and learn as much as we can from them.  No counseling jokes.
DR. HAMMER:  Any other questions on question 2?  We need to move on to question 3.  Again, just to summarize for the agency, what studies are needed to further define performance characteristics of available assays in order for them to be useful in drug development.  I think the processes that are going on now are defining them.  I think we have raised a couple of issues here.
Everyone is interested in greater sensitivity of these assays.  Personally, I think that is coming.  I think the issue of picking up the mixtures is also important and some assays are better at this than others.  We will never be able to be perfect because we know that even less than 1Êpercent of a viral subpopulation can emerge under the right conditions and will likely not get quite there.
But I think the two areas for further improving the performance characteristics of available assays are lower limits of sensitivity on amplification and the ability to detect mixtures.
Anyone want to add to that?
Question 3; it is already sort of introduced a bit by Dr. Stanley.  What quality‑control data are needed to support use of these assays in clinical trials and drug development.  What additional studies should be conducted with respect to reproducibility and quality control
Among others, I would like to hear from Dr. Jackson on this question.  Sorry to put you on the spot, Brooks.
DR. JACKSON:  I think Doug presented a number of the performance characteristics that need to be quality‑controlled such as sensitivity, specificity, the precision.  I think, in judging from the ENVA panel, there is still a real need for this type of data and standardization of this sort of quality‑control data that needs to be developed both for the kit performance characteristics as well as for the performance of the laboratories.
I guess the latter is really not part of FDA's thinking on this at this point in terms of this discussion.
DR. HAMMER:  Although, I think we should separate two things.  One is kit approval issues through CBER and drug application packages that come to this division and this committee.  That is different.  That is not an approval process.  That is sort of what should the data package look like.
Of course, the assays underneath that have to have a degree of validation but I think we can make suggestions about what a data package should look like in the drug‑development process which I think is what we are being asked to do.
DR. JACKSON:  Just like any diagnostic assay, I think the same sort of things that are required when data are submitted in terms of sensitivity, precision, the specificity, all those sorts of things in different populations, different sets of populations that are looked at in terms of how these assays perform in those respects are going to be needed.
I can't think of anything other than those specific topics that Doug brought up but clearly there is a need for that.
DR. HAMMER:  Other comments?  This could be a short discussion.
DR. HAMILTON:  I have a question of the agency which may not be answerable but I wonder if, in the preclinical mode and prelicensing mode, the agency provides any advice to applicants, to sponsor, relative to the testing procedures they are proposing to use in a subsequent study.
DR. IACONO‑CONNORS:  What we normally tell sponsors when they propose a series of studies, we basically ask them to identify what assays they would like to use to support their clinical studies.  If those assays are unlicensed or unapproved, as would be the case with these types of studies, then we ask them to provide us with additional performance information that would allow us to be able to interpret the data that those assays would generate.
So we don't give them any direct advice.  We wait for them to come in with their proposals and then see what kind of performance information packages that the drug companies would need to get from the kit companies in order for us to understand the data.
So it becomes a bit of a problem.
DR. POMERANTZ:  I have a question for Doug.  You presented a nice picture of how the ViroLogic assay goes for phenotypic analysis.  My question is, as you are thinking about generalizing things, you pointed out that they are in‑house.  That is basically an HIV‑based pseudotyped vector.
If we are going to start using phenotypic assays as we are talking about now more globally, that is going to be an interesting way of straining the ability to use what is still a complicated assay.  How do you see it developing or how would you see the company developing it so that it could come to use in all these different studies and non‑studies for patients that we are talking about now.
DR. RICHMAN:  I can't speak for the two companies but I have talked to them.  I think they think they can or hope to expand to accommodate the phenotype needs of investigators and practitioners.  Now, if a third or fourth company comes along with performance characteristics that are similar, that is fine.
I think I can say they have been talking about trying to document that they come up with similar results on the same specimens as Doug Mayers tried to raise.  I am not concerned with the numbers that they come up with in terms of the fold change because I think I showed slides from both companies showing how tight their values from wild‑type isolates were around the control value.
The issue of what value is important is of practical concern.  You will see data this afternoon giving some indications that for many of the drugs we are using now, four is a better number than ten.  But, for each drug, we do need to know some data.
There are, in fact, I think good data regarding abacavir, retrospective data with monotherapy, indicating that somewhere about four‑ or six‑fold is probably enough to be enough to make it not work.
That is probably true with a number of the drugs, but the pharmacologic issues with some of the protease inhibitors raised a question about more moderate levels.  I think there are really no alternatives because the data from the PBMC‑based assays‑‑blinded comparisons have been done with these other assays and they certainly can outperform speed, cost, precision and everything else.
There are a lot of internal problems that we have all observed with the data that we have generated with our own assay.  So, for these two targets, I don't see an alternative from a practical point of view.  I am not sure if that answers your question.
DR. POMERANTZ:  It does.  The main point is that you think that one company in the United States can, in one site, handle this?
DR. RICHMAN:  You will have to ask‑‑
DR. POMERANTZ:  Because we are talking about doing a lot of trials and moving phenotypic analysis‑‑
DR. RICHMAN:  There are representatives from both Virco and ViroLogic.
DR. POMERANTZ:  Would you think about having subsites?  How are you going to do this?  This is not a trivial‑‑it may not be the PBMC assay but it is still not a trivial process.
DR. RICHMAN:  These are not assays that can go out of house.
DR. POMERANTZ:  Exactly.
DR. HAMMER:  I think it is fair to say that we are talking about market forces here.  If the demand is there, I think that it will be met.  I don't know that we need a specific‑‑since we were asked, really, not to be product specific, I would rather not ask representatives of ViroLogic or Virco to answer this directly, officially, but during a break, I think this question can be asked.
Given entrepreneurial skill and market forces, one would think the need could be met.
DR. JACKSON:  Just to follow up on some of my earlier comments that Dr. Dayton brought up I thought would be relevant that things we have found with viral‑load testing, for example, in terms of these assays being used in clinical trials over time, I think it is important to see quality‑control data regarding lot‑to‑lot variation, that sort of thing.
We have seen, in past trials, various deteriorations sometimes with the stability of reagents.  These sorts of things will probably need to be shown as well as validation panels that are done showing the performance of the laboratory personnel using the assays in these trials.  These are fairly standard procedures but I think they are important.
DR. HAMMER:  I think there are a couple of things.  One is we should probably think about phenotype and genotype a little bit separately here.  There are two commercial groups in the phenotype field at the moment.  That may change in the future, but I think the data we have seen about the descriptions of their assay performance and at least the approvals that have come from some regulatory groups gives a fair amount of credence, I think, to the data that are generated and help in any submission in a packet.
But those are the kinds of things we need to see.  I think, as Brooks outlined and as was outlined by the earlier‑‑Dr. Dayton's talk, that is sort of the standard things.  On a genotyping side, there are the classic kit issues, reproducibility and lot‑to‑lot variations.
I think one other thing just to mention with regard to PCR and sequencing, as Betty Korper has, more than once, made us aware of the issues even in very good laboratories of contamination and that when large datasets or even moderate datasets are sent in of sequencing in a single laboratory, what procedures that laboratory goes through to be sure they have not had a problem with contamination‑‑that is, sort of checking their sequences across their isolates and against the isolates that are commonly used in the laboratory is a reasonable question to ask of any laboratory, whether that be in something that is submitted for publication or in something that is submitted for drug approvals because that problem is one even the best labs have occasionally.
Are there any other comments on the quality‑control issues and issues of reproducibility?
DR. WONG:  I guess I just want to make one comment and that is that I am a little concerned that phenotypic testing seems to be going to route of adopting a heterologous expression technology exclusively.  I understand why this is necessary and that it is easier and cheaper and more reproducible for now, but I guess I would hope that we not abandon completely the idea that the virus, in the context of the virus itself, has to be assessed as well.
Just taking out a gene, expressing the protein in a tester strain and then testing that strain is not, necessarily, the whole story.  Although practically it may be the way to do it today, I wouldn't want us to get locked into that sort of system.
DR. MATHEWS:  I had a question about the assays for reproducibility.  It seemed that most of the ones that Doug mentioned were assay reproducibility and not really looking at sources of biological variability besides drug pressure.  When the viral‑load assays were looked at, when CD4 assays were developed, biological variability was one of the big issues that had to be grappled with.
To what extent has this been looked at, variability in patients not on drug at different time points and under different conditions?
DR. HAMMER:  You mean multiple sample from the same patient, either off drug or on the stable regimen and the reproducibility‑‑
DR. MATHEWS:  If they are on drug, that is a problematic issue because, obviously, if they are not suppressed, they may evolve.  Is there a sort of steady‑state background biological variability that needs to be taken into account?
DR. RICHMAN:  In the interest of time, I took out slides.  Richard Haubrich, whom you may know‑‑there is a strategic study looking at the utility of phenotypic testing versus not done by the California Collaborative Treatment Group.  He presented at ICAAC the results of two consecutive tests taken on each of those people who entered at that time.
I could show you the slides, if you like, or he could‑‑but, basically, the reproducibility on two consecutive assays was quite impressive.
DR. HAMMER:  What was the interval between the sampling?
DR. RICHMAN:  It was 30 days.
DR. HAMMER:  Thank you.
I think, on that note, we will break for lunch.  We will return in an hour at 1:05.
[Whereupon, at 12:05 p.m., the proceedings were recessed to be resumed at 1:05 p.m.]

A F T E R N O O N   P R O C E E D I N G S
[1:10 p.m.]
DR. HAMMER:  We will begin this afternoon with Session 2 entitled the Evaluate of Relationships Between Genotype, Phenotype and Treatment Outcome.  I would like to introduce Dr. Girish Aras who will introduce the afternoon session.
SESSION 2
Evaluation of Relationships Between Genotype, Phenotype
and Treatment Outcome
Introduction
DR. ARAS:  Good afternoon.
[Slide.]
I am Girish Aras from the Division of Biometrics 3 and the Division of Antiviral Drug Products, CDER, FDA.  I am going to introduce Session 2 that is devoted to the evaluation of relationships between genotype, phenotype and treatment outcome.
The session objectives are: one, to discuss approaches for categorizing mutational patterns for assessing their prognostic value on treatment outcome; two, to discuss approaches for categorizing susceptibility profiles for assessing their prognostic value on treatment outcome; three, to determine whether available evidence supports the clinical utility of HIV genotyping in drug development and to determine what additional information is needed; four, to determine whether available evidence supports the clinical utility of phenotypic testing in drug development and what additional information is needed.
Prospective studies will be presented first followed by retrospective studies.  The prospective studies are VIRADAPT and GART and will be presented by Drs. Clevenbergh and Baxter, respectively.
The Resistance Collaborative Group developed a Data Analysis Plan abbreviated as DAP to statistical model the link between treatment outcome at the end of the trial to the baseline covariates including genotypic and phenotypic baseline covariates.
Dr. John Mellors will present an introduction to the Resistance Collaborative Group's reanalysis of selected studies using the DAP methodology.  That will be followed by Dr. DeGruttola's presentation of the DAP methodology.  Dr. John Mellors will then present an overview of retrospective and prospective studies reanalyzed using DAP.
Drs. Para, Ait‑Khaled and Miller will present some of the retrospective studies analyzed according to DAP followed by a summary by Dr. Mellors.  Finally, I will make a few statistical comments on retrospective analysis.
We look forward to an interesting and productive discussion.  I will introduce the first speaker and then turn the session over to our committee chair, Dr. Scott Hammer.  Dr. Phillipe Clevenbergh will now present the VIRADAPT study.
Thank you.
Prospective Studies
VIRADAPT Study
DR. CLEVENBERGH:  Good afternoon.
[Slide.]
This is an overview of the VIRADAPT study as it has been published and presented at the San Diego meeting regarding the long‑term follow up and the pharmacological data.
[Slide.]
Numerous retrospective studies have linked the presence of resistance mutations with drug failure.  Two prospective pilot studies have shown short‑term virological benefit of using genotyping technology to manage patients failing therapy.
[Slide.]
In VIRADAPT, heavily pretreated patients failing therapy were randomized into two arms, a control arm and a genotypic arm in which the antiretroviral treatment changes were based on the detection of resistance mutations.  Entry criteria were a plasma HIV RNA remaining over 10,000 copies/ml despite treatment with at least six months of a nucleoside analogue and at least three months of a PI.  Clinical status, CD4 cell count, HIV RNA and genotype were assessed every three months.
If viral load failed to decline at least 0.5 log after treatment change, therapy could be changed again based on the latest available genotype in the study arm or standard of care in the control arm.
From March, 1997 until March, 1998, 106 patients were enrolled in the study, 43 in the control arm and 65 in the study arm.  The study protocol was approved by the institutional ethical committee in our hospital and informed consent was obtained from all patients.
[Slide.]
We report here the 12‑month follow up.  After the month 6 interim analysis, a statistically significant advantage was formed for the genotypic group and, therefore, we decided to allow genotyping for all patients.
[Slide.]
Therefore, the first six months was a randomized study in which the study arm received genotypic guided treatment, yellow, and the control arm received standard of care, blue, whereas, during the second six months, both arms received genotypic guided treatment changes in an open label fashion.
[Slide.]
The primary endpoint of the study was the variation of HIV RNA from baseline at months 3 and 6.  Analysis was performed by intent‑to‑treat.
[Slide.]
For the 6 to 12 months, open‑label follow‑up study, the primary endpoint was the evolution of HIV RNA from baseline at months 9 and 12.  All data collected were included in the analysis which was performed by on‑treatment analysis.
Of note, since some patients in the control arm had already progressed beyond six months, genotypic‑guided treatment change was performed only at month 9 and for those who were already completing the first year, no genotyping could be performed.  Genotyping was performed for 30 of the 43 patients in the control arm‑‑that is, 69 percent of the patients.
[Slide.]
Complete sequencing of the major part of the reverse transcriptase gene and the entire protease gene was performed in plasma HIV RNA.  During the first six months of the study, a number of sequencing technologies were used including, initially, a home brew ABI‑based sequencing followed by the prereleased version of the VGI TrueGene assay and, ultimately, the VGU TrueGene kit.
After the first few months, technology was standardized and the 6‑to‑12‑month follow‑up study was performed using the VGI True Gene HIV system as depicted on this slide.
[Slide.]
Decisions for genotypic guided therapeutic changes were based on correlations linking specific mutational patterns to decreased activity of specific drugs.  This table is rapidly evolving over time as knowledge about resistance pattern increases.  Shown here is the actual version of that table used to guide our decisions regarding RT inhibitors and protease inhibitors during the study.
[Slide.]
As can be seen on the slide, baseline characteristics were similar between the two arms.  Mean HIV RNA about 4.8 log.
[Slide.]
All patients were heavily pretreated with antiretroviral drugs.  They were exposed to a mean of 3.9 nucleoside analogues and to a mean of 1.8 protease inhibitors.  This exposure was similar in both groups.
[Slide.]
The overall prevalence of primary mutations for the reverse‑transcriptase gene was 90 percent.  All patients presented at least one or more secondary mutations.  We found one strain with the 151 and one strain with 69S insertion mutation conferring multidrug resistance.  Mutations at positions 67, 69 and 215 on the RT gene were statistical more prevalent at baseline in the study arm than in the control arm.
[Slide.]
The overall prevalence of primary mutations in the protease gene was 48 percent.  All patients had at least one secondary mutation in the protease gene resulting in an average of 6.2 primary or secondary mutations.
[Slide.]
103, 99 and 92 of 108 patients were evaluable at months 3, 6 and 12, respectively and included in the analysis.
[Slide.]
Depicted are the changes in HIV RNA from baseline for patients in the genotypic arm, in red, and for the control arm, green.  Shown are both, the first six months presenting the randomized study and the second six months in which both arms received genotypic‑guided treatment.
In the genotyping arm, reduction in HIV RNA was maintained throughout the twelve months of study with a mean drop in HIV RNA of 1.15 log.  In the control arm, at the completion of the randomized study, viral load had dropped 0.67 log.  During the second six months, open‑label genotyping phase, there was an additional drop of 2.98 but since this phase was not controlled, no conclusions can be drawn regarding the cause of causes of this additional reduction.
[Slide.]
Shown is the proportion of patients with an HIV RNA below level of detection of 200 copies in both arms.  In the genotypic arm, in red, the percentage of patients with an HIV RNA below level of detection remains stable, around 30 percent throughout the 12‑month follow up.  In the control arm, in green, the proportion of patients with an HIV RNA below the level of detection rose from 14 percent at month 6 to 30 percent at month 12.
As noted earlier, some patients in the control arm had a delay in receiving genotyping‑guided treatment to month 9 which may partly explain the delayed increase in percent of patients undetectable.
[Slide.]
We performed additional analysis to determine predictive factors effecting HIV RNA responses.  The presence or absence of primary protease gene mutations at baseline were correlated with reduction in viral load at three and six months.  Shown are the changes in HIV RNA from baseline at three and six months according to the treatment arm and the presence or absence of primary protease mutations.
The greatest reduction was seen in patients who did not have primary protease mutations and received genotypic‑guided treatment, yellow symbol, with a drop of 1.5 log.  The poorest response was seen in those in whom primary protease mutations were present and received standard of care, pink.  Intermediate results were seen in groups in whom protease mutations were absent and received standard of care, orange or patients in whom primary protease mutations were present and received genotypic‑guided treatment, green.
[Slide.]
In this heavily pretreated patient population, genotypic‑guided therapy resulted in a sustained reduction in HIV RNA of greater than one log throughout one year follow up.  Performance of genotypic‑guided therapy may have contributed to additional viral‑load reduction seen in control patients.  Multivariate analysis showed that the presence of primary protease mutations and performance of genotypic‑guided treatment both independently affect virological response.
[Slide.]
Even with the use of genotypic guided treatment, 70Êpercent of the patients in the VIRADAPT study did not achieve complete viral suppression.  Multiple parameters determine the response to antiretroviral therapy and many causes other than development of drug resistance should be considered when analyzing drug failure.  Among these factors, suboptimal concentrations of antiretroviral drugs may play a major role.
[Slide.]
In contrast to the nucleoside reverse‑transcriptase inhibitors, significant correlation has been found between antiviral activity and plasma concentrations of protease inhibitors and low plasma levels of PI have been linked to rebounds in HIV RNA.
[Slide.]
Our objective was to correlate protease inhibitors plasma levels with the changes in HIV RNA and to determine the multiple factors contributing to the efficacy of antiretroviral therapy in treatment‑experienced patients.
[Slide.]
Of the 108 patients included in the study, the 87 patients from both arms participating at the Nice center were included in the pharmacological substudy.  Several PI plasma trough levels were performed in these patients during the twelve months study periods.
[Slide.]
Plasma PI concentrations were measured by HPLC.  Patients were instructed not to take the morning dose until after drug levels were drawn.  The analysis was performed on batched frozen samples and levels were determined for the four PI utilized in the study.  Data were analyzed only for patients having at least three samples obtained.
[Slide.]
Eighty‑one patients were included in the substudy.  Similar to the parent study, the two groups, according to genotype utilizations, were comparable for risk factor, age, sex, previous treatment, CD4 cell count and HIV RNA at baseline.
[Slide.]
Shown are the mean and median plasma levels obtained for each protease inhibitor from patients in the standard of care and the genotypic‑guided treatment groups.  Concentrations are expressed in micrograms per ml.  The median plasma levels were not significantly different between the two randomization arms.
An efficacy threshold level, as will be explained later, was defined, permitting to categorized patients with optimal or suboptimal concentrations.  Between 27 percent for nelfinavir and 43 percent for ritonavir of the patients had suboptimal drug concentrations following our criteria.
Among these patients with suboptimal concentrations, the majority even had no detectable PI plasma levels.
[Slide.]
Shown is the correlation between PI plasma levels and HIV RNA for all individual time points where both measurements were available.  Linear regression analysis showed a statistically significant correlation between plasma concentrations and HIV RNA for each PI.  Higher drug cons correlated with lower HIV RNA levels.
[Slide.]
A wide range performance pharmacokinetic data is available in the literature for the various PIs.  We did not find a consensus statement standardizing these values.  Presented in this table are values for IC95, Cmax, Cmin and efficacy threshold concentrations we used for the various PI.  The threshold value of twice the IC95 was used as a cutoff for optimal PI level.
[Slide.]
Patients were divided into two groups; suboptimal concentrations for the patients having had a PI plasma concentration below the defined threshold on at least two occasions during the study period, optimal concentrations for patients having at no more than one level below two times the IC95.
According to our efficacy threshold, 32 percent of the patients had suboptimal concentrations and 68 percent had optimal concentrations.  The distribution was similar in both groups.
[Slide.]
Shown are the changes in HIV RNA from baseline for the groups of patients segregated based on optimal or suboptimal PI concentrations regardless of randomization arm.  HIV RNA decreased 1.2 log at month 12 in patients in the optimal concentration group, in pink, versus 0.36 log at month 12 for patients in the suboptimal concentration group, orange.
[Slide.]
Patients were categorized based on randomization arm and drug level: group 1, suboptimal PI concentrations in the control group; group 2, optimal PI concentrations in the control arm; group 3, suboptimal PI concentrations managed with genotypic guided treatment; and group 4, optimal PI concentrations managed with genotypic‑guided treatment.
[Slide.]
Shown are the changes in HIV RNA according to PI concentration and genotype usage at six months.  The smallest reduction in was obtained in patients with suboptimal PI concentrations managed on the control group.  The greatest reduction in HIV RNA was obtained for patients with optimal PI concentration and genotypic‑guided treatment.  Intermediate results were seen in patients with suboptimal PI concentrations managed with genotype, light blue, and in patients with optimal concentrations in the control arm, orange.
[Slide.]
Shown are the proportion of patients with an HIV RNA below detection limit according to PI concentration and genotype usage at six months.  The highest proportion is seen in patients with optimal concentrations managed with genotype, yellow.  No patients with suboptimal PI concentrations managed with standard of care reached undetectability, orange symbol.  Intermediate results were seen in patients with optimal concentrations and standard of care, in red, and suboptimal concentrations managed with genotype, in blue.
[Slide.]
To summarize our data, multivariate analysis of the predictive factors of virological response showed that optimal PI concentrations, genotypic‑guided treatment and the absence of primary protease mutations were all independent significant predictors of favorable virological outcome.  The use of different cutoffs for optimal drug levels and definition for primary protease mutations may have influenced these results.
[Slide.]
In conclusion, exposure to drugs was inversely correlated with plasma HIV RNA levels for all PI.  Genotypic‑guided therapy, drug concentrations and the presence of primary protease mutations are all factors which independently affect the response to therapy in experienced patients.  Therefore, assays to determine both drug levels and resistance mutations may be useful in improving therapeutic responses in drug‑experienced patients.
[Slide.]
This is the list of the contributors, particularly Dr. Gerome, Dr. Garrafo, Professor Dellamonico, Professor Schapiro.  The VIRADAPT st was sponsored by Roche, Abbott and Visible Genetics.
DR. HAMMER:  Thank you.
Are there clarification questions for Dr. Clevenbergh?
DR. KUMAR:  Would you clarify for me, in your control group, who chose the salvage regimen?  Was it the same group of physicians who chose the salvage regimen?
DR. CLEVENBERGH:  The study was conducted on three sites and for patients, as it is written in The Lancet, considered difficult to treat, there was a discussion between the physician in charge of the patients and the team running the study to decide which was the best treatment to give to these patients.
DR. HAMMER:  Other clarification questions?
DR. WOOLSON:  Even under the best of circumstances, it is difficult to interpret subgroup analyses that are done in clinical trials.  I was wondering if you had done any modeling of those variables to try to see which of those might be more important, in particular with some of the comparisons you were representing a moment ago with the optimal and suboptimal concentration in those groups.
DR. CLEVENBERGH:  I think that the most important of the variables analyzed is the pharmacological effect on the genotypic effect.
DR. WOOLSON:  Let me try to ask a follow‑up question, actually a somewhat different question.  You mentioned, at twelve months, there are some 85 percent of the individuals for whom you have evaluable data.  The 15Êpercent of individuals who are not evaluable, can you describe those individuals in terms of their outcomes?
DR. CLEVENBERGH:  I know that there were six deaths.  Six patients were lost to follow up.  Six patients died and four did not complete the study.  I have no idea of what happened to these last four patients.
DR. WONG:  Can you say something about the numbers of drugs and the classes of drugs that the patients in the two groups received?  Were they comparable, except that on one group, they were chosen specifically based on their resistance pattern?
DR. CLEVENBERGH:  The number of new drugs given to the patients is similar and, also, the new classes, because, at the time the study was done, there were not a lot of new classes.  There was just nelfinavir arriving on the market and NNRTI appeared at the end of the study.  So the distribution of new classes of drugs is similar between the two groups.
DR. HAMILTON:  Since the endpoints that you have chosen to analyze were virologic, were statistical tests done to compare the differences, actually, in these lines that you generated?  It appears to me that, in this one table, at least, or this one graph, for the person who actually doesn't take his drug optimally or at least has suboptimal levels, genotypic analysis doesn't add that much, if anything.
Were there, in fact, statistical differences between these lines?
DR. CLEVENBERGH:  I didn't get the question; I'm sorry.
DR. HAMILTON:  This graph here which shows‑‑
DR. CLEVENBERGH:  Ah, yes; the impact of the pharmacological concentrations.
DR. HAMILTON:  Right.  Were there statistically significant differences between these lines?
DR. CLEVENBERGH:  I don't think that we performed a p‑value on this.
DR. HAMILTON:  A follow‑up question, and it is a very nice study.  I am very interested in the outcome here.  It would be a shame not to collect clinical events in the course of this even though they might be small in number.  Are there clinical events that are occurring during this period of time that are related to HIV?
DR. CLEVENBERGH:  Yes; these are depicted in The Lancet.  I don't remember them that we defined.  I think that four new defining events in each arm.
DR. HAMILTON:  Each arm being‑‑which arms, now?  Genotype or‑‑
DR. CLEVENBERGH:  Genotype and control; yes.
DR. GULICK:  It looks like very few patients had taken prior non‑nucleosides.  Could you comment on the number of patients who used a non‑nucleoside in their salvage regimen?
DR. CLEVENBERGH:  There were only a few patients who received a non‑nucleoside because it arrives only at the end of the study.  The number is written exactly in The Lancet, also.
DR. KAPLAN:  This is not included in the presentation but I am wondering if there is any phenotypic resistance data done for some of the protease inhibitors used.  Based on some discussion we heard this morning, it would be very interesting to see if there was a correlation between phenotype resistance and the virologic responses.
DR. CLEVENBERGH:  We have baseline phenotype for about 90 patients of the 108 and we will present this data  on a later meeting.
DR. HAMMER:  Thank you very much.
I think we need to move on.  The next speaker is John Baxter.  Dr. Baxter will speak and describe the GART study.
GART Study
DR. BAXTER:  Thank you.
[Slide.]
CPCRA 046 was a randomized study of antiretroviral management based on plasma genotypic antiretroviral resistance testing, or GART, in patients failing antiretroviral therapy.
[Slide.]
To enter the study, patients had to have at least a three‑fold rise in viral load while taking at least 16 weeks of two nucleosides and a protease inhibitor.  Patients also had to have a CD4 count between 50 and 500 and a total history of at least twelve months of antiretroviral therapy.
Patients came in for a baseline visit at which time plasma was collected for viral load testing and genotyping.  The genotype results were then reviewed by the protocol virologists and the protocol virologists also had the treatment history.  They then prepared a GART report which included the mutations as well as the interpretation and up to four treatment suggestions.
Patients were randomized between July of 1997 and December of 1998.  There were 153 patients randomized, 78 to the GART group, 75 to the no‑GART group.  Prior to randomization, we required clinicians to prespecify what salvage regimen they would prescribe in the absence of the GART report.  For the no‑GART patients, these regimens were prescribed.
For the GART patients, in contrast, those clinicians prescribed a regimen after reviewing the GART report.  Patients then had follow‑up visits at 4, 8 and 12 weeks.  At 8 weeks, the clinicians were permitted to change the salvage regimen.  Patients were encouraged to stay on their salvage regimen at least until 8 weeks unless toxicity occurred.  The clinician could change at 8 weeks based on the 4‑week viral‑load result.
[Slide.]
This is the algorithm that the protocol virologists used for interpreting the genotypic mutations.  What you can see here is the major mutations in yellow, and for the YD4 mutation, for example, we defined this as conferring full resistance to 3TC, ddI and ddC.
For the non‑nucleoside mutations, they are listed here.  However, only about 10 percent of patients entering the study had prior non‑nucleoside exposure.
[Slide.]
This is the algorithm for protease showing, again, the major mutations in yellow.  The most complex portion of this algorithm was the definition for indinavir/ritonavir resistance which is shown here.  Patients had to have at least three of these mutations listed here including one of the primary mutations, either 46, 82 or 84.
The GART report is shown in the next slide.
[Slide.]
This is just a sample.  This is not an actual patient from the study but it just illustrates what the report looked like.  We listed the resistance mutations here for RT and protease; the interpretation here, which was either resistant, possible resistant or sensitive.  There were up to four treatment suggestions.
There was no direct contact between the protocol virologists and the study clinicians.  Furthermore, the study clinicians did not need to follow one of the treatment suggestions.
[Slide.]
The baseline characteristics for the two groups were very similar, a mean CD4 count of about 230, median viral load of about 27,000 copies.  The most common failure regimen that patients were entering the study on was either AZT and 3TC or d4T and 3TC in combination with either indinavir or nelfinavir.  There were a small number of patients entering failing ritonavir or saquinavir and about half of the patients were failing on their first protease inhibitor.
[Slide.]
This shows the distribution of major mutations.  Overall, 73 percent of patients had both a major RT and major protease mutation.  20 percent had a major RT mutation only.  4.6 percent had no major mutations and 2 percent had a major protease mutation only.
The major mutations are shown here on the left side of the slide showing that the most common failure genotype was 82 percent having the 184V mutation.  61 percent had either 215 F or Y.  For protease, 14Êpercent had 30N, 31 percent had 46I or L.  34 percent had 82F or The and 31 percent had L90M.
[Slide.]
This slides shows the impact of the GART report for patients randomized to the genotype arm of the study showing that the GART report influenced the choice of the salvage regimen for 83 percent of the patients.  So, in other words, the report altered what the clinician would have done in the absence of the report.
However, for only 54 percent of the patients did the clinician actually prescribe one of the suggested treatment regimens.  The reason why some clinicians chose not to follow one of the suggested regimens included concerns about drug toxicity, patient preference and also saving classes of drugs such as non‑nukes for subsequent regimens.
[Slide.]
The primary endpoint of the study is shown here which was the change in viral load from baseline to the average of the 4 and 8‑week visits showing that the in GART arm, the mean viral‑load reduction was 1.19 logs versus 0.61 logs for the no‑GART arm with a highly significant difference.  The 95 percent confidence interval ranged from ‑0.29 to ‑0.77.
[Slide.]
This slide shows the viral‑load change by study week showing that, at each time point, there was a significant difference favoring the GART arm.  Out at 12Êweeks, the difference was 0.44 logs with a p‑value of 0.003.
[Slide.]
This slide shows the percent of patients undetectable by study week showing a significant difference between the two arms at the 4 and 8‑week visit.  By 12 weeks, the p‑value was 0.1.  The difference here in the GART arm, 34 percent were undetectable versus 22 percent in the no‑GART arm.
[Slide.]
One of the things we looked at was changes in viral load by the number of active drugs prescribed.  Active drugs here were defined as drugs which the study virologists interpreted as either sensitive or possibly resistant.  What we found is that for each additional active drug prescribed, when we combined both treatment groups, there was a 0.37‑log reduction in viral load for each additional active drug.
So, for example, those patients who received one active drug in their salvage regimen, the viral‑load reduction was 0.1 and, for those who received four active drugs, the viral‑load reduction was 1.25 logs.
What you can see here is that the GART group received a greater‑‑there were actually a greater number of GART patients who received three or more active drugs, 86Êpercent, and in the GART arm received three or more active drugs as compared to 45 percent in the no‑GART arm.  We believe that this explains part of the treatment difference we saw between the two groups.
The other interesting finding is that, for each additional inactive drug, there was a 0.17‑log reduction in viral load.
[Slide.]
We also looked at patients in both arms who received the same number of drugs in their salvage regimen. What we found is that patients in the GART arm were more likely to receive three or more active drugs.  So, for example, if you compare patients in both arms who received four drugs, 91 percent in the GART group received three or more active drugs as compared to 62 percent in the no‑GART arm.  This was similar for those that received three or five or more drugs.
[Slide.]
The other part of the study that we looked at was baseline predefined subgroups and the effect of GART versus no‑GART.  We found that there was a favorable effect of GART across all of the baseline predefined subgroups so, for example, looking at patients who had CD4 counts above and below 200, the effect of GART was significant.
Also looking at patients that were above and below the median viral load, 25,000 copies, there was a favorable effect for the GART arm.
[Slide.]
Also, looking at patients who were failing their first protease inhibitor versus those who had failed two or more proteases, there was a similar favorable effect of GART.  Also when looking at the individual proteases the patients were failing on at study entry, there was also a favorable effect of GART for each protease inhibitor.  The error bars are wide here for ritonavir and saquinavir because there were small numbers entering on those drugs.
[Slide.]
When looking at the presence of baseline RT and protease mutations, we looked at those who had both major RT and PI mutations and found a favorable effect for GART and also for those who did not have both a major RT and PI mutation.
Also, in looking at those patients where the clinician proposed using a non‑nuke and a salvage regimen, we found there was a favorable effect for GART.  So, in this group of patients, essentially all of them in both arms received a non‑nucleoside but you can see that there is quite a difference between the response favoring the GART patients.
For those where the site clinician did not propose to use a non‑nuke, there was also a favorable benefit from GART.  What this suggests is that when you are adding a new class of drugs to a patient failing a protease‑containing regimen, genotyping helps you choose drugs to use along with that new class of drug, in this class, the non‑nuke.
[Slide.]
We also looked at the impact of baseline mutations on virologic outcome and found something that was rather interesting.  We found that those patients that were entering the study that had the 30N mutation versus those who did not have the 30N mutation in protease had an average viral load change of ‑0.41 logs.  In contrast, the 90M mutation had an adverse impact on virologic response with those patients with the 90M mutation having a 0.31‑log higher viral load as compared with those patients without the 90M mutation.
We found that 46, 82 and 84 did not have an impact on virologist response.
[Slide.]
So, in conclusion, GART with expert advice in patients failing therapy was superior to no‑GART as measured by short‑term viral‑load responses.
[Slide.]
The greater viral‑load reduction with GART is attributed to the greater number of active drugs prescribed in the GART arm and we found that those patients who received three or more active drugs had a better outcome.  Furthermore, the impact of GART was similar for patients failing their first protease inhibitor than for those who had received multiple proteases.  Baseline genotype was associated with virologist response to salvage therapy.
[Slide.]
I would like to acknowledge the protocol team members, especially the protocol co‑chairs, Doug Mayers and Tom Merigan as well as the protocol statistician, Jim Neaton and the protocol manager, Debbie Wentworth.
Thank you.
DR. HAMMER:  Thank you very much.
Let me start off the questioning by asking about the clinicians who were influenced by the GART report but didn't prescribe one of the specific regimens.  Have you looked at is there any consistency in how those clinicians were influenced, because I think that is a very interesting potentially practical derivative of this study.
DR. BAXTER:  It was interesting.  We found that the report impacted 86 percent of the clinicians.  It changed what they would have done in the absence of the report, but, apparently, 54 percent did they actually follow one of our treatment suggestions.
The other thing that we found is that clinicians who were more likely to follow one of our suggested treatment regimens, those patients tended to have a better outcome virologically.  Although it was not significant, there was a trend suggesting that those clinicians that followed our treatment suggestions, those patients did better.
DR. HAMMER:  The ones who were influenced but, again, didn't specifically use one of the recommended regimens, did they tend to use more drugs or a different class or go along with the general results of the study that suggest the more active drugs you use, the better one is‑‑I am just trying to sort of think about how they interpreted this.
They may have avoided a drug because of potential toxicity or a drug interaction but then they would substitute what the physician might have thought would be a comparable agent along the lines of the suggestion of the committee or is it just not possible to really take a look at that.
DR. BAXTER:  We have looked at that.  For four patients, the regimens that were prescribed were very similar except there was a substitution for the non‑nuke, for example.  For the rest of them, they tended to use less drugs than what we had prescribed but I can't give you the specific details.
DR. HAMMER:  Thank you.
Other clarifying questions?
DR. POMERANTZ:  A couple of quick questions.  When you showed that the level of undetectability became insignificant at twelve weeks, that was a p‑value of 0.1, I think it was.
DR. BAXTER:  Correct.
DR. POMERANTZ:  400 copies?  50 copies?
DR. BAXTER:  We used the bDNA second‑generation assay, 500 copies.  But even though there was a loss of significance for undetectable, that wasn't the primary endpoint.
DR. POMERANTZ:  No; I understand.
DR. BAXTER:  But there was still a significant difference overall between the two groups.
DR. POMERANTZ:  Did you see any difference in effects on CD4 counts?
DR. BAXTER:  CD4 counts; no, there was not a significant difference over twelve weeks in CD4 count.  I think, by twelve weeks, it was +25 CD4 cells for the GART group and +18 for the no‑GART group.
DR. POMERANTZ:  Do you have any longer‑term follow up than twelve weeks?
DR. BAXTER:  We are actually rolling‑‑we have rolled patients over into a long‑term monitoring study so we will be able to get some additional follow up on these patients.  But this was really a short‑term look.  Clinicians could change therapy at eight weeks and we felt that going much further than twelve weeks probably wouldn't yield much.
DR. YOGEV:  In the slide which you are showing HIV RNA changes by number of active drugs prescribed, was GART suggesting one drug only in two?  Those are the physicians who did not follow GART and are still called GART?
DR. BAXTER:  For that slide, what we did was to combine both treatment groups.  So we looked at all patients and we looked to see how many drugs prescribed in a salvage regimen would have been sensitive or possible resistant according to the algorithm that the protocol virologists used.
So those were for drugs that were either considered sensitive or possibly resistant in the salvage regimen.  The greater number of active drugs, the better the virologic response for both groups overall.
DR. YOGEV:  So the GART would suggest, let's say, three but the physician would use one which is sensitive?  It will be in the category of GART?
DR. BAXTER:  That is combining both the GART and the no‑GART group so that for the GART group, those patients received a greater number of active drugs.  So 86 percent received three or more active drugs in the GART group versus 45 percent in the no‑GART group.
But if you are asking how many drugs we recommended in the treatment suggestions, it varied per patient.  In retrospect, looking back, what the three protocol virologists were actually doing was trying to recommend at least two or three active drugs in that regimen.  So that is how we, in retrospect, were making our treatment suggestions.
DR. YOGEV:  It is a little bit puzzling that when two drugs in the GART were used, were less effective percentagewise than‑‑
DR. BAXTER:  That is active drugs.  So, for example, in that group where we are looking at two active drugs, those patients could have been prescribed four drugs but only two of them were active.
Does that make sense?  That is just looking at active drugs.  That is not looking at total drugs.
DR. YOGEV:  It is just surprising that, through the GART which supposedly by genotype is more sensitive, there was less percentage in that group.
DR. BAXTER:  Yes.  The GART patients were less likely to receive fewer active drugs.  They were more likely to get more active drugs in their salvage regimen.
DR. HAMMER:  Not to go into two many specifics, but how did the expert panel deal with multidrug pain resistance?
DR. BAXTER:  Those were the most difficult patients.  Those were the longest conference calls.  We tried to come up with regimens where there were at least two active drugs but there were some patients who really had very few options and, when we specifically looked at the outcome for those patients, they didn't do well regardless of which treatment arm they were in.
DR. MATHEWS:  It looks like about 25 percent of the patients either had only RT mutations or no mutations.  Did you look at whether GART was less effective in that subgroup at all?
DR. BAXTER:  We looked not specifically in that subgroup.  We looked at those who had both RT and protease major mutations versus those that did not and we found that for those that did not have both a major RT and protease mutation that there was a favorable effect for GART, favoring GART.
DR. HAMMER:  So is that similar to VIRADAPT?  Would genotyping help even when the virus was wild type at baseline?  Essentially, is that sort of what you are asking, Chris?
DR. MATHEWS:  Yes.
DR. BAXTER:  There were so few patients.  There were only 4.6 percent of patients who were really wild types so the numbers are too small to really say anything about those individuals.
DR. KAPLAN:  Sorry if I missed this but I wonder about the people in the GART arm who did not follow the advice regardless of the number of active drugs.  When you showed the slide of mean HIV RNA by study week, where would that group fall into that slide?  Do they become, then, nondifferentiable from the no‑GART arm?
DR. BAXTER:  I don't know that we specifically looked at that group's virologic response.  We basically just looked at the GART treatment group.  I think that, from looking at it the other way which is how well clinicians adhered and their response, we found that those clinicians that adhered less well to the treatment suggestions, their patients had a weaker response or a less vigorous response as compared to the clinicians that followed the treatment suggestions.  So it did dampen the response when they did not follow treatment suggestions.
What is interesting is that when we looked at which patients the clinicians were more likely to accept one of our treatment suggestions, it was typically patients who were more advanced and had lower CD4 counts and higher viral loads.
DR. HAMMER:  Thanks again.
I think we should move on.  We are now going to move into the Resistance Collaborative Group's analysis.  I would to just introduce Dr. John Mellors.
Retrospective Studies
Introduction to Resistance Collaborative Group's Reanalysis
of Selected Studies Using the RCG Data
DR. MELLORS:  Thank you, Scott.
[Slide.]
It is a pleasure to present the work of the Resistance Collaborative Group.  What I would like to do is just give you a little history to start out with, to tell a story.  What we are going to do in the next hour or so is review the goals of the Clinical Validation Subcommittee, review the subcommittee workshop that occurred in April of this year, work through the development of a standardized data‑analysis plan.  Victor DeGruttola will present that to you.
I will then review the mutation table used for the DAP and I will describe the reanalyzed studies and then present the composite data from the reanalyzed studies.  Then there will individual presentations of representative studies, ACTG 33 by Mike Para, CNAA2007 by Mounir Ait‑Khaled and data from the Frankfort cohort by Veronica Miller.
I will then review prospective studies in progress and make some summary points about the analysis of the data presented.
[Slide.]
The Resistance Collaborative Group Clinical Validation Subcommittee members are shown here; Rich D'Aquila, who had a key role in developing the DAP table, Veronica Miller, Louise Pedneault, Amy Patick, Victor, Andrew Phillips, Lynn Dix, Dan Holder and Jeff Murray from FDA.
Our progress was rapid and steady.  There was a temporary slowing of progress while the Boston Red Sox were losing to the Yankees.  There was some anger and depression that we had to work through but we persevered.
[Slide.]
The goals of the Clinical Validation Subcommittee were simply to compile and evaluate existing data on validation of resistance tests and to review issues relevant to that clinical validation including study design, patient populations studied, definitions of drug sensitivity and resistance as we heard this morning are critical, definitions of the virologic endpoints used in these studies and, importantly, the methods of analysis and how important covariates such as HIV RNA and new drugs were controlled for in the analysis.
[Slide.]
Let's review the workshop that occurred in April of this year.  The goal of that workshop was to review existing clinical data on the relationship between genotype or phenotype and virologic response.  We identified studies by review of meeting abstracts.  There were presentations by lead investigators that flew in from all over the globe.
There were questions and clarifications by the subcommittee similar to what you all have been doing for the last 48 hours.  The studies presented and reviewed were thirteen retrospective studies and two prospective intervention‑based studies.
[Slide.]
The impressions of the subcommittee were that there were consistent associations presented between baseline genotype or phenotype and virologic response but there were highly variable methods of analysis including different definitions of resistance, different types of mutations analyzed, different cutoffs for phenotypic assays, different virologic endpoints, methods of analysis and control for key covariates.
It was clear at the end of the subcommittee meeting that there was a need for a standardized data analysis of these studies to make sense out of them collectively.  So the action item from the committee was to develop a data‑analysis plan for standardized reanalysis of the retrospective studies and the prospective studies presented.
[Slide.]
So we developed a standardized analysis plan and it is a royal "we."
[Slide.]
Because it was really Victor DeGruttola, the chair, Dan Holder, Andrew Phillips and Lynn Dix who are all in the audience.
Now I would like Victor to come up and present the data analysis plan.
DAP Statistical Methodology
DR. DeGRUTTOLA:  Thank you.  If we could have the first slide which gives the objective of the data‑analysis plan.
[Slide.]
The primary objective of the analysis plan was to specify an analysis that could be applied to a wide variety of clinical HIV resistance studies to relate genotypic and phenotypic testing to response to treatment.  The goal was not to try and come up with the most subtle or scientifically powerful analysis of each individual study but to try and come up with an analysis plan that would address the fundamental question and, most importantly, be applied consistently and could be applied consistently across studies so that we could put all of the information together.
Both intervention and non‑intervention studies were included but the objectives of the intervention studies that we have just seen presented are different from those of the non‑intervention studies so they would be analyzed and presented separately.
Of course, the focus was on resistance data in the experienced population.  Where studies may have followed naive patients, they would be analyzed separately.
[Slide.]
The non‑intervention studies; the first request was to characterize the study population in terms of summarizing baseline RNA and CD4, prior ART experience, and summary of duration of therapy on a HART regimen.
[Slide.]
The most important purposes of the DAP was to try and get a consistently applied definition of an endpoint.  Our primary endpoint was virologic failure by week 24 which meant an RNA value about threshold.  For studies that were of duration 8 to 16 weeks, change from HIV RNA could be used as an endpoint.  Once again, those would be handled separately.
In determining virologic failure, we excluded or asked the individual analysts to exclude transient increases in HIV RNA.  If a week‑24 value was greater than threshold but the next determination was below, despite no change in therapy, we would not call that a failure.
Our week‑24 window was defined to be week 16 to week 32 to try to keep things as inclusive as possible but, for studies whose duration was 16 to 24 weeks, we used the last week with sufficient data.  The failure threshold should have been taken to be whatever was specified in the original protocol but if it was not specified, we used 400Êcopies/ml.
[Slide.]
Patients who withdrew early from study medication were handled in two different ways.  The goal here was to handle them in different ways statistically to try and get some sense of how much study results are affected by the withdrawals.  So we proposed two different analyses, a DAC, dropout as censored, and the DAF, dropout as failure, analysis.
In the dropout or censored, patients who withdrew early from study medication without evidence of virologic failure were treated as censored and then excluded from these analyses, but there were provisions given to define an early virologic failure for patients who did not make it out to 24 weeks.
For example, if two of the RNA values were above baseline, they would be scored as a failure even in this DAC analysis.  If the reduction from baseline was less than half a log between weeks 4 and 8, they were failure or if the nadir was below threshold.
Patients who were on a study regimen for at least 16 weeks but who had no RNA values in the week‑24 window were excluded from all analyses.  Finally, we get to the dropout as failure analysis where we had patients who withdrew early from treatment were counted as failures.
[Slide.]
There were two measures of genotypic information that was explored.  The first one was a genotypic sensitivity score which, combined across all the drugs in a study regimen, to which a patient had genotypic sensitivity.  For each drug in the regimen, the genotypic sensitivity was generally 1, if the patient genotypic has no resistance mutations and 0 otherwise.
I say generally because there were a few exceptions that John Mellors will describe in more detail in a minute.  So the genotypic sensitivity score is the sum of the genotypic sensitivities over all the drugs in the regimen, basically the number of drugs to which a patient is genotypically sensitive.
In addition to that summary score, genotypic information was used as three separate variables.  The numbers of mutations in each class, protease, NRTI and NNRTI, for classes of drugs represented in this study regimen but not limited to drugs in the patient's actual regimen.
[Slide.]
For phenotypic resistance, we first used two different metrics of phenotypic resistance, the first using the minimum cutoff for the assay, sensitive if the value was less than or equal to the minimum cutoff and resistant otherwise.  In addition to that, a ten‑fold cutoff was used.  For each drug in the regimen, the patient's phenotypic resistance score was defined as 1 if sensitive or 0 if resistant.
[Slide.]
Once again, there were two measures of phenotypic sensitivity.  The first was aggregated against across all the drugs in the study regimen.  The PSS is defined as the sum of the phenotypic sensitivities over all drugs in the regimen.  The second one disaggregated this and looked at phenotypic sensitivity in each one of the drug classes for classes of drugs that were represented in the study.
[Slide.]
Our analyses also included investigation of the effect of confounding variables measured prior to initiation of therapy.  Those included baseline HIV RNA, whether or not the patient was on the potent PI or NNRTI.  This would be a yes if the patient was naive to protease and there was protease in the regimen or if they were naive to NNRTIs and there was an NNRTI in the regimen.
So this was just a yes/no variable.  Finally, the number of new drugs in the regimen.  Once again, nothing is totally straightforward here because after much discussion, it was decided that in ddI‑naive subjects, ddI plus hydroxyurea would count as a new drug, but if patients were ddI experienced and added hydroxyurea, that counts as half a new drug.  A mini‑dose of ritonavir did not count at all.
Once again, the goal here is to try and get something which is reasonable and consistently applied across all studies although some might argue about where the 0.5 is the perfect choice.
[Slide.]
Method of analysis was logistic regression of the binary response, just failure versus success, by week 24 with a common set of covariates.  There were six models for studies with genotypic data.  The first one is just looking at the effective baseline HIV‑1 RNA; second, looking at new drug covariates, whether there is a potent PI or NNRTI and number of new drugs; C, the one, of course, of most interest, the genotypic sensitivity score; D, the number of mutations in each one of the drug classes; E looks at the GSS, genotypic sensitivity score adjusted for the baseline RNA and for the new drug covariates so it combined the variables in models A, B and C.
F looks at the number of mutations in each one of the drug classes and also includes baseline HIV RNA and the new drug covariates.  So F includes all the variables from models A, B and D.
[Slide.]
For the phenotypic data, there were ten models because of the fact that we were interested in both the assay minimum cutoff and the ten‑fold cutoff.  Models A and B are the same as before.  C is now the phenotypic sensitivity score using an assay minimum cutoff.  D is the phenotypic sensitivity score using the ten‑fold cutoff.
E is the disaggregated phenotypic sensitivity score for each one of the classes of drugs separately using assay minimum cutoff.  F is using the ten‑fold cutoff.
[Slide.]
G is the phenotypic sensitivity score adjusted for or including baseline RNA and the new drug covariates, the covariates from models A, B and C put together.
H is the same thing using the ten‑fold cutoff for the phenotypic sensitivity.  I is just the phenotypic sensitivity scores disaggregated to look in each drug class adjusted for RNA new drug covariates and J is the same as I but using the ten‑fold cutoff.
[Slide.]
In order to summarize results, the summary statistic was the odds ratio for a unit change in each covariate and the 95 percent confidence interval for all the covariates in each model.  P‑values for testing each covariate individually were also developed.  P‑values were calculated either using likelihood ratio tests from nested models or from the wild statistic.
[Slide.]
The final slide, for the intervention studies that we just heard presented, we requested that, first of all, the studies examine the effect of the treatment, the randomization to receiving genotypic information versus not‑‑obviously, studies would have already done that‑‑then to do all the models which I just went through and, finally, go through all these models again with the addition of the treatment covariate.
That was the DAP analysis plan which was sent out to everyone who participated in the Resistance Collaborative Group.
DR. HAMMER:  Thank you.
Just for the committee sake, I think what we will do is go through all of the presentations and then take questions as a group so that we can get through the retrospective studies.
DR. MELLORS:  Thank you, Victor.
Overview of Retrospective and Prospective Studies
Reanalyzed Using the DAP
DR. MELLORS:  What I would like to do next is review the mutation table used for the DAP.
[Slide.]
The goal of the mutation table is a standardization of genotype analysis.  It is really not intended for patient management.  It was a consensus, and a hard‑fought consensus, of the RCG members and it was based on clinical data and opinion.  I think the collective number of e‑mails was probably over 500 and approached 1,000 surrounding the development of the DAP.
Let me stress that the table was developed before there was any reanalysis of study so it was a priori.  We focussed on primary mutations for each drug that would be expected to markedly reduce response to that drug.  The table that is before you is not inclusive of all possible mutations that could influence susceptibility.
The table was used to calculate genotypic sensitivity score and the number of mutations present for each drug class.
[Slide.]
You can review this table.  It is your materials.  It is self‑explanatory.  I won't read through the individual mutations because that is even more lethal than the presentations of the data analysis plan.
[Slide.]
We had a group for nucleoside‑‑I hesitate to show this drug because I think that we have heard enough about adefovir for a while, but, suffice it to say, 65, 70E and the second pathway to multinucleoside resistance was scored as a 0 for adefovir but the 184V was scored as 1.5 because it increased susceptibility.
There are two multinucleoside resistance pathways, one involving the 151M and the second involving the 69 insert complex.  Here are the non‑nucleoside reverse‑transcriptase inhibitor mutations.
[Slide.]
And, finally, the protease inhibitor mutations.
[Slide.]
Let me just review the calculation of GSS.  As Victor said, when a mutation was present for a drug received, the score was 0.  When a mutation was not present for a drug received, the score was given as 1.  When a mutation was not present for a drug received, the score was given as 1.  The exceptions, as I mentioned, 184V for adefovir was given a score of 1.5.  Because AZT resistance mutations can reduce the response to subsequent nucleoside therapy, when these were present, d4T, ddI, or ddC were given a score of 0.75. The total score is the sum of the individual drug scores.
[Slide.]
For the calculation of PSS, similar; resistance present, score 0; resistance absent, score 1.  The total score was the sum of the individual drug scores that the patient received and resistance was defined as either greater than four‑fold or greater than ten‑fold decrease in susceptibility or increase in IC50.  Separate analyses were done for each of these cutoffs.
[Slide.]
I would like, in the next two slides, to describe the studies that were reanalyzed.  First, the criteria that were used to select studies for reanalysis were that the study was completed, that there was adequate size for multivariate analysis and twelve of the fifteen studies we reviewed qualified ten retrospective, two prospective, intervention‑based.
These tables are difficult.  These are summary tables of the develop studies including abacavir pooled data, ACTG 333, ACTG 364, 372, CNAA2007.  These are studies with treatment defined by the protocol.  The investigators are listed and the number of patients with genotype and phenotype is shown.  Note that the numbers, overall, are relatively low.
The treatment experience was varied from nucleoside experience only to heavily nucleoside experience and PI experience with 42 percent non‑nucleoside experience.  The resistance technology was ABI sequencing or, for these studies, phenotyping was done by Virco.  Median baseline RNA shows a broad range as does CD4 cell count.
[Slide.]
Here are the cohort studies in the GS408 study that you heard about yesterday.  The cohort studies were relatively small in size, experience ranging but generally heavily pretreated.  Resistance technology is shown and baseline RNA and CD4 showing a broad range as with the protocols that had treatments specified in them.
You can look at these at your leisure.
[Slide.]
Now let's move on to the composite data from the reanalyzed studies.  What I am going to present is first the eight retrospective studies that were reanalyzed and then the two prospective studies.  The GS408 and Swiss studies will not be included because HIV RNA change from baseline was modeled rather than the failure endpoint, and this is difficult to show as a metric with the other studies.
I will show you the dropout as failure analyses.  The models that I will show you, both unadjusted and adjusted, are the odds ratio of failure for HIV RNA, GSS, number of mutations by drug class and PSS.  The other analyses and models have been provided in the documents.
[Slide.]
To put these data that I am about to show in perspective, let's look at the results of the Surrogate Marker Working Group, their metaanalysis of RNA relationship with outcome.  These were studies of dual nucleoside therapy, familiar old names, NUCA 3001, 2, ACTG 116a, VA 298 and other studies.
This metaanalysis looked at the reduction in risk of progression to AIDS and death per log reduction in RNA.  Here we see the point estimates for the reduction in risk across this metaanalysis.  Two things are obvious.  First, most of the points are less than 1 showing that there is a lower risk of progression with a log reduction in RNA, per log reduction in RNA, but not all the studies show the point estimates less than 1.
The second thing that is obvious is that there are broad confidence intervals around these estimates.  The one thing I would like to point out is here is a composite estimate showing a narrower confidence interval of all the studies combined.  We did not do that because we did not have time nor did we have the logistics to do that quickly.
So that is background for the data that I am about to present.
[Slide.]
Let's warm up to the data by looking at a tried‑and‑true variable, baseline HIV RNA, as a predictor of failure.  What we have here is the odds ratio on a log scale, the points estimates and the 95 percent confidence intervals for baseline RNA.  This is the odds ratio per 1‑lot unit increase in RNA unadjusted.  This is the retrospective studies with the dropouts as failures.
What you can see, and the reason we showed this, is that we showed a consistent finding that has been reported in the literature that baseline RNA is highly predictive of treatment failure with an odds ratio above 1 in all the studies examined.
The confidence intervals are broad but, with one exception, they do not intersect 1 unless they are statistically significant.  So this tells us that there is internal validity to the data set.
[Slide.]
This shows the same variable, HIV RNA, adjusted for genotypic sensitivity score and the new drug covariates, whether or not they received a potent PI and NNRTI and the number of new drugs.
What we can see is the unadjusted and, beneath it, the adjusted point estimate and confidence interval.  We see that there is some shift in some of the studies to a lower power of the variable but, in general, there are not major shifts.  The majority of the studies, the confidence band did not intersect 1 and, thus, show a statistically significant association of RNA with failure after adjusting for the GSS and new drug covariates.
[Slide.]
Let's move on to the baseline genotypic sensitivity score.  Let me say that this is a rather crude estimate of the activity of drugs and the odds ratio is shown per one unit increase.  This is unadjusted, again retrospective, analysis, dropouts as failures.
What you can see, again, is the point estimates with one exception, ACTG 372, fall less than 1, meaning the higher the GSS score, the lower the risk of failure, the opposite of RNA.  So the more sensitive drugs, the lower the risk of failure and the confidence intervals do not intersect 1 in five out of the eight studies.
[Slide.]
When we adjust for baseline RNA and new drug covariates, we find that there is a general shift in the point estimates towards 1 and that the confidence intervals broaden, but there are no major differences after adjusting for RNA and new drug covariates, and the majority of the point estimates, again with the exception of 372, group b, fall less than 1.
[Slide.]
Lets shift to the baseline number of nucleoside mutations.  The odds ratio is shown for additional mutation.  This is adjusted for other mutations in classes used in the regimen.  In this dataset, we see that the point estimates generally are above 1, meaning that for each additional mutation, nucleoside resistance mutation, the risk of failure is increased.
The confidence intervals intersect 1 in several of the studies but the point estimates are consistently greater than 1.
[Slide.]
This is after adjustment for HIV RNA and new drug covariates, again adjusting for mutations in other classes.  After all this adjustment, we still see that the point estimates for the majority of studies are greater than 1 although the magnitude is not as great as the GSS score.
[Slide.]
Let's move on to number of PI mutations.  Similar analysis.  Point estimates are all greater than 1, unadjusted.
[Slide.]
And adjusted, we see some diminution of the effect of the number of PI mutations but, still, there is consistency among the data in that the number of mutations in protease is associated with a greater risk of failure.
[Slide.]
When we looked at phenotypic, the first analysis we did was the four‑fold phenotypic sensitivity score meaning that if the IC50 was less than four‑fold, there was one point, if greater than or equal to four‑fold zero points.  This looks at the four‑fold PSS score odds ratio per one unit increase adjusted for baseline HIV RNA and new drug covariates, unadjusted in white, adjusted in yellow.
What you see here is consistency, again, in the relationship between the PSS and the risk of failure.  Each of these point estimates substantially less than 1, meaning the higher the PSS, the lower the risk of failure and, before adjustment, one of the analyses intersects 1, after adjustment 2.
But I think the important point is not how many confidence intervals intersect 1 but the consistency of the point estimates of the odds ratio.
[Slide.]
When we look at the ten‑fold phenotypic sensitivity score adjusted for RNA and new drug covariates, we see a little less effect of that variable in the model.  The point estimates are generally unadjusted, less than 1, and there is some movement after adjusting for RNA and new drug covariates.
But, in general, the magnitude of the effect is less for a ten‑fold cutoff than a four‑fold cutoff.
[Slide.]
I am going to show you just a few slides looking at the reanalysis of the prospective studies, VIRADAPT and GART.  Each of these studies is broken down by whether there was genotyping or no genotyping.  Here, GART, no genotyping, VIRADAPT, no genotyping; GART genotyping and VIRADAPT genotyping.
This looks at baseline HIV RNA unadjusted and adjusted for GSS and new drug covariates.  The dropout is failure analysis.  What we see is that for GART, it is consistent with our previous datasets that the higher the RNA, the greater the risk of failure for VIRADAPT.  In the no‑genotyping arm, we don't see that effect.
For GART, in the genotyping arm, there is a curious effect in that the baseline RNA is no longer predictive of failure and suggests that the intervention altered that effect, meaning that the resistance test interpretation and therapies prescribed negated the effect of baseline RNA on outcome.
In VIRADAPT, in the genotyping arm, we see an effect similar to previous studies that I presented showing the greater risk of failure with higher RNA.
[Slide.]
When we look at the baseline genotypic sensitivity score, again for the same studies, divided by treatment arm, we see that, with the exception of the VIRADAPT no‑genotyping arm, the point estimates are less than 1 suggesting, again, that the higher scores associated with a lower risk of failure, broad confidence intervals and some intersect 1.
[Slide.]
So that is a composite data analysis of the retrospective studies and prospective studies that we did.  Now I would like to turn it over to the individual presenters.
[Slide.]
There will be presentations first by Mike Para and then the other individuals listed.
Thank you.
DR. HAMMER:  Thank you.
Mike?
Key Retrospective Studies Analyzed
in a Standard Fashion
ACTG 333
DR. PARA:  Good afternoon.  Thank you for the opportunity of presenting briefly the results of ACTG 333 which was the study titled The Antiviral Effect of Switching from Saquinavir, Hard Capsule, to the New Formulation of Saquinavir Versus Switching to Indinavir After Greater than One Year of Prior Use of the Saquinavir Hard Capsule.  This will be the DAP analysis that you just heard about.
[Slide.]
The objectives were to determine if, after prolonged use of the saquinavir hard capsule, Invirase, there was a fall in viral load upon switching to either indinavir or saquinavir, soft gel, the Fortovase.  The objective of the DAP was to determine if the amino‑acid substitutions at the protease positions associated with in vitro saquinavir or indinavir resistance at baseline were predictive of the RNA response.
[Slide.]
This is the protocol design.  Eligible patients had been on saquinavir‑‑that is, Invirase‑‑for more than 48 weeks prior to study entry.  Of note, they had no antiretroviral change for the two months prior to admission and they were naive to the other protease inhibitors.
Upon entry, they were randomized in an open‑label fashion, to continue their saquinavir hard capsule or have a single drug switch, the protease switch, to the saquinavir soft gel, or a single‑drug switch from the hard capsule to indinavir.  Viral loads were then followed for the next six months.
 For the purpose of the DAP, a success was being suppressed below the level of 500 at 24 weeks.  Within the DAP, because these individuals were only placed on this drug for sixteen weeks, they were not further analyzed.  In the study, patients who failed this were allowed to cross arms.  Those failures were considered in the DAP analysis as failures.
[Slide.]
Baseline characteristics of the population are here.  Note that these are extensively treated patients with over two years of prior saquinavir, hard capsule.  They had been on greater than or equal to, at the time of entry, to nucleosides.  85 percent were on two or more nucleosides.  Their baseline viral load was 4.1 log‑base 10.  Their baseline CD4 count was 240.
[Slide.]
The next slide shows the virologic response for the first eight weeks.  You can see with a single drug switch from the hard capsule to the saquinavir soft gel, viral load fell about 0.25 logs at eight weeks.  With a single‑drug switch to indinavir, viral load fell about 0.6 log at eight weeks.
[Slide.]
The purpose was to look at the predictive power of the baseline genotype.  We did genotyping by two methods.  One was population‑based sequencing performed by Charles Bouchet with ABI sequencer.  The second was a multiple PCR independent clonal sequencing also with ABI which was performed in the laboratory of John Condra.
The amino‑acid positions that were analyzed were those described in the DAP and are shown here.  Of note, if people were found to have a mixture of both wild type and resistant at a particular position, they were considered to be mutant at that position.
Differences by the two methods were resolved by repeat sequencing.
[Slide.]
The next slide shows the distribution of the number of protease mutations for the 89 subjects enrolled in the study.  As you can see, in spite of the fact that these patients had a median of two years of prior saquinavir hard capsule, 31 percent had none of the mutations that were selected to be analyzed.
[Slide.]
The next slide shows baseline covariates and virologic response.  This is the baseline RNA.  As you can see, with increasing baseline RNA, there was a greater likelihood of virologic failure, as John just said.  That is not surprising.  But you can also see with increasing number of protease mutations, there was also an increasing likelihood that these individuals would fail their new drug regimen.
[Slide.]
Statistical analysis of this shows that these relationships were highly statistically significant in that a one‑log change of baseline RNA was associated with a seven‑fold increased risk of failure.  Also, for every additional protease mutation, there was a two‑fold increased risk that they would not be successfully suppressed.
In the multivariate analysis, it was found that these two were independent of each other; that is, the more mutations, the less likely they would be suppressed.
[Slide.]
The next slide is my conclusion.  RNA change was greater for indinavir than saquinavir, soft gel, and saquinavir, soft gel, than the hard capsule.  I didn't show this data but we found that there was a trend between increasing number of protease mutations in greater saquinavir prior use, more mutations the higher the baseline RNA, more mutations the lower the baseline CD4 count.
What we showed here was the number of protease mutations and the viral load at baseline were independently predictive of RNA response.  Patients with 0 or 1 protease mutation had a greater RNA decrease.  Patients with 2 or more protease mutations had a blunted RNA response.
[Slide.]
Lastly is the team, shown here.  Ann Collier was the vice chair.  Bob Coombs was the protocol virologist, and then our virologists and collaborators.
Thank you.
DR. HAMMER:  Thank you.
Dr Ait‑Kahled will present the CNAA2007 study.
CNAA2007 Study
DR. AIT‑KAHLED:  Good afternoon.
[Slide.]
I am going to present the retrospective analysis of the baseline resistance in CNAA2007 to investigate the predictive value and the predictive quality of genotype and phenotype data on biological response to salvage therapy in HIV‑infected patients.
[Slide.]
The objective of that study was to evaluate the treatment with abacavir, amprenavir and efavirenz as salvage therapy in heavily experienced patients who experienced viral rebound on the PI‑containing HART.
Ninety‑nine subjects were exposed to the study drugs and patients in this trial were heavily pretreated with 44 percent NNRTI‑experienced.  This reflects the inclusion of an NNRTI‑naive patient in the protocol.  60Êpercent of patients had experience with three or four PIs and over 70 percent had prior experience to four to five NRTI.
[Slide.]
The methods used in that study were as follows; the plasma HIV RNA was measured by the Roche Amplicor.  Genotyping was carried out by a plasma‑population sequencing using the ABI technology.  Phenotyping was obtained by the recombinant virus assay using the antiviral grown at Virco.  The statistical analysis was carried out according to the RCG data‑analysis plan using the virological endpoint of HIV RNA greater than 400 copies/ml in the week 24 time window.
[Slide.]
The baseline characteristics of the study population is shown here.  Genotyping was obtained for 94 patients and phenotype values were obtained for 64 patients.  Both populations had high baseline HIV RNA of about five logs or 100,000 copies/ml, a low baseline median CD4 count of around 100,000 cells/microL.  The prior NNRTI usage was similar in both populations with 40 percent being NNRTI‑experienced prior to study entry.
[Slide.]
In accordance with the numerous prior therapies of these heavily pretreated patients, a large number of mutations were found in reverse transcriptase and protease.  65 percent of the baseline isolates had more than four nucleoside reverse‑transcriptase‑inhibitor‑associated mutation.  45 percent had more than one non‑nucleoside reverse‑transcriptase‑inhibitor mutation with 21 percent having more than two.  81 percent of viruses had more than four protease‑inhibitor‑associated mutations with 50Êpercent having more than five PI‑associated mutations.
Looking at the baseline phenotypic data for the study drug in CNAA2007 at the two different cutoffs, at the four‑fold cutoff, 45 percent of the isolates were sensitive to abacavir, 58 percent sensitive to amprenavir and 67Êpercent sensitive to efavirenz.
Looking at the ten‑fold cutoff, over 70 percent of the baseline isolates were susceptible to all study drug according to that cutoff, so 94 percent to abacavir, 86 to amprenavir and 73 to efavirenz.  In that particular study, the ten‑fold cutoff had less discriminatory power to discriminate the baseline isolates according to phenotype.
[Slide.]
I am now going to present the results of the various univariate and multivariate analyses described in the data‑analysis plan for both the dropouts‑as‑failure and dropouts‑as‑ censored population.  This graph‑‑luckily, Dr. Mellors had made an excellent job by describing the way this data is presented.
The graphs here show the odds ratio or the risk of virological failure and the X axis represents the variable used in the model presented.  The risk of 1 is marked for comparison and the 95 percent confidence intervals are also shown.
So here in the univariate analysis, you can see that a higher baseline HIV RNA is, as stated, with an increased risk of failure.  The introduction of a potent new drug and, in this case, efavirenz in the NNRTI‑naive patient is associated with a lower risk of failure, about five‑fold, and the higher genotypic sensitivity score, again, associated with a decreased risk of virological failure.
[Slide.]
The multivariate analysis using those three variables has shown that the genotypic sensitivity score remained the only factor associated with virological failure.
[Slide.]
Looking at the genotype analysis using the number of mutations for each drug class, the mutations for NRTI, NNRTI and PIs, after adjustment for baseline RNA, potent new drug, one can see that the number of NRTI mutations and NNRTI mutations remained the only factor associated with an increased risk of virological failure.
[Slide.]
Looking now at the phenotypic analysis, this is the univariate analysis of each of the variables with the baseline HIV RNA, potent and the four‑fold cutoff phenotypic sensitivity score and the ten‑fold cutoff sensitivity score.  What is apparent here is that increased baseline HIV RNA was associated with increased risk of virological failure and a higher phenotypic sensitivity score at the four‑fold cutoff was associated with a decreased risk of failure but not the ten‑fold cutoff.
[Slide.]
In the multivariate analysis, if you concentrate just on the left‑hand side of the graph, looking at the  phenotypic sensitivity score based on RNA and potent new drug, the baseline HIV RNA and phenotypic sensitivity scores remained the only factors associated with virological response.
[Slide.]
In this multivariate analysis, looking at the phenotypic sensitivities for each of the drug classes, the baseline HIV RNA, an increased baseline HIV RNA, was associated with an increased risk of about six‑fold of virological failure.  NRTI sensitivity score at the four‑fold cutoff and the NNRTI sensitivity score were‑‑sorry; in this multivariate analysis, the only sensitivity score that remained associated with a decreased risk of failure was the sensitivity score for the nucleoside reverse‑transcriptase inhibitors.
The possible explanation for the loss of association between the NNRTI sensitivity score and virological failure is probably the use of the presence of confounding variables in the model such as the use of potent new drug which was the use of the efavirenz in NNRTI‑naive patients, so, in other words, looking at two different variables of two identical effects and of two different variable measures.
[Slide.]
In conclusion, this retrospective study has shown that the genotypic sensitivity score was the only predictor of virological response in the multivariate analysis using baseline HIV RNA and new drug covariate as the variables.  The genotypic analysis has also shown that the number of nucleoside reverse‑transcriptase inhibitor and non‑nucleoside reverse‑transcriptase inhibitor mutations were also associated with biological response after adjustment for baseline HIV RNA and new drug covariate.
[Slide.]
The phenotypic analysis has shown that, in the univariate analysis, the four‑fold cutoff was more predictive of virological response than the ten‑fold cutoff but here, again, it was population‑dependent.
Finally, the study seems to suggest that, in heavily pretreated patients, the phenotypic sensitivity score using the four‑fold cutoff was the best predictor of virological response independently of baseline HIV RNA and new drug covariates.
[Slide.]
Finally, I would like to acknowledge all the investigators, team members and patients who participated in this study.
Thank, you for your attention.
DR. HAMMER:  Thank you very much.
Again, we will hold questions.  Dr. Veronica Miller will now present the study results from the Frankfort Cohort analysis.
Frankfort Cohort
DR. MILLER:  Hi.
[Slide.]
What I will be talking today about is the association between phenotypic resistance and virologic response to mega‑HART regimens in patients from the Frankfort HIV cohort.  What I want to point out here is that this is going to be different from the other trials that you have just heard about because here we have a total mixture of the kinds of pretreatments these patients have seen and, also, complete freedom as to the types of drugs that the patients could receive in their treatment.
[Slide.]
So the Frankfort HIV cohort consists of all patients attending the clinic who had presented at least once since January 1 of 1995.  Data pertaining to antiretroviral history, immune status, HIV‑1 viral load, HIV‑associated clinical events and demographics are in this database.
Patients with multiple treatment failures were treated with a salvage regimen consisting of six or more antiretroviral drugs and phenotypic resistance tests were performed from stored plasma samples.
[Slide.]
So, in terms of the patients and methods, we had 50 patients who met the definition criteria and the DAP requirements.  Viral load was assessed in the Amplicor system.  Resistance was assessed using the Antivirogram.  Virologic failure was defined as having more than 400Êcopes at the week‑24 window.  And the statistical analysis basically followed the DAP requirements very closely.
[Slide.]
Here are the baseline characteristics of these patients.  As you can see, they were relatively advanced with the virus meeting viral load of 5.52 and a CD4 cell count of 95.  They had been on HART for a median time of 18.3 months.
[Slide.]
This is the summary of previous ART divided as to less than one week, one week to one year in yellow and more than one year in red.  So you can see drugs like ZDV, 3TC these patients had seen quite a bit and, also, 90 percent of these patients had been on HART for more than one year.
[Slide.]
The susceptibility status at baseline looked like this.  As you can see, there was a lot of resistance to most of the drugs, a lot of resistance to the nucleosides, especially ZDV.  3TC; most of the patients did have resistance to 3TC, cross‑resistance to abacavir although this patient population hadn't seen this drug very much, extensive resistance to the non‑nucleosides as we presented here as well as to the protease inhibitors.
[Slide.]
These were the drugs that were used on treatment.  About 90 percent of the patients used six or seven drugs and 10 percent of the patients used eight or nine drugs.  You can see that all of the drugs were represented and 100Êpercent of the patients did receive protease inhibitors, most of them two and, in some cases, even three.
[Slide.]
This is the univariate analysis where we looked at the variables of RNA, number of new drugs, sensitivity of all the drugs with the cutoff of 4, the same with a cutoff of 10, NNRTI sensitivity cutoff of 4, PI sensitivity cutoff of 4 and the same with the cutoff of 10.
But we see what comes out statistically significant is the RNA level using both types of analysis, the sensitivity cutoff score of 4 as well as a 10.  Then, when we get into the individual drugs, the PI sensitivity of 4 and of 10 remains statistically significant.  So we see about a two‑fold increase in the OR here and about a 50Êpercent reduction in the OR for the amount of sensitive drug that the patients were receiving.
[Slide.]
Here we have the different multivariate analyses.  We first looked at an RNA level at baseline, the number of new drugs, sensitivity score of 4 of sensitivity score using the ten‑fold cutoff assay.  What remained significant was the RNA with about a four‑fold increase in the OR and the sensitivity score at 4 as well as at ten with about a 60 percent reduction in the OR.
[Slide.]
Looking at the multivariate analysis 3 and 4, in this case we also kept RNA, number of new drugs, but now looked at the sensitivities to NRTIs and 2PIs using a cutoff of 4 or of 10.  In this analysis, the only thing that really remained significant was the PI sensitivity either at 4 or at 10.
[Slide.]
So, in conclusion, in this retrospective analysis, based on a small group of extensively experienced patients, phenotypic resistance was a significant predictor of virologic response independent of baseline virus load and previous antiretroviral history.  Either a four‑fold or a ten‑fold cutoff provided significant independent information.
In models looking at resistance within individual drug classes, resistance to PI was more predictive than resistance to NRTI.
Thank you.
DR. HAMMER:  Thank you very much
John?
Summary of Key Points
DR. MELLORS:  Thank you, Veronica.
[Slide.]
I would just to like conclude the session by review of prospective studies in progress.  There are quite a few.  They are listed here by name, location, design and status.  RESA 2026 is closed and will be presented at the retrovirus conference.  VIRA 3001 just went through an interim analysis and the FDA has asked me to tantalize you with three slides from that interim analysis.
CERT compares phenotyping/genotyping to standard of care.  It is fully enrolled. CTCG 575 is phenotyping versus standard of care.  It is enrolling.  Nick Hellman from ViroLogics told me this morning he thinks it is fully enrolled.  There is NARVAL, SEARCH, HAVANNA, which is a two‑by‑two factorial design of genotyping versus phenotyping versus standard of care.  They are all enrolling.
ERA is a study in the UK, phenotype/genotype versus standard of care.  It is opening soon.  A5076 is phenotyping versus genotyping versus both.  That is in development.  So there are really quite a number of studies that are ongoing or planned.
[Slide.]
I would like to just spend the next few minutes to present some preliminary results from VIRA 3001 which was an open‑label, randomized trial comparing the effect of phenotyping, immediate phenotyping, on viral load compared with standard of care.
[Slide.]
Here is the study design.  Individuals were screened.  There were no therapy change permitted during the screening period.  After the baseline individuals received treatment based on the Antivirogram or standard of care, there were 274 patients enrolled and follow up was at 2, 4, 8, 12 and 16 weeks.
[Slide.]
The patient population studied was prior therapy history of two or more nucleosides and one PI, plasma viral load above 2000 copies, stable regimen for one month prior to screening and no prior phenotypic sensitivity testing.
[Slide.]
Here shows the interim analysis results.  This is the HIV RNA response in a modified ITT analysis showing all the observed data.
Let me share with you that there are about 127 patients in this analysis.  By modified ITT, is those that dropped out before enrollment and treatment change‑‑in other words, those who dropped out during the screening period were excluded from this analysis.  They were balanced between the phenotyping and non‑phenotyping arm.  This is the median log change in baseline RNA.
What you can see in yellow is the control arm and, in light blue, is the Antivirogram arm and at each of the time points in this analysis, after week 4, there is a statistically significant difference in the viral load response amounting to half a log or greater.
[Slide.]
A more conservative analysis of the last observation carried forward, we see similar results.  The control arm and antigram arm, again half a log difference or more statistically significant at each time point.  The proportion undetectable at 16 weeks was 62 percent in the Antivirogram arm and 33 percent in the control arm.
The sponsor, Glaxo Wellcome, and Neil Graham are in the audience if individuals want to ask additional questions about this data analysis.
[Slide.]
Let me just end by summarizing some key points.  I think the standardized reanalysis of the retrospective studies has generally confirmed associations between baseline genotype or phenotype and virologic response.  The small datasets has led to some variability in the point estimates and broad confidence intervals but, nonetheless, there is consistency after reanalysis.
The placebo intervention‑based trials that you have heard about support the clinical value of resistance testing for selection of treatment regimens in experienced patients and, lastly, the data accumulating from ongoing clinical trials of improved and investigational agents will refine the interpretation and improve the predictive value of specific resistance test results.
[Slide.]
I would like to close by acknowledging the tremendous effort that was put forth under very tight time lines by all the investigators involved.  I don't have enough slides or time to acknowledge all of them.  Some individuals that were not mentioned were Richard Harrigan, Andrew Zolopa and other individuals that you have heard about who contributed datasets as well as their statistical support staff.
Thank you.
DR. HAMMER:  Thank you very much.  On behalf of the committee, we would like to really thank you and the members of the RCG.  It is really a privilege to see these data first‑hand and for the first time.
Why don't we continue with Dr. Aras to wrap up this section.  Then we will take a break and then we will have a question period.
Summary of Studies Analyzed by DAP:
Statistical Comments
DR. ARAS:  Good afternoon, once again.
[Slide.]
You recall the data analysis plan discussed earlier by Dr. DeGruttola and Dr. Mellors.  Briefly, the virological failure, as to type dichotomous variable, is the dependent variable and it is to be explained by independent variables, namely baseline covariates.
This is achieved using a logistic regression model.  I will make a few comments here to remind ourselves of the limitations of retrospective analysis.  My first set of comments are about odds ratios.
[Slide.]
These are the covariates that are listed in the data analysis plan.  You have already seen them.  The extent to which a covariate explains the dependent variable is captured through the odds ratio.  Hence, let's remind ourselves what is an odds ratio and its relationship to a related concept, a relative risk.
[Slide.]
Here is a definition of relative risk.  Let p‑0 be the chance of virological failure when a covariate is at a given level and let p‑1 be the change of virological failure when a covariate is one unit higher than the previous level.  The relative risk is defined as the ratio of p‑1 and p‑0.
For example, if the relative risk is 2, it means that the chance of failure is twice as high when the covariate is increased by one unit.  The same interpretation may not be shared by the odds ratio, as we will soon see.
[Slide.]
Relative risk is the quantity of interest but, typically, not estimable from a retrospective study.  Hence, a related quantity odds ratio is many times looked.  An odds ratio is defined as the ratio of the odds of failure for the covariate at a higher level versus the covariate at the lower level.
The odds ratio can be estimated from a retrospective study but may not be of interest, of itself.  When p‑1 and p‑0 are small, relative risk is approximately equal to the odds ratios.  In such a situation, an odds ratio can be interpreted as a relative risk.
In the present situation, however, such an interpretation of the odds ratio may not be possible since p‑1 and p‑0 are not small for all of the covariates listed in DAP.  For some covariates in some of the studies, the relative risk can be estimated directly since the randomization scheme took into account the covariate by stratification.
Treatment, for example, is a stratified covariate in VIRADAPT and GART.
[Slide.]
Here are a couple of graphs that describe the relationship between odds ratios and relative risk.  You can see that when p‑0 and p‑1 are small, odds ratio and relative risk are nearly the same.  But, as p‑0 and p‑1 increase and p‑1 is much larger than p‑0, the gap between the two concepts increase.
You can see, as an example, if p‑1 is at 0.5 and p‑0 is at .025, the relative risk is 2 but the odds ratio is 3.  If the gap is even larger, if the p‑0 is, say, at 0.025, 0‑1 and 0.75, then the risk is 3.0 but the odds ratio is 9.0.
[Slide.]
The same information as in the previous slide is depicted here slightly differently.  Here, again, you can see that if p‑0 is at 0.4 and the relative risk is at 2.0, then the odds ratio is at 6.0, so it is exactly three times.  It is the odds ratios and not the relative risks that are generated by DAP methodology.
One is cautioned here not to interpret odds ratios as relative risk.
[Slide.]
Let's move on to discuss the crucial table 1 in DAP.  Table 1 in DAP titled Mutations Associated with Resistance to Specific Antiretroviral Drugs is the crucial table there.  It is largely based on in vitro data and/or exploratory subgroup analysis. A general consensus is yet to emerge on this table.
[Slide.]
Most of the studies were available to the Resistance Collaborative Group before the DAP was developed.  Hence, in principle, a possibility exists that the DAP and, in particular, table 1, was developed interactively to fit the studies.  We recognize that these are useful and valid exploratory practices to generate specific hypotheses that could be tested in confirmatory studies later.
With this caveat, let's look at some of the results of these retrospective studies.
[Slide.]
For illustration, I have chosen only a few of the models from those listed in DAP.  Actually, these slides are very similar to Dr. Miller's presentation and reiterate his findings; hence, I will move through them quickly.
Model C has only one covariate, namely genotypic sensitivity score whereas model E is a multivariate model in which the odds ratios for genotypic sensitivity score are computed in the presence of other covariates; namely, new drug covariates and baseline log HIV‑1 RNA.
The x axis represents the odds ratio.  The y axis lists the retrospective studies.  The vertical line is the odds ratio equal to 1.  In all these figures, dropouts as failures is depicted.  The analysis was similar for dropouts as censored.
The 95 percent confidence interval entirely on one side of the vertical line at 1 indicates statistical significance for that odds ratio.  More adjustment for multiple comparisons are made here.  Notice that the multivariate model, in most cases, has generated slightly wider devices and hence, sometimes, the statistical significance is lost.  But, overall, you see the same consistency that Dr. Miller pointed out.
[Slide.]
Here we have the 95 percent confidence intervals for the number of PI mutations.  The number of studies vary across the slides since data was either not available to us on all the covariates on the studies or a specific covariate was not relevant in a given study.
[Slide.]
Here we have confidence intervals for odds ratios with phenotypic sensitivity score as the predictive covariate, again a univariate setup and a multivariate setup.  The SA minimum cutoff is the phenotypic that has been looked at.
[Slide.]
These exploratory analyses suggest a predictive link between virological failure and genotypic and phenotypic measures at the baseline.  They will provide insight in generating specific hypotheses to be tested in future confirmatory studies.  We look forward to the results of DAP from ongoing prospective studies that Dr. Mellors mentioned.
Thank you.
DR. HAMMER:  Thank you very much.
This was really very impressive.  In order to avoid the risk of the data‑overload meter getting into the red zone, I am going to suggest that we take a twenty‑minute break and return at 3:30‑ish.
[Break.]
DR. HAMMER:  I would like to officially reconvene the committee session for this afternoon.  We will do this in two parts.  The first will be a little bit of a discussion of the data presentations and some clarifications with questions and discussion, and then there are questions specifically for the advisory committee to consider that we will bring to open discussion.
I would like to initiate the second half of this afternoon's session by asking Dr. DeGruttola, perhaps, to comment on some of the statistical issues that were raised in Dr. Aras' presentation.  Perhaps we can have some clarification for the group based on some of the issues that we didn't have a chance to discuss.
DR. DeGRUTTOLA:  There are a couple of issues that Dr. Aras raised.  As he correctly pointed out, an odds ratio is not a risk ratio and shouldn't be interpreted that way.  So if you see an odds ratio of 2, you shouldn't say the risk had doubled unless the risk was, in fact, very small.
On the other hand, as Dr. Andrew Phillips was just pointing out, it isn't true that, in this case, we were required to use an odds ratio rather than a risk ratio because this was a retrospective study.  If, in fact, we had been confident that we could collect all the data that would allow us to get not only whether or not there was a virologic failure but the time of virologic failure, it would be possible, from the retrospective data, to get a risk ratio.
The reason we didn't was twofold.  One was the primary goal was to get consistent analysis across all the range of studies and keep things as simple as possible.  From that perspective, it make more sense to define a yes/no variable of virologic or not rather than trying to find exactly when those failures would occur, which can get pretty complicated.	
The other thing is that it was easier to do the adjustments that we spoke of for baseline covariates, new‑treatment covariates, et cetera, by keeping it as a dichotomous variable.  And odds ratio is, we thought, the appropriate way to present data when you have a dichotomous outcome.  Although you cannot interpret the odds ratio as a risk ratio, as we have said, it is, nonetheless, a valid measure of association between the covariates and the outcome and that is why it was used.
On the second point, to what extent that Dr. Aras mentioned‑‑to what extent was the DAP developed specifically in response to some of the features of the individual datasets regarding the mutations that were important.  Was it the case, for example, that investigators knew that the 215 mutation was important in six of these studies, and therefore, we wanted to make sure to get the 215 mutation into the table to confirm what we already knew?
I don't think that describes the process at all.  I think that the table of mutations was based on experience of virologists and investigators over a broad range of studies and did not reflect just was important in these individual studies.
So I think that this analysis does move closer to the kind of confirmatory analysis that Dr. Aras mentioned rather than simply an exploratory analysis although I would agree with Dr. Aras that we need to apply this methodology to other studies where the data haven't even been developed yet to try to learn more about the association between these different clusters of mutations and outcome.
But, in this particular case, the tables were not developed based on specific knowledge about these studies.  In fact, I think the fact that we get fairly consistent results across the studies even though the mutations that were important in those studies was highly variable, the nature of the drugs being used and so on tends to support the value of these analyses as providing some confirmation.
But, perhaps, Dr. Mellors or Dr. D'Aquila would like to comment further on the development of the table.
DR. MELLORS:  I think, Victor, you have described the process well.  It had very little to do with the studies to be reanalyzed.  This was, really, a collective consensus of, Rich, how many individuals, a dozen or more‑‑a dozen or more individuals some of whom had no, really, knowledge of the studies that were even to be reanalyzed.  I think is the important thing.
The other important thing is that, in terms of the phenotypic analysis, we used the assay minimum cutoff that was defined by the company and that seemed to be validated in this study.  That was done without knowledge of the phenotypic information from the reanalyzed studies.
Maybe Rich would like to add some subtlety or major point that I have missed.
DR. D'AQUILA:  Richard D'Aquila, Mass General Hospital, Boston.  No; I don't think anybody missed anything major.  I just wanted to point out another aspect of what was mentioned about the mutation.  There were different people involved in these exchange of e‑mails, both virologists and clinicians.
I think the bulk of the data that led to defining these mutations came from in vitro studies of site‑directed mutants with the specific mutations.  I think some of the people who were involved in these discussions were also aware of clinical data that correlated viral load responses with specific mutations, but I think the bulk of the information really came from study of site‑directed mutants in vitro and I think that data is relevant and essential.
DR. HAMMER:  Thank you.
Dr. Aras, did you have any other questions that you would wish to have clarified?
DR. ARAS:  Yes.  I do agree with most of you.  The data shows tremendous consistency.  I did agree with that.  My only fear was that, in the epidemiology of rare diseases, typically logistic regression is fitted and there odds ratio and relative risk are one and the same because the probabilities are very small.
We did see a couple of speakers who did talk about odds ratio as a five‑fold increase, three‑fold increase.  So there is a tendency there.  I just wanted to point that out.
DR. HAMMER:  It is important.
DR. ARAS:  Just to keep that in our mind.
DR. HAMMER:  Thank you.
Before we get to the six questions that have been addressed to the committee, I would like to give the committee members a chance to ask some questions.  Once again, I would like to thank the speakers that we had this afternoon and the privilege to hear the VIRADAPT, GART and RCG analyses here and particularly some of the new data analyses that have not been presented anywhere else previously.  All of that is quite a privilege.
It also created data overload to some extent so it is a bit hard to absorb everything from the RCG analysis in this large group setting and frame questions but I would, perhaps, in order to first have some takeaway point beyond the consistency of the analyses maybe to ask Dr. Mellors one question.
If one could look at some of the resistance analyses that were included in the analysis plan which was on phenotype four‑fold and ten‑fold cutoff, a genotypic susceptibility score, numbers of resistance mutations, are you willing to make a broad conclusion as to relative prioritization of the predictability and relationship to outcome for those measures across the studies.
I think some of us can draw conclusions from those slides but they went by quickly and you know the data better than anyone.
DR. MELLORS:  I think the GSS and the PSS were the most consistent.  I think that we were limited in the individual class genotype or phenotype by the number of studies that could look at that.  I did not show anything for NNRTIs because there were only two studies that could analyze that.	
I think the take‑home message from the DAP analysis is that some score related to genotype or phenotype is very important for determining response.  I think it is not just the DAP.  It is John Baxter's GART.  It is other studies that have looked at it.  It is prospective and retrospective. But I would say that those two fell out most importantly.
DR. YOGEV:  Can I follow up with that.  Did you make a correlation between the GSS and the PSS?
DR. MELLORS:  No; not in this analysis.  There was no analysis of the relation between GSS and PSS.
DR. YOGEV:  Are there any plans to do that?  The reason I ask is it may be more practical because phenotype today is a thousand and the genotype is cheaper.  Should one just go to one, two?  This is a practical issue more than scientific.
DR. MELLORS:  There is no reason we can't do correlations.  You saw some data earlier today from ACTG 372.  I think Doug Richman summarized it very nicely.  The correlation will never be 1.0 because the virus is always a little bit ahead of us.  But, as databases build, we can improve the correlation.  This is one dataset that can be used but it is a relatively small dataset.
DR. HAMMER:  Clarifying questions, again on the afternoon's presentations.
DR. WONG:  John, I was very impressed that the data really seem to be very consistent.  But I am concerned that they are aggregate data, kind of all drugs at a time analyzed together, study by study.
Can you tease out from all of the studies that were analyzed in the metaanalysis any conclusions about individual drugs by crossing through studies and looking at individual drugs.
DR. MELLORS:  The one study that looked at an individual drug was the abacavir pooled data.  Otherwise, I think we are limited again by what is the lack of frequency or the low frequency and number of studies in which a drug was used consistently.  So it makes it different.
We could look at classes and, depending on the prior treatment experience and what was used in the new regimen, we saw, in the example of ACTG 333, a very important relation between protease and response to saquinavir or indinavir.
So there are, within the aggregate data, examples of individual response, abacavir, saquinavir, indinavir.  The problem gets into the fact that new therapies, particularly in experienced patients, are not going to look at individual drugs.  They are going to look at combinations.  We talked about that this morning and it is a problem.
So there are bits and pieces but we can't make statements about each of the fourteen antiretrovirals; no.
DR. HAMMER:  I guess it is fair to say that, even with certain studies like 372 where individual drugs were looked at in relation to virologic response in the study‑specific analysis, no relationship was found although a summary measure of susceptibility, there was a correlation.  So, in fact, within a study you can look at the question you raised.  In fact, it creates the complexity of needing a summary measure of susceptibility in combination regimens or at least looking to that as helpful.
DR. MELLORS:  I think Scott raised an excellent point that I missed.  In 372, the relationship between individual drug susceptibility and response was not apparent.  It is only in aggregate that we see a correlation with response because the response is dependent not on single drugs but multiple drugs.
DR. POMERANTZ:  I am going to let John sit down, finally.  Two of the trials gave some very surprising findings.  The one was led by Dr. Khaled and the other one by Veronica Miller, Dr. Miller.  The one by Khaled showed that when you looked at phenotypic analysis for virologic failure that the four‑fold rather than ten‑fold difference was statistically significant.
Then, when you looked at the one by Miller, either four or ten‑fold phenotypic differences were statistically significant.  Now, based on this morning's discussion, I am sort of surprised at that because part of that is trying to dissect out what is a level of phenotypic resistance that has clinical correlates.  This is some of the first that showed a true cutoff.
Maybe Doug or Dr. Little would like to comment on these two trials.
DR. RICHMAN:  Those conclusions are, to some extent, a function of the population on whom‑‑the data that Dr. Ait‑Khaled showed was that the ten‑fold cutoff, this 80 or 90 percent of the patients, didn't fit about that threshold so that there is no discriminatory power to show a difference while the data of Dr. Miller, that breakpoint might have been different so you have had the discriminatory power.
So I don't think that that observation that his data showed a difference between four and ten addresses the question about individual drugs.  I think we know from the abacavir monotherapy data that probably four or six‑fold is enough to kill it.
With a number of drugs, I think you just need a few‑fold to probably make it inadequate but there may be others in which that threshold, you have more margin of error and you can have a higher cutoff.
So there are two points.  I think, in general, four‑fold has more discriminatory power and clearly was as good or better in all of these studies but for making decisions about individual drugs, we still need more data except we have the benefit of the abacavir which had a lot of monotherapy experience that they could look at retrospectively.
That is not a privilege or an opportunity we are going to have with future drugs, I hope.
DR. POMERANTZ:  Maybe we will talk about it more tomorrow, but then why pick tenfold as a cutoff for true resistance rather than four‑fold or three‑fold.
DR. RICHMAN:  No; I wasn't.  The reason the analysis had two different cutoffs is because we didn't know what the right one was and we were interested in finding out.  I think one other point that has been mentioned is the pharmacologic issue where, with, for example, a number of the protease inhibitors that have significant pharmacologic enhancement by ritonavir, as monotherapy, a low level may be enough to bump them off where you may be able to get away with a higher level with ritonavir.
But that is the type of information that is going to have to be generated in study designs in order to convince all of us that that is true.
DR. POMERANTZ:  I still remain a little bit confused.  If you were to look now at phenotypic analyses and pick it, this is a resistant virus, this is not a resistant virus, would you pick it at four‑fold for most drugs?
DR. RICHMAN:  If I were managing a patient and got some phenotype results, I would try to pick a combination of drugs that were all less than four‑fold.  But, if I had no choices‑‑
DR. POMERANTZ:  Thank you.
DR. HAMMER:  Perhaps Dr. Ait‑Khaled and Miller might want to comment since their studies were mentioned.
DR. MELLORS:  Roger, I think we have to be careful in this analysis.  The important thing I tried to stress was that the point estimates are not that different between four‑ and ten‑fold.  The confidence interval is greater because the proportion of individuals who have greater than ten‑fold resistance is smaller.
When we are dealing with tiny datasets of 50 and 70 patients, the confidence interval can result in it intersection 1 and the holy grail of the p‑value is lost.  But I don't think we should be too swayed by that.  I think if you look in the 2007 study that the point estimates were very similar between four‑ and ten‑fold.
So I really think‑‑maybe Victor would like to comment on that, agree or disagree.  The other thing‑‑I think Doug said it.  For many of the approved drugs, particularly the approved PIs, a four‑fold change in susceptibility may be critical because, at trough, there may be three‑ to five‑fold trough to IC50 ratio.
With a newer generation of protease inhibitors, ten‑fold may not be the cutoff.  It may be thirty.
DR. DeGRUTTOLA:  Just to comment briefly.  I agree with what John said.  I wanted to go back to what the objective of the DAP was, too, which was just to say that you could find these summary scores, either phenotype or genotype, that did show an association between the testing and the response to the drug.
They are obviously not intended to try and resolve the question of what is the cutoff, either the best cutoff overall or the best cutoff for individual drugs, which is going to take a lot more intensive research.
DR. HAMMER:  Other clarifying questions?
DR. GULICK:  A question for John Mellors.  The GSS relies a lot of table 1.  You implied in your presentation that this table engendered a lot of controversy to settle on what it was.  One of the critiques from the agency was that there is no general consensus on the mutations in table 1.
How do we reach consensus on this and how close are we to reaching consensus on a chart like table 1?
DR. MELLORS:  I think it depends on who you are trying to reach consensus with.  At home, it might be relatively easy but, in a complicated field like HIV, I think it is very, very difficult.
And we will never reach consensus on every mutation in a mutation table probably while we are still breathing.  I think, though, that we will hammer down‑‑no pun intended‑‑mutations that have been shown in studies to be related to outcome.
They will be black‑and‑white ones, as Doug said.  They will be grey ones and they will be highly contested ones.  So I don't think we are close to reaching a consensus.
DR. HAMMER:  Wouldn't it be fair to say that the controversy does not extend to the whole table, that 80 percent of that table was probably easily reached and that it was the fine points, if you will, of particular mutations and particular drugs that led to the endless debate, or near endless debate, that ended today.  Is that fair?
DR. GULICK:  That was my next question.  So you think 80 percent is pretty solid?
DR. MELLORS:  I think that probably 80‑‑yes; 80 percent or higher.
DR. RICHMAN:  A lot of the mutations were, as a participant in this 500 to 1000 e‑mail exchange over a couple‑of‑week‑period, these are talmudic debates over whether something is a primary or secondary mutation in protease, and it is the seventh or eighth most important‑‑you know.  Where do you put it on the table because the whole analysis plan, if something goes up or down one point, if it is on one side or the other.
But most of the debates were over three or four little things here or there, should 3TC resistance‑‑how do you deal with that with adefovir.  In general, we agreed on almost everything.
DR. HAMMER:  We should give Dr. D'Aquila a chance to make a comment.
DR. D'AQUILA:  The way I think we will reach consensus is over time to build large databases that look at genotypes and phenotypes on the one hand and viral‑load responses on the other.
I think we are just beginning to build those kinds of databases.  What we need are correlations now with the in vitro behavior of the virus but how well we can treat it with our current regimen.  Those databases will take a long time to develop and they will never be static because our drugs will keep changing.
DR. JACKSON:  Just a couple of questions on the VIRADAPT and the GART study in terms of the turnaround time of the genotype or phenotypic assays, how long that actually took.  Clearly, this virus evolves over days and a lot over weeks.  With the better assays, maybe we are underestimating the effect if we get these back fast.
I don't know how long it took to get these back.  And then one other question was that, on the other hand, against that, there seems to be no difference, as you pointed out, in CD4 levels between the two arms later which makes you wonder whether there is going to be a clinical benefit of those.
DR. BAXTER:  The average turnaround time was about twenty‑one days and the average time to randomization was four weeks.  In terms of the CD4‑count issue, we found that patients benefitted from GART regardless of their CD4 count, whether they were between 50 and 200, and 200 to 500.
DR. JACKSON:  When you say the "benefitted‑‑"
DR. HAMMER:  I think he was talking about the CD4 response in the two arms, not the baseline stratification.
DR. BAXTER:  Oh, right.  Part of that effect, in the GART arm‑‑there was more hydroxyurea used in the GART arm which could have blended the CD4 response for those patients, but also it was a very short‑term study.  It was twelve weeks.
DR. HAMMER:  Short‑term pilot proof of hypothesis, if you will; right?
DR. BAXTER:  Right.  We were really looking at viral‑load outcome.
DR. CLEVENBERGH:  For the VIRADAPT, the turnover time at the beginning of the study was quite long, about four weeks.  But now, we get the results in less than two weeks.  About the CD4, there was a wide range of variation between the patients and also all the patients are already on the PI regimen which has been shown to increase already the CD4 even if the viral load was raised, so maybe there is a confounding effect.
DR. PETTINELLI:  I have a question for Victor.  I notice that in the different models, baseline CD4 was not one of the covariates.  Is there a reason for that?
DR. DeGRUTTOLA:  I think it might have been interesting to look at baseline CD4 and to look at other possible confounders as well, but the main goal here was to try and get an analysis plan that was as simple as possible that really could be conducted in a fairly short period of time consistently rather than to try and do analyses of everything that was potentially of importance.
Given that we wanted to include one primary indicator of patient status, baseline HIV RNA was selected.  Obviously, with our treatment, virologic failure of treatment being the primary endpoint that was expected to be the most important.
DR. HAMMER:  Thank you.  I think we should move on to the questions that are before the committee.
Questions to the Advisory Committee
There are six questions, a couple of which we, perhaps, can consider together.  I will read them for the record.
The first question is, for use in drug development, what are some important principles for developing algorithms that classify mutational constellations as "drug resistant?"  How should mutational algorithms and/or phenotypic breakpoints be developed in the future?
This is quite a question, or two questions.  Who would like to tackle this?  Again, not everyone at once, here.
DR. MAYERS:  I would agree with Rich D'Aquila.  I think, for the short term, what genotypes you like, phenotypes, is what we can do.  But what we ultimately want is what correlates with failure to respond to the drug or to loss of response to a drug.
So I think that the ultimate goal would be to get genotypes and clinical and phenotypic breakpoints that correlate with loss of antiviral activity of your drug, if you can acquire that.  The issue is going to be how to do that in the context of combination therapies.
The other issue, which I think is going to come into here is that all failure is not resistance and how you discriminate the patients who have decided that you drug is too toxic to live with from the patients who are failing virologically is not necessarily easy.
So some level of compliance or looking at drug exposure is going to have to be factored in when you make those breakpoints.  Otherwise, you can, for many drugs, mix in patients who have stopped taking the drug because it is too difficult with patients who are losing the activity of the drug because of drug resistance.
Somehow, the company is going to have to factor both of those into their analyses of breakpoint determinations.
DR. HAMMER:  You put your finger on the key issue.  How do you develop these algorithms in the current context of combination therapy and the inability, and appropriate inability, to add a single agent to background therapy and tease this out.
DR. MAYERS:  I think that, probably, you are going to get more data from the patients who are failing, who have been experienced, where you can, perhaps, see the mutations that you have lost with your drug because the other drugs may not be contributing much.
But you are going to have to analyze it in the context of the other drugs the patients are receiving.  That is not going to be easy, as you said.  I think that, looking at your naive patients who are on the drug and failing will have some use, but the problem may be‑‑for example, with a protease inhibitor, what you will find is that all your patients are failing with 3TC resistance which might make the company happy but isn't going to answer the question as to what causes you to lose the drug.
DR. HAMMER:  I think one point that comes out is the first failure in naive subjects who have wild‑type virus at baseline will, depending upon which drug you are testing, you might be able to tease out where the resistance is and it might be the test drug, in fact, because it depends on the genetic barrier.  So the first failure may give you a fair amount of information, depending, of course, on what is the drug of interest.
Other comments?
DR. YOGEV:  I just wonder if you take a naive patient who starts breaking through and then starts to level off at a certain level, isn't that a population for resistance because there is some selection, maybe by the virus, to go with one resistant and not for all the three or four drugs which you are using.
So I would suggest that, if you have‑‑you can tease out who is taking the drug for toxicity.  Hopefully, we develop better mechanisms for compliance to see those out and then you have to take those who are just coming out of begin‑‑suppose the drug is effective‑‑and then start moving away from it.  The is maybe the time to tease out.
We know, by consensus, hopefully we will develop sometime what the mutations are for the drugs which are not new, to look into the other mutations and see if there is any repetition of one.  That is how we develop the resistance now, knowledge.  So there is no reason not to do that.
Also, I thought that, in vitro, you can develop the resistance in vitro by increasing different factors to at least identify what part of the 1500 or 2000, whatever you are going to screen, would be of interest to look in that variation.
So I think the company should maybe supply, in the developing of the drug, what is the resistant development in vitro to then look later in vivo for this variation.
DR. HAMMER:  Other comments?
DR. MATHEWS:  There are some modeling things that I am sure the investigators probably dealt with but didn't have time to discuss.  There is an assumption that there is a linear relationship, the way the variables were coded and the creating of the GSS and the PSS.  So you don't really get a sense of the explanatory power of that compared to other ways of categorizing the variable, the cutpoints and whether there is a ceiling effect and so on.
And model discrimination and calibration; were those things looked at?
DR. DeGRUTTOLA:  I think that that is a very important point.  I think what we have to keep in mind is that, in developing these algorithms, there are two approaches.  There is sort of an exploratory approach where you are trying to find out are there any new mutations that you don't know about, how are these mutations interacting when they affect the response to a drug, and confirmatory analyses where you just trying to show that, with some kind of metric you have developed, there actually is the relationship that you think should exist.
As Dr. Mathews just pointed out, the analyses that we did here were quite straightforward.  We weren't trying to model the specifics of the relationship between the development or the presence of certain mutations or the phenotype and the response.
In order to do that kind of modeling using something that really might be appropriate, look for nonlinearities, look for threshold effects, ceiling effects, and so on, you would need a lot more information.  I think, to proceed with two different kinds of analyses, the more exploratory one in which you don't set up all the rules of the analysis in advance but you really try and let the data sort of speak to you about what is going on to try and understand these relationships.
Then, when you do understand them, you sort of iterate and then say we are going to take what we now believe is the appropriate model and apply it to another dataset.
In this particular case, once again, these analyses are done very quickly in the course of a month and the size of the datasets were small.  So we really couldn't investigate in that period of time with this information, the question that Dr. Mathews raised.
But, as Richard D'Aquila mentioned, only by developing large databases and really trying to mine them for this information, can that be addressed.
DR. MAYERS:  Victor, before you sit down, do you think, with the datasets and the analysis that you did, it would be possible, over time, to develop parameters for each of the individual drugs in the model for response models such that when a new drug came long, you could potentially use all the data you had previously accumulated for predictions for the individual drugs and the regimen to try and then get the parameters for the new drugs that you are evaluating?
DR. DeGRUTTOLA:  I think there are two parts to that question.  The first part is it appropriate to tease out the mutations that are associated with each drug and to view them separately.  As long as those mutations don't interact in the way that they affect the response to treatment, that is a reasonable thing to do.
But, as I understood this morning's presentations and others, sometimes they do interact.  For example, the 184V mutation has different effects on different drugs.  So it may not be appropriate to try and look at the relationship between each drug and each mutation but to look at the relationship between patterns of mutations and the combinations of drugs.
To the extent that that is not true, that you can tease them out and allocate the mutations to specific drugs, then you certainly can use the information in these data and other data sources to try and do that.  That should provide a big help.  Even if it is not perfectly true that they are completely independent should provide a big help towards understanding what is going on with a new drug.
DR. HAMMER:  I don't think we have been particularly helpful, but let me try to summarize question 1 for the agency.  This may stimulate discussion or additions, but we also need to move on.
The issue of developing algorithms and other important principles, I think one thing to keep in mind is the history here and that the table that we have been discussing and other similar tables have benefitted by the history of drug development and some of the limitations of that because, during serial areas of monotherapy or addition of single drugs that allowed the mutational patterns to be evolved and defined, and it is has been iterated several times but needs to be reiterated that that is going to become more and more difficult.
However, it would seem to me that the basic principles derived from what we talked about this morning from a number of different ways.  First, the in vitro selection studies that will done for new drugs and development and the mutations that are seen in vitro and their proof of what they do by site‑directed mutagenesis, those basic virology assays are important.
The testing of new drugs against panels of viruses with characterized mutational backgrounds including multidrug resistant profiles is important.  Obviously, the good characterization of isolates from patients who fail therapy and taking good looks at those viruses in vitro and testing them,  Looking at the mutational patterns, testing those mutations in site‑directed mutagenesis assays and phenotyping them are all important.
I think it is also important to mention that relational databases are also going to help us, organizations.  Both commercial and academic are involved in developing large relational databases that will help us determine, or at least make hypotheses about what certain patterns will look like for resistance to the drug of interest and for cross‑resistance.
We didn't really talk about breakpoint issues, and that is part of this question.  But I think that essentially has been answered that nobody knows the breakpoints.  What we know, at least for phenotypic assays, what individual assay performance can tell us, is what is the cutoff level at which there is a clear, statistically proven difference against a control isolate.
But how that translates to activity in vivo is a complex issue related to the particular susceptibility of the drug, the susceptibility of the isolate and the drug exposure.  I think one thing, perhaps, to reiterate, is that we do need more and more data about achievable levels in vivo and be able to relate IC50s or IC90s or IC95s to that and then make some determination of how many fold above that we want to be.
So I think no simple answer about determination of breakpoints can come but just as MICs are different for various bacterial agents in relation to achievable levels and activity of drugs, et cetera, the same thing holds here.
Does anyone want to add to that modest summary?  Dr. Murray is not happy.
DR. MURRAY:  No; I am happy with it.  I just wanted to know logistically if anybody would like to comment who and how could this be accomplished.  Should it be left up to the sponsors to develop their own individual breakpoint for their drugs?  Ideally, would it be nice to have a committee?
DR. HAMMER:  I wasn't really being facetious earlier when I mentioned the NCCLS because I actually think that, at some point in our development, we need to bring a consensus body together to try to actually put a dataset together that can be used and built upon.
That would be my own feeling.
DR. CHARACHE:  I am also on the NCCLS direction at this point.  They have just been working, as you know, on susceptibility testing for other viruses.  Some of the thoughts might be helpful putting in here.
But I did want to add just the practical consideration that it really will be key to standardize the methodology that is used to determine phenotypic breakpoints or whatever of the IC50s and IC90s because I am not certain, in some of the various reports we have heard today, whether all are using the same methods.  It is the details that would make a difference between the results and whether four times or ten times makes a difference.
DR. HAMMER:  I would just say that, of the two recombinant methodologies that are out there, the first‑level cutoff and the second‑level cutoff vary with the manufacturer but they are clearly definable and, I think, well characterized on an assay‑performance basis.  They differ slightly one to the other, but they are pretty clear cut.
Does anyone else want to comment on Dr. Murray's question?
DR. IACONO‑CONNORS:  Before we move on, I just wanted to ask the committee sort of a basic question.  It is a little bit backwards but with respect to all the genotypic data we have been hearing about today, I am curious as to whether we should be thinking about any mutations in terms of their linkages.
Most of the data that we see, it is all population‑based sequencing so we really don't have a sense of their linkage.  I wonder if any of the speakers have an opinion on whether we should be concerned about this.
DR. HAMMER:  Since the question was first directed to the speakers, does anyone want to talk about that?
DR. D'AQUILA:  I do have a few slides about that in the talk for tomorrow morning, but just to directly address it, to date, there is good evidence that population sequencing is adequate for detecting linkage.
DR. HAMMER:  Other questions or comments on No. 1 before we move on?
Question 2, which I think we can deal with reasonably quickly as far as targeted responses are, in addition to the covariates and methodology for assessing treatment response included in the DAP, what additional covariates or methodologies should be considered when evaluating resistance data.
I think some statistical input and other clinical research input is important here.  Dr. Pettinelli already raised the issue of the CD4 count.  Who would like to add to this?
DR. MASUR:  One of the correlates that I guess I am not clear about in terms of pharmacology is we seem to be relaying everything to Cmin in terms of the likelihood of response.  Is it unequivocal that Cmin, rather than area under the curve or some other pharmacologic variable, is the correlate that we want when we are looking for correlating drug levels to the likelihood of response?
DR. HAMMER:  I will turn to Dr. Fletcher as the resident expert on this committee.
DR. FLETCHER:  No; I don't think it is.  I think, for the three classes of drugs that are available, I wouldn't be surprised that it is not different for each class of drug.  I am persuaded that, for the PIs, troughs are important.  For the non‑nucleosides, I am not.  There is some data on troughs but also area under the curve.
For the nucleosides, where the active moiety is the intracellular triphosphate, I am still really not sure what it might be.  I think this is one of the real challenges, as you try to move forward.
Virology and pharmacology here, I think, are fundamentally intertwined.  That doesn't mean that you have to absolutely understand exactly how to move forward with virology, but I think if you want to have a vision for optimal patient management and being able to understand everything that contributes to it, we are going to have to lay out a plan so that we can quantitatively understand what does pharmacology contribute, what does adherence contribute, immunology and so on.
So back the pharmacology, I think one of the real challenges is trying to understand what metric is it that is going to tell you the most about response.
DR. MASUR:  I guess, then, the corollary to this is it is just going to be too complicated, as we sort out the correlation of genotyping and phenotyping to failure, to make sure that patients are, in fact, failing in the presence of adequate drug or whether they are failing for other reasons.
There is going to be a lot of background noise here when the patients are failing for all the reasons that a number of speakers have pointed out other than lack of activity of the drug, but are we just going to have to accept all that as background noise that we are going to have to surmount with these assays?
DR. FLETCHER:  I think initially you might because just the virologic data alone, I am fairly persuaded that phenotype and genotype information contributes to the information on why you have success or failure.  I think that question is can you do better if you add additional information into that.
I don't know to what degree‑‑for example, take the CNAA2007 study where there is an adverse interaction between amprenavir and efavirenz.  In the back of my mind, is that, somehow, contributing to this difference between a fourfold resistance falling out and a ten‑fold not.
I can't come up with how it might be but I keep wondering about what confounders that may be adding.  I think you have to lay out a plan to start somewhere.
DR. HAMILTON:  It strikes me that there is a variable that we haven't heard much of anything about concerning the durability of the benefit conferred by genotypic testing.  What we have are relatively short‑term studies and a limited number of tricks in our bag.
I wonder if there are not some projections that have been made about what happens next.  Do we go back to time zero and the same series of events happen again?  Or are we in a better position or what?  I am not even now talking necessarily, although I could be persuaded to talk about it, what the long‑term implications are here.  I am talking just about recurrence of viral loads at relatively higher levels.
DR. JOLSON:  It is a very important point.  I don't think we have the data to answer that.  It all relates to what the armamentarium is going to be at the second and third and fourth round.  But a hint of that, of the utility of resistance testing on the second round, at least, is from the open phase of the VIRADAPT study where the deferred use of genotyping allowed a response in the second six months.
Now, that is an open phase of that study but, again, it says that something is up even when you use genotyping in a deferred fashion.  So, to me, that is encouraging although obviously not proof in a randomized fashion over a strategic trial over three or four years.
DR. MAYERS:  I think an issue, as we try and relate pharmacology to resistance is going to be that we are going to have an agreed‑upon methodology for protein‑adjusted IC95s or IC50s because it is clear that these drugs have very different protein binding and it is free drug that is important so that if you are going to try and relate and IC95 in a relatively protein‑free culture media with the patient's blood, you are going to have to do the adjustment and it would be nice to actually get an agreed‑upon method of adjusting drugs for their protein binding and then to be able to make the correlation between IC90 in a test tube and drug levels attained in the patient.
DR. HAMMER:  Let me ask Dr. Fletcher a follow up to that.  It is obviously important but even it is more complicated.  It is not just the free drug but it is the off rate and the protein binding, the tightness of the binding that may influence it, not just the proportion of free drug versus bound drug.  Is that correct or not correct?
DR. FLETCHER:  It is.  When you get into some of the multiple, let's say, PI‑PI combination or a PI‑non‑new combination where you now have two highly protein‑bound drugs, I don't think we understand can there be, then‑‑if they are bound to the same binding site, can there be competition.
I suspect that the final best way to sort that out will actually be quantitation of the unbound concentrations in plasma.
DR. HAMMER:  Let me ask Dr. Murray a question related to the question that has been put to us because the question includes the specifics of the DAP and the issues of covariates and methodologies, particularly statistical methodologies.
Are you referring specifically to the RCG analysis that we have seen and how those data should be looked at more thoroughly or in the next round or is this a more generic statement about other covariates that should be looked at because the RCG and the DAP is a very specific project?
I don't know if this is a project‑specific question or a more generic question that is being placed to us.
DR. MURRAY:  I think it was a more generic question for analyzing data that might come to the committee in the form of maybe even an NDA for a new drug where they want to include resistance data, just standardizing ways for evaluating resistance data in the future.
DR. HAMMER:  I would just mention a few things.  Again, it depends on the dataset.  I think Dr. DeGruttola made it very clear as to the streamlined nature of this for the purposes at hand.  To bring all these datasets together, streamlining was critical.
Other baseline covariates besides what Dr. Pettinelli mentioned; obviously, gender, age, some of the normal demographics would have to be looked at even if one doesn't suspect that there is going to be much of an issue.  But I think subtype and other virologic characteristics as we move into more spread of viruses beyond group B around the world into areas where there is drug development going on are going to be important to look at.
But I don't know if there are any statistical issues that Dr. Woolson, maybe you want to comment on?
DR. WOOLSON:  I think one thing is, as Dr. DeGruttola had mentioned, that the intent with the Data Analysis Plan was to have an overall summary the would work across a number of studies.  If we are talking specifically about projects that would come before our committee for review, I think it really is good for us not to lose sight of the fact that the continuous variables, like the log of the HIV RNA, actually provide very valuable information.
We saw a number of the presentations today where we had a dichotomy based on failure‑‑for example, the GART summary‑‑and also had the actual log of the HIV RNA.  I think that information, in terms of those changes, is very valuable.  The Data Analysis Plan, as I understand it now, doesn't speak directly to hanging on to those values, but I would imagine that, as they go through future iterations, they will probably look at other kinds of endpoints.
But I think that is one issue, that we would need to, I think, continue to focus on the continuous variables.  At least, I think that is very important.
The other issue I wanted to raise about the data analysis, and maybe this is more of a specific question to Victor, is the very last slide that was up, and I think that does deal with question 2, as well, for us.  Your very last slide indicated how you would apply these, this Data Analysis Plan, to intervention studies.
You didn't mention specifically that you would look at a treatment by these covariate interactions yet, in some of the studies that were presented today, there were some very clear treatments by covariate interactions.  Again, I was assuming that that was something would be worked out at a later point.
DR. DeGRUTTOLA:  I think those issues are very important.  But I think it would have to be left to the investigators of GART and VIRADAPT to work out those interactions and present them for those studies.  Again, the purpose of this was to just come up with an analysis that could be applied consistently across studies with a very pointed question for the issues that you raised.
I think the investigators could probably best do that on their own and, as you mentioned, some of those points were addressed in their presentations.
DR. PETTINELLI:  I might just say the obvious, but when you were looking at covariates in modeling, I think we also have to make sure that we have the right number of patients to look at because I was a little bit concerned, for example, for some of the statements that were made following the analysis of the Frankfort cohort which stated that when you are looking at‑‑like, resistance to PI was more predictive than resistance than NRTI, to look at the confidence interval at least when we are looking for fold differences.
That is the difference.  I don't know if it is because there are less patients, there is variability.  So, what I am saying is we have to be careful in terms of what we are working with.
DR. MATHEWS:  Jeff's question reminded me that, very often, the committee and the division have to look at several studies in different populations to evaluate a product for licensure.  In the future, if claims are going to be made that a product is effective in the setting of a certain mutational pattern, you are going to have to be able to make cross‑study comparisons even within the same NDA application.
That means, based on some of the VIRADAPT data and other studies we haven't talked about yet, that there has to be comparability in variables like adherence and drug levels and those kinds of things which are increasingly being incorporated into clinical trials but are not uniformly there.
Now, for example, there is data already that adherence, itself, can make over a log and a half difference, a population at three or six months which is larger than the effects that were described for genotyping testing.
So I think that these things are covariates that need to be looked at and incorporated up front in some standardized fashion into clinical trials.  It also affects the issue of poolability for large cross‑study comparisons like what was presented this afternoon because, unless those things are standardized in some way, they will just increase the overall variability and the quality of the studies is not the same.
DR. YOGEV:  I just wondered, from the modeling, we are almost accepting now that less than 400 is the cutpoint.  The clinical studies suggested there is a negative effect, even if you have about 50.  So shouldn't the modeling be changed to go to 50 as a prediction of failure to try to find that resistance that maybe there are earlier in that system than waiting for the 400 which is a convenience which I think we should leave at point.
DR. HAMMER:  Just on your comment on the RCG, I think 400 was taken because it was an endpoint that could be seen across all of these studies and it wasn't making a statement that that is the way that all of these analyses should be done.
DR. YOGEV:  I have no quarrel with what was done, I am saying, for the future, what covariate to look at.  I was wondering should we just also lower the modeling for future presentation to go to 50.  We know that when we say 50, we are still in the billions in the body.  So maybe we need to go even lower.  But, at this point, I think that already 50 for the future study, not for what was done.
DR. HAMMER:  Any other comments on question 2 as far as far a covariates are concerned and resistance data?  I am not sure I can really summarize this for the agency.  I think some of the issues have been obvious, that the populations need to be well defined.  RNA has been mentioned and was already included.  Obviously, CD4, basic demographics, gender, age, viral subtype is important where the populations expand and, I think, most importantly, the covariates of drug exposure or measures of drug exposure, adherence and PK.	
I am also thinking, taking Dr. Mathews statement one step further in what he said earlier today, whether one is looking at resistance retrospectively in a study, particularly in a substudy, or in a prospective fashion for  drug that is being developed specifically to go after resistant virus.
I think some of the issues of defining the populations and powering the studies appropriately are important when these are done in a prospective fashion to prove a drug's validity and efficacy in a drug‑resistant population rather than in retrospective studies.
More and more we will see that as drugs that look particularly good as salvage agents come through.
Any additions to that?
MR. HARRINGTON:  Scott, just one.  I want to just go back to what Dr. Hamilton said how the patients are doing now.  Ideally, if you have these huge, large relational databases, it would nice to know how the patients are actually doing physically, clinically.  That would be nice.
DR. HAMMER:  I don't think anyone would argue with that.  At least one of the relational databases that I am aware of doesn't really have that kind of clinical follow‑up data but certain groups can develop that and should develop it, certainly, in the academic environment where people are being entered through clinical trials and long‑term follow‑up is being attempted.
DR. STANLEY:  I just want to add to the things that can contribute to resistance that are not intrinsic to the virus.  We have to keep in mind it hasn't been a big issue for us so far in HIV virology, but if we go back to other infectious diseases, thinking about things like Dr. Richman mentioned this morning, proton pumps and other things that may be intrinsic to the cells or limiting nutrients in the cells or other things like that.
Presumably, that would try to be controlled for in the in vitro preclinical stage, but those are other non‑viral intrinsic resistance factors.
DR. HAMMER:  Thank you.  I think we are going to see a wave of information about that in the Nature and Medicine report.
We are going to move on to questions 3 and 4 which I think we can combine since they are identical except for one word; do the data presented support the clinical utility of HIV genotypic testing for use in drug development.  That is question 3.  Question 4 is identical replacing genotyping testing with phenotypic testing.
This is an easier question, at least to me, it is.  Who would like to start this discussion?
DR. YOGEV:  Yes.
DR. JACKSON:  Just to play the devil's advocate here, though, based on the prospective studies of the VIRADAPT and the GART study, one could argue, getting back to this adherence issue, these were open‑label studies.  I assume that the patients knew whether they were on the study arm or the control arm and whether that may have made a difference in adherence in a claim, this difference.
There were also differences in the study group.  Not only did they have the benefit of GART but they had the benefit of expert opinion that was based, not just one the GART results, but on the antiretroviral history.
If the control arm had had the benefit of expert opinion, just based on the antiretroviral history, would that have made a potential difference.  There are also no differences in CD4 counts by the different arms, as we have already mentioned.
These were relatively sick populations with about 200 CD4 counts.  Whether this really will clinically benefit this relatively sick population, I am not sure the data is really there to say that.  My personal opinion is I think it probably will, but the data are the data and there are these potential confounders that I think we can't ignore.
DR. HAMMER:  Just to add to that, I think the per‑proportion below the copy number limit chosen in VIRADAPT and GART at 12 weeks actually is not dissimilar than salvage‑regimen studies whether it be 359 or 372 that were doing it without prospective testing.  But I think, again, these were pilot groups of hypotheses studies and it relates, again, to the armamentarium used.
But the data are consistent.  I think this question, I would say, although potentially primarily driven by the prospective studies, our thinking needs to be influenced by the new prospective study on phenotyping that we got a glimpse of here but also the retrospective analyses that are pretty consistent.
You have to ask yourself has any study shown the negative that the application of resistance testing leads to a worse outcome.
The people who wouldn't speak into the microphone were murmuring budgetarily and financially.
DR. GULICK:  Just to follow up on what you just said, Scott.  At first glance, it is disappointing that the best arms in VIRADAPT and GART have rates that are very similar to the early salvage studies where we didn't have the benefit of resistance testing.
But then when you go back and look at the studies that you mentioned, 372, 359 even CNAA 2007, what they have in common, as a big success rate, are people that were non‑nuke naive and then got a new nucleoside.  So you wonder if that what it balances it out.
DR. HAMMER:  It also brings up that it is more than just having a resistance test and someone to interpret it.  It is intervening at the right moment at the lower viral load rather than a higher viral load, choosing the right drugs, et cetera.  It is complex, as everything in this disease is.
DR. MAYERS:  I don't think the clinical management question is what the FDA asked here.  What they asked is what is useful in drug development and understanding how your drug succeeds and fails and, perhaps, defining populations that your drug would be better suited to and not suited to.
I think, for that area, the answer is yes and yes.  And then, for how we use the test and who should use them and when, I think the answer is a bit more open.  But for the drug‑development issue, I think the answers are fairly clearly "yes" and "no."
DR. HAMMER:  Although I think they are interlinked because the drug development process is going to use the application of resistance testing to either do randomizations up front or strategically use the drug in a population that fails.  The immediate derivative of that, again, which we will talk about tomorrow, is the clinical application.  So I think these are somewhat inextricably linked.
But the question is, specifically, in drug development which, if anything, should make Dr. Jackson more happy about saying "yes" without the words in his mouth.
DR. PETTINELLI:  I also agree that we are ready to use the genotypic and phenotypic testing drug development.  I really refer to our discussion this morning which was presented by Doug in terms of where and when.  Again, the characterization of post‑treatment isolates, is important, most important, to try the specific patients that were failing, particularly if the drug is presenting some very positive effect over the specific use and population.
DR. HAMMER:  Any other additions?  We did touch a little bit, as Carla mentioned, on this question earlier this morning looking at applications in phase I and phase II‑III.
I think this is easy to summarize.  The questions 3 and 4 are "yes" and "yes."
Question 5.  What are the limitations of the data correlating mutational patterns with clinical income?  Clinical outcome; thank you.  It brings us back, full circle, to the psychologic issues that were raised this morning that I am quite suggestible when someone mentions financial matters in my ear next to me, here.  Both are true, however.
Who would like to talk about this, clinical outcome and mutational patterns.
DR. PETTINELLI:  To me, the major limitation today is that we have only short‑term data and I think it is very important to develop a long‑term database.  I don't know if the several studies that are ongoing that were discussed earlier, indeed, there was the duration.  But I think that would be important.
DR. JOLSON:  I just had a quick follow‑up question to Carla's to direct to Dr. Mellors.  In terms of the prospective ongoing studies that you showed, can you just give us a general sense of how large they are and how long the follow‑up is of patients so we can kind of expect what might be coming with additional data?
DR. MELLORS:  They range in size from a couple of hundred to over 500.  Follow up is as far as 48 weeks.  But that is the initial protocol.  It could be extended.
DR. HAMMER:  Can I ask a follow‑up question, John?  RCG, phase II, as far as analyzing the database further but, also, with the number of prospective trials that you have, in follow‑up to Dr. Jolson's question, of bringing analyses together for prospective trials looks interesting, as well as longer‑term follow up in a cooperative fashion.
DR. MELLORS:  The RCG is holding out for a retirement package, a pension, similar to what you get in the military.  So, we haven't talked about phase II.  We are not quite done with phase I until tomorrow at 5:30.  As Doug said, we were hoping to declare victory and disband.
DR. HAMMER:  But you can be reborn with another acronym.
DR. MELLORS:  It will take more than that.
DR. MASUR:  Just to follow up on what Mark Harrington said before.  This question asks what are the  limitations regarding mutational patterns with clinical outcome.  I guess, at this point during the day, we have accepted the fact that clinical outcome equates to virologic status whereas, in fact, clinical outcome is obviously a much more complex issue.
When we talk about the limitations of data concerning the correlation with clinical outcome, I guess the question is do we really have some way of looking at whether or not the patient is better off far down the road in ways other than simply virologic load, or in terms of a long‑term strategy.
DR. POMERANTZ:  I think that is a very important point.  We talked about it a little bit yesterday as well as this morning is that clearly it would be great if we could everyone to undetectable or to profoundly decrease their viral load.  I think everyone, now, would agree with that.  But with a lot of these salvage therapies where the genotypic and phenotypic analyses are going to be used, you are not going to be able to do that, at least in this first generation or second generation of drugs that we have.
So there are these patients that are being more and more described, some anecdotally, some non‑anecdotally, that do not get a great benefit in the CD4 count, do not have profound changes in their viral load, yet seem to do fairly well and may do better than those that did not have changes in their antiretrovirals.
I think that was the point is that we are not only going to follow viral load but, as this goes out, I think you are going to see this disconnect in certain patients where some will do better than you might think just looking at the viral load and CD4 count.
Understanding those parameters on a basic biological level is important but understanding them clinically with these correlations is going to be very important because a lot of these people will not have great changes in the viral load as these studies sometimes have shown even this afternoon.
So I would just reiterate that and say that I think that you are going to have more of that problem rather than less of it.
DR. MAYERS:  I want to stay away from the issue of long‑term benefit of resistance testing.  On the other hand, there is another resource.  Both the ACTG and, I think, the CTCRA have large mutation datasets now.  We are going to have over 1200 patients fairly soon in CTCRA and every one of them is enrolled in the clinical‑endpoint long‑term monitoring study.
So I think the issue of other mutational patterns that would be better or worse against the true clinical data, virological data, will be emerging over the next few years in both the ACTG and the CTCRA and, if we are truly smart, we will make the datasets meldable.
DR. KUMAR:  I want to follow up what Dr. Pomerantz just said.  We continue to see in clinical practice a number of patients who clearly have failed therapy by all measurements that they are set for but continue to do clinically well.
I am not sure taking the genotype testing and altering the antiretroviral treatment without looking into toxicity data, quality‑of‑life measurements, that we are really doing them a service.  Without looking into those type of data whether we can say yes.  On a short‑term basis, there is clearly a decline in viral load.  Whether that includes the long‑term benefits, I really don't think we know as yet.
DR. HAMMER:  Although I would just make a plea.  I agree we need cohorts and long‑term follow up and to see the fruits of what we are doing over the next five to seven years in a more cohesive fashion, I don't think we are suggesting, or can go back or should go back to clinical endpoint issues here.
At least that is my own feeling.  I think what is being raised is, in fact, the fact of discordance and viral fitness issues and replicative capacity when viruses are impaired.  But even some of the groups that have reported discordant responses and hanging up of CD4 counts and clinical benefit, clinical status patients that have remained well over time despite virologic failure are now beginning to demonstrate that those patients do go on to failure immunologically as well.
So those data will emerge.  But I think it is complicated because viral load doesn't tell you everything pathogenetically that is going on and you need to look at other factors.  I think that is what we are saying and it is getting more complex than some of the issues about whether certain mutations impair the virus more than others is clearly important and one of the limitations, I think, in response to this question.
Hopefully, we will stimulate some discussion.
DR. MAYERS:  I think there has been a confusion between the strategy of using a resistance test‑‑let's just make it generic‑‑a resistance test to manage patients, the strategy of using a resistance test to manage patients every time a viral load becomes detectable.
I think that is a very different issue.  I think that you have two‑‑it looks like three prospective studies that show that when you make the decision that it is time to change drugs, the testing will help you to get your patient‑‑your patient is twice as likely to go below the limits of detection with the test than without out.
But that is very different than how you choose the test and at what time you choose the test so that it is quite rational to let people float through for two years with discordance in viral load and do nothing.  But when you decide you want to change therapies, I think, at least in the short term, you are twice as likely to get undetectable with genotyping or phenotyping, per your preference, at that point that you choose to change.
So it is going to be hard to address that in any serious way for long‑term clinical benefit.
DR. HAMMER:  Personally, I agree.  I think we are falling a little into the trap, which is obviously easy in discussions like this and is the same thing we did with viral load.  Everyone focussed on a number.  We can't focus on a resistance result and think that you are going to manage an HIV patient properly if that is the only thing you are thinking about.
DR. HAMILTON:  This just actually emphasizes the point I wanted to make which was that to extrapolate the data that was presented today, as compelling and as interesting and as important as it is‑‑extrapolating that information to the population at large, I think, would be a big mistake at this moment.
I think we need a lot more information in terms of how this actually benefits patient outcome in very real terms.  You can call them clinical endpoints if you like.  I call them life.  I would like to think that what we are doing is going to have a long‑term impact.
How we go about measuring that, assessing it, implementing the current set of guidelines that undoubtedly will appear as a result of this impressive dataset is another matter.  But I think we do not want to lose sight of the larger picture here.
DR. CHARACHE:  I think, in terms of new‑drug development, a very key issue here is to define what is meant by clinical outcome.  Is it CD4 count?  Is it other parameters?  I think this will have to be agreed upon.
DR. HAMMER:  I am not sure, in this question, whether it is marker RNA outcome, CD4 outcome or ultimate clinical outcome.  Some of us reserve clinical outcome for‑‑purely what is truly meant by that.  I thought that was the implication of the question.
DR. KAPLAN:  One thing I have always been impressed with in the GART data is, in a sense, it represents kind of a real‑life situation in that about half of the providers in the GART arm didn't do exactly what they were asked to do.
This happens in clinical practice.  They make their own judgments and so forth.  In the study, which only went out to week 12, the curves start to come together and the percentage of patients that were undetectable becomes insignificantly different in twelve weeks.  So I think, again, it raises the issue‑‑here you have, perhaps, even a short‑term virologic benefit.
Now, VIRADAPT showed a longer term, but what exactly does that short‑term virologic benefit, just for three months or so, mean in terms of a long‑term endpoint for the patient.  I think we are hearing that over and over.  I wouldn't argue about the value of phenotypic or genotypic testing and producing some virologic benefit, but question No. 5 specifically says what are the limitations.
I think we are all owning up to what the limitations are.
DR. MATHEWS:  I think, also, and it is really not a limitation since these are very preliminary analyses, but I think the kind of confirmatory studies that Dr. DeGruttola was talking about are really essential if this kind of data is going to be useful.  Develop a model in one study, apply it to another dataset and see if it is equally predictive, look at alternative parameterization of the measurements to the scores that are developed and just, really, see if you can get the biggest bang for the buck out of the data that is already there.
That is going to take a lot of time and energy and I certainly appreciate all the work that has already been put into it but you can milk it for a great deal more.
DR. HAMMER:  Let me just try to summarize questionÊ5.  The limitations of the data correlating mutation patterns with clinical income‑‑clinical outcome.  Something else comes to mind in my new role outside of this committee.  Clinical divisional income is probably the thing we deal with on the most frequent basis.  It sounds like I need to talk to John about his counseling.
The limitations, I think, in what we have seen today, the data summarized, et cetera, relate to, I think, some of the numbers we were‑‑some of the studies don't sort of have enough numbers within certain drug‑class mutational patterns to help us.  So I think there are a number of issues.  Dr. Pettinelli raised the issue of follow up.
Obviously, resistance mutational interactions are difficult, sometimes difficult, to infer from just looking at mutational patterns and that is where phenotypic testing comes in.  We talked about that there may be impaired‑‑what these mutations are doing to the functional replicate of capacity of the virus which there may be mutations which, if just scored as mutations, may actually be helpful to the patient by impairing the virus.
There are, obviously, issues of what we don't know.  It was only two‑plus years ago that we didn't really know‑‑or at least I didn't know about the 69 insertion.  And then there was a wave of presentations at the Resistance meeting, two meetings ago, and then just everywhere.
So there are other multidrug patterns that we are going to need to know about.  So there will always be limitations on a mutational pattern as well as the fact that we do not discover everything we need to discover unless we start doing multiple clones on patients.
So there are many limitations but it doesn't, I think, obviate the importance of defining the‑‑it emphasizes long‑term follow up, it emphasizes the importance of relational databases.
Other additions to that?
The last question is another classic ending question for the day; what additional clinical studies are needed to further define the clinical utility of resistance testing?  Dr. Mathews talked about that in relation to datasets that currently exist and particularly in relation to the RCG analysis, but would anyone like to add‑‑
DR. STANLEY:  I just to add the caveat, define the clinical utility of resistance testing in drug development which is, I think, what we are here for today.  I think that in studies that are planned that we need to take into account the nonvirologic factors.  I don't think it is too much to ask of a drug company, if they are going to use resistance testing to show data about their new drug, that they show that they have accounted for absorption and the pharmacokinetics and adherence and other issues.
DR. GULICK:  One thing we haven't mentioned today; every study that we have heard today, and this is a bit tangential, but every study we have heard today took people who were failing their regimen, did resistance testing and then switched them to a new regimen immediately.
We have also been hearing, and I guess Veronica Miller has presented some interesting data, about stopping the so‑called course of treatment interruption and using resistance testing in the context of treatment interruption to try to maximize future options.
I guess it is more of a clinical management, the drug development question, but it directly speaks to the question, I think.
MR. HARRINGTON:  Just to follow up slightly on what Trip said, if you can restore sensitivity to some drugs during SGIs and you can also maybe get a more potent effect on the salvage regimen which might actually make appealing for companies to study the new agent in this population, which is something that has been hard for to persuade industry to do until now.
So, using the resistance test pre‑ and post‑treatment interruption would actually be very important.
DR. KAPLAN:  In terms of what the question  specifically says, and what we have been talking about today, drug‑experienced patients and salvage therapy, obviously, we need to study, to look at, other uses such as in antiretroviral‑naive persons.  I presume we are going to be getting into that tomorrow morning from what I am seeing on the agenda, that there are such things going on but we haven't even gotten into that area, the utility of resistance testing in that arena.
DR. PETTINELLI:  I think it would be important to establish the relative utility of phenotypic versus genotypic resistance assay.  That is a little bit different from drug development but I think, again, it would be important to compare each other how they perform in terms of outcome of the patient.
DR. HAMMER:  Thank you.  It looked like there were some studies, some that I am aware of and others that I wasn't aware of, that are listed on Dr. Mellors' slide of prospective studies that are enrolling or have enrolled or will enroll.
Other comments?  I think we have really touched on this.  As far as further studies, I think the important point that Dr. Stanley made about not just looking at resistance but I think taking the VIRADAPT pharmacologic study further that other cofactors are really important.  I think the pharmacology study within VIRADAPT is very helpful, in fact, in showing that there are independent predictive power to resistance testing and to optimal levels, how they were defined in that study.
So other factors need to be prospectively defined within studies.  Within the context of defining the clinical utility, we certainly have more retrospective analyses, but I think the key will be in the prospective studies and with the relation of drug development, it is going to be in defined populations depending upon the drug and its characteristics.
I think there are two ways‑‑more than two ways‑‑to do this and one is, at baseline, as far as the population that one recruits and one could, prospectively, do resistance testing to see, as it evolves‑‑one could randomize on the basis of resistance testing if one has the drug.
We talked about this before, that may be useful, particularly with a particular genotype or phenotype.  And then one could use it strategically in a clinical trial as far as management which gets into how long the benefit and the continued benefit of resistance testing can be in first failure, multiple failure and over time.
So I think the studies that we have seen so far, retrospective and prospective, are really the tip of the iceberg.  As to how to apply them, it really is better‑defined populations, up‑front randomizations, proper stratifications, and use in a strategic fashion that will tell us where these will ultimately be placed.
I think it is also worth stating philosophically that, like every other assay in HIV care, it is here, it is being used, it is out in front of how we know how to use it, and that will be true of the next major assay in HIV as well.
So I don't think we will actually ever catch up.  The technology is ahead of us.  It has taught us a lot and it will continue to tell us a lot, so I don't think we will ever actually catch up with our clinical trials with the advance in technology, and maybe that is a good thing.
Other statements or additions to question 6?
We are twenty‑one minutes ahead of schedule.  I would ask Dr. Jolson or Dr. Murray what else you would like us to‑‑
DR. JOLSON:  I just wanted to thank a moment to thank all the invited speakers today and, also, in particular to thank all of the people who helped put together the DAP presentation.  We recognize it is an extraordinary amount of work.  It was all done as part of a cooperative effort.
It has been enormously helpful for us to see these analyses.  I think we have learned a lot from it.  I really can't thank you enough for the time and effort that went into it.
I would also like to make just a personal appeal not to disband the RCG after this because I think, particularly as tomorrow's discussion evolves, it will become clear that the issues are going to get more and more complex as we start thinking of different scenarios to apply to resistance testing and drug development.
It would be impossible for the agency to think through these issues unilaterally.  We really need comment and participation by industry and academics and others working in the field to help further the thinking.  So that would just be a personal appeal that there be some sort of an ongoing effort to work collaboratively on that.
DR. HAMMER:  I would echo that.  I think that the presentations were fantastic this afternoon, and impressive, but, in no way, can they be absorbed by this committee or by the agency within a couple of hours.  I think that the data need to be mulled over and thought about and there are whole issues of analysis and interpretation that can help understand resistance testing and its application on the  drug‑development process.
So I would hope that there would be further communications between the agency and the RCG and other groups that have datasets that have datasets that have an impact on this analysis.  It has really been impressive.
So I would, in closing, thank the participants, the speakers, the agency, our guests and consultants of the committee and, really, everyone that put a lot of effort into today's event.  It is not over.  Tomorrow is part 2 and we welcome you back here at 8:30 tomorrow morning.
[Whereupon, at 5:15 p.m., the proceedings were recessed, to be resumed at 8:30 a.m., Wednesday, November 3, 1999.]
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