AT DEPARTMENT OF HEALTH AND HUMAN SERVICES

> FOOD AND DRUG ADMINISTRATION
>
> **SCIENCE BOARD ADVISORY COMMITTEE MEETING**
>
> Wednesday, April 9, 2003
>
> 8:10 a.m.
>
> Advisors and Consultants Staff Conference Room
>
> 5630 Fishers Lane
>
> Rockville, Maryland
>
> PARTICIPANTS
>
> Michael P. Doyle, Ph.D.Chair
>
> Susan F. Bond, M.S., Executive Secretary
>
> MEMBERS
>
> Robert M. Nerem, Ph.D.
>
> Harold Davis, D.V.M., Ph.D.
>
> Martin Rosenberg, Ph.D.
>
> Cecil B. Pickett, Ph.D.
>
> Josephine Grima, Ph.D. (Consumer Representative)
>
> Jim E. Riviere, D.V.M.
>
> Cato T. Laurencin, M.D., Ph.D.
>
> Katherine M.J. Swanson, Ph.D.
>
> Kenneth I. Shine, M.D.
>
> John A. Thomas, Ph.D.
>
> FDA
>
> Norris E. Alderson, Ph.D.
>
> Robert Buchanan, Ph.D.
>
> Kathryn Carbone, M.D.
>
> Daniel A. Casciano, Ph.D.
>
> Lester W. Crawford, D.V.M., Ph.D.
>
> David Feigal, Jr., M.D., M.P.H.
>
> Jesse Goodman, M.D.
>
> John Marzilli
>
> Mark B. McClellan, M.D., Ph.D.
>
> Stephen Sundlof, D.V.M., Ph.D.
>
> Janet Woodcock, M.D.
>
> [C O N T E N T S]{.underline}
>
> Call to Order:
>
> Michael Doyle, Ph.D. 5
>
> Waiver Statements:
>
> Susan Bond 8
>
> Introductory Remarks:
>
> Norris E. Alderson, Ph.D. 10
>
> Welcome and Overview of FDA\'s Initiative to Improve
>
> the Development and Availability of Innovative
>
> Medical Products:
>
> Mark B. McClellan, M.D., Ph.D. 13
>
> Quality Systems Approach to Medical Product Review:
>
> Janet Woodcock, M.D. 41
>
> Quality Teams to Improve Regulatory Processes:
>
> David W. Feigal, J.D, M.D., Ph.D. 50
>
> Quality Systems for Clinical Pharmacology
>
> and Biopharmacology Review:
>
> Larry Lesko, Ph.D. 59
>
> Quality Systems for CMC Review:
>
> Yuan-yuan Chiu, Ph.D. 82
>
> Questions and Discussion with Board/Presenters 94
>
> Update on Pharmaceutical Manufacturing Initiative:
>
> Ajaz Hussain, Ph.D. 119
>
> Update on Patient Safety Initiative:
>
> Kelly Cronin 139
>
> Questions and Discussion with Board/Presenters 154
>
> Open Public Comment 173
>
> Fostering Technology Development\--Pharmacogenomics
>
> Janet Woodcock, M.D. 174
>
> Pharmacogenomics\--Preclinical Studies:
>
> Frank Sistare, Ph.D. 193
>
> Pharmacogenomics\--Drug Metabolism/Dosage:
>
> Larry Lesko, Ph.D. 234
>
> Industry Use of Pharmacogenomics and Regulatory Issues:
>
> Brian Speak, Ph.D. 245
>
> [C O N T E N T S]{.underline} (Continued)
>
> Fostering Technology Development\--Pharmacogenomics
>
> (Continued):
>
> Janet Woodcock, M.D. 267
>
> Ethical Issues with Regulatory Review of
>
> Pharmacogenomic Data:
>
> Benjamin Wilfond, M.D. 277
>
> Questions and Discussion with the Board/Presenters 283
>
> Closing Remarks/Future Directions:
>
> Michael P. Doyle, Ph.D. 308
>
> [P]{.underline} [R]{.underline} [O]{.underline} [C]{.underline}
> [E]{.underline} [E]{.underline} [D]{.underline} [I]{.underline}
> [N]{.underline} [G]{.underline} [S]{.underline}
>
> **Call to Order**
>
> DR. DOYLE: Good morning. I am Mike Doyle. I am the incoming Chair of
> the Science Board and I want to welcome you all to this spring meeting
> of the FDA Science Board.
>
> We might begin by introducing the Board to you. I am going to have
> each Board member introduce him or herself, briefly state what you do
> and where you are from. Then we will move on with the meeting.
>
> I am Mike Doyle. I am the Director of the Center for Food Safety at
> the University of Georgia. I am a food microbiologist by training.
>
> DR. THOMAS: I am John Thomas, Vice President Retired, Professor
> Emeritus, Pharmacology and Toxicology at the University of Texas
> Medical Center at San Antonio.
>
> DR. RIVIERE: Hi. I am Jim Riviere. I am a pharmacologist/toxicologist
> at North Carolina State University. I direct the Center of Chemical
> Toxicology.
>
> DR. GRIMA: I am Josephine Grima. I am from the National Morphine
> Foundation. I am the Director of Research and Legislative Affairs and
> I am the consumer representative.
>
> DR. LAURENCIN: I am Cato Laurencin. I am Professor and Chair of
> Orthopedic Surgery and Professor of Chemical Engineering and
> Biomedical Engineering at the University of Virginia.
>
> DR. SHINE: I am Ken Shine. I am Senior Policy Fellow and Head of the
> Center for Domestic and International Health Security at the RAND
> Corporation and I am still going to keep an eye of what kind of food
> you will eat today because I am a cardiologist.
>
> DR. SWANSON: I am Katie Swanson. I am a food microbiologist. I am
> currently Director of Quality and Regulatory Operations for Yoplait,
> Columbo and soon to be the Director of Global Product Safety for
> General Mills.
>
> DR. PICKETT: I am Cecil Pickett. I am President of Research and
> Development at the Schering-Plough Research Institute which is the R&D
> arm of the Schering-Plough Corporation.
>
> DR. ROSENBERG: I am Marty Rosenberg. I am a microbiologist. I have
> recently retired from GlaxoSmithKline, Head of Infectious Disease.
>
> DR. DAVIS: I am Harold Davis. I am a toxicologist and pathologist. I
> am Vice President of Preclinical Safety for Amgen, Inc.
>
> DR. NEREM: I am Bob Nerem. I am from Georgia Tech where I am Director
> of the Institute for Bioengineering and Bioscience and also direct the
> Tissue Engineering Center. I am basically a biomedical engineer.
>
> DR. DOYLE: Thank you. As incoming Chair of the Science Board, it is
> difficult for me to start the meeting with what I consider to be very
> sad news, and that is, we have had with us for three years Ms. Susan
> Bond who has been what I call the glue that has held the Science Board
> together.
>
> Susan has been our Senior Science Policy Analyst for the Science Board
> and she is moving up. She is now going to be the Special Assistant to
> Deputy Commissioner Crawford. We are going to miss Susan, but we do
> want to, as a Board, give her some mementos of our appreciation for
> Susan.
>
> So, Susan, if you would join me up here, I will share with you some of
> our mementos. Susan is a big fan of bulldogs. We, at the University of
> Georgia, are also big fans of bulldogs. So we wanted to give to her
> some bulldogs for all occasions.
>
> This one here\--you can cuddle up at night with that one. This
> one\--you can keep warm in the wintertime with this one. This one will
> keep you covered up in the summertime.
>
> Susan, we really appreciate all that you have done for the Board and
> do wish you all the best with Dr. Crawford.
>
> \[Applause.\]
>
> MS. BOND: Thank you. I appreciate it.
>
> DR. DOYLE: Next, Susan is going to talk about the waiver disclosures.
>
> **Waiver Statements**
>
> MS. BOND: If you could just bear with me. I have to read these for the
> public record.
>
> The following announcement addresses the issue of conflict of interest
> with respect to this meeting and is made part of the public record to
> preclude even the appearance of such at the meeting.
>
> The Food and Drug Administration has prepared general matter waivers
> for Drs. Nerem, Davis, Grima, Riviere, Rosenberg, Doyle, Laurencin,
> Shine, Swanson, Pickett and Thomas. A copy of the waiver statements
> may be obtained by submitting a written request to our Freedom of
> Information Office. The waivers permit them to participate in the
> committee\'s discussion of the FDA\'s Initiative to improve the
> development and availability of innovative medical products and to
> discuss the agency\'s initiatives in pharmaceutical manufacturing on
> patient safety.
>
> The topics of today\'s meeting are of broad applicability and, unlike
> issues before a committee in which a particular product is discussed,
> issues of broader applicability involve many industrial sponsors and
> academic institutions.
>
> The participating committee members have been screened for their
> financial interests as they may apply to these general topics at hand.
> Because general topics impact so many institutions, it is not prudent
> to recite all potential conflicts of interest as they apply to each
> participant.
>
> The FDA acknowledges there may be potential conflicts of interest but,
> because of the general nature of the discussion before the committee,
> these potential conflicts are mitigated.
>
> With all that said, I will just give you some housekeeping notes to
> that. \[Housekeeping notes.\] We have open public comment scheduled
> for 1 o\'clock. I would just remind everybody to turn your microphones
> on when you speak so that the transcriber can pick everything up.
>
> That\'s it.
>
> DR. DOYLE: Next, we have some introductory comments from Norris
> Anderson.
>
> **Introductory Comments**
>
> DR. ALDERSON: Thanks, Mike.
>
> In addition to you members that introduced themselves, I do want to
> add some comments about each of those to give you an idea of the
> expertise and experience these four individuals to the Board. Dr.
> Laurencin is the Lillian T. Pratt Distinguished Professor of
> Orthopaedic Surgery and Professor of Chemical and Biomedical
> Engineering at the University of Virginia. He just moved there and he
> is telling me he really likes Charlottesville.
>
> He and I had an experience last night. I wound up with his bags and
> he, by chance, just had my cell-phone number before I got away. So we
> have a little something to share now. But his experience is in
> chemical and biochemical engineering and orthopaedic surgery.
>
> Dr. Katherine Swanson is the Director of Quality and Regulatory
> Operations for the Yoplait, Colombo at General Mills and former
> Director of Microbiology and Food Safety at Pillsbury. Her expertise
> is in food microbiology and food science and its impact on public
> health.
>
> John Thomas is Professor Emeritus of Pharmacology and Toxicology at
> the University of Texas Health Science Center. His expertise is in
> toxicology and pharmacology.
>
> Last, Dr. Ken Shine is a Founding Director of the RAND Center for
> Domestic and International Health Security and former President of the
> Institute of Medicine and former President of the American Heart
> Association. His clinical experience is in internal medicine and
> cardiology. He has extensive experience working with global issues and
> emerging infectious diseases, bioethics and access to care.
>
> Certainly, we welcome these four new members to the Science Board.
>
> Mike advised everyone of Sue\'s departure. I want to add to that. I
> have been in this position now almost two years. Sue is one of those
> people I have totally enjoyed during that period. She and I have a lot
> of things we share. She will tell you I am her chief advisor now and I
> keep telling her, now that she has moved to Dr. Crawford, that that
> service is no longer free, that I am starting to charge next week.
>
> But she still works with me until the end of the month. Her other big
> responsibility, and a huge one, is this annual FDA Science Forum. I do
> want to remind everybody of that. It is April 24 and 25 at the new
> Washington Convention Center. We think we have an outstanding program
> this year with both Dr. Crawford and Dr. McClellan being major
> participants.
>
> We also have participants, members of CDC and NIH, and, hopefully, the
> Department. We hope to know that next week. But there is a large
> poster session of FDA research. The three tracks of the presentation
> are Risk Management, Counterterrorism and Novel FDA Science. We have
> included a copy of the program with your notebooks, so if you have
> questions about that and you are interested in attending, please see
> Sue or me.
>
> We are starting to recruit immediately to backfill Sue\'s position. In
> the interim, until we get somebody named, if you have questions or
> issues you need to address for the Science Board, you can easily call
> me and we will take care of it for you.
>
> Thanks, Mike.
>
> DR. DOYLE: Thank you, Norris.
>
> Next, it is a real pleasure for me to introduce our next presenter,
> Dr. Mark McClellan who is the Commissioner of the Food and Drug
> Administration. We, at the Board, had the opportunity to have dinner
> with Mark last night. For many of us, it was the first time to
> interact with Mark. But I will tell you, I think the FDA is very
> fortunate to have someone with his capabilities in charge.
>
> So I, at least speaking for me, personally, Mark, appreciate you being
> the leader of FDA.
>
> Dr. McClellan is going to give us an overview of FDA\'s initiative to
> improve the development and availability of innovative medical
> products. So, the stage is yours.
>
> **Welcome and Overview of FDA\'s Initiative to Improve**
>
> **the Development and Availability**
>
> **of Innovative Medical Products**
>
> DR. McCLELLAN: I want to thank all of you for coming this morning,
> especially Mike for his leadership of our critically important Science
> Board. This is a very integral part of FDA\'s policy-development
> process, as many of you know, and that is why I am very pleased to see
> so many representatives from the public here today. We are looking
> forward to hearing from you later.
>
> We need this kind of scientific input now more than ever. I talked
> about this a little bit last night with our Science Board highlighting
> how the challenges that we face today are, in many ways, more complex
> than the challenges that we faced ever before. But opportunities for
> addressing those challenges are also better.
>
> We have had some significant achievements on the legislative front in
> the past year with new authorities and new resources for both
> priorities like addressing terrorism threats and our challenges on
> approving safe and effective medical products more efficiently. I am
> going to talk more about that this morning.
>
> But figuring out how to use these new authorities as well as the
> changes that are taking place more rapidly than ever in the fields of
> science is an important and ongoing challenge for the agency. That is
> why this kind of interaction is a critical part of our efforts to do
> our job of protecting and promoting the public health as efficiently
> as possible.
>
> All of you on the Science Board have a major responsibility to play in
> this. We very much appreciate your stepping up and being willing to
> take on that responsibility. We look forward to the discussions here
> today and I am going to look forward to some more extensive
> interactions with you in the months ahead as we move forward on these
> critical initiatives for the public health.
>
> I, too, want to thank Norris and Susan Bond for their terrific work in
> putting together this effort. I hope you all are going to find the
> day\'s presentation by FDA staff informative. I know that they have
> put a tremendous amount of new thinking into the initiatives and ideas
> that they are going to be summarizing for you here today and they,
> like I, are very much looking forward to your critical feedback and
> appraisals of how we can fulfill these initiatives as effectively as
> possible.
>
> The FDA team is one that I have come to enjoy working with
> tremendously in my four months at the agency. Les Crawford, in
> particular, has done a great job under difficult circumstances,
> working as Acting Commissioner for the better part of a year while I
> was getting on board and those types of things were happening. I will
> talk about this more in a minute.
>
> During that time, the FDA did manage to make some major strides
> forward on important issues. Janet Woodcock has done a tremendous
> amount to organize the presentations for today. Yesterday, you all got
> to hear about some of the activities going on in more detail at our
> Drug Center. I hope you, like I, have found that work to be very
> impressive efforts to make the most of the limited resources that we
> have available to fulfill our mission.
>
> Our center directors and senior leadership represented here today will
> be participating in the meeting, too. Again, I want to thank them for
> their ongoing efforts to help us fulfill our mission.
>
> I hope this meeting offers something for our Science Board in return.
> We are expecting a lot from you in terms of input and ideas about how
> we can use the best available science to fulfill our mission
> effectively but I hope this is also an opportunity for you to hear
> back from us about some of the front-line challenges that we are
> facing in trying to think creatively about the practical implications
> of many of the new scientific developments and new public-health
> threats facing us today.
>
> As I mentioned, the challenges of promoting and protecting the public
> health today are greater than ever. We are facing new threats of
> terrorism. We are facing new opportunities to bring more complex but
> potentially much more effective medical products to the market and to
> the public in the months and years ahead. For the sake of public
> health, we need to find the most effective ways possible to address
> these new challenges.
>
> We don\'t have unlimited resources at the FDA. We don\'t have
> unlimited staff. So we have to prioritize. We have to identify the
> best opportunities for improving the public health and the best way to
> fulfill those opportunities. In many ways, this is requiring a
> reexamination and updating of the way that FDA does its job. This is
> true in each and every one of our centers, our Veterinary Center, our
> Food Center. All of our centers are making some fundamental changes in
> the way that they approach fulfilling our mission given the greater
> challenges that we face today and some of the new resources and
> opportunities that I already outlined.
>
> What we are going to focus on today is illustrating some of the key
> ideas that are part of FDA\'s new Strategic Action Plan in the context
> of working to lower the cost of developing new pharmaceuticals, new
> devices, and other new medical products. They must still continue to
> meet our standards of safety and effectiveness but we are trying to
> make sure we are using the best science, the best biomedical science,
> the best regulatory science, even the best economic science, in
> fulfilling this mission as efficiently as possible.
>
> This is a particularly important challenge today because of some of
> the changes that are occurring in the biomedical industry. This is why
> I think it is a very good opportunity to illustrate to you some of the
> key ideas that we are trying to implement as part of our goal of
> efficient risk management throughout our activities here at the
> agency.
>
> Why is this a special challenge today? As many of you know, the
> approval of new drugs and biologics is now at its lowest level since
> the Prescription Drug User Fee Act was implemented over a decade ago.
> The number of truly new drugs, the so-called new molecular entities,
> that have reached the agency is down around to half of what it was as
> recently as 1996. The number of new biologics is down significantly as
> well.
>
> These challenges are not confined to drugs but are common to all
> medical technologies. At our Devices Center, for example, the average
> review time for a major new device, as so-called PMA, is now almost
> fourteen months, probably not surprising given the staff level of the
> Devices Center has dropped by 8 percent since 1995 and we are facing
> more complex and difficult device review decisions than ever before.
>
> So, one possibility for the future is that this kind of trend
> continues, that the cost of developing new medical technologies keeps
> rising. It is easy to see how this might happen. The kinds of
> blockbuster drugs that were a hallmark of medical product development
> in the 1980s and 1990s are fewer and more far between today. Many of
> the receptor sites that those drugs have effectively exploited, based
> on a good understanding of molecular biology in humans, have been
> exploited.
>
> We expect to see more of these drugs coming along in the upcoming
> years but not so many as before. In contrast, while there has been a
> tremendous increase in biomedical research, a doubling of the NIH
> budget is being completed this year, over \$27 billion. Many in the
> public do not appreciate the fact that there has also been a
> substantial increase in private-sector research and development
> contributions. For example, the research activities of the
> pharmaceutical companies has doubled since 1995.
>
> We are seeing a tremendous increase in research and development
> spending but, so far, it hasn\'t translated into new products being
> approved by the agency. The decline in new-product approvals that I
> just talked about is a direct reflection of fewer new-product
> applications coming in to the agency.
>
> A lot of people think this has multiple causes. One important cause
> that has been cited is that the kinds of products being developed now
> are going to be a bit different. Much of the new investment has gone
> into new, but more basic, types of biomedical research than has
> occurred in the past. A new understanding of the genome and genomics,
> a new understanding of how proteins function at the individual protein
> level, proteomics.
>
> There are many other \"omics\" fields at well that are still at
> relatively young ages. Remember, it was just a few years ago that the
> genome was actually sequenced. So, translating all this new
> information into medical products is posing a challenge for product
> developers and it may also be a challenge for our agency, as I will
> talk more about in a while.
>
> So one possible course for the future is, as we get all these
> additional sources of information, we simply add to the cost and
> complexity of developing new breakthrough medical products. Again, it
> is easy to see how this could happen. If we don\'t get a solid and
> efficient understanding of what all this pharmacogenomic and all this
> proteomic information in data mean, it could mean additional
> preclinical tests or additional tests in conjunction with our usual
> review process.
>
> Because these enzyme-activity changes that are associated with
> potentially thousands of different testing sites don\'t have clearly
> understood implications for the human body and human systems, we may
> also end up adding on more testing at the clinical stage to just
> understand whether a particular abnormality or particular deviation in
> microarray testing has any clinical consequences for a patient.
>
> If we take that approach, we are going to be adding further to the
> cost and time for product development and that is already going up
> rapidly. According to that Tufts University study group on this issue,
> the cost of developing a truly new drug is now over \$800 million in
> present value terms. While people may quarrel with what the exact
> number for this cost ought to be and how it should be calculated,
> nobody can quarrel with the fact that that number has gone up a lot.
> According to the Tufts University estimates of a decade or so ago, it
> was less than half as much.
>
> A lot of the added costs are coming in at the clinical-testing phase
> but more costs are coming in in other parts of the development as
> well. The time to get a product approved at the agency is going up,
> too. So, all of these factors are contributing to increasing costs of
> product development. If these trends continue, I don\'t think it would
> be a huge surprise that we wouldn\'t see a significant increase in the
> number of new products approved, especially if you keep in mind that
> these new products are not blockbuster drugs that are intended for a
> market of 5 or 10 or 50 million people but rather of more
> individualized treatments that are likely to be highly effective but
> that would be targeted to more particular segments of the patient
> population.
>
> It might involve not just a drug but a drug paired with a diagnostic
> test or a novel drug-delivery system in order to make the treatment
> particularly effective for a subgroup of patients. If that costs \$800
> million or trillion plus, we are not going to see a whole lot of those
> breakthroughs.
>
> I don\'t think that the future needs to be that way but it does mean
> that we need creative solutions in all kinds of dimension. First,
> policy makers need to find new approaches to the problems of
> health-policy reforms that we are facing today. As healthcare costs
> keep going up, healthcare affordability is a front-and-center issue
> and is going to remain so. So we need to find better ways in policy
> making to make healthcare more affordable for all Americans while
> still encouraging innovation.
>
> This is a very complex debate. It involves a lot of money. There is a
> lot at stake. My only concern is that, if we don\'t find good,
> creative solutions here, we may, in fact, implement reforms that do
> not encourage this kind of innovation, all this potential for
> breakthrough and more effective treatments in the 21st Century.
>
> So we need to adopt, in the policy setting, ways to realize more value
> in what we do in healthcare in order to promote the availability of
> even safer and more effective treatments today and in the years ahead.
>
> Clinical researchers need to change the way that they are undertaking
> their activities, too. I have had a number of very interesting
> discussions with Elias Zerhouni and other leaders from the National
> Institutes of Health in recent months. They are talking, over there,
> about nothing short of taking steps to re-engineer the
> clinical-research enterprise to help get around some of the issues
> that I talked about before.
>
> We need clinical research that is integrated more than ever with some
> of these basic science biomedical breakthroughs in fields like
> genomics so that we don\'t end up having a longer and more costly and
> more time-consuming process for getting effective products from the
> stage of basic research ideas to the stage of proof of concept.
>
> But, beyond there, FDA has a critical role to play as well, so we need
> creative thinking in the area of regulatory initiatives as well. Going
> from a proof of concept of a new medical product to a product that we
> can actually have confidence can be used safely and effectively by the
> public and can be produced using techniques that reliably get the
> treatment that is desired to the public is not a simple matter. It
> requires solving countless more or less difficult and more or less
> challenging problems in the process of product development,
> effectiveness testing and manufacturing as well as more effective
> monitoring, I think, in the postmarket setting, too.
>
> So we have a major role to play in trying to facilitate this
> development. FDA can\'t cause medical products to come to the public
> quickly and effectively by itself. That is primarily the
> responsibility of the product developers. But we do have a
> responsibility to make sure that our regulatory processes are keeping
> up with the changes that are occurring in medical technology and are
> responding to the very critical health-policy problems that we are
> facing today.
>
> Again, at the top of that list is healthcare affordability, making
> these treatments available, making sure they are safe and effective
> but making them available at no greater expense to the public than is
> necessary.
>
> So we are undertaking a lot of activities to try to fulfill our
> responsibilities in this area. Many of these were announced as part of
> our medical technology initiative back earlier this year. I know you
> all have some materials on that and have been hearing updates from us
> by e-mail and through other means as has been going along.
>
> I just want to review some of the main features of that initiative
> right now. It had three prongs. The first prong involved conducting a
> root-cause analysis of the so-called multiple-cycle product approvals
> that have occurred recently. These are products that took more than
> one round of review before they could be approved.
>
> An additional round of review means at least an additional ten months
> or so of time before a product can be approved as well as millions of
> dollars of additional costs in product development. In undertaking
> this effort, we found, in our preliminary looks, that, in many cases,
> these situations could not have been avoided but, in some cases,
> perhaps they could have been through earlier communication and
> feedback, for example, with the companies that are developing
> products.
>
> As part of our Prescription Drug User Fee Reauthorization, we are in
> the process of implementing some pilot programs to test whether
> frequent early consultation with product developers and whether
> reviewing applications a piece at a time, rather than waiting for the
> whole application to come in, can help us avoid some of these
> multiple-cycle delays by getting the information the companies need to
> understand their way through the regulatory pathway to, then, more
> quickly and more effectively, as they are designing their studies and
> the process, to get to a determination of safety and effectiveness.
>
> It is going to require some more resources up front through these
> earlier consultations. We want to investigate whether that has an
> offsetting impact in terms of reducing our total amount of time and
> effort in the product-development process by requiring fewer repeat
> cycles and we want to see what the impact is on the time and the cost
> of developing products as well.
>
> Second, we are developing a quality-systems approach for our review
> procedures. The idea here is to apply the best-management practices
> internally as effectively and widely as possible as we undertake
> reviews. You are going to hear much more about this today. David
> Feigal is going to talk about a quality-teams approach that is being
> implemented at our Devices Center.
>
> Larry Lesko and Yuan-yuan Chiu will be talking about initiatives in
> the two implement quality systems in clinical pharmacology and in our
> new drug-chemistry activities as well. I want to emphasize that this
> is a fairly fundamental change that is ongoing in the agency and it is
> ongoing in all of our product-development centers. So we will value
> your feedback on the initial steps that we are taking in this
> direction.
>
> A third part of this initiative is that we are working to publish new
> guidance documents in areas where we think regulatory pathways could
> be improved or better defined. This includes some areas that we have
> identified as priorities for new product development where we don\'t
> think our regulatory standards have been defined or have been
> communicated as clearly to the outside world as they could be for
> diseases where the opportunities for reducing the burden on the public
> health is actually quite substantial.
>
> These include new guidances, for example, in such areas as obesity and
> diabetes treatment as well as many areas of cancer care. We are also
> developing new guidances that will address emerging areas of product
> development. So, just as a couple \"for examples\" here, these
> guidances may consider the use of bioimaging tools to help us more
> quickly and accurately map drug distribution in particular areas of
> the body where the drugs are intended to reach.
>
> To the extent that we can identify valid markers or valid biomarkers
> that are clearly related to important later health outcomes of
> interest to clinicians, that can help us in getting through the review
> process efficiently. Validated biomarkers may be able to streamline
> clinical trials by allowing for shorter follow-up times and more
> confidence that a product is going to have the desired clinical effect
> and it may allow us to enroll patients who are more likely to respond
> based on a molecular signature from these kinds of biomarkers as well
> as to make sure that, when these treatments are actually approved, we
> can use them in conjunction with diagnostic tests and biomarkers to
> help make sure that patients are getting a maximal benefit from the
> treatments that they actually use.
>
> As part of this effort, we are going to be running a number of joint
> workshops with outside experts including clinical groups like the
> American Society for Clinical Oncologists and the National Cancer
> Institute to help in the development of these biomarkers.
>
> For other technologies such as cell and gene-based therapies and
> pharmacogenomics, we also intend to establish partnerships with
> outside experts including some with the National Institutes of Health
> to help guide research programs and activities that can lead to
> approval.
>
> I want to say a little bit more about pharmacogenomics in this context
> because this is a major focus of our presentations to you today. This
> is one of the areas where we think guidance development can help and
> where we think there are significant opportunities for improving the
> regulatory process if we can use the emerging pharmacogenomics
> information effectively.
>
> Many people have argued that a stream of new medical breakthroughs
> could well be the result of integrating genomics information into
> medicine and making medicine even more of an information science than
> an art as it is today. So you are going to be hearing a lot about
> these efforts in the agency to work with drug developers and other
> product developers to use these tools and this information to
> accommodate the transformation here.
>
> Right now, much of the kind of data that could be used from molecular
> genetics information technology and related areas is lost to the FDA.
> We are not able to take advantage of it because either the product
> developers don\'t do certain studies that we think would be helpful or
> they don\'t submit the results to us for concern that this is an early
> science and it is unclear how the information fits into the regulatory
> process other than to potentially raise some red flags.
>
> It is true that most scientists, including our reviewers, still don\'t
> know what many of the pharmacogenomic patterns that we are starting to
> see in all these microarray studies really mean for their impact or
> their predictive value for the response or potential safety problems
> with the treatment.
>
> This is a problem. We cannot improve our regulatory process unless we
> can use this new information efficiently. Now, our hope is that this
> science will progress and that, one day, these kinds of genomic
> markers will be used to accurately predict a patient\'s propensity
> from suffering from a drug toxicology based on their genomic profile
> to being able to allow us to approve treatments and label treatments
> so that they can be targeted effectively to the patients who are most
> likely to benefit, again increasing the value of medical services by
> avoiding the use of treatments in cases where they are unlikely to
> have a benefit.
>
> So pharmacogenomics, if it actually works, is a tremendous tool for
> providing doctors and our reviewers with a science-based approach to
> risk management and risk assessment related to new products. But, in
> order for us to be in a position to evaluate this science and to help
> support its productive development, we have to come along the learning
> curve on this new science along with product developers and
> researchers.
>
> We are starting to do this already with work that we are undertaking.
> The National Center for Toxicological Research has a major library
> development program ongoing for genomic and proteomic information. But
> we also need help from outside experts. We need product developers and
> researchers to have efficient ways of sharing their results with us so
> that we can incorporate it into what we are doing in our review
> processes.
>
> We need to discuss this new science collaboratively, especially at
> this experimental-research phase of its development. So, during this
> meeting, we are going to be seeking your advice about ways that we can
> make that happen. Janet, Larry Lesko and industry and NIH
> representatives here today are going to all be making presentations on
> this issue.
>
> Another area where we think we can increase the value of medical
> products involves our application of risk-management principles to
> overhaul pharmaceutical good-manufacturing practices. These are the
> regulations that govern how medical products can be produced in a way
> that is recognized by FDA as leading to safe and effective reliable
> treatments.
>
> Manufacturing processes often don\'t get as much attention in the
> areas of biomedical science as they deserve. But we are working to
> change that thanks to Les Crawford\'s leadership on this issue when it
> was announced last August. And Janet Woodcock is chairing\--how many
> work groups do we have in this, like fourteen or something like that?
> It is really not a small effort.
>
> But that is appropriate here. Our GMP regulations have not been
> significantly updated or fundamentally updated in a quarter of a
> century. Meanwhile, during this time, best-manufacturing practices in
> other industries that have virtually zero tolerance for impurities or
> errors have changed fundamentally.
>
> Think about the semiconductor industry, where it was in the mid-1970s
> versus where it is today. Those companies were struggling along then
> and were wondering if there was even going to be a U.S. industry in
> this area. Instead, they adopted some fundamental changes in their
> manufacturing practices such as 6-sigma manufacturing, total
> quality-control systems with a goal of zero defects in production.
>
> As a result, they have been able to march through significant
> improvements in the productivity of manufacturing, big improvements in
> output, reductions in cost, enormous improvements in productivity that
> have translated into better value for consumers without sacrificing
> quality.
>
> In fact, they have improved quality. Their error rates, their
> precision problems, are lower than ever. Those kinds of techniques
> have not been applied to nearly the same kind of degree in the area of
> pharmaceutical and biologics production as they have been in other
> industries. We think there are lots of opportunities whereby, through
> regulatory reforms, we can facilitate these much needed changes to
> improve productivity and reduce costs of medical products.
>
> Ajaz Hussain will be talking to you about an update on the
> implementation of this program today and I am very proud to say that,
> even though this program has only been in existence for seven months
> or so, we are already implementing changes in our regulatory processes
> to take advantage of these new insights.
>
> Even if you develop drugs more efficiently, even if you produce them
> more efficiently, that is not the end of the story. Too often, the
> drugs and devices and other products that we regulate are involved in
> preventable adverse events. As many as 20 percent of Americans have
> experienced some kind of medical error and, according to the latest
> surveys I have seen, more than a third of Americans and an even higher
> percentage of doctors have family members that have been affected by
> significant medical errors.
>
> So we need to do more about that, about understanding how we can help
> our products to be used effectively not just under the idealized
> conditions of clinical trials but in the real-world conditions where
> they are actually going to be used.
>
> I am particularly concerned there about issues related to people who
> are using our products in conjunction with a number of comorbid
> conditions or other medications that are very difficult to test in a
> preclinical setting, minority populations, other special populations,
> where particular issues of safety or effectiveness may arise. We need
> to do more in the postmarket setting to uncover these adverse events
> and understand better how to prevent them. So we are working on
> developing a range of information-technology tools in addition to some
> regulatory changes that we have announced just in the past month to do
> a better job here.
>
> I would like to highlight, as well, that we have got new authorities
> in this area thanks to the Prescription Drug User Fee Act
> Reauthorization and the new Medical Device User Fee Program that we
> intend to take advantage of. So, for example, we have announced
> partnerships with hospitals such as Columbia Presbyterian under our
> so-called Marconi Program.
>
> We are expanding the MedSun Program in our Center for Medical Devices
> to, hopefully, a couple of hundred hospitals and other healthcare
> institutions in the coming year to collect more data automatically,
> not just relying on the so-called spontaneous reports from product
> developers and health professionals but more automatic data
> collections based on modern IT systems to understand quickly when and
> why there is a problem with some of the products that we have approved
> and to give us a two-way street for providing more quick and effective
> feedback to the health professionals and to the general population
> that is involved in using these treatments.
>
> You will hear an update on this program from Kelly Cronin today. This
> is, I think, a very important way to increase the value of the
> products that we regulate.
>
> Another agency priority involves providing better information to
> consumers and to patients. We are not going to have much time to talk
> about this today given everything else that is going on, but,
> obviously, it is extremely important that people be able to get
> accurate information about risks and benefits of a product to help use
> it effectively and we need effective labeling for physicians as well
> so that they can get the information they need on how a product has
> been proven to be safe and effective in guiding their own treatment
> decisions.
>
> So we are undertaking a fundamental look at our product labeling
> requirements for physicians and we are also going to have some
> activities in the coming year on improving information available to
> patients. We are working on some new guidance about risk/benefit
> information in the brief summary of direct-to-consumer advertising as
> well, lots of work in these areas.
>
> Generic drugs are another area where we can increase the value of
> products available to the American public. We need to promote the
> availability of more low-cost safe and effective options for consumers
> in this area. There are several hundred major pharmaceuticals that are
> coming off patent in the next few years that provide opportunities
> here. Generic drug manufacturers provide medications that are just as
> safe and effective as brand-name counterparts.
>
> So here is an area where we will be expanding. We propose some major
> budget expansions in this area and we are going to be implementing
> some fundamental reforms. Many people don\'t know this but the actual
> time to approve a generic drug is significantly longer than the time
> to approve a new drug even though it is a less-complex process. The
> reason for that is that multiple cycles of review are a way of life in
> the generic-drug approval process.
>
> Only 7 percent of generic-drug applications are approved the first
> time around. We are going to change that through some fundamental
> reforms in our generic review process. We are also in the process of
> implementing some regulatory reforms in the Hatch-Waxman law that
> governs generic competition to make that work more efficiently as
> well.
>
> So these are just some examples of what we are trying to do here at
> the agency, to apply the principles of efficient risk management
> throughout our activities in the context of making safe and effective,
> better, medical products available at a lower cost. That involves our
> premarket review processes. It involves our manufacturing regulatory
> processes. And it involves our postmarket activities to address
> medical errors and adverse events as well as getting better
> information to consumers.
>
> This kind of comprehensive approach is something that the expert staff
> at FDA have been doing a tremendous amount to implement. That is why I
> think the schedule here today is so packed and we really are just
> focussing on one major, but only one, area where we are trying to
> apply these principles of efficient risk management throughout the
> agency.
>
> I am going to stop my remarks there. I wanted to give you an overview
> of what you are going to be hearing about today and, again, give you a
> major plea for critical and useful input as we work to meet these new
> challenges facing the agency and these more critical challenges for
> promoting and protecting the public health that we face today,
> challenges that are more difficult and complex than ever before.
>
> Thank you very much for listening to me and thank you again for your
> contributions to the tremendously important work of this agency. We
> very much appreciate it.
>
> DR. DOYLE: Thank you for that excellent overview.
>
> Next we are going to hear from Dr. Janet Woodcock who is the Director
> of the Center for Drug Evaluation and Research. Dr. Woodcock is going
> to tell us about the quality-systems approach to medical product
> review.
>
> **Quality Systems Approach to Medical Product Review**
>
> DR. WOODCOCK: Thank you and good morning.
>
> \[Slide.\]
>
> As Dr. McClellan said, one of the parts of our new initiative on
> innovation\--one of the major efforts in our Improving Innovation
> Initiative that was announced earlier this year is the idea of,
> \"instituting a continuous quality improvement or quality-systems
> approach throughout the premarket review.\" I am quoting from the
> announcement of the initiative.
>
> So, first, I would like to run through what we actually mean by that
> because I think that probably has confused some folks. David
> Feigal\--I was interested to see, David, in your talk you just handed
> out that the Science Board review recommended this to you all in
> Devices as part of the scientific review; is that right? So,
> apparently, this is not a new idea and it was already reinforced by
> the Science Board in their previous review.
>
> \[Slide.\]
>
> What do we mean? Well, a review is a scientific assessment of
> submitted documents and data where we draw conclusions about
> conformance to scientific, technical and regulatory standards. So the
> review that we are doing within the agency on the medical-product side
> is really a scientific activity of checking conformance to standards.
>
> In the review document that we produce, we have to produce
> documentation of this. These activities are often controversial and we
> have to write our assessment down. We have review documents that are
> written documentation.
>
> \[Slide.\]
>
> However, the issue with the review process is it is not just this
> gentlemanly process of doing reviews. It is a production-scale
> activity. For new drugs, this is just a ballpark estimate that I
> pulled off our tracking systems. We do an annual number of scientific
> reviews just in the new drug review side. About 21,000 scientific
> reviews are produced. We issue about 2,500 letters out of a new drug
> side which have multiple, usually, scientific issues within them. The
> generics program estimates they produce about 5,000 scientific reviews
> a year.
>
> \[Slide.\]
>
> Although this is mass production, each of these reviews must be
> scientifically correct, apply the regulatory and scientific standards
> that have been established appropriately. They are subject, many of
> them, to intense stakeholder and scientific legal scrutiny for various
> reasons because they are implementing standards.
>
> We need to incorporate any new scientific findings or new regulatory
> guidance as we go along in these reviews, so we need to make sure each
> of these 21,000 reviews, for example, has brought in any new
> principles or any new guidance or anything that we have developed over
> the recent past.
>
> \[Slide.\]
>
> How do we assure quality of these scientific activities? That is
> really the question I am addressing here. Obviously, execution is very
> important in this area, in the regulatory area. If we have scientific
> advances, we have to make sure they are incorporated in a uniform and
> high-quality manner into our regulatory process.
>
> What has been done traditionally, and I don\'t know whether this is
> because this is a medical setting or for other reasons, but,
> traditionally, there has been sort of a craft or guild approach where
> there is a hierarchical system of control. So we have successive or
> serial checking by different levels of expertise or management over
> each one of these reviews to assure its quality.
>
> As a model of quality, that is 19th Century or earlier, I would say,
> as far as how to insure quality. It is most successful as this craft
> model for one-of-a-kind types of products.
>
> \[Slide.\]
>
> As was already alluded to, there has been a revolution in how to
> insure consistency in mass production. That started out with
> standardization issues back with Henry Ford and others and has moved
> through quality control, then the concepts of quality assurance and,
> finally, to quality systems and quality-management approaches which
> are systemic, systemswide, approaches to assuring quality.
>
> \[Slide.\]
>
> I know this is the Science Board, but I would just like to take you
> through. Some of you are familiar with this, but some of you may not
> be and I am not going include any jargon here. I am just going to say
> what the principles are sort of in a general English-language way.
>
> I like this set of statements about it. The quality-systems approach
> is; say what you do, do what you say, prove it and then improve it.
> That applies to the system as a whole. What does that mean?
>
> \[Slide.\]
>
> This sounds simple, but it is actually much more powerful and I think
> we may illustrate with some of our presentations how this can be
> applied. To say what you do, you need to identify what your vision and
> the purpose of your organization is and who the customers of your
> organization are, and even subparts of the organization.
>
> You need to define quality. We are struggling with that right now in
> the GMP world, how do you define the quality of a manufactured
> product, a pharmaceutical, another medical product.
>
> Then you define the attributes that you are going to measure the
> quality by and you define the processes by which you are going to
> produce this quality. That is saying what you do.
>
> \[Slide.\]
>
> Doing what you say, you measure and produce things that have quality
> attributes and you also have another important step which I think the
> FDA has pretty much accomplished in many areas, which is process
> management; that is, you define the process you are going to follow.
> You standardize the process. You track things within the process and
> you control the process.
>
> \[Slide.\]
>
> To prove it, you make sure that whatever your customers are expecting
> that you are producing that. You do trend analysis and have other
> metrics of whether you are achieving the quality attributes that you
> set out to achieve. And you do audits and evaluation.
>
> \[Slide.\]
>
> Then improve it has been the most challenging and difficult, I think,
> for anyone involved in the quality area because that is where you need
> to do corrective and preventive actions. You need to do feedback and
> training. If it is about science, you need to incorporate that science
> in and prove\--the organization needs to learn and change as you are
> moving forward.
>
> So that is sort of a short description of quality systems free of any
> particular jargon.
>
> \[Slide.\]
>
> But you might say to me, and that is what the reviewers say to us,
> \"Review is an intellectual activity. The review product is a
> scientific document.\"
>
> \[Slide.\]
>
> How can it be reconciled? How can we apply on to this a quality-system
> framework that really did originate in mass manufacturing? How can we
> bring these things together? That is what we are here to ask you
> because we don\'t know totally, precisely, either.
>
> \[Slide.\]
>
> But, in fact, the systems approach works very well for many processes.
> Sure, it originated in mass manufacturing but it is now being applied
> many places. I read this morning in the newspaper that a healthcare
> system won the Malcolm Baldrige award this year for applying
> quality-management principles to healthcare demonstrating,
> demonstrably improving the quality of the care within their system.
>
> There are a variety of tools and methods that can be selected for any
> specific application so we don\'t have to pretend we are making
> widgets and then apply quality principles of widgets to our process.
> We are exploring examples of how these approaches can be used in
> scientific activities; in other words, getting people in to talk to us
> about how they are applying these principles in science.
>
> \[Slide.\]
>
> I think David is going to talk a little about CDRH and the situation
> there. In the Center for Drugs, we have completed considerable work on
> process-management aspects such as procedures, tracking and training.
> We have also done considerable work on some work-product standards. I
> already told you our work product is a review. So we have gone through
> and we have established templates for the review documents, what they
> should look like, and some directions on good review practices; in
> other words, what the review activity should be, what items should be
> covered and so forth.
>
> We have done some work on quality assurance mechanisms and feedback.
> We are instituting audits and so forth to see how well these standards
> are followed. We have done very little on systems aspects such as peer
> discussion and assessment, CAPA, organizational learning and also the
> metrics, the overall metrics. So some of the presentations we are
> going give you this morning are to show where we are in a couple of
> different areas, not every discipline, and then get some of your
> feedback.
>
> \[Slide.\]
>
> The next presenters are going to give examples of some progress and
> also some proposals for moving forward in this area. Our question to
> you is really what steps are most important do you think, as we move
> forward in this, for integrating evolving science into the review
> process.
>
> There are many things we need to do, obviously. Which ones do you
> think will be most important for us to implement first with a focus on
> integrating the new science, making sure that our reviews contain the
> up-to-date scientific information and apply that to the review
> process.
>
> So our next speaker will be David Feigal. He is going to talk about
> what the Device Center is doing in this area.
>
> **Quality Teams to Improve**
>
> **Regulatory Processes**
>
> DR. FEIGAL: Thanks.
>
> \[Slide\]
>
> I would like to actually tie some themes together and this is a bit of
> a progress report. Fifteen months ago we had a presentation to this
> Board of Science Review that was led by Robert Nerem. It was a process
> that the Center spent about a year preparing for, and we took the
> recommendations of that group very seriously and actually promised to
> perhaps even come back with a more comprehensive follow-up report and
> actually pull the committee back together.
>
> But there were two of the recommendations of the science review to the
> Center and one of them was about quality systems. CDRH should develop
> and implement a quality evaluation and improvement program, and this
> should include metrics in addition to timeliness of reports. We have
> always had metrics for timeliness. We produced about a 40-page annual
> report that slices timeliness about a hundred different ways. But it
> has been more difficult for us to grapple with quality, and this is
> one of the things that the Science Board recommended to us.
>
> \[Slide\]
>
> It is also a theme that is really a theme government-wide and the
> whole issue of how do we measure performance, how do we measure value
> added\--this is just a clip from one of the federal government trade
> presses about the government-wide effort to be able to actually
> quantify the value that we add. Historically that has been done in
> terms of review times but as the Center began the process of having
> user fees to speed review times, the Center became even more
> interested in showing that we can improve the quality at the same time
> we improve the speed.
>
> \[Slide\]
>
> This fits in with a number of themes. It fits in With Dr. McClellan\'s
> initiatives that he spoke of this morning. It fits in also with the
> strategic plan that the Center developed, and as part of that
> strategic plan we have begun a process of developing a scorecard where
> we will actually be accountable, and describe in real time the areas
> that we think are key to our performance and key to some of the
> changes we need to accomplish.
>
> \[Slide\]
>
> I just wanted to show you the areas that we think are areas for key
> results and how we will indicate these. Some of these relate to our
> central mission of public health protection. We want to look at the
> effectiveness with which we identify hazards and resolve them. Some of
> them are on promotion in terms of timely availability of novel
> products. But in terms of operational accountability and quality,
> which is where we have had most of our metric, there we want to
> actually look at our application activities, all of the conformity
> assessment activities including our field programs and compliance
> activities and even business accountability and look at the quality of
> those systems.
>
> \[Slide\]
>
> One of the things that we did as a strategy to do this, we actually
> began working with an author in this area, Dr. Richard Cheng, and one
> of the things that we contracted with him to do is to actually train
> our staff in a method of continuous process improvement to actually
> take to some of our staff. We have identified 18 people and they have
> worked half time in training for the last year to actually learn how
> to be leaders and coaches in one of the methods. Many of the quality
> systems have similar elements and this is one that Dr. Cheng uses for
> teaching materials, but to actually have an intellectual framework for
> this and to have a team that actually can move out into the Center.
> So, working with one contractor we actually leverage that off 18 of
> our staff to begin this process.
>
> \[Slide\]
>
> What I just wanted to show you is one of the processes. I am just
> clipping three slides from the middle of the 20-slide presentation of
> one of the teams. What the teams do, they pick a continuous process
> improvement area and they take the review process and break it down
> into the components. They take many of the elements that Janet talks
> about, not just looking at the process but also looking at the
> quality.
>
> For example, they took a look at PMA filing decisions. This is just
> simply the facts, we didn\'t file 11 percent of them in the study
> period, which was an 18-month study period. But what was done is a
> little bit different. Instead of just looking at the ones that
> weren\'t filed, and that is an activity all the centers do because
> those get appealed and those are under scrutiny, but they also did
> peer review analysis of the applications that were filed because there
> is often the sense that there is tremendous pressure to file and can
> we identify whether or not the filing clearly met our guidelines and
> standards or whether there were some issues with that.
>
> \[Slide\]
>
> The primary issue, and this will actually help us work with industry
> to improve the quality of the submissions\--the primary issue, even in
> the applications that we filed, were concerns about inadequate
> clinical studies in the applications and disorganized applications;
> then, less frequently, lack of information about the product. I only
> bring this up as an example of a process. This is one of 18 projects
> that are implemented in the Center by this team.
>
> \[Slide\]
>
> One of the ways that they take this issue apart, and this is again
> just focusing in on the filing issue, is to look at what are the
> opportunities to really make improvements; what are the root causes of
> the problems. Some of them deal with our staff; some of them deal with
> relationships between different units in our group; and some of them
> deal with industry.
>
> \[Slide\]
>
> These are just examples of the way that this team approached this
> problem, looking at various options that could be taken both to staff
> and to the outside stakeholders. Specifying minimum filing criteria,
> we have this. The question is, is it up to date. Providing feedback,
> we actually don\'t do that very much about the quality of
> applications, except in an informal way. Education.
>
> \[Slide\]
>
> For turbo filing we actually have successful applications that are
> computerized and that have been nicknamed turbo, so that is what turbo
> refers to. Then, the issue of actually having operational
> accountability for this, a lot of times there is a feeling,
> particularly when time frames are tight, that standards have fallen or
> things have changed. If we are actually measuring this and monitoring
> this in an ongoing fashion we can actually talk about this. Of course,
> we are very interested in the stakeholder collaboration.
>
> \[Slide\]
>
> So, I show that just as an example to highlight the strategy that we
> took, which is to develop some internal competence in this area. As
> busy as we are, and all of these people had day jobs and useful things
> that they did, one of the things that struck us was that we needed to
> develop some internal resources. One of the questions to you is are
> there opportunities to actually learn from business or other
> opportunities to learn from university systems that do similar things.
>
> The other thing I wanted to give you some quick feedback on is a
> second science review recommendation which I think also relates to
> quality and peer review in a perhaps more indirect way. That was a
> recommendation about the use of expertise, particularly external
> expertise. Dr. McClellan alluded to this a little bit as well.
>
> \[Slide\]
>
> Since the science review we have developed a device fellowship
> program. The types of people that have participated in this range from
> the very senior, including a sabbatical that is planned this fall with
> a chairman in surgery from a medical school, to more mid-career
> faculty, to fellow, residents, medical students, engineering
> disciplines and other health science disciplines. This is a program
> that we have already had formal and informal relationships and fellows
> join us from all of these different universities and, actually, more
> contacts to come with this.
>
> \[Slide\]
>
> We have had cardiology fellows from Brigham and Women\'s come and
> spend six months with us. We have begun co-op programs with
> engineering schools and working with faculty. So, this is something
> that we are actually quite excited about and take quite seriously.
>
> \[Slide\]
>
> One of the things that was very helpful for this board\--this is just
> a list of the kinds of expertise that we are looking for. One of the
> ways that this board was very helpful and the science review was very
> helpful is that when it came time to actually negotiate the user fee
> bill, the science board really very clearly laid out the need for
> resources for external expertise. In the language of the letters that
> went back and forth about what was expected from the user fees it was
> actually specified that we would actually try and develop enough
> external expertise that would consume about 20 FTEs of staff. To put
> that into perspective, the Center has 300 special government employees
> on its advisory panels. There are 17 panels. They each meet about
> twice per year and that is one of our major sources of external
> expertise. All of that time from those 17 panels each year only adds
> up to 3 FTEs. With bringing in the fellows program, we have already
> added in part-time people the number of hours equal to all of the
> consultation from those panel meetings. But that still only is about
> one-sixth or one-seventh of where we intend to go with this and what
> we intend to develop.
>
> So we appreciate your support. I think that the strong recommendation
> to do this and the strong interest in external expertise from industry
> combine to actually make this part of the user fee bill so that it
> wasn\'t just focused on review time with us.
>
> \[Slide\]
>
> Implementation\--we always feel in the hot seat, and we appreciate
> your support. Thanks very much.
>
> DR. DOYLE: Thank you, Dr. Feigal. We are going to have a break and
> then two more presentations and then we will get into a discussion for
> about 30 minutes. So, at this point we can take a 15-minute break and
> reconvene at 9:30.
>
> \[Brief recess\]
>
> DR. DOYLE: Next we are going to hear from Dr. Larry Lesko, who is with
> the Office of Clinical Pharmacology and Biopharmaceutics with CDER. He
> is going to tell us about quality systems for clinical pharmacology
> and biopharmaceutics review.
>
> **Quality Systems for Clinical Pharmacology and**
>
> **Biopharmacology Review**
>
> DR. LESKO: Thank you, Dr. Doyle and good morning, everyone.
>
> \[Slide\]
>
> Thank you for having me here to talk about quality systems for
> clinical pharmacology and biopharmaceutics. I look forward to hearing
> what your comments are after the combined presentations that you are
> going to hear this morning.
>
> \[Slide\]
>
> What I am going to do in the time I have is cover five topics. I will
> talk about the organization and responsibilities of our Office. I will
> talk about what we have instituted as quality systems approaches to
> reviews; the benefits to customers, both internal and external; the
> metrics of improvement, although these won\'t necessarily be hard
> numbers; and some of the future goals that we have in the area of
> quality systems.
>
> \[Slide\]
>
> Let me begin by introducing the Office to you. You can see in the
> organizational chart, down to our divisions and teams, we are
> structured very much like any other office with an upper management
> level responsible for the strategic management and implementation of
> the program. We have three divisions, each in three separate locations
> throughout the Rockville area. Then, we have 15 therapeutic teams,
> each led by a team leader with anywhere from three to nine reviewers
> per team depending on the business of that therapeutic area.
>
> \[Slide\]
>
> This will give you a sense of the demographics of our scientific
> staff, at least with respect to their educational background. All of
> our reviewers have advanced degrees. Many of them have post doctoral
> experience of anywhere from one to three years. Several have dual
> degrees. Most of those dual degrees are M.D., M.P.H.D. degrees. We
> have a fairly active recruitment program because it is necessary.
> There is a relative shortage of clinical pharmacologists within the
> profession and we are in deep competition with the pharmaceutical
> industry to get talented people. We recruit actively at colleges of
> pharmacy, from clinical pharmacology training programs, schools of
> medicine, and while we don\'t recruit from the pharmaceutical
> industry, we average about three scientists per year coming to the
> Office from the pharmaceutical industry with experience.
>
> Our current staffing is about 95, and 70 of those represent the
> scientific review staff. On the right-hand side you can see the
> breakdown of our review staff in terms of their degree. Most of them
> are Ph.D. but we have a sprinkling throughout our three divisions of
> both Pharm.D. and M.D. as terminal degrees.
>
> \[Slide\]
>
> Let me define for you briefly what I mean by clinical pharmacology. It
> is a science dealing with human properties of the drug substance\--and
> I have underlined \"drug substance\" to differentiate it from the drug
> product\--which occur after the release of the drug from the dosage
> form. So, these are pretty much systemic properties of a drug
> substance, as well as nonclinical characteristics of the drug
> substance that relate to these human properties.
>
> To give you an example, clinical pharmacology would encompass the
> pharmacokinetics of the drug substance, the absorption, distribution,
> metabolism and excretion properties either in healthy volunteers or,
> for example, patients with renal disease. Another example of clinical
> pharmacology data that would be nonclinical would be the solubility
> and permeability characteristics of the drug substance that are
> important prerequisites to getting the drug absorbed.
>
> \[Slide\]
>
> Compare that and contrast that to biopharmaceutics, this is a body of
> science that deals with the [in vivo]{.underline} performance of the
> drug product once the drug is formulated into a drug delivery system
> and the [in vitro]{.underline} properties that relate to the drug
> product.
>
> I don\'t mean to separate the two sciences. They are integrated and
> they are overlapping but they do have distinguishing features.
> Examples would be bioavailability of a tablet. It is the percent
> absorbed or perhaps bioequivalence of two capsules. Another example
> might be the rate of dissolution of the drug product.
>
> \[Slide\]
>
> This is an important aspect of our quality system because it
> represents the flow of information throughout the review process and
> it is categorized in terms of the scientific and information content
> of what we receive in the Office. It beings at the top with data that
> comes in as part of the NDA. I consider data simply facts. It is raw
> measurements and some of the examples of that are illustrated on the
> slide. The review process, however, takes that data and begins to make
> connections between it, associations, cause and effect. I call this
> information and this is what is important in terms of understanding
> what is going on with the drug product.
>
> As a review proceeds, we move down into the third hierarchy of
> knowledge, and this is very important because it really represents the
> context for applying the information that comes out of the review
> process. This is what we know to be relevant to the therapeutic
> situation, and this is also a prerequisite to an effective review in
> terms of the risk management aspects that we have heard about today.
>
> All of this informational content, all of these hierarchies are in the
> past because they rely upon the review of the NDA. We try to go a step
> further in our review process and look at the knowledge component or
> the fourth category which is more futuristic. We attempt to understand
> things a little bit better, to synthesize new information that may be
> relevant to future reviews. In this category of knowledge we will
> frequently apply modeling and simulation, predictive tools to try to
> look at \"what if\" situations that may be relevant to the current
> review or may be relevant to some future review. So, as we proceed
> through the review these are the stages that we hope the review
> process takes on.
>
> \[Slide\]
>
> Where the information comes from is, of course, the drug development
> process. I have illustrated it here in a very simplistic linear
> fashion. But, as you can see, the clinical pharmacology and
> biopharmaceutic information comes from all of the clinical phases of
> drug development as well as the nonclinical portion of drug
> development, and under each of those boxes of clinical pharmacology
> and biopharmaceutics I have highlighted just a sample of the types of
> information that comes out of the drug development process that is
> subject to review.
>
> \[Slide\]
>
> The regulatory review process then is intended to move us through the
> data to the knowledge hierarchy and convert that information to
> customer-related knowledge. I am referring here to the internal
> customer. I will talk about external a little bit later, but this is
> the internal customer who I see as the other disciplines that we
> integrate our review with.
>
> We expect that the reviewer becomes an interpreter of data. While they
> may assess all data in an application, we ask them to prioritize and
> selectively analyze only that data that has the most impact on the
> risk/benefit assessment. We ask them to look at the factors that most
> likely impact efficacy and safety and, just as important, we ask them
> to look for data that is missing, the gaps of information that we will
> have to deal with in the product label or in a Phase IV commitment.
>
> We emphasize mechanistic understanding at all levels from the cell to
> the patient, and we view our goals as translating science into
> therapeutics. We view clinical pharmacology as a means to an end, and
> the end is an effective risk/benefit assessment when coupled with the
> other disciplines.
>
> Our primary scientific focus in the Office currently, as it has been,
> is on adverse events and managing the risk of adverse events and
> understanding the likelihood of those adverse events. For that reason,
> we focus in particular in our review on exposure-response
> relationships, drug-drug interactions which, we understand, is a major
> cause of preventable adverse events; and on the integration of new
> technology and evolving science into the review process.
>
> \[Slide\]
>
> Let\'s get to the quality systems approach to review and speak in
> terms of the customer. This is the internal customer. Our primary
> customers are the medical officers who rely on our assessments, along
> with their own, to make some judgments about risk/benefit. Our systems
> approach involves placing as much emphasis on reviewing and describing
> the connections between data and studies as on reviewing the data in
> studies themselves. In other words, we emphasize an integrative type
> of review.
>
> The figure on the left-hand side shows our Office as a matrix. it
> covers the five ODEs in the Office of New Drugs. On the right-hand
> side, under good review practices, are the ideal goals of our review.
> It really reflects our vision and mission within the Office. We want
> to generate knowledge as part of our good review practice. We want our
> reviewers to be decision makers. We want our reviewers to understand
> patient context for what they are doing and to recognize the medical
> need for knowledge that medical officers have and, most importantly,
> to communicate the science in a very clear and useful way, and not in
> the jargon of the individual scientists.
>
> \[Slide\]
>
> Our GRP is based on something we call the question-based review. It
> occurred to us some years ago that when NDAs come into the agency they
> don\'t come in with studies in any particular order. They may come in
> the order that they were conducted in for drug development, or they
> may come in the order that the sponsor would want us to look at.
> Instead of reviewing studies one by one in sequence as they appear in
> an NDA, we felt it was more important to put those aside and focus on
> the pertinent questions, not the studies or the sequence of studies.
>
> So, we developed a question-based review that is intended to integrate
> knowledge across different nonclinical and clinical studies in a way
> that addresses what the reviewer perceives as the key safety and
> efficacy issues. In addition, the reviewer looks at the information in
> a way that links it to the claims being made by the sponsor in the
> label of the drug product.
>
> As I said, there are often gaps in information so it is important for
> the review process to know the risks associated with uncertainty or
> gaps of information that are in an NDA and use appropriate label
> language, or perhaps Phase IV commitments, to manage those risks.
>
> \[Slide\]
>
> Our good review practice timeline is not that old; it is still
> evolving. But, as you can see, we began using a question-based review
> in July of 1999 and went through a series of educational steps. We had
> an Office retreat to talk about it within our Office. We had a
> voluntary implementation of it and a formal requirement of it in
> October of 2001. We are currently at the point where we are going to
> stand back and assess our GRP and question-based review for compliance
> and for effectiveness and determine where else it might be applied
> within the review process.
>
> \[Slide\]
>
> These are the five quality subsystems of our systems approach, and I
> have numbered them in a clockwise direction to give you a sense of the
> quality subsystems. It begins at the top with a template. We call it
> the clin. pharm./biopharm. review template. It is a very important
> template because it brings uniformity and consistency to the review
> process across three divisions and 70 reviewers.
>
> What we like about it, and I hope the users of it like about it is
> that it standardizes the order and placement of the subject matter. It
> doesn\'t dictate the content. That is still an individual scientific
> endeavor. But when you pick up one of our briefing packages, over time
> and in different therapeutic areas, it should look the same in terms
> of the format and consistency.
>
> We build our quality system not only around the regulations but the
> domestic and international guidances that relate to our area. Coupled
> with the regulations, these form our review standards. We have
> guidances for reviewers we call MAPPs or SOPs. These are both
> scientific and a process that creates the construct for the review
> process. In those MAPPs are frequent decision trees that aid the
> reviewer in analyzing their data and making decisions. We stay on top
> of new issues and new science through our team leader meetings, from
> which oftentimes new scientific policies emanate.
>
> Then, number four, we get into a very important part of our quality
> systems, and that is our NDA clin. pharm./biopharm. briefing package.
> This package, which represents the product of the review, is posted on
> the Internet. It is a very open, transparent process. It contains
> something for everyone. It contains an executive summary, a listing of
> key issues and important unresolved questions and recommendations
> related to the approvability of this section of the NDA.
>
> Finally, the quality systems approach ends with the NDA clinical
> pharmacology/biopharm. briefing. This is the highlight of our quality
> systems. This is a formal presentation by the primary reviewer of the
> NDA. They make a presentation to an inter-disciplinary audience. The
> meeting itself is characterized by a very good interactive dialogue
> and it represents in many ways the vehicle that we use to assess the
> quality of the review and a reality check in terms of its relevance to
> the needs of other disciplines. Following that briefing, the review
> package is reconsidered as necessary and then finalized for the
> records.
>
> \[Slide\]
>
> As I said, we are talking about internal customers and what have been
> sort of the expectations and the benefits to the reviewers with this
> approach. The quality systems approach has been excellent. I think
> what it has done for us is that it has standardized our method of
> delivering a comprehensive review. This has led, I believe, to a
> higher quality of review. Our reviews usually meet and usually exceed
> and frequently anticipate our internal customer expectations, and I
> believe this has built over the years a significant amount of trust
> with our other disciplines.
>
> I think our review process collectively has a greater ability to
> derive clinical inferences from the data. This has helped
> communication and I believe it has led to a more effective review. I
> think the process has enhanced the critical thinking of our reviewers
> through the question-based review and this has led to some
> efficiencies in our review process.
>
> Finally, I think there is an ability to recognize efficiencies more in
> our clin. pharm./biopharm. data set than we had in the past. I think
> this has instilled in our reviewers a large amount of confidence. I
> think it brings a large amount of job satisfaction and high morale to
> our staff as they go about their business of intellectual reviews.
>
> \[Slide\]
>
> As I mentioned, the briefing is really a critical component of our
> quality system. Because it is a formal presentation, I believe it
> compels reviewers to think very deeply about their review. There is
> individual accountability as you stand up in front of a group of 20 or
> 30 individuals. There is a pride of ownership. The feedback and the
> seeding of new ideas from our briefing is also critical. The
> attendees, as I said, are medical officers, toxicologists, chemists
> and this is critical to our Office in terms of continual learning and
> knowledge sharing. There aren\'t many opportunities to do this within
> a review process.
>
> For the attendees, I think in this setting there is a greater
> consideration of the comments before speaking because it is a showcase
> of people\'s comments and I think, therefore, the quality of what is
> being discussed is high. We also invite other reviewers. They may be
> new; they may be inexperienced. We invite them to the briefing. It is
> a good teaching tool and it is a form of professional growth and the
> presenter, the reviewer can lead by example in this presentation.
>
> \[Slide\]
>
> I have talked pretty much about internal customers. Let me turn to
> external customers and what we think about in a quality systems
> approach here. We think about their needs and expectations, and I am
> only going to use one example, drug-drug interactions, because it is
> such a predominant part of the adverse drug reactions scene. With
> health professionals, we know and they know that drug interactions
> represent a high percentage of preventable ADRs and are an important
> contributor to ER visits and hospital admissions. We know that from
> the literature; we know that from hearing from them. Patients, what do
> they worry about? Their top two concerns are getting the wrong drug
> and getting two drugs or more that interact. So, the problem is very
> real.
>
> We feel the work product of our review is the delivery of prescribing
> information in the label that addresses the needs of health
> professionals and patients. In the past the drug interaction section
> was very much a fact-based presentation of pharmacokinetics, area
> under the curve, Cmax. What does that do for the physician and the
> patient?
>
> There are many potentially harmful drug combinations. This information
> was often poorly utilized by consumers. Number one, they had a hard
> time finding it in the label. Number two, when they found it and they
> looked at two labels the presentation may have been inconsistent and
> unclear. Finally, what is the significance of increases in area under
> the curve, and what is the significance of this increase of 20 percent
> versus that increase of 40 percent? So, it was a real difficult
> interpretive problem.
>
> \[Slide\]
>
> What we have tried to do over the last year in particular is to bring
> more added value to the work product and improve the labels, using
> drug interactions as an example. We still, by law, by regulation, have
> to provide fact-based changes in pharmacokinetics in the label.
> However, along with the agency\'s label initiative, that information
> is now being moved to a more prominent part of the label so it isn\'t
> lost in the shuffle of clinical pharmacology information so it is
> moved to the top of the label.
>
> We have begun to establish a common language to describe drug-drug
> interactions. We now classify drug interactions as being strong,
> moderate or mild. That gives the physician and the patient a sense of
> risk when they use two drugs together.
>
> Along with that, we have also a parallel process of standardizing the
> method of interpreting drug interactions so that we can say something
> about what to do in the label, and this usually involves an analysis
> of pharmacokinetic changes using what is known about the
> exposure-response relationship for the drug.
>
> We are also moving towards expressing results in terms that are
> meaningful to clinicians such as probabilities or odds ratio so that
> somebody can make an informed decision. We don\'t know the success of
> this. It is in progress. We hope to survey customers at some point to
> assess the value.
>
> \[Slide\]
>
> As far as metrics go of quality systems, our metrics of process
> improvements since we have introduced our question-based review, good
> review practices are maybe soft metrics but valuable nevertheless. We
> feel the consistent format of reviews that we have brought to the
> process has made our review packages very readable, which is a very
> important aspect in a busy scientific environment and, as I said,
> various levels of detail. It is a predictable package.
>
> High quality reviews that I feel we produce address issues of
> relevance to therapeutics, and I think this has resulted in more
> informative questions of sponsor data that comes in an NDA. I have
> noticed over the last five years a significant increase in attendance
> at our briefings. People only attend meetings if they are valuable and
> I think this is indicative of a metric. We have had greater staff
> participation at advisory committee meetings.
>
> I think also through different means\--CDER awards and other things
> like that\--we have achieved greater recognition of the expertise and
> leadership in our Office. What I like best to say is we have frequent
> requests from the Office of New Drugs for more reviewers so I hope we
> are providing a need\--additional invitations to speak at advisory
> committees and an obviously increasing role at industry meetings.
>
> \[Slide\]
>
> Some other metrics\--efficiency and informativeness of drug
> development. I think what we have done over the last five years is
> actually identified studies that may be more germane to regulatory
> decision-making and public health. As an example, through our efforts
> and our interactions with industry we see an increasing number of high
> quality drug-drug interactions which is a major issue in public
> health, but along the way we have also been able to identify and
> convey to industry the idea that some studies aren\'t necessary for
> regulatory decision-making. I don\'t know if the companies did them
> for reasons that they had of their own or they thought we wanted them
> but, for example, the number of bioequivalence studies in applications
> has decreased significantly since we indicated clearly, through
> guidances and one-on-one meetings with companies, where they are
> necessary and where they are not. I think this allows us to shift
> emphasis.
>
> Our most recent initiative is to emphasize exposure response studies.
> While they are good, they could be much better and much more
> informative in regulatory decision-making and we are currently
> communicating that to industry through guidances and public meetings.
>
> \[Slide\]
>
> Let me just say a few words about this. Dose response and PK/PD
> represent exposure response studies and we feel they are really the
> core or the hub question in our good review practice as it applies to
> the submissions from sponsors. It is a quantitative approach to
> assessment of efficacy and safety. The reason that it is important is
> that it allows in the review to assess safety and risk of dose
> selection in quantitative ways. It allows us to evaluate the risk of
> exposure changes in special populations because of drug interactions
> or disease and come up with a rational basis for dose adjustment. As
> formulations evolve during drug development, it allows us to assess
> the clinical significance of differences in formulation.
>
> In order to implement this in the review process we have had to make
> use of extensive pharmacometric tools such as modeling and simulation
> and other statistical approaches. It has been an intellectual
> challenge for us. In the near future we will have a guidance for
> industry that describes a lot of the principles that I am referring
> to.
>
> \[Slide\]
>
> This has had a significant impact on quality of our review process and
> I think on drug development because, if I look at three categories of
> impact of our involvement with exposure response, I find that we have
> case studies that have demonstrated improved study design that assess
> benefit and risk during the end of Phase II review. In the long term,
> this can increase efficiency in drug development through greater
> interactions at this point in drug development. We know that some of
> our case studies support approval of doses that are deemed effective
> and safe during the NDA review, particularly when the doses are
> different than those that the sponsor proposed, or sometimes without
> additional clinical efficacy and safety studies which is a powerful
> impact on efficiency of drug development.
>
> Thirdly, our examples of impact have identified absent exposure
> response information that, had it been available, could have supported
> efficacy and safety and avoided delays in approval that resulted from
> conducting additional clinical trials.
>
> \[Slide\]
>
> Let me conclude with saying that we have future goals. We are
> currently in the process of a QC/QA check of our NDA reviews. We want
> to check two things. One is the compliance with our good review
> practice MAPP. We want to get reviewer feedback on the experience of
> the last 18 months and make revisions or tweaking of our process as
> necessary, and also focus on more added value areas, both internally
> and externally.
>
> We want to look at the application of good review practice to INDs and
> supplements. We have only focused on NDAs, for the most part. We want
> to establish a standardized method of assessing experience response,
> particularly for dose adjustment when intrinsic and extrinsic factors
> change exposure.
>
> Finally, we think it would be valuable, and we have said this publicly
> in other settings, to interact with the industry at a much earlier
> stage of the drug development process to talk not only about specific
> studies but about drug development plans and to jointly maybe think
> about what is necessary and what is not necessary for the decisions we
> have to make when that NDA eventually comes through the door.
>
> With that, I will conclude and say thank you for your attention and
> turn it back over to Dr. Doyle.
>
> DR. DOYLE: Thank you very much, Dr. Lesko. Next we are going to hear
> from Dr. Yuan-Yuan Chiu, who is the Director of the Office of New Drug
> Chemistry at CDER. She is going to address quality systems for CMC
> review.
>
> **Quality Systems for CMC Review**
>
> DR. CHIU: Good morning.
>
> \[Slide\]
>
> It is my pleasure to be here to present to you our CMC product quality
> review. CMC stands for chemistry manufacturing controls. Usually this
> is what people call our reviews. I am going to give you a brief
> summary of the current status of the CMC review and our
> responsibility, and then I am going to explain to you what we plan to
> do and what we are hoping we can achieve in the future.
>
> \[Slide\]
>
> As Dr. Lesko said, his office is structured based on the therapeutic
> class of drugs and, in the same way, we are have 19 chemistry teams
> and each team is responsible for drugs of a specific therapeutic
> class.
>
> \[Slide\]
>
> We also have three divisions. Our review staff are called chemists
> because they all have the basic training in chemistry. However, many
> of them actually are specializing in very different fields and they
> have academic training, some in chemical engineering, biochemistry,
> molecular biology. This is because we review a diverse variety of
> product types so we need people with different expertise. We hire
> people not just right out of university. They all have working
> experience. Some of them actually were professors and some have
> research experience, and a small number of them come from industry. We
> are not very competitive in salary and when they come, at minimum,
> they have to have their pay cut by 30 percent. That is why we are
> never successful in recruiting people from industry.
>
> \[Slide\]
>
> The purposes of the CMC review are multiple. The first one is to
> assure the quality of the investigational new drugs. We want to make
> sure that clinical trial material is safe for all phases of the
> clinical studies. We also want to make sure that clinical material is
> properly characterized and with adequate consistency and the data
> generated from expanded studies are reliable. We don\'t want the
> products to be not properly monitored with inadequate quality so that
> you cannot be sure whether the batches, the material taken by the
> patients actually is what it is supposed to be. We also review the
> NDAs and we also monitor some of the possible changes after a product
> is marketed. Therefore, we really monitor the quality of the products
> throughout their life cycle.
>
> \[Slide\]
>
> We also see ourselves as the facilitators of product development and
> approval. So, we have written guidances provided them to industry for
> various phases of drug development and for NDA submissions. We
> participate in international activities to standardize the NDA
> submissions. We encourage dialogue with sponsors to discuss their drug
> development programs.
>
> \[Slide\]
>
> I want to give you a flavor of the diversity of the product types we
> evaluate. In terms of drug substances, we have very simple compounds
> sometimes. However, we also have very complex compounds. In fact, our
> Center is the one that approved the very first biotech products. So
> far we have approved more than four NDAs in the biotech area. We also
> have products which are complex mixtures of many, many substances
> derived from natural sources.
>
> \[Slide\]
>
> The diversity of dosage forms is also very important. We have simple
> oral tablets for immediate release but we also have very complex
> dosage forms which are targeted for an action site or which are
> designed to reduce toxicity, such as liposomal products, inhalation
> products. Surprisingly probably to you, we also evaluate some devices.
> We evaluate IUDs. We have a number of NDAs for IUDs.
>
> \[Slide\]
>
> So our scientific evaluation of the drug substance includes a lot of
> different studies, data and information. I have several slides to
> explain that. I am not going to go into detail in the interest of
> time, however, you can see that even within a product the knowledge
> required to evaluate it will be very important because the product
> types are so different. Even within a product, the data and
> information will cross multiple disciplines.
>
> I also want to point out that even though in a submission we have the
> manufacturing process and control description, however, the majority
> of that data and information actually is the responsibility of our
> field offices. They assess the process validation, demonstration of
> product batch analysis, and those data are evaluated outside.
>
> \[Slide\]
>
> With all the data submitted and the studies submitted to the NDA, and
> with analysis such as the impurity profile and analysis methodology
> validation, study and clinical batches results, the NDA establishes
> specification for a drug substance at release time and also through
> shelf life. I would also like to point out that most of the time the
> NDA specification eventually becomes the published standard at a later
> time.
>
> \[Slide\]
>
> We evaluate the stability, the design of the study and the results so
> that we can establish the storage conditions and expiry or retest data
> for a drug substance.
>
> \[Slide\]
>
> The second major product of our work is to evaluate the drug product
> information. There are also multiple elements which are listed in the
> slide but I will not go over them piece by piece, however, it is
> important to point out that recently, because of the implementation of
> ICH, we are able to get some knowledge from the data for
> pharmaceutical development. Therefore, now we can understand the
> formulation design, the dosage form characteristics and the critical
> components and attributes. So, this will help us to have a critical
> evaluation of the process, of the specification we are going to set.
> Just like a drug substance, the manufacturing and the process control
> information are mostly a field evaluation function.
>
> \[Slide\]
>
> In the same way, the NDA specifications for a drug product eventually
> also become public standards.
>
> \[Slide\]
>
> Some of the container closure systems actually are used as a delivery
> device and in those cases we will evaluate that the container closure
> system is appropriate for the use.
>
> \[Slide\]
>
> We establish the shelf life for the dosage form and, because of the
> study, we will know whether you need special handling instructions.
> Whether it needs to be protected from light, things like that. We also
> review all the container labels and certain sections of the package
> insert. So, we want to make sure we have clear and accurate
> information for patients and the healthcare providers.
>
> \[Slide\]
>
> I think this gives you a flavor for the diversity of the data and the
> information we have to evaluate, and the challenges we have been
> facing over the years. The first one is, as I explained, that some of
> the information is actually reviewed by the field office so,
> therefore, the reviews actually lack of full manufacturing information
> to make a final decision. The CMC review is separate from inspection,
> with sometimes limited or no communication between the reviewers and
> the investigators. I will explain later how we rectify those things.
> The reviewers have no access to inspection results. The inspection
> observation of deficiencies we don\'t get to see. Turbo 483, we are
> not on line and we don\'t see established inspection reports, the
> final report.
>
> In the past few years, two or three years, we also experienced change.
> Because of the compressed drug development time, because the companies
> would like to launch the product as quickly as possible, there is not
> enough time to really do a thorough manufacturing process development
> and optimization becomes a post-approval activity. Therefore, in our
> submissions when we do the review, often we do not know the
> manufacturing capability.
>
> \[Slide\]
>
> As I mentioned, the data and information cover multiple scientific
> areas and the emerging new science and technology in formulating
> design, manufacturing process and novel dosage forms require the
> reviewers to be on the cutting edge of science and technologies.
> Reviewers are expected to have broad knowledge to address all
> scientific and technical issues for all types of products.
>
> \[Slide\]
>
> We also observe an increased work load of generic products. There are
> many, many interests of industry to launch generic products as soon as
> possible so there will be an increasing need for our Office to
> coordinate with the generic chemistry review. There are changes in
> patient involvement in medicine and patient expectations which we have
> to fulfill. We have to be able to meet their needs. The last remark is
> that this is consolidation of CDER and CBER products. We need to have
> the same review standards.
>
> \[Slide\]
>
> Facing those challenges, what have we done? In our Office we have
> participated in ICH activities, and because of the change in product
> process development and the shortened time or the compressed
> development time, we don\'t have enough data so what we have done is
> to implement interim specification at approval time.
>
> We use the limited clinical data and we use statistical approaches and
> set an interim specification. The final specification would be set
> post-approval when we have additional data. We also implement skip lot
> testing and sunset testing because, once we know more about the
> process or we are confident about the data, a certain test may not be
> necessary to be repeated every time and certain tests may be able to
> be dropped.
>
> We also implement limited involvement in the pre-approval inspections
> so our reviewers will be able to resolve some of the issues on site
> with the firms and we will be able to evaluate some of the data on
> site.
>
> We also have expert review teams for special products. Because of the
> uniqueness of biotech products or metered-dose inhalation products and
> botanical products, we have special people for consultation review or
> for primary review.
>
> In terms of sharing information, the lessons learned, we have
> symposia. We also have peer review. We select certain INDs and NDAs
> and present them to the entire staff at the office level or at the
> division level or at the team levels so we have multiple levels of
> peer review.
>
> \[Slide\]
>
> Like any other office, we have the good review practice which we
> implemented more than a year ago. I am proud to say the Center has
> evaluated the implementation of GRP across the offices and our Office
> received the highest score. In addition, we also established some
> unique programs so we can continue to train our reviewers in terms of
> manufacturing processes, in terms of learning the cutting edge of
> science and technology.
>
> We have coordinating committees to address common issues across
> generic drugs and new drugs. We participated in the 21st century GMP
> initiative, and we are very hopeful about integration of quality
> review and inspection to bring us additional knowledge so we can make
> more intelligent decisions.
>
> \[Slide\]
>
> The GMP initiatives mapped out the desired states of product quality,
> the product review and the inspection. Dr. Hussain will give you a
> detailed explanation of the desired state so I am not going to go over
> that. However, in order to reach the desired state we believe we need
> a better quality system for our review.
>
> \[Slide\]
>
> The benefits of a quality system are multiple. I am not going to go
> over them one by one but I think a couple of them are very important.
> First and foremost is that we will have improved scientific
> underpinning in reviews. We will have question- and risk-based review
> and the risk assessment will be based on science and the decisions
> will be based on critical analysis and thinking.
>
> \[Slide\]
>
> In addition, I think a quality system will make us dynamic; make our
> causes dynamic; make our review dynamic. Therefore, it will be easy to
> adopt changes and have agility for the future.
>
> \[Slide\]
>
> So, what are our initiatives? To have a quality system; to have
> multiple internal conceptual discussion. We have formed a steering
> committee. The committee members consist of the management of Office
> of Pharmaceutical Science and our Office. We also formed a working
> group consisting of a strong team, the best and the most dynamic
> people in our Office. We have high hopes that this team\--they are
> actually in the audience\--will be able to bring us to the future.
>
> \[Slide\]
>
> We also actually already hired a quality systems specialist as a
> consultant and he has started to interview the members. We would like
> to do certain things. A few of them are listed here. We would like to
> identify our customers. We know that patients have care providers, our
> customers, and we would like to identify all of them. Then, we would
> like to survey customers to determine their needs and to establish
> metrics for accomplishment and to establish milestones, short-term and
> long-term goals. With that, we would like to determine an
> implementation process and, hopefully, at the end we will have a
> continuous improvement program.
>
> \[Slide\]
>
> This concludes my presentation and I thank you for your attention. I
> am looking forward to having feedback from all of you.
>
> **Questions and Discussion with Board/Presenters**
>
> DR. DOYLE: Thank you very much, Dr. Chiu. This now brings us to the
> time where we can ask of the presenters any questions or present any
> comments that we may have. We have had a couple of questions presented
> to us that we should try to address as well. Specifically, what steps
> are most important for integrating evolving science into the review
> process? And, are there opportunities to learn from business or
> universities in improving the review process? With that, do we have
> any thoughts or comments? Yes, Jim?
>
> DR. RIVIERE: I have one question for Dr. Lesko. I am just curious, on
> the template that you have provided for the structure of the review,
> have you made that available to the sponsors applying and what those
> criteria are for the information in that document?
>
> DR. LESKO: That is actually a very good question. I think we have made
> it available informally through presentations at professional
> meetings. We have certainly made it available through our meetings
> with PhRMA. Whether it is publicly available on an Internet site, I
> don\'t believe it is.
>
> DR. RIVIERE: Very similar to an NIH current review, you have very
> specific areas and you know a study section is looking for that
> information. So, you know, the whole point of this is to improve the
> approval process and improve the quality of the data. If that is now
> formalized to the point where your reviewers are looking for this
> information in that format, I would think it would be very useful for
> PhRMA to look at that so they know exactly that the information they
> provide is consistent with your goal of early communication.
>
> DR. LESKO: Right. I think it is a great idea. It has been somewhat of
> an experimental phase for us using this approach, and it looks like we
> are going to move to make it more firmly established in our process.
> That might be the time to do exactly what you are saying, to make it
> available on an Internet site as opposed to an internal site and
> continue to advance it within our public contact with the industry.
>
> DR. DOYLE: Dr. Shine?
>
> DR. SHINE: First let me congratulate you on the efforts to bring
> continuous quality improvement to the process. I think this is a very
> important development and it is one that should be strongly supported.
>
> I would just make a couple of observations. First of all, Janet, when
> we started to worry about quality in the healthcare system we were
> told it couldn\'t be measured. Scientists will tell you that there are
> all kinds of ways in which people resist the notion of addressing
> these issues. I think the key in many regards is data, and I want to
> come back to that in a moment.
>
> I guess my concern is that for a long time the efforts in improvement
> in quality in the healthcare system were very much focused on process.
> JCAHO kept looking at the processes of hospitals and things of this
> sort. One of my concerns in these presentations is that there has been
> so much emphasis on process and not as much on outcomes, as I always
> like to see. Outcomes can be measured in a whole variety of ways and I
> am not talking about simply the time frame in each area, but outcomes
> can be measured when you want to talk, for example, about the
> confidence of your customers or how can one set up a way to measure
> that.
>
> We heard about different ways in which new drug applications come in.
> Are the outcomes different depending on how, in fact, the new drug
> application comes in, whether it comes in, in an orderly way or
> whether it comes in, in bits and pieces, whatever? Depending on what
> the data shows, what approaches can one take in order to improve the
> quality of the submissions and what are the outcomes you want to
> measure in that regard?
>
> But I want to emphasize that there are many, many ways to define the
> outcomes in each segment of the activity and I would urge you to look
> much more closely at that. Again, we heard metrics used but in some
> cases the metrics were about the quality of the meeting. Again, that
> is soft. Can you come up with any better outcome measure in terms of
> helping people to tell whether things are getting better?
>
> The second observation I would make is that we have certainly learned
> in the healthcare environment that it is systems that count. As you
> know, ever since we brought out the patient safety report at the IOM,
> our principal message has been \"it\'s the system, stupid\" in terms
> of drug safety and not blaming an individual.
>
> Again, we saw some approaches to subsystems. Dr. Lesko presented a
> subsystem. But I would submit to you that each of those items, one
> through whatever it is, five or six, could be described internally as
> a system, that is, the very system which allows one to move within
> each of those areas. And, I would like to see some real attention
> given to the microsystems, if you will, that underlie each of those
> because those microsystems are, in fact, where you have the greatest
> leverage in terms of improving quality, and if you have a relationship
> between some outcomes and how that microsystems works, then you
> expose, if you will, the way in which the interstices work to a much
> better extent than I think the broader conceptual activities.
>
> Finally, I think one of the things we have learned, at least from some
> industries, is the notion that one can encourage the worker, in this
> case the staff, to innovate and to do things in different ways
> provided you have an outcome measure. In other words, in the context
> of some kinds of outcome measures you can give people the
> opportunity\--and it has to be open and transparent and explicit, but
> you can give them a chance to do things somewhat differently and then
> demonstrate that they can produce a better, or if it doesn\'t work a
> worse, outcome but at least you have a context in which you create the
> opportunity for innovation.
>
> One of the things we learned, that you may or may not know, is that on
> the cardiac advisory committee that has been reporting on the outcomes
> of cardiac surgery and angioplasty, pediatric care and so forth, in
> New York State, what was remarkable was the pride that people took,
> once they got the data, in improving the system. It wasn\'t about the
> doctors choosing better hospitals or the patients choosing better
> hospitals. It was when one group saw that they weren\'t doing as well
> as another group that they tried to figure out ways to improve. So, I
> am suggesting that thinking about a way to do that could be
> constructive.
>
> DR. DOYLE: Excellent points. Thank you, Dr. Shine. Dr. Pickett?
>
> DR. PICKETT: I also want to congratulate the group for thinking about
> quality systems. The industry also has given, as you know, a lot of
> thought to quality systems. One of the things that I did not hear
> today, because in my own experience it has been difficult implementing
> quality systems without increasing head count\--I heard absolutely
> nothing about what the plans were to increase head count, I assume, to
> meet the demands of implementing quality systems across your
> organization.
>
> DR. MCCLELLAN: I just want to take a moment to respond to Dr. Pickett
> on head count. We are increasing head count. The agency is larger than
> it ever has been and this is a direct result of legislation passed
> last year. On our food side, we have added over 700 new people,
> primarily in the field to help improve our inspection activities, and
> the like, but that creates some opportunities for taking a fresh look
> at how we are undertaking our food security and food safety programs,
> which we are doing.
>
> Here, on the medical products side, we had legislation last year that
> implemented some new programs and provided some new resources as part
> of the Prescription Drug User Fee Act and the Medical Devices User Fee
> Act. The PDUFA new authorizations with increased fees is going to
> provide some new personnel, including personnel in key areas like IT
> and in post-market monitoring, that will help us implement these
> activities. The Medical Device User Fee Act, which is off to a little
> bit of a rocky start because of some issues about the right level of
> appropriated funds to go along with it, is also providing some new
> resources and we are fully committed to implementing that program
> effectively too.
>
> We are actively pushing right now for new legislation on animal drug
> user fees and there is considerable bipartisan support for that. I
> think it is mainly a matter of just getting the focus in Congress to
> get it on through, given all the other very urgent priorities that
> Congress has to deal with. These programs together provide some new
> resources as well as some new authorities to undertake these
> activities.
>
> But I don\'t want to kid you, the financial situation facing the
> government is tight. It looks like it is going to be tight for a while
> to come. So, I think we are going to have to try to make the best
> progress we can without huge new infusions of new resources and that
> is difficult given the increasing complexity and the increasing number
> of products that we are facing as a result of all the increases in
> research funding that I was talking about earlier, and all the
> information that is still at a stage of being transformed in
> understanding, as you heard in the presentations this morning. So,
> that is an area of challenge. It is one that we are trying to respond
> to as best we can given the constraints that we face.
>
> DR. DOYLE: Yes, Dr. Shine?
>
> DR. SHINE: Yes, I just would emphasize the world is filled with
> quality experts. I know that Cecil is not implying this, but you have
> to have some support people in terms of facilitation but this kind of
> quality improvement in this kind of an organization has to be
> generated internally by the people who are doing the work, who know
> how it has to be done, who can interact with each other and do it
> well. So, I think the notion of additional help is important in terms
> of facilitating that but they are not going to do the planning; they
> are not going to do the quality improvement themselves in a
> professional organization like this. I think people have to buy into
> it and they have to be committed to it in terms of their own part of
> the organization.
>
> DR. PICKETT: Could I respond to this for a second? I don\'t disagree
> with Ken. The quality obviously starts at the bench and individuals at
> the bench really are critical for quality. But there are certain other
> parts of the organizational structure\--quality control; quality
> assurance\--that do need expertise and will require additional head
> count if you want to fulfill that aspect of the overall quality
> system. I think that has certainly been my own personal experience in
> industry and I think those types of individuals are necessary.
>
> DR. SHINE: I would agree with that, not disagree.
>
> DR. DOYLE: Dr. Thomas?
>
> DR. THOMAS: Mike, I would like to comment on one of your opening
> remarks with regard to how to integrate the new sciences, and perhaps
> a question directed to any of the previous speakers. How much effort
> is actually devoted to what I will call test validation, replication,
> particularly in the area of biopharmaceutics or agro. biotech.
> products? Without this validation and replication of these assays that
> you are going to be looking at in the future, and you probably are
> already looking at some, I think you need to have some internal
> expertise within given units to assess these things. I don\'t know who
> to direct the question to specifically, but I would like to know what
> sort of effort or energies are devoted to this type of activity.
>
> DR. LESKO: I think that is a good point. In particular in our clinical
> pharmacology area when we think about things like pharmacogenomics or
> things like modeling and simulation, it would be unrealistic to think
> that everyone in the Office is going to have those skills.
>
> What we have tried to do over time is identify those skills that we
> think are relevant in proportion to sort of the integration of that
> science of drug development and tried to stay ahead of that a little
> bit and, through the recruitment process or through some internal
> training, try to get that expertise built up and sprinkled throughout
> the Office and the respective divisions in a way that gives us the
> coverage appropriate for the therapeutic areas.
>
> The reality is that all therapeutic areas don\'t need all of the new
> science and it always happens that certain therapeutic areas sort of
> advance faster than others in terms of integrating some new science
> such as pharmacogenomics or such as modeling and simulation. So, we
> try to do it that way and begin to utilize on site\--by on site,
> meaning in a division\--experts to begin to mentor other people in
> that division on the skills that we are talking about and really come
> to the reality that what we have is sort of a puzzle where each piece
> doesn\'t necessarily equal each other but together they provide us the
> coverage that we need in terms of assessing the science coming in.
>
> DR. THOMAS: Thank you.
>
> DR. DOYLE: Thank you, Dr. Lesko. Kathy?
>
> DR. CARBONE: Hi. I am Kathy Carbone, sitting in for Jesse Goodman who
> is unavailable for this morning. At CBER we have a fairly extensive
> program where we actually do some in-house testing. This is assisted
> by Sherry Lard and Deborah Jensen, who are focused on quality issues
> and specifically laboratory quality issues. We are working with all
> the laboratories which do testing to make sure that the systems are up
> to quality assurance and quality control laboratory testing standards.
>
> We also have an extensive interaction with manufacturers where experts
> on board actually assist in development of tests that have good
> utility, from our point of view, and consistent products. That is
> driven, of course, by the complicated nature of the products and the
> way in which biologic products are produced that require specific
> interaction. So, we have a fairly extensive program in-house.
>
> DR. DOYLE: Thank you. Dr. Crawford?
>
> DR. CRAWFORD: I believe, Dr. Thomas, you also asked about ag. biotech.
> That is done, as you know, in our Center for Food Safety and Applied
> Nutrition, and was put in place in terms of reviews of these products
> as they were being developed in 1992 when we formed the Food Advisory
> Committee which I was on. Since that time, they have refined those
> procedures I think very well under Jim Maryanski from the Center for
> Food Safety and Applied Nutrition. I would characterize it as a very
> reproducible system that is working well. For those that they have
> cleared, they haven\'t had any recalls or any product cancellations as
> a result.
>
> But they are continuing to look at it because the next generation of
> those kinds of products will be those that are specifically doing
> something to change the food. In the past it has just been processes
> that cause product to grow faster, and so forth.
>
> DR. THOMAS: Not substantial equivalence?
>
> DR. CRAWFORD: Yes, substantially equivalent to the products already on
> the market. As the science gets more refined there are going to be
> products that will have more vitamin C or whatever and, as you know,
> those products will have to be labeled. So, the enterprise will have
> to be ratcheted up a number of steps and quality control is going to
> be a critical part of that.
>
> DR. DOYLE: Thank you, Dr. Crawford. We have time for one more.
>
> DR. CHIU: I would also like to answer the question about new methods,
> new assays. Whenever there is a new technology or new methodology
> developed by the pharmaceutical company for new products, our
> laboratory will repeat the tests and verify the rigidity of the tests.
>
> DR. DOYLE: Thank you, Dr. Chiu. Dr. Nerem asked for the next question
> so we are going to respect that.
>
> DR. NEREM: Thanks. I didn\'t want David Feigal to get off without any
> questions, but actually my questions could be submitted to any of the
> centers.
>
> David, through the CDRH science review it was very clear that one of
> the challenges is to bring the right science to bear, and there were a
> couple of things that were talked about and I would appreciate your
> providing some kind of an update. One was that as you look ahead over
> the next five years and you have a number of people, for whatever
> reason, leaving that you actually developed some kind of a plan as to
> what you want your science to look like five years from now as opposed
> to simply falling into the trap of when someone leaves replacing that
> person by someone that looks sort of like that person. So, I am
> wondering where you are in terms of having such a plan.
>
> The second thing was in terms of using outside people. You indicated
> the 20 FTEs. Part of the suggestion from the group was that you have
> in critical areas where you don\'t have the science expertise, at
> least not to the extent you would like, have people that are sort of
> consultants on call because when you need to talk to someone you
> don\'t want to wait weeks for paperwork to go through the bureaucracy.
> One of your staff needs to be able to pick up the phone and simply
> talk to the person then. So, where are we on these kinds of issues?
>
> DR. FEIGAL: A couple of great questions. On the issue of how do we
> plan for the future, let me describe what we did with planning for the
> new hires with the user fee positions. We didn\'t simply look at the
> organizational structure and see which groups were busy, where there
> was need and proportionally distribute the resources according to the
> organizational chart. What we did instead was that we recognized that
> we had divided the product areas into six different working groups.
> For example, one of our busy groups is the cardiovascular group.
>
> What we did, we asked each of the division directors responsible for a
> product area to actually pull together a team from across the whole
> Center, not just from new product review but also from our laboratory
> programs, from our post-marketing programs or compliance programs, and
> for them to look at the kinds of things that need to get accomplished
> by the user fee goals, as well as the critical needs of the Center
> from our initiatives, from the initiatives from Dr. McClellan and from
> the Department and some of the President\'s objectives. What we asked
> each of those groups to do, Bob, was to actually give us a prioritized
> list of the hiring they needed without respect to organizational
> structure. So, the cardiovascular group could, in fact, say, well,
> they need to have some expertise in biomaterials; they need to have
> some expertise in electrophysiology; they need to have an engineer who
> does computer software without respect to where organizationally those
> people sit, and prioritize them. We essentially asked them to take
> responsibility for our overall responsibilities for the whole product
> area.
>
> Those requests are ranked and voted on, just like a study section, by
> the office directors. That is the way we do our prioritized hiring.
> So, it isn\'t on the basis that, well, if you have lost someone you
> can back-fill that position. In fact, you have to actually request it
> through this process, that there is a critical need to back-fill that
> position and time to stop to think do you want to change and reshape
> the organization, and also not to get groups into the bind of, well, I
> lost a person but the only kind of people in my unit are
> statisticians, or the only kind of people in my unit are engineers and
> clinicians. We invited people to actually think about the whole
> product area. I think that is actually going to allow the Center to
> identify needs for the future.
>
> DR. NEREM: This will be a continuing process?
>
> DR. FEIGAL: A continuing process. As the resources become available
> through the collection of fees, which is just starting now, we will
> release a certain number of slots for hiring in batches because you
> never know exactly who you are going to find and in what order, and
> have authorization to hire sort of from a pool of identified needs.
> But it is a way of actually getting involvement of the whole Center in
> thinking about where does the Center need to go rather than simply
> asking each organizational unit to do its best. Historically, that
> hasn\'t worked so badly but this actually allows you to not tie the
> needs to the organizational structure.
>
> Your second question, our biggest pool of outside experts that are
> ready-made are actually advisory committee and advisory panel members
> like yourselves who are already qualified as special government
> employees who are available, if used singly, for consultation, which
> we nicknames homework in the Center. We will often actually send out
> parts of a review or parts of a protocol, sometimes the whole thing,
> to an advisory committee member and ask them to provide expertise. It
> is not unusual to invite a single advisory committee member to
> participate in a telephone conference call with a company over an
> issue. So, that gives us a pool of about 300 people who have been
> selected for their expertise on the advisory panel system to start
> with.
>
> Then, above and beyond that, we have actually looked at one of the
> things that we have in the works which I think will be out in the next
> month or two, and that is a process for actually contracting with
> universities, with individuals who will be available to us on a
> part-time basis as essentially part-time staff, except located in
> universities. We think this might work particularly well with more
> junior faculty that don\'t have as complex relationships with industry
> as more senior faculty often do. We have started in our very busy
> cardiovascular area to see if we can develop a contract proposal for
> universities to bid on where, in fact, a young cardiology faculty
> member could review applications, could review protocols as a special
> government employee. We would probably hire them through an IPA, or
> there are many, many different mechanisms.
>
> But those are some of the things we are pursuing and moving ahead on.
> The thing we had to do to make progress was to hire someone whose job
> it was full-time to make this happen. You know, as long as we talked
> about an interest in this nobody had time to get through all the
> contracting details and punch these things through. Dr. Susan Homire
> has been very effective in putting these programs in place over the
> last year.
>
> DR. DOYLE: Thank you, Dr. Feigal. Dr. Buchanan?
>
> DR. BUCHANAN: Thank you, Mike. I would like to also follow-up a little
> bit on Bob\'s questions and reflect on our experiences in the last few
> years in terms of the evolution of our scientific staff and our needs.
> Strategic planning for your scientific hires is a critical component
> but the lesson that it has also taught us in the last few years, as we
> have had tremendous changes take place in our program orientation, is
> that we also have to try to hire some of the brightest people we can
> and have a learning organization. So, it needs an equivalent
> commitment, for example, to an FDA university where we can quickly
> retrain people to fill in the needs that we have immediately to get
> started on critical attributes. We found that you can\'t do one or the
> other. You can\'t rely on future hires. You also have to be able to
> respond quickly with your own internal learning organization.
>
> DR. DOYLE: Dr. Rosenberg?
>
> DR. ROSENBERG: I just have a comment and maybe a question. Dr. Lesko
> mentioned the initiative by the agency to get involved earlier in the
> process of interaction with some of the people that are starting to do
> that science and to participate much earlier with industry in the
> process of going through clinical development.
>
> I think I applaud that initiative but it strikes also of moving kind
> of to a dynamic role of review rather than kind of just a static role
> of review. I was wondering if the agency has considered some of the
> problems that they may run into. It sounds good on paper and I think
> it would be applauded. The problem, of course, is that these processes
> tend to be fairly long processes. They can go on for years and,
> therefore, the agency is going to have to maintain very careful
> consistency in how it works with industry through that process.
>
> I think as Janet mentioned, the more you think of this as a guild or
> an artist in science, of course, then scientific opinion and as people
> turn over in these groups, as things go on in time, particularly with
> people turnover, you end up with different opinions along that
> process. Therefore, it will be very important that when you start a
> conversation or begin that process with industry you have a way to
> maintain the consistency you are going to need so that what they are
> hearing at the end of the process is consistent with what they heard
> at the beginning of the process. It is something that I think people
> in industry have been concerned about in the past in terms of
> maintaining consistency in conversations with the agency.
>
> DR. LESKO: I agree with what you have said. I think we have the
> context with industry now and interface in meetings where decisions
> and discussions are made about the Phase III program where that is
> equally important. But I think you are right, there are things that
> have to be thought through for these earlier interactions. I believe
> we should be dynamic in the sense of interacting with companies
> earlier in drug development as opposed to living with what we get at
> the end of the day with the NDA, where we have unanswered questions or
> perhaps even some wasted dollars in terms of generating information
> that perhaps the sponsor thought we needed and we didn\'t need.
>
> In particular in the area of dosing strategies for drug development
> programs, I believe dosing strategies are set very early in the drug
> development program, even preclinical, and that is a critical area in
> terms of regulatory assessment later on in terms of efficacy, safety
> and dosing adjustments.
>
> So, I think we should do both. I mean, I think we should be both
> dynamic and facilitate efficient and informative drug development, but
> also be in our traditional role of review. This has implications and I
> would expect not all companies would welcome this sort of early
> interaction and, like any meeting, I think it would be up to the
> sponsor to determine whether this has value to them or not.
>
> It also requires resources on our part. This is not a small issue. But
> I think it is worth exploring. I think it is worth exploring in
> selected cases or with very clear roles in mind to pilot something
> like this to see what the value of that would be, both for us
> internally as well as for companies that engage in this activity.
>
> DR. DOYLE: Thank you, Dr. Lesko. Dr. Sundlof?
>
> DR. SUNDLOF: Thank you, Mike. I think Dr. Rosenberg raises a very
> critical question. The Center for Veterinary Medicine has had what we
> call a phased review process for about ten years now in which sponsors
> meet with us in the very beginning, even before they have decided
> whether or not they are going to go forward with the development. All
> of the problems that you have just mentioned we have run into but we
> have been able to work our way through all of those problems. Some of
> them we are still working on, just to be honest.
>
> Some of the things that we have done have been that we have chopped it
> up into various technical sections, the review process. When we get
> through with each one of those sections we send the company a letter
> that says this is a technical section complete and you can move on.
> You don\'t have to worry about that. One of the disciplines that we
> had to maintain is that with our reviews, especially as we have
> turnover and new things come up is that once we make a commitment to a
> company that we have reviewed their protocol and have come to
> agreement we will stand by those. We are not going to change unless it
> is something that would put the public health at risk. So, we do have
> some caveats to that but primarily, once we have made that commitment
> to the company, then we honor that commitment and that is very
> important to us.
>
> It does have some additional problems just because the submission is
> broken up into so many different pieces and it is going in so many
> different directions throughout the Center, it is hard to maintain
> centralized control over where all the pieces are. So, that has been
> our biggest challenge in this phased review process.
>
> DR. DOYLE: All right, thank you, Dr. Sundlof. I think we need to move
> on if we are going to have lunch. Next we are going to hear from Dr.
> Ajaz Hussain, who is the Deputy Director of the Office of
> Pharmaceutical Science in CDER. He is going to provide to us an update
> on pharmaceutical manufacturing initiative.
>
> **Update on Pharmaceutical Manufacturing Initiative**
>
> DR. HUSSAIN: Good morning.
>
> \[Slide\]
>
> I am very pleased to be here to share with you a progress report on
> our manufacturing initiative. There are actually two initiatives but I
> think my goal is to show you just one initiative as we move forward
> from here.
>
> \[Slide\]
>
> What I would like to do today is to share with you a progress report
> on the process analytical technology initiative that we first
> discussed with you in November of 2001 and subsequently in April of
> 2002. Then came the cGMP initiative for the 21st century. And, how
> these two sort of come together now, and to move forward I would like
> to sort of spend my time discussing a desired state.
>
> We have a number of activities, a number of programs, a number of
> initiatives ongoing and unless we define very clearly what the desired
> state is, I think alignment and the challenges associated would be
> greater. So, I would like to get some input from you on how we have
> articulated our desired state for the future and, hopefully, you will
> provide us some ways to improve that desired state.
>
> \[Slide\]
>
> The PAT initiative, the process analytical technology initiative, was,
> as I said earlier, first proposed to you in November of 2001. We
> presented this as an emerging science issue in pharmaceutical
> manufacturing. We presented several different perspectives. We had an
> academic and an economic perspective by G.K. Raju from MIT and Doug
> Dean from Price Waterhouse Cooper, who essentially outlined the
> opportunities for improvement, looking at the current efficiency
> levels in manufacturing; some of the challenges; some of the reasons,
> root causes for why things are as they are.
>
> We heard from Norm Winskill and Steve Hammond from Pfizer about their
> perspective on new technology and innovation in manufacturing.
> Essentially, they outlined for us that industry has adopted a \"don\'t
> use\" or a \"don\'t tell\" approach to new technology and innovation.
> Under the \"don\'t use\" scenario, essentially because of the
> regulatory uncertainty, industry says let\'s not use any new
> technology. Or, if they desperately need new technology they will do
> it, but then they will provide the routine type of information to the
> FDA and be in the \"don\'t tell\" scenario. We were extremely
> concerned by this because this is not in the interest of public health
> and this is not in the interest of the country. So, we really needed
> to address this.
>
> In April we had a presentation by Ray Scherzer, who is senior vice
> president of manufacturing at GlaxoSmithKline. Essentially, he came
> and sort of challenged the industry itself, and his presentation was
> titled \"challenge to PhRMA industry\--quality by design.\" The
> perspective he shared was that manufacturing, although being treated
> as a stepchild in some cases, is a significant part of the industry; a
> critical part of the industry, as well as that the technology that
> exists for manufacturing and improving the efficiency of manufacturing
> exists already outside and not within the pharmaceutical manufacturing
> arena.
>
> Therefore, I think we actually posed a question to you, is it
> appropriate for FDA to take the lead on this initiative and actually
> facilitate introduction of innovation and new technology in
> manufacturing? Essentially you sort of endorsed that proposal and we
> moved forward from there.
>
> \[Slide\]
>
> Following our proposal to you, we set up an advisory committee under
> the Advisory Committee for Pharmaceutical Science, a PAT subcommittee,
> and we held three meetings. These meetings and deliberations were
> focused on definitions of what process analytical technology really is
> in the pharmaceutical context, benefits and scope. Essentially, the
> discussion led to the scientific underpinning of process
> understanding. PAT is essentially the science of manufacturing and
> understanding the process itself, not just having a sensor on-line,
> and so forth.
>
> We identified perceived and real regulatory hurdles. I think the point
> I want to make here is that we have perceived hurdles that have a way
> of becoming real hurdles. I think that we saw that in the internal
> hurdles a company has. I was actually a bit pleased but I sort of held
> myself back at the end of the third meeting, when the industry
> representatives on the PAT subcommittee said that FDA is not the
> hurdle; industry itself is the hurdle for us. I would like to believe
> that but I think we are all part of the same system; we are all part
> of the problem; so we all have to be a part of the solution so I think
> we all have to work together to remove these hurdles.
>
> We also identified the need for cross-discipline communications.
> Pharmaceutical manufacturing in particular essentially works from the
> art of pharmacy compounding and to some degree I think our thinking is
> somewhat similar in that context. To give an example, I think we make
> tablets the same we made tablets a hundred years ago. The scientific
> underpinning of that in terms of predictability and generalization has
> not really occurred. So, we really need to bring pharmacy, chemistry
> and engineering together as a discipline of pharmaceutical
> engineering.
>
> We also identified approaches for removing these hurdles. I will share
> some of those with you. We also had excellent case studies presented
> by company representatives such as Bristol-Myers Squibb, Pfizer, Glaxo
> and others. We also discussed a general approach to process
> validation. But I think most importantly, we actually developed a
> training and certification curriculum for our staff. We are proceeding
> with that.
>
> \[Slide\]
>
> But before I sort of outline some of this to you, let me just share
> with you the people involved in this initiative. This is the Office of
> Regulatory Affairs, Center for Drugs and Center for Veterinary
> Medicine initiative right now. We have a steering committee, a policy
> development team, training coordination, as well as a team of
> reviewers, inspectors and compliance officers that are being trained
> and being certified in some of these new technologies. The
> certification program is based on the curriculum we developed through
> our discussion and deliberations of the PAT subcommittee.
>
> Three universities, University of Washington, Seattle, University of
> Tennessee, Chemical Engineering and University of Purdue, Pharmacy
> have been brought together to provide this training. This training has
> both didactic and hands-on experience at these sites.
>
> \[Slide\]
>
> In summary, what the consensus of these deliberations was is that
> process analytical technology provides an opportunity to move from the
> current \"testing to document quality\" paradigm to a \"continuous
> quality assurance\" paradigm that can improve our ability to ensure
> quality was built in or was by design. We felt that this was actually
> the ultimate realization of the true spirit of cGMP.
>
> This provides greater insight and understanding of the process
> itself\--at, on, or in-line measurement of performance attributes. I
> want to sort of distinguish that from measurement of, say, pH,
> temperature and so forth. We are not talking about those type of
> measurements. We are talking about measurements that can predict
> product performance. Real-time or rapid feedback controls focus on
> prevention; potential for significant reduction in production as well
> as development cycle time; and minimized risks of poor process
> quality, thereby reducing risk itself and also reducing regulatory
> concerns.
>
> \[Slide\]
>
> What we were able to do was to create a conceptual framework for PAT,
> not piecemeal. This is quite a complex slide and I am not going to
> walk you through this slide. The slide is just to illustrate that we
> have addressed every part of the manufacturing process, including
> development optimization, continuous improvement and how do you bring
> a multivariate systems approach to risk classification and mitigation
> strategy to be part of the PAT system. This is the framework that we
> are using to develop our draft guidance. I will not, as I said, walk
> you through all of the elements of this.
>
> But I would like to sort of emphasize that, as we move forward,
> product and process quality has to abe based on knowledge. I have
> distinguished knowledge from data. I would sort of submit to you that
> I think many of our decisions are data-driven decisions where we
> cannot generalize, we can not predict and, therefore, the level of
> sophistication is not what it can be.
>
> So, if the question to the FDA review staff as well as inspection
> staff is to assess whether quality was by design, what is the
> information that they use to make that assessment? I think that is the
> key. The information that is submitted to us in the review or when it
> is held at the site tends to be more data driven. What I mean by that
> is that you will have bits and pieces of data arranged to form
> information and we say, all right, this drug is stable, therefore, it
> is all right. But when there is a change in any manufacturing process
> you either have to repeat and retest every package to say the change
> did not impact and in a knowledge-based system you will have
> understood the critical formulation variables, and so forth, and be
> able to say this is not a critical change and, therefore, you can make
> a decision in a different way.
>
> Today I would submit to you we are at the bottom of this knowledge
> pyramid where our decisions are driven by data that are generally
> derived by experiments and as we move up this knowledge pyramid, going
> towards mechanistic understanding and first principles of these
> systems, I think that is the direction we want to move in. The PAT
> process actually brings us in the middle of this pyramid where
> actually we are moving towards establishment of causal links that are
> a predictor of performance.
>
> \[Slide\]
>
> So, the regulatory framework under which this initiative will sort of
> conclude in terms of a draft guidance is that the PAT tools are not a
> requirement. The current manufacturing processes do provide quality
> product but they are not as efficient, but the technology is really
> new so it cannot be a requirement. Industry and companies will need to
> decide whether it makes sense for them, whether they have the know-how
> to move forward. So, that is an important question that we have posed
> to you and you sort of have endorsed that.
>
> Research exemption I think is an important part of this because one of
> the biggest hurdles we have is if you put new technology on an
> existing manufacturing line you will see problems, or you will see
> trends which might be considered as problems, whereas, if you had not
> done so, there would not have been an issue at all. So, continuous
> improvement without the fear of being considered non-compliant is a
> major hurdle and a research exemption framework. Again, Dr. Woodcock
> presented a case study on how we intend to use sound statistical
> principles for addressing some of these issues that will be part of
> this draft guidance.
>
> Regulatory support and flexibility during development and
> implementation\--this is the PAT review and inspection team. This is
> to eliminate the fear of delayed approval and actually dispute
> avoidance as well as resolution for some of these issues.
>
> Science- and risk-based approach\--low risk categorization based on a
> higher level of process understanding. So, if you link regulatory
> scrutiny to the level of process understanding you are able to provide
> better incentives.
>
> \[Slide\]
>
> The strategy for moving forward has been to conduct workshops, and we
> have done several workshops now, both in the U.S. and Europe. We have
> participated in some workshops in Japan also. I forgot to mention
> that. The scientific discussion and debate that occurred following our
> PAT subcommittee was very important and it brought in debate across
> disciplines\--pharmacy, chemistry, chemical engineering. And, some of
> this debate was emotional because I think you are dealing with
> different disciplines and some of the common issues and how different
> disciplines address that.
>
> We had debate across organizational units between development and
> manufacturing. For example, manufacturing folks say you develop a
> product and throw it over the wall and we have to deal with it, that
> type of debate and, then, the regulatory departments and what sort of
> regulatory policy and process there should be.
>
> So, the general guidance that we will issue in a few months as a draft
> will sort of encompass all these aspects. It will not be a technical
> guidance; it will be more of an information guidance on process and so
> forth. We plan to bring other guidances out on the scientific and
> technical aspects. But when we release this guidance we will bring
> together the engineers, the chemists, the pharmacists together as part
> of the workshop for training.
>
> \[Slide\]
>
> I think the most important point that I am very proud to share with
> you is that although FDA initiated this initiative, we cannot champion
> it. We need champions to take over from us, and the champions have
> been created. Champions to drive this initiative towards a shared
> desired state are coming from industry. Some examples of companies
> which have come forward are listed there but, more importantly,
> academia. We started with the MIT presentation by Purdue, Washington,
> Tennessee, Michigan\--you can look at the list of schools in the U.S.
> where we have established contact and they have already introduced PAT
> in their curriculum as well as research programs. But also London,
> Bradford, Basel have already started working towards their PAT-based
> curriculum and research program. We plan to work with several
> universities in Japan this summer. But more importantly, I think you
> have pharmaceutical engineering programs as part of clinical
> engineering departments now, and PAT has been introduced in these
> programs at Purdue, Michigan, Rutgers and so forth.
>
> One challenge that we are still working on is that the instrument
> manufacturers, the sensors and so forth, do not have a common voice.
> We want to bring them together as an association to deal with the
> common issues.
>
> \[Slide\]
>
> Moving forward, we have hired several experts. We have an intramural
> research program focused on our needs. We are learning from other
> industries, especially linking to ASTM and their organizations where
> other industries have already done this. We are in the process of
> finalizing a collaborate development agreement with Pfizer on issues
> with respect to chemical imaging and other on-line technologies. We
> are in the process of finalizing an inter-agency agreement with the
> National Science Foundation to be part of the Center for
> Pharmaceutical Processing Research.
>
> \[Slide\]
>
> I think the main point I want to make here is that I think this
> started as a small initiative but it could not have proceeded to its
> ultimate goal without the cGMP initiative. So, this is going to be
> part and parcel of the entire initiative that was introduced, and this
> will be an example of how a science- and risk-based system approach
> could be brought to bear.
>
> \[Slide\]
>
> The cGMP initiative was introduced on 21st of August of 2002. I would
> like to call that initiative a drug quality system for the 21st
> century initiative because it is broader than just GMPs. It also
> includes review. It also includes all aspects.
>
> The objective here was to sort of take time to step back and evaluate
> the currency of our current programs on product quality, such that the
> most up to date concept of risk management and quality system
> approaches are incorporated while continuing to assure product
> quality. The latest scientific advances in pharmaceutical
> manufacturing and technology are encouraged. The management of the
> program encourages innovation in the pharmaceutical manufacturing
> sector.
>
> \[Slide\]
>
> The submission review program and inspection program operate in a
> coordinated and synergistic manner. You heard from Dr. Yuan-Yuan Chiu
> about some gaps that we need to fill. Regulations and manufacturing
> standards are applied consistently and FDA resources are used most
> effectively and efficiently to address the most significant health
> risks.
>
> \[Slide\]
>
> The scope and timeline of this initiative\--this is veterinary drugs
> and human drugs, including human biological drug products.
> Organizations involved are ORA, Office of the Commissioner, CBER,
> CDER, CVM with involvement of CFSAN and CDRH depending on the issues
> to be discussed, for example, electronic records. We have identified
> 14 task groups. We have 13 active. Dr. Woodcock is chairing that. We
> have a two years timeline for this. The immediate goals were to
> complete certain tasks, which we announced in February of 2003. We
> have intermediate and long-term projects.
>
> \[Slide\]
>
> In your handout packet you have a summary progress report so I will
> not go through, line by line, all the activities but I want to share
> with you the major highlights. We have issued a draft guidance on 21
> CFR Part 11. We have issued a draft guidance on comparability
> protocols for CMC information and manufacturing changes in a more
> proactive way than what we do today.
>
> \[Slide\]
>
> We are working on different aspects: Center involvement in the cGMP
> warning letters, a process for the Center to review that. A technical
> dispute resolution process for cGMP disputes is being considered;
> emphasizing risk base appropriate to the work planning process;
> including product specialists on the inspection teams. I think there
> are some best practices that CBER has that I think we can learn from
> and bring best practices to work so we want to sort of evolve a
> program which integrates the review and inspection in a synergistic
> manner; improving the operations of team biologics; enhancing
> expertise in pharmaceutical technologies; pharmaceutical inspect rate;
> quality management system; international collaboration; and holding a
> scientific workshop with stakeholders. As I said, you have a progress
> report in your handout. I will not go through each one of those but if
> you have any questions I will be glad to try to answer those
> questions.
>
> \[Slide\]
>
> I would like to sort of have your feedback on the desired state. It is
> I think very important that we all define the desired state for
> pharmacology manufacturing for the 21st century, and this desired
> state has to become a shared vision for public health not only from an
> FDA perspective, but from an industry perspective as well as from
> academia. I think it will not only help us move in a synergistic
> manner towards this desired state, but also identify scientific and
> engineering gaps that need to be filled and how to fill those.
>
> \[Slide\]
>
> In our announcement on February 20th we articulated the desired state,
> and the principles that this articulation was based was as follows: We
> recognize that pharmaceutical manufacturing is evolving from an art
> form to one that is now science and engineering based.
>
> Effectively using this knowledge-and I underscore knowledge\--in
> regulatory decisions in establishing specifications and for evaluating
> manufacturing processes can substantially improve the efficiency of
> manufacturing as well as our regulatory processes.
>
> The initiative that we have started is designed to do just that
> through an integrated systems approach to product quality regulation
> founded on sound science and engineering principles for assessing and
> mitigating risks of poor product and process quality in the context of
> the intended use of pharmaceutical products.
>
> \[Slide\]
>
> So, that is our framework to sort of defining the desired state which
> is as follows: Product quality and performance is achieved and assured
> by design of effective and efficient manufacturing processes. Product
> specifications are based on mechanistic understanding of how
> formulation and process factors impact product performance.
>
> I want to sort of underscore that. If the information available in
> submissions is limited, for example, if you have done seven or eight
> pilot batches and that is what you have used for clinical development
> and these are your specifications for those batches, if you don\'t
> have a mechanistic understanding you say, well, this was your lowest
> dissolving tablet so this is your specification. So, you are setting
> specifications based on the capability of your pilot batches and then
> you set up a system later on because you cannot achieve that
> capability during routine manufacturing. So, if you move away from
> that modality of specification setting to more mechanistic
> understanding, it really will improve the process.
>
> \[Slide\]
>
> Continuous real time assurance of quality, that would be sort of the
> manufacturing perspective. From an FDA perspective, how can we
> facilitate that? I think we can do that as follows, if our regulatory
> policies are tailored to recognize the level of scientific knowledge
> supporting product applications, process validation and process
> capability. Today I think we have done an excellent job of harmonizing
> and coming up with minimum standards, but in doing so we do not have
> good means for saying this company has done better science than this.
> So, we treat the same company the same without recognizing the level
> of scientific understanding underpinning their application in this
> area. So, we could sort of open that up and actually understand and
> reward or provide incentives for good science.
>
> So, risk-based regulatory scrutiny would lead to a level of scientific
> understanding of how formulation and manufacturing process factors
> affect quality and performance, and the capability of process control
> strategies to prevent or mitigate risk of producing a poor quality
> product. So, you can see the regulatory incentives removing the
> hurdles of good science sort of comes through some of these
> statements.
>
> \[Slide\]
>
> I will stop here and would really welcome any suggestions that you may
> have on how we may improve our articulation of the desired state; what
> considerations we should have when we sort of discuss this with our
> stakeholders; and how we should align our processes better. Thank you.
>
> DR. DOYLE: Thank you, Dr. Hussain. It is heartening to see the
> progress that you are making in this area, and thank you for that
> update. Next we are going to hear from Kelly Cronin, who is Senior
> Advisor for the Office of Policy and Planning at FDA. She will provide
> an update on the patient safety initiative.
>
> **Update on Patient Safety Initiative**
>
> MS. CRONIN: Thank you and good morning.
>
> \[Slide\]
>
> I am going to spend the next 15 minutes trying to give you an overview
> of all that we have been working on in recent years regarding patient
> safety, but with a particular focus on our recent efforts with
> strategic planning.
>
> \[Slide\]
>
> I am going to start by just giving you an overview of this strategic
> planning effort, and while I go through the various areas of our focus
> I will give you an update on ongoing initiatives, many of which you
> might have read about recently in our announcement last month.
>
> I also want to try to articulate our vision for the future and get
> your feedback on that, as well as articulating some of the challenges
> that we are going to have in trying to make that a reality.
>
> \[Slide\]
>
> I think as mentioned briefly this morning, there are five key areas to
> the strategic plan:strong FDA; risk management that has a focus on
> really pre-market activities and programs; better informed consumers
> and there are many different initiatives trying to get more
> information out to consumers; and patient safety, and most recently we
> have expanded this to consumer safety with the intent that we would
> like to be broader than medical products and also including foods,
> dietary supplements and animal health products. Then, the last major
> area of this strategic plan is counter-terrorism. But this will give
> you a sort of overall idea of how this fits into the larger effort.
>
> \[Slide\]
>
> Our primary goal with patient safety is obviously to improve both
> patient and consumer safety by reducing risks of regulated products,
> and by regulated products as I just mentioned, we mean medical
> products, devices, drugs, biologics, vaccines, blood, as well as
> products for animal health. We have three primary objectives within
> this effort. We want to enhance our ability to more rapidly identify
> risks associated with these products. We want to increase our capacity
> to analyze these risks. We also want to be taking appropriate actions
> to communicate risks and actually correct problems that are identified
> and known.
>
> \[Slide\]
>
> In order to think through all these issues and come up with a thorough
> strategic plan and action plan that will be implemented in the next
> two years, we have put together four different agency-wide working
> groups. There are 55 people involved across five centers at this
> point.
>
> The first one is focusing on how we can improve our current reporting
> systems. So, the adverse event reporting systems that are across CFSAN
> and CDER or CBER, CVM are all our focus.
>
> We are also trying to take a more careful evaluation of all the
> external data sources that exist to better identify risks. We
> currently have access to many in-house healthcare databases from
> payers, from the CDC, from AHRQ that we do actively use, but we are
> trying to do a more comprehensive inventory and address how else we
> could be accessing data that is outside the agency.
>
> We would like to improve our risk communication efforts both to
> consumers and healthcare professionals. Historically, we have been
> very reliant on certain types of dissemination vehicles and we are
> trying to think more broadly now about how we can partner with people
> and implement some new ways of communicating directly to stakeholders.
>
> We are also trying to develop other approaches to control risks and
> reduce risks. A good example most recently is the bar coding rule.
>
> \[Slide\]
>
> Relevant to the adverse event reporting systems, we recently announced
> the proposed suspected adverse drug reaction rule which has been many
> years in the making. It was largely developed in conjunction with
> industry and many different foreign regulatory bodies. In essence, it
> proposes new standards to improve both the quality and the usefulness
> of the data that we are collecting, but it is also going to reduce
> burden in that it does allow for one common set of definitions and
> procedures for reporting across all of these countries.
>
> There are many different features to this rule. We will be getting
> expedited critical safety information. We will be getting more volume
> of information on medication errors. But it is a 500-page rule and we
> don\'t have time to get into it in too much detail today.
>
> \[Slide\]
>
> Again, with adverse event reporting systems we have been continuing to
> implement the MedSun program. We are currently across 80 facilities in
> the U.S. and we hope to expand to additional 100 in the next year. For
> those of you who are not familiar with this, it is an Internet-based
> reporting system that is allowing more direct interaction with
> healthcare professionals to get device-related adverse event reports
> in.
>
> We are also trying to identify potential partners that are already
> active in the patient safety arena, that are collecting various types
> of reports related to our products. One company in particular we have
> recently met with has 50,000 reports already collected across 100
> different hospitals in the country, and we would like to be able to
> develop the capacity to be able to get this information in on a
> regular basis since it is really above and beyond anything we would
> normally have access to.
>
> \[Slide\]
>
> As I mentioned before, we are also trying to look carefully at the
> databases we currently do have in-house to identify and analyze risks,
> but we are thinking beyond that now and trying to look across 24
> different databases, both public and private, to ascertain how we can
> better measure exposure and risks associated with all of the products
> of interest. Many of them we have identified and tried to target to
> address the concerns that we will likely have under products
> specifically approved under PDUFA III since there will be some funding
> available to do some surveillance of those products. We would also
> like to plan forward, not just with 03 but plan to tie in to the risk
> management programs that will be proposed under PDUFA III but to look
> at future years and see how we can meet these needs.
>
> \[Slide\]
>
> Another important project we have going on related to external data
> sources i the Marconi Project. This has been recently mentioned in
> announcements. It is coming out of a private-public collaboration with
> the Marconi Foundation and an initiative called connecting for health
> that is involved with setting clinical standards to enable exchange of
> healthcare information. Essentially, what we are doing is partnering
> with healthcare facilities that will be sending in periodic reports,
> but it will be automatic. So, if a woman on thalidomide is tested
> positive in a pregnancy test, it will automatically facilitate a
> report that will go into the agency.
>
> We actually plan to get data starting in June and this really will be
> our proof of concept study. We hope to expand this effort with the
> healthcare providers that are participating now and look more towards
> signal detection and trying to identify unexpected events, as well as
> try to perhaps tie this into some active surveillance of the products
> that will be marketed or approved under PDUFA III.
>
> \[Slide\]
>
> We are also going to be developing a strategy specifically to
> facilitate this ability and we have the interest of many of the
> providers that are already engaged in this effort. There have also
> been other activities ongoing in the Department of Defense in this
> area. So we hope to try to learn from everyone who has already been
> involved to try to use the tools and come up with a better way for
> real-time assessment of product risks.
>
> Of course, there are some concerns with HIPAA given that we will
> likely have access to electronic medical records. We have attorneys
> with expertise in this area who are very much involved with the
> working groups and are thinking through these issues carefully.
>
> \[Slide\]
>
> In risk communication, we have recently completed an inventory of all
> the types of risk communication efforts that we have going on across
> the agency to try to get a better handle on our baseline. We think
> there are some known weaknesses but we do really need a more careful
> evaluation of some of our key ways of communicating to better
> understand how we can improve them and also build on them. For
> example, we have known historically that the package insert has a lot
> of very valuable information but it is perhaps as user-friendly as it
> should be to most physicians.
>
> We have also started exploring new ideas as to how we could perhaps
> get more information out on a regular basis to providers, in
> particular, who are prescribing products with some known risks. While
> we do have many different mechanisms for doing that now, we would like
> to try to develop better ways of doing that, perhaps emailing certain
> groups of physicians who are prescribing products, and doing that
> based upon the idea that we would be facilitating the collection of
> additional information so we could better ascertain the true risks.
>
> \[Slide\]
>
> We also have an ongoing initiative, called the DailyMed. This is in
> conjunction with the National Library of Medicine. This is also
> related to two different rules, both the physician labeling rule as
> well as the electronic labeling rule. What we hope to accomplish with
> this initiative over the next couple of years is providing healthcare
> information systems with product labeling that will be updated on a
> real-time basis so that that can be fed into various types of tools
> like decision support technologies that could be used at the point of
> care.
>
> We also have a variety of proposed initiatives. Right now we would
> like to get better information out over the Internet and we don\'t
> have a very well coordinated place for people to go that would give
> you all the information that would be relevant and important for all
> of our products. We would also like to expand on our electronic
> communication of tools we have already developed, like the patient
> safety news, which should be relatively low cost and easy to do.
>
> \[Slide\]
>
> I think I mentioned before that we have recently proposed the bar
> coding rule, several weeks ago. This will require bar codes on all
> prescription drugs, over-the-counter drugs packaged for hospital use,
> vaccines, blood and blood components. This will allow for unique
> identification of these products. They will be able to scan them in
> and identify them through NDC codes. This is expected to facilitate
> the uptake of scanners across the country at hospitals and, in
> essence, prevent a lot of medication administration errors which are
> very prevalent. So, things like the wrong dose, the wrong drug or the
> wrong time to administer a product could be avoided.
>
> I mentioned before that our last group has focused on risk prevention
> and control. We are identifying many different actions that we could
> take under our current statutes to try to correct or prevent problems.
> Most recently we have been talking about how to take a more risk-based
> approach to recalls. While these ideas are early in our thinking, we
> are thinking that this could perhaps improve in a more systematic way
> how we handle a lot of our actions.
>
> We also have recently released concept papers on risk management and
> pharmacovigiliance and there is actually a series of public meetings
> ongoing this week downtown to discuss these concepts. We hope that in
> conjunction with these efforts we will be able to better prevent risk
> given that, at the time of approval, it is more likely that the
> industry will be engaging with us to figure out how we can work
> together to prevent risks.
>
> \[Slide\]
>
> We have various different partnerships that are under way or at least
> being explored. With CDC we have had several discussions about how we
> could improve our data collection by adding on specific modules for
> drugs and devices and potentially foods through their existing
> surveillance systems. There seems to be a lot of interest and support
> of this idea, and it really could drastically improve our ability to
> collect information related to device-related infections and adverse
> events that occur in the emergency room.
>
> With AHRQ we have been exploring different ways that we can facilitate
> data collection specifically with adverse events. There has been a
> portal that has been under discussion for quite some time now that
> could perhaps facilitate the collection of data from hospitals.
>
> We also like to share the expertise that we have in this area. Since
> legislation is pending on patient safety right now, that could create
> a national patient safety database. We would like to work with them
> and be involved in that effort in thinking through how we might design
> and access data that would be coming into that database.
>
> With the VA we have been under discussions as to how to sort of
> continue our collaboration with them. Historically, we have used their
> pharmacy benefit management database which has been very useful for
> specific projects. But we would like to expand our efforts with them
> and use their data sources on a more regular basis. They are actually
> quite ahead of the curve in terms of electronic medical records. They
> have a variety of health services researchers and
> pharmacoepidemiologists who are very interested in collaborating with
> us.
>
> \[Slide\]
>
> We have also been trying to think about how we can work with different
> outside organizations to improve communication. Most recently we have
> been discussing this with the Joint Commission. We have several
> projects that we would like to pilot with them. In fact, we have a
> meeting this afternoon with them. They, in particular, have a lot of
> interest in root cause analysis associated with medical errors and
> would like to be a part of getting risk messages out to various
> organizations.
>
> \[Slide\]
>
> In the future, we would like to obviously build up our spontaneous
> reporting systems but, given the voluntary nature and the fact that it
> is passive surveillance, we would really like to move to a system that
> would be much more automated and less reliant on healthcare
> professionals to take time out of their busy days to report to
> manufacturers and to us. So, we really would like to be using more
> automated tools to collect data, to analyze data and the Marconi
> Project is really the most relevant example of an effort there.
>
> We also need to make sure that we are going to have a flow of
> information across information systems. Right now there is a lot of
> effort under way to develop standards to facilitate this but we feel
> we really need to be part of this effort to make this type of thing
> work. Again, we want to be continuing to use healthcare databases and
> other outside sources so we can improve our ability to monitor risk on
> a more real time basis.
>
> \[Slide\]
>
> We feel that the better we become at rapid detection of adverse events
> and medical errors, the better able we will be to take steps to
> actually prevent them. We really do want to improve our risk
> communication with the intent that informed healthcare professionals
> and consumer are going to be able to make better decisions about their
> role in the healthcare system, and also be able to prevent obviously
> the many adverse events, morbidity and mortality and associated
> healthcare costs.
>
> \[Slide\]
>
> As I mentioned before, we have many different challenges. Of course,
> we have budget constraints. We have our two-year action plan that is
> focused on being resource neutral, meaning that we are not
> anticipating any additional funds. So, we are trying to establish what
> is feasible given the FTEs we have across the centers and the
> expertise that we have across the centers, but we also realize that we
> are going to have to rely on partnerships with healthcare providers,
> with other government agencies, with accreditation bodies to really
> carry a lot of this through.
>
> Our new initiatives are also very IT intensive. We realize that we
> have to be thinking about a lot of new and innovative ideas in the
> context of consolidation which is being directed by the Department. We
> really do at this point have less control over how we are spending our
> IT resources when we really need to be thinking creatively about how
> we are going to be making electronic labeling a reality and getting
> that directly to the provider. Also with some limited resources, we
> have some issues with trying to retain and recruit talent.
>
> We also have legacy IT systems that we are going to need to transition
> out of but, yet, we can\'t interrupt the functioning of our programs.
> So, these are all considerations that we need to keep in mind as we
> are moving forward. I think that we are trying to deal with our
> barriers and our obstacles as best we can, and we are being very
> creative in our use of partnering and what-not, but these are
> challenges that are not going to go away. Thank you.
>
> **Questions and Discussion**
>
> DR. DOYLE: All right, we are getting a little bit close to lunch time
> so, with Dr. Woodcock\'s permission, we will save your presentation
> until after lunch and initiate some discussion of our last two
> presentations. So, if anyone has any thoughts or comments, questions?
> Dr. Hussain did give us questions that he would like for us to comment
> on relative to how FDA might improve their articulation of desired
> state considerations for communicating the desired state to
> stakeholders, and any additional considerations for aligning FDA\'s
> activities to ensure efficient progress. So, with that, any thoughts?
> Dr. Shine?
>
> DR. SHINE: A question for Dr. Hussain and also for Kelly. Dr. Hussain,
> there is a little paragraph about team biologics in your report but
> the overall flavor of the report is about drugs and drug
> manufacturing, and the expertise that you described was pharmacy,
> chemical, chemical engineering. Clearly, one of the areas that has
> been most troubling has been manufacturing processes for vaccines and
> the biological soups that have to be created to do that, and also the
> concerns about what has happened with vaccine manufacturers not being
> in a position to continue production. Could you give us some sense as
> to the extent to which that is a focus for this initiative and what we
> are doing with regard to addressing that particular element? Then I
> have a question for Kelly.
>
> DR. HUSSAIN: My presentation started with the PAT initiative which is
> focused more on traditional pharmaceutical manufacturing, and within
> that context I said, you know, chemistry, pharmacy, engineering. So,
> that was the focus of the PAT initiative and that continues to be the
> focus.
>
> I think in many ways the opportunities for improving efficiency are
> tremendous but with respect to the level of complexity we have in
> terms of scientific understanding and so forth because vaccines, and
> so forth, are far more complex systems so the PAT initiative started
> with that focus in mind. The GMP initiative actually is trying to
> address all pharmaceutical human drug manufacturing including
> vaccines. So, within that context that is being addressed, but with
> respect to the PAT, we have kept that limited at this time to the
> traditional pharmaceutics.
>
> DR. CARBONE: We do have representation in that group, and I think the
> encouraging component is what was mentioned by Dr. Hussain, CBER\'s
> plans include product specialists in inspections, and this is a plan
> that is under consideration for adaptation and adoption. So, I think
> that the biologic specific issues, although not prominent, certainly
> will be part of the consideration and can be increasingly so as the
> need arises because you are absolutely right about the complexity and
> specific issues in manufacture of biologics, and vaccines are an
> example.
>
> DR. SHINE: Given the challenge in this area, Mr. Chairman, I would
> hope that sometime in a future meeting we might address the question
> of the status of vaccines and vaccine manufacture. I think with the
> crises that have occurred with regard to childhood vaccines, vaccines
> for terrorism and so forth, it is a really pressing issue that I think
> the agency has a great deal to contribute to.
>
> I just wanted to ask Kelly a couple of things. First of all,
> congratulations on getting bar coding. As you well know better than
> any of us, the VA have been doing that for a long time. There are two
> issues. One, Marconi is a hospital-based experiment?
>
> MS. CRONIN: Right now it is, although there are some providers that
> are involved that have vertically integrated healthcare systems where
> we could potentially be getting data across settings of care.
>
> DR. SHINE: That would be terrific, and the VA is another example of
> being able to do that because we really don\'t know very much about
> what is going on in the ambulatory arena, and beginning to get some
> meaningful data there would be extremely useful.
>
> The other question I have is, as you are probably aware, when you try
> to get healthcare providers to pay attention to the future the
> hospital administrators are interested in a microsecond but the docs
> are not. The docs are resistant. Recent polls have demonstrated that
> only about a quarter to a third of docs really acknowledge the
> importance of errors, and so forth. Part of the reason is they believe
> it is something that takes place somewhere else. The most successful
> programs have been ones in which a hospital has looked within its own
> organization and identified the error rate in their institution and
> being able to say to the medical staff we have just as much of a
> problem here as they do at the hospital across the street where you
> think everything is happening. Is there any mechanism, as you develop
> this information\--I am not talking about public reporting or
> whatever; I am talking about the question of any kind of feedback
> mechanisms so that we can get information to people that will get them
> to take seriously the notion that there is a problem in their
> environment as opposed to somewhere else?
>
> MS. CRONIN: We are thinking about various ways we can facilitate
> communication and feedback once risks are identified. We haven\'t
> really worked through all those ideas yet. We hope to discuss that at
> an upcoming brain-storming session with many of the providers that are
> involved in Marconi. But we realize that quality improvement and
> prevention of risk is really an integral part of this effort and in
> order to really get people to buy into this effort, since it is really
> above and beyond any regulatory effort and is really just relying on
> their interest and their participation, we will have to be
> communicating what we learn back to them in an effective way so that
> they can then turn to prevent risks and improve quality in their
> systems.
>
> DR. DOYLE: Dr. Woodcock?
>
> DR. WOODCOCK: Yes, I have a comment about this. One of the more
> promising discussions I feel we have had has been with some of the
> payers because for the data you are referring to, you have to talk to
> the practitioner about their practice pattern. Some of the payers are
> actually willing to collaborate with us in feedback mechanisms around
> prescribing patterns and consequences, and so forth. That type of
> feedback is very personal, of course, but it also is very effective in
> changing behavior compared to exhortations or general communications,
> and so forth. So, there could be some interest in actually
> communicating to patients who are on specific medicines about the risk
> that would pertain to that medicine.
>
> DR. DOYLE: Dr. Thomas?
>
> DR. THOMAS: I have a question for Dr. Hussain. Where do you think you
> are going to get the next generation of PAT experts or professionals?
> For example, in schools of pharmacy it is very difficult to find a
> so-called physical pharmacist. Schools of engineering, while some may
> have very strong chem. engineering may not be interested in this sort
> of manufacturing process. Also departments of chemistry, I mean, the
> medicinal chemist obviously isn\'t going to fulfill the criteria that
> you need for a PAT. Has anyone done any manpower surveys? Has anyone
> looked at curricula within the U.S. academic institutions with regard
> to this type of training? It used to be more prevalent but, for
> whatever reason, it is not something that young people are going into.
>
> DR. HUSSAIN: Just to sort of reflect on that fact, before I came to
> the agency I was on the faculty of a school of pharmacy for nine years
> and I saw the erosion of the physical sciences from those curricula. I
> think the sensitive issue is in the sense that schools of pharmacy
> have a very important function from a patient care perspective and
> that is where the focus has been and will continue to be. So, the gap
> that remains is industrial pharmacy, physical pharmacy and where this
> information will be utilized to train individuals. The trend has been
> that schools of engineering, especially chemical engineering, have
> picked that up. For example, the Rutgers School of Pharmacy
> essentially has moved away from industrial pharmacy program but the
> School of Chemical Engineering now has a very mature pharmaceutical
> engineering program. So does the University of Michigan. Purdue has
> still maintained its strong focus on industrial pharmacy with the
> PAT-based applications.
>
> So, there are now at least three focused programs that at least we
> have direct contact with which have already moved in this direction.
> But what I would like to say is that from a chemistry perspective,
> process analytical chemistry actually has evolved over the last 30
> years as a mature science. So, you do have opportunity to tap into
> that pool of talent and then sort of combine that with the
> pharmaceutical know-how. I think it has to be a team approach.
>
> So, in the short term the solution is to bring a team concept to this
> with engineers from other sectors which have the know-how for some of
> these. Eventually what I see is that some of these programs will
> mature and will provide the know-how or the talent base for the U.S.
> But that is a major concern when we have spoken to a number of
> companies about where the new plants are going to be, PAT-based, many
> have said it is going to be Germany, not U.S., and that is precisely
> the reason why. The German education system has maintained a strong
> base and is providing that know-how. But I think the concern I have
> always had is that we need to have that base in the U.S. also. That
> was the reason for sort of partnering with the National Science
> Foundation and creating some opportunities to have this talent pool.
>
> In short, I think we will have the talent pool as a team concept but
> eventually this will take off. The University of Michigan actually
> will have a distance learning program based on PAT systems and so will
> the University of Purdue. So, those curricula will be established very
> soon.
>
> DR. THOMAS: Thank you.
>
> DR. DAVIS: You mentioned, Dr. Hussain, the use of case reports to help
> work up the desired state. I would suggest, sort of thinking out of
> the box, because you don\'t have a lot of programs, training programs
> that you might try case reports as a teaching tool to provide
> information to those schools that aren\'t so strong. You know, I am
> not business or an MBA person but I often look at the [Harvard
> Business Review]{.underline} from a learning perspective. It serves as
> a great way to get information out using case reports to study. So, I
> would suggest scientists need to consider doing the same thing where
> we have training programs that are deficient in some specific region
> so instructors might take this information and at least use it to talk
> about.
>
> The question I have for you though, I notice you had ORA as a part of
> the team. How are you anticipating these new technologies in CFR Part
> 11 from the start? I was very pleased to see you had ORA. I am just
> wondering what is the input there in using CFR Part 11 with these new
> technologies getting started at the ground level?
>
> DR. HUSSAIN: Let me go to the first question. I think the point is
> well taken in the sense that I think case studies, case reports would
> really be examples. What has been quite gratifying is that companies
> have stepped forward with some examples and have shared those
> publicly. For example, I think Bristol-Myers Squibb came up with a
> complete case study presentation at our PAT subcommittee, and this is
> how it can be done; this is how they are doing it. So have Pfizer and
> others. So, those really have helped us sort of conceptualize the
> concept itself. At the same time, I think the curriculum that we have
> developed for PAT staff training and certification actually has become
> a model for academia to adopt. So, in this instance we are actually
> providing a curriculum to academia. So I think that was a positive
> step of our deliberations on that advisory committee.
>
> On the issue of Part 11, I think we did realize that the
> interpretation of Part 11 could have created some of the hurdles for
> new technology and innovation. Therefore, as part of that I think we
> should draft a guidance as part of the cGMP initiative. As we move
> forward, I think the integrated team concept is the only way forward
> and I think the centers are working hand-in-hand to get there not only
> with respect to computer validation, software validation and Part 11
> issues but every aspect of science because we will be moving to a more
> sophisticated statistical base analysis and I think that is a learning
> curve that needs to be sort of understood as well as applied
> appropriately.
>
> MR. MARZILLI: This is John Marzilli, from ORA. I wanted to echo that
> sentiment and just to add that Dr. Hussain, with Dr. Woodcock\'s
> leadership, has been involved with our senior management staff at our
> past two senior staff meetings where we have presented this to our
> regional directors and district directors. On the GMP initiative since
> its inception, Dr. Woodcock and company have worked closely with our
> Division of Field Investigations and our field staff from across the
> country to our drug field committee. Our Chair, the District Director,
> Doug Ellsworth from the New Jersey District, as well as our Regional
> Director, Susan Suderberg from the Central Region and members of that
> committee have worked closely with these work groups and we have
> members on each of these work groups.
>
> Because we are dispersed across the country, we try to have our folks
> come in as much as possible to participate and generally we have been
> active participants from day one in these initiatives. I want to thank
> Dr. Woodcock for bringing us on board, because it is an important
> aspect of the field organization, at the table from day one. So, we
> have been there. Thank you.
>
> DR. DOYLE: Did that cover your points, Dr. Davis? Dr. Shine?
>
> DR. SHINE: What kind of metrics are you going to use to evaluate this
> program in the future?
>
> DR. HUSSAIN: Well, I think we have been giving some thought to that.
> One clear metric is actual real-life applications and moving towards
> adoption of some of these technologies, in that regard, bringing
> technologies that are already existing in the \"don\'t tell\" mode and
> actually having some regulatory utility and benefit from that.
>
> In one sense, I think it would be prudent on industry\'s part to adopt
> a move in this direction, and that essentially would indicate that the
> hurdles that are perceived or real are sort of being removed. The
> metric for that would be simply the applications that will come
> through. That will happen over a period of time. But most of all, I
> think sort of a metric in terms of what has already occurred is the
> almost emotional debate of the current state of pharmaceutical
> manufacturing in terms of what it was and what it should be. The
> workshop that we have held I think brought that debate out, moving
> towards a consensus to the desired state. I think we have established
> that.
>
> But in terms of collaboration between the centers and the ORA, I think
> with respect to technical issues and disputes that might come about,
> whether we resolve those quickly or we actually prevent those issues
> from happening, those are all a potential source of future metrics
> that we could come up with. So.
>
> DR. SHINE: For example, will there be any attention paid to cost
> issues, to interruptions in production, some of the things which are
> sort of the end measures of what the outcome is\--quality of product?
> I just want to get some sense at the end of the day of what does this
> do for the industry.
>
> DR. WOODCOCK: Yes, or for the public. We are going to look at that. We
> have an evaluation group that is trying to devise how we would measure
> we are achieving the desired outcome of this initiative. So, your
> suggestion is a good one and we agree. I mean, we would hope, like
> Ajaz said, for some work examples from industry because the
> preliminary indications we have, which were presented previously at
> the Science Board, is that there could be a major reduction in cost
> after the initial investment. So, we look forward to those. Those are
> real-life applications that would demonstrate some of the benefits.
> But we also need to further develop how we measure whether it is
> successful or not.
>
> DR. DOYLE: Thank you, Dr. Woodcock. Dr. Laurencin?
>
> DR. LAURENCIN: This is for Kelly Cronin. How do the adverse event
> reporting strategies that you have compare to other countries, say,
> like Canada?
>
> MS. CRONIN: We actually haven\'t considered comparing our systems to
> other countries. I think with the SADAR rule there are probably going
> to be more similarities than differences, once that gets finalized.
> But we do hope to improve our systems in terms of trying to have
> better outreach and increased awareness of the importance of
> reporting. We also hope to come up with better ways of signal
> detection through the adverse event reporting databases that we have
> in-house. So, we have a variety of different initiatives that we are
> thinking through now. Whether they will differentiate us more from
> other countries, you know, I am not certain.
>
> DR. WOODCOCK: We have actually been talking to Canada. They want to
> get onto our database and add their reports and be able to analyze
> because they don\'t have the power with a much smaller population to
> be detecting rare adverse events. Their system is somewhat similar to
> ours. European countries are very difficult for pharmaceuticals at
> least because they have a national healthcare system so much of their
> reporting is because they are kind of administering the payment of
> drugs through one system. In Japan for pharmaceuticals, I don\'t know
> for devices, it is a totally different healthcare system where the
> practitioner actually dispenses the drugs. They have put in place some
> adverse event reporting systems that are focused on the practitioner
> when a drug is newly launched and their early experience with
> dispensing that drug to patients. So, we are talking about a lot of
> different kinds of systems. It is very interesting but it is hard to
> make direct comparisons because of the differences in the underlying
> healthcare systems.
>
> MS. CRONIN: Yes, I think it is also fair to say that they are all
> voluntary as well so it is passive reporting. Several years ago I did
> look at data across various different countries that reported to the
> WHO collaborating center in Sweden. The data, at least for this
> particular class of products, was sort of spotty across many different
> countries. So, I don\'t think that any other system is going to have a
> better way of identifying the true safety profile given that they are
> all passive systems.
>
> DR. DOYLE: Dr. Feigal?
>
> DR. FEIGAL: It is interesting to look at the system in the U.K. for
> medical devices. In the U.S. 90 percent of our reports come from
> manufacturers. In the U.K. 90 percent of their reports come from
> health professionals. So, they have a very different system and it is
> in part related to the fact that the same public health system that
> runs the hospital systems and the primary care systems also is
> responsible for the MDA, which is the regulatory authority for
> devices.
>
> They have the ability to make some linkages that we don\'t. For
> example, they look at recalling devices as something where they have
> responsibility for the whole system. So they have recall coordinators
> within hospitals that are accountable to the same body, the device
> authority. So there are some interesting different systems.
>
> There is another layer that exists probably for all products, which is
> communication between regulators of the problems they are analyzing.
> It is one thing to have robust systems for signal identification and
> triaging and data mining and refining systems, but one of the benefits
> of the harmonization efforts of pharmaceuticals and devices has been
> more opportunities to share between regulators.
>
> For example, a few years back problems began developing with a new
> heart valve and we had reports that we heard from the U.K. and from
> Canada. It was a product that had an earlier launch in Europe than it
> had in the U.S. that alerted us to the problem while there were still
> clinical trials ongoing and early marketing in the United States where
> we could look at this system together. So, I think it is going to be
> necessary to take a whole systems approach to the situation and
> recognize that it is really a global marketplace, that these same
> products are appearing throughout the world in somewhat different
> orders, and if we want early signals we are not going to be able to
> get them just by turning up the detection in our own systems.
>
> DR. DOYLE: Any other questions or comments? I think we are getting
> hungry. So, I think we are going to take a break now and reconvene at
> one o\'clock.
>
> \[Whereupon, at 11:55 a.m., the proceedings were recessed to reconvene
> at 1:00 p.m.\]
>
> \- - -
>
> [A F T E R N O O N P R O C E E D I N G S]{.underline}
>
> **Open Public Hearing**
>
> DR. DOYLE: I guess we are ready to reconvene. Next on the agenda is
> public comment. We don\'t have anyone officially registered for public
> comment but is there anyone in the public audience here who would have
> a comment? I see no hands so I guess no public comments.
>
> DR. BOND: I do have one written comment that I am just going to submit
> to the transcriber for the record.
>
> \[Written comment submitted for the record: \"It is clear that FDA is
> not protecting the public from cancer or other illness risks. It is
> clear that FDA is allowing industry to sell any old product to the
> public so that the seller makes money, and the public gets sick. The
> FDA is there primarily to protect the public and all standards should
> do so. If it takes 20 years of testing to prove a product safe, that
> should be the standard, not anything less. These are my comments for
> inclusion in the public record since I cannot attend Washington
> conference on scientific standards. B. Sachau, 15 Elm St., Florham
> Park, NJ 07932.\"\]
>
> DR. DOYLE: Very good. We will submit that for the record and move on.
> Next we are going to hear from Dr. Woodcock again. We are going to
> make her earn her keep today. She is going to talk about fostering
> technology development in relation to pharmacogenomics.
>
> **Fostering Technology Development\--Pharmacogenomics**
>
> DR. WOODCOCK: Thank you.
>
> \[Slide\]
>
> Good afternoon, folks. I hope we will have a lively discussion this
> afternoon and keep everybody awake after lunch. This continues the
> theme of facilitating the introduction of new technologies through
> regulation. This afternoon we are going to talk about
> pharmacogenomics, pharmacogenetics. I want to say that many of the
> centers are involved in different aspects of this.
>
> As I said earlier, NCTR obviously, as was said earlier, is extensively
> involved in certain scientific assays and so forth. The Center for
> Biologics has a very extensive genomics/proteomics program. But today
> what I wanted to talk about is the relationship of genomics and, by
> extrapolation, really the new proteomic techniques and similar
> techniques. I am going to talk about the genomic techniques but really
> these issues apply to all these techniques in the development and
> regulation of drugs. So, that is the \"pharmaco\" in the genomics.
>
> \[Slide\]
>
> As I said, this is really about translating innovative science to
> bedside medicine. We know this is often a very rocky road and there
> are a lot of bumps along the road, and part of the initiative that Dr.
> McClellan spoke about this morning is trying to smooth that pathway
> and help these new innovations translate into benefit to patients. As
> Dr. Shine would say, that it the outcome measure we want to see here.
> We want to see the actual benefit to patients.
>
> \[Slide\]
>
> What is the issue we are talking about here? This new science of
> pharmacogenomics, and I will define this later, and proteomics and
> other similar technologies are actually being applied extensively by
> the pharmaceutical industry in drug development.
>
> They have the potential to revolutionize a number of the processes.
> Most of the data that is being generated is not being seen by
> regulatory agencies, partly out of concern for how the data will be
> used. All right? We need an approach to this new data that is going to
> be generated, and is being generated right now, that will enable the
> free exchange of information between regulators, companies and,
> hopefully, to the extent possible the scientific community at large.
> It will help advance the science and technology and, very importantly,
> aid in the timely development of appropriate regulatory policies
> because we cannot have this revolution upon us before we start
> formulating our policy development. There are many scientific and
> technical issues that are going to have to be solved and some of the
> later speakers are going to address some of these.
>
> \[Slide\]
>
> Basically what am I talking about here? I am going to give you a
> little bit of background, if you will, on why this is important. There
> is tremendous variability in how people respond to medicines, and that
> is such a commonly understood fact that most people don\'t even think
> about it but that is really a major barrier to having effective
> therapeutics. It is also a major barrier to developing drugs and
> biologics because we can\'t predict who is going to respond.
>
> \[Slide\]
>
> This is true for effectiveness. For many drugs, if you leave aside
> antibiotics, antivirals and so forth that are directed at some other
> organism, other than a person, for many drugs the size of treatment
> effect when we do randomized trials is less than 10 percent of some
> outcome measure. It is a very small effect. Many people conclude from
> this that the effectiveness of drugs is very small. Even some of our
> own staff will tell me this drug doesn\'t work; it is such a small
> effect that we observe in the population.
>
> \[Slide\]
>
> You expose a population; you randomize them and you see something like
> this. You see that the mean response of the drug is enough over the
> placebo that, in fact, you can demonstrate a statistically significant
> difference in a population but it is not anything to really write home
> about clinically. This is very common and one of the problems in drug
> development because you have to have very large trials to show
> effectiveness of drugs.
>
> \[Slide\]
>
> If you did trials in a different way and you set up the responders,
> what often you may find is that most of the subjects on the drug arm
> look just like the placebo subjects. In other words, they don\'t
> respond or, you know, some of them get better, some of them get worse,
> just like the placebo. All right? But there is a little group of
> people that respond very much to the drug. This is a common
> observation, except we don\'t usually conduct trials using this type
> of hypothesis and we don\'t usually report them in medical literature
> according to the magnitude of the response; we look at the mean
> difference between populations. One of the goals and one of the issues
> here is to predict who these people are, the small group of people who
> actually do respond to a specific drug in a very positive way.
>
> \[Slide\]
>
> What about drug toxicity? Again, this observation is so common we
> don\'t even think about it. Often not everybody gets the drug
> toxicity. If you study drug versus placebo you see each drug has a
> consistent pattern of side effects, and that is repeated over and over
> again in different trials when you compare it to placebo. That is
> observed with the common as well as the rare events. Some of these
> effects are attributable to known pharmacologic effects of the drug
> and others, in medicine we have always called them idiosyncratic.
> Idiosyncratic is not defined this way in Webster\'s but what we mean
> is we don\'t know what this means at all; you know, it just happened.
> Actually, from a science perspective you have to say there was a
> reason; there was a reason for this. There is a reason that some
> people suffer more from the pharmacologic effects of the drug than
> others. It can\'t be just random but that is how we have thought about
> it. Random is another word for we don\'t understand the causes; we
> don\'t understand the underlying cause.
>
> So, the current physician medical approach is kind of at the level of
> organ function. We say, well, people have compromised liver function
> and you have to be careful here, or whatever, or we just observe the
> patients and we wait for something to happen that we try to prevent
> from getting worse. We can\'t really often predict who are the people
> who are going to experience these adverse events and why; what is the
> underlying cause that they get them and some other people don\'t get
> them?
>
> \[Slide\]
>
> It is commonly agreed, and we will probably convince you in our
> presentations today, that there is an inherited or a genetic component
> to variability in drug response. Here are your definitions.
> Pharmacogenomics we are defining, just for the purposes of this
> meeting since these are controversial definitions but this is what we
> are going to talk about today, pharmacogenomics: application of
> genome-wide RNA or DNA analysis to study differences in drug actions
> or drug effects.
>
> The earlier term, pharmacogenetics, is by many people considered to be
> study of genetic basis for individual differences in pharmacokinetics.
> The reason for that distinction is pharmacogenetics was discovered
> fairly early. We have known for a long time that different people
> metabolize drugs differently, the same drug differently. There was
> even early study of this and some things have been known for a long
> time, using probes and so forth to find out people\'s phenotypes. So
> we were able to learn about pharmacogenetics a long time ago for some
> drugs but we are learning a lot more now because we have better
> methods of studying it. Today I am going to talk about the broad issue
> of pharmacogenomics and some subsequent speakers will focus more or
> less on the drug metabolism issues.
>
> \[Slide\]
>
> What genetic background issues could affect efficacy and what do we
> know about this? Well, we know there can be genetic diversity of
> disease pathogenesis. So people have studied, for example,
> hyperlipidemia and there are many different genetic differences in
> people and how they handle lipids\--lipid transport, lipid receptors,
> all sorts of things. In some cases there is a suggestion that perhaps
> that may, in fact, alter their responsiveness to certain drug
> interventions, and that makes sense depending on where the variability
> is and how they handle the lipids.
>
> Variable drug metabolism is very well understood as a cause of lack of
> efficacy. There are some people called hypermetabolizers. Larry Lesko
> probably talked about this. You give them the usual dose of drug and
> their body chews it right up so they don\'t really have any drug
> around and so they can\'t respond; they need a much higher dose. So
> that is a cause of lack of efficacy and, depending on how big the
> population of hypermetabolizers is for that particular group, you have
> a certain group of people who are genetically fated not to respond to
> that drug at that dose, at the usual dose.
>
> As I showed you earlier, we often determine mean population effects so
> we determine a mean dose for the population and that is what everybody
> gets. Well, that doesn\'t work when the drug is one that is variably
> metabolized in a human population.
>
> Then, there are genetically based pharmacodynamic effects. What does
> that mean? Well, that means that the beta-adrenergic receptor may be
> an example of this. People have genetic differences in their
> beta-adrenergic receptor. That is not believed to cause them to
> experience asthma at a higher rate but certain people whose adrenergic
> receptor is different may not respond the same way to beta-adrenergic
> agents. In other words, their drug responsiveness is modified by the
> fact of their different genetic background.
>
> Most of these things I have been mentioning here are somewhat
> controversial. There are studies to support to this; studies that
> don\'t support this. But basically the story is that there are
> probably three different ways where efficacy can be impacted by
> genetic differences.
>
> \[Slide\]
>
> What about toxicity? Again, there are underlying genetic contributions
> to the variability in the toxic response. For example, if you have
> certain inherited syndromes that affect cardiac repolarization and
> then you are also given a drug that happens to also affect cardiac
> repolarization you are going to be much worse off than a person who
> didn\'t have that genetic background. This, along with a long QT
> syndrome is something you can identify phenotypically. In other words,
> you can measure someone\'s electrocardiogram and find out about this
> but there are probably many underlying genetic problems where we
> can\'t easily measure them with our current measures and so we give
> them a drug and there is a bad interaction.
>
> Again, differences in drug metabolism also go the other way. We have
> people who are outliers in the population who metabolize drugs very
> slowly. A good example is thiopurine methyltransferase. Drugs like
> azathioprine or 6MP are metabolized through the pathway and the
> population has normal metabolizers\--it has various metabolizers but
> there are people who are very slow who lack the ordinary metabolic
> path. Their AUC will be about ten times higher on administration of
> these drugs than the normal person\--well, these are all normal
> variants\--than the most common person. This is very important because
> 6MP for example is a cancer chemotherapy agent, and so forth. It is
> used in the treatment of inflammatory bowel disease, chronic use. So,
> it is a very important thing that may result in various drug
> toxicities. There is a lot of information about this in the literature
> and similar cases.
>
> Another example of a different kind of genetic contribution might be
> what I call a toxicodynamic interaction. That is not where you have a
> genetically based abnormality or you don\'t have a metabolism
> difference; you have some normal existing state that somehow interacts
> with the drug that can cause a severe reaction. People have postulated
> that a hypersensitivity reaction to abacavir might be caused by
> association with a certain MHC type, for example. Again, these are
> still being studied. We are not really sure of the association but
> this is the kind of prediction that could be made.
>
> \[Slide\]
>
> How important are these differences? You could say, well, they exist
> but they are not that important. How much of the variability could we
> predict if we knew the genetic substrate? That is an important
> question.
>
> \[Slide\]
>
> At the level of an individual\--and that is what people always ask us,
> they say what drug is right for me? What dose is right for me? They
> want individualized therapy. Obviously, at the level of an individual
> a genetic difference may actually determine the drug response. I am
> using a very obvious one here, enzyme deficiency. Obviously, if you
> pick the wrong replacement enzyme for somebody then they are not going
> to respond and that may not be completely clear phenotypically.
>
> Also, it may highly influence drug response. That is what I have
> already talked about, the polymorphic drug metabolizing enzymes. In
> some cases these are not that important because there are multiple
> pathways; because the therapeutic index of the drug is very broad; for
> a variety of reasons. In other cases these are extremely important and
> will predict to some extent who gets toxicity; who fails to get
> efficacy if you don\'t control the dose correctly.
>
> \[Slide\]
>
> Unfortunately, we have to recognize that many responses of people to
> drugs, just like everything else in life, are going to be an emergent
> property of multiple gene products interacting with each other and
> with other environmental factors so you get a system where you have
> multiple gene products. They all can have genetic differences. They
> interact with each other and it can be very difficult to predict how
> that will affect their drug response.
>
> So, many individual genetic differences or even the patterns of
> differences, that Frank Sistare is going to talk about, even
> transcriptional differences may not be that highly predictive of drug
> response. So, we are going to have to sort this out over time.
>
> \[Slide\]
>
> This is kind of a picture that was just in [Science]{.underline} about
> the body and actually how it is organized. The point is that there is
> a level\--we are talking at the genetic level, the RNA level and the
> protein level, expression level and so forth\--the body though has
> larger systems imposed on top of that to become a very large-scale
> complex interacting system. So, we are probably naive, if we look at a
> few things at the bottom, here, that we are going to tell how the
> system is going to read this out. All right? So, genetics is not
> determinative, except in some cases.
>
> \[Slide\]
>
> Let\'s go back to drug development, the background of how the genetics
> could affect drug development. Currently, in drug development we have
> a satisfactory way to determine efficacy. As I said, it has to be done
> on a population basis. We randomize a whole bunch of people; we look
> at what happens and then we decide whether a drug works or not. It is
> the same with a determination of drug toxicity.
>
> It is observational. First we expose a bunch of animals\--and I
> apologize to the toxicologists here, I am sure it is much more
> scientific than that, but basically you expose a bunch of animals and
> then we expose a lot of people and, again, it is observational. We
> expose a lot of people to the drug and we write down what happens. If
> we are lucky we compare it to a placebo or something so we can tell
> which ones are real. In fact, even for approved drugs the carcinogenic
> potential and repro. tox. potential is based on [in vitro]{.underline}
> and animal studies because obviously we are not going to find that out
> in people.
>
> \[Slide\]
>
> So, that is drug development. It does well; gets stuff on the market.
> But how can we\--we, meaning the whole enterprise\--use
> pharmacogenetics in drug development? Number one, and that is being
> done now and this doesn\'t really involve the FDA, we can improve
> candidate drug selection. Obviously that is in drug discovery.
>
> We could develop new sets of biomarkers, and that is being worked on
> too and I think that is what NCTR is working on as well as others for
> toxic response in animals and humans which will eventually perhaps
> decrease or minimize animal studies, or at least make them much more
> predictive and be able to understand the underlying mechanisms.
>
> Perhaps we could predict who will respond to a drug at some level;
> predict who will have serious side effects and rationalize drug
> dosing, which would be a very helpful thing to do.
>
> \[Slide\]
>
> In fact, Ajaz and I were talking about this, this has a lot of
> parallels to the PAT GMP initiative we are doing. The pharmacogenomics
> has the potential to move us from a current empirical process\--and
> that is really what drug development is right now; it is empirical,
> not mechanistic\--to a mechanism-based process that is hypothesis
> driven. Really that is pretty far in the future, I admit, but we ought
> to be trying to go there because that is the science we are seeking.
>
> This could in the future result in a lower cost, a faster drug
> development process that would, nevertheless, result in more
> effective, less toxic drugs but for smaller a population, the people
> to whom it was targeted.
>
> \[Slide\]
>
> How is pharmacogenomics being used now by the pharmaceutical industry?
> We are going to have a later presentation by a member of the
> pharmaceutical industry so I will skip over this pretty quickly, but I
> want to just give you a little bit more of the background before we
> get to the next speakers.
>
> As I said, it is being used in discovery and identification of lead
> candidates, identifying targets, evaluating cellular or animal
> responses to different candidates. But it is hard to make a lot of
> sense right now, I think, out of a lot of this data and this is not
> part of regulatory submissions, not required. This is in the discovery
> and nonclinical part.
>
> \[Slide\]
>
> Within the nonclinical it is in the path toward a regulatory
> submission. There are a lot of exploratory studies going on. There are
> a lot of directed studies of genes of interest. Sometimes we see what
> I call an explanatory study. Well, what is that? How is it different
> from exploratory? Explanatory studies\--well, you find a toxic effect
> in animals. You don\'t think it is related to people or other animals
> and you can look at the responses elicited during that toxic response
> and you can perhaps show they are different than in other animals or
> maybe even in human cells, or whatever, and that explains that this
> can\'t be generalized. Then, people are working on predictive toxicity
> patterns.
>
> \[Slide\]
>
> What about human studies? Well, the real hope is that we can sort
> disease syndromes into subgroups based on genetic differences and
> pathogenesis and these subgroups may have different responses to
> treatment. This is undoubtedly true because many of our diagnoses of
> disease are, again, observational based on syndromes and these
> syndromes may contain many different disease pathogenesis groups.
> Almost by definition, it is like these are going to respond
> differently to different drugs also in humans, looking at genetic or
> phenotypic tests for metabolizer status to predict dosing, rational
> dosing, and see how metabolism interacts with dosing.
>
> \[Slide\]
>
> Then you can go the other way and you can look for genetic differences
> in responders versus non-responders. Some of that is being done.
> People respond to a drug. Are they somehow different and you find out
> in the genetics versus non-responders? Can you look at RNA expression
> and look for pharmacodynamic differences, different pharmacodynamic
> responses?
>
> There is a hope that soon\--and I know nothing about this and whether
> this is a reasonable hope or not that some day soon we could find some
> genetic explanation for the severe or catastrophic side effects that
> people experience nowadays from drugs. This is really a bad problem
> because we have drugs out there that really help people, help large
> numbers of people and, yet, every once in a while, and completely
> unpredictably from what we know now, they cause catastrophic side
> effects that are fatal or terrible. We don\'t have a way to predict
> who is at risk right now, and that is a huge problem I think for
> medicine.
>
> This again is a typo and I am sorry. This is supposed to be SMP and
> gene expression screening. Other folks are going to talk about this
> more but these are the kind of things that are being done in people.
>
> What I am going to do now is pause and I am going to have the other
> speakers come up. We are going to hear from two FDA speakers next
> about what we are seeing and what we are doing from our side. Then we
> are going to hear from industry about what they are doing and what
> their concerns are about the sharing of these data. Then I am going to
> come back and present our proposal to deal with this and then we are
> going to hear from a medical ethicist from NIH, who has kindly
> consented to talk about this problem, and then we can have a general
> discussion of the proposal. Thank you.
>
> DR. DOYLE: Thank you, Dr. Woodcock. Next we are going to hear from Dr.
> Frank Sistare, who is the Acting Director in the Office of Testing and
> Research at CDER. Dr. Sistare is going to share with us a presentation
> on pharmacogenetics in preclinical studies.
>
> **Pharmacogenomics\--Preclinical Studies**
>
> DR. SISTARE: This is a good time slot because we saved an hour for the
> open public discussion so that means I have an hour to talk\--
>
> \[Laughter\]
>
> I think I have 40 slides here. I am going to breeze through a bunch of
> them but it is going to be a challenge to get through in 30 minutes
> but I am willing to take on that challenge. Let the record reflect it
> is 1:27!
>
> \[Slide\]
>
> In fact, I am going to alter my original charge. My original charge
> was going to focus just on preclinical but we realize that there would
> be a big gap missing if I focused only on the preclinical and we
> decided that a fair divvying up of the action was for me to focus on
> RNA and Larry is going to focus on DNA.
>
> \[Slide\]
>
> What I am going to do is give you an overview first of the technology
> and get everybody up on the same page, and then talk about some of the
> medically related applications of pharmacogenomics relevant to CDER\'s
> responsibilities.
>
> I am going to highlight some of the concerns and issues that we have
> already heard or that we have already experienced ourselves, and I
> categorize those in three major areas, technical, biological and
> procedural, that have been raised at that triad interface between
> patient care, drug development and regulatory oversight. Finally, I am
> going to talk about what CDER is doing to address those issues
> presently.
>
> \[Slide\]
>
> Again, to get everybody on the same page, this is a very simplistic
> cartoon that shows the double helix DNA molecule. From the DNA, RNA is
> made and then from RNA protein is made. They are not all made in the
> same place obviously. There is a little trafficking that needs to go
> on there. But what I am going to talk to you about is the RNA
> component of this process.
>
> \[Slide\]
>
> RNA is measured in a number of different ways. RNA can be converted to
> cDNA and cDNA or DNA can be amplified through a process of PCR. This
> has been a well tried and well tested and highly depended upon method
> of measuring RNA in a quantitative way.
>
> \[Slide\]
>
> But in 1996 what really stimulated the focus on measuring RNA
> molecules as a function of disease and drug intervention was the
> application of the DNA microarray. Again, this cartoon is very
> simplistic but what it shows is a two-dimensional surface to which DNA
> sequences are attached. These serve as probes. A sample can be
> prepared. RNA is isolated from those samples of interest and they are
> labeled in some way, copies of those things are labeled in some way.
> Then, the labeled copies of the samples, together with the
> two-dimensional capture matrix, are hybridized and you can measure a
> signal ultimately. There are a lot of technical aspects to being able
> to get the data out of this process but this is essentially the
> process.
>
> \[Slide\]
>
> There are a number of different platforms, a number of different
> specific technologies that have been used to measure RNA or gene
> expression. There is the dual color approach in which a control and a
> treated sample or a normal and a diseased sample can be hybridized to
> the same exact same platform. That platform exists in several
> different ways. You can have a long 500 to 2000 base pair length probe
> sequence attached to that surface, called the cDNA microarray. You can
> have small oligonucleotides attached to that capture surface, on the
> order of 30-60 length of nucleotides. Or, it can have [in
> situ]{.underline} synthesized oligo microarrays. These all exist out
> there in the marketplace.
>
> \[Slide\]
>
> Then there is another whole species of microarrays. There is the
> GeneChip from Affymetrix which is a high density probe array and
> 500,000 specific 25mer oligonucleotides are synthesized, and this is a
> single color approach. So, controlled, treated, diseased or normal are
> applied to two different probes.
>
> I said that just as background to get into some of the other issues I
> will be talking about later about technical aspects of the technology
> that need to be established.
>
> \[Slide\]
>
> What I am doing here is just giving you a feel for the fact that these
> things are probably not a flash in the pan. We are not talking about
> cold fusion here I think. This is a technology that is on the rise and
> it is probably here to stay, and it is going to continue to evolve.
> There are probably a lot of different ways of making this point. We
> can look at publications; we can look at commercial sales. This is
> just showing GeneChip sales and the sales of the machinery to analyze
> these have quadrupled in the last four years.
>
> \[Slide\]
>
> Why are people really so interested in measuring RNA? Essentially, the
> RNA is serving as a protein function signal. Proteins are what is
> making alterations in activity of the cell. So, something is
> perturbing the cell, working through proteins, to alter changes in RNA
> concentration and we have the capability of measuring tens of
> thousands of these things simultaneously\--an incredible capability.
>
> Well, what are you going to do with tens of thousands of measurements
> at one time, in one experiment, at one dose, at one time in one
> animal? You have to measure collections of these things. You really
> need computers obviously. You need a database. You need to be able to
> really efficiently analyze the experiments, individual sets of data
> across patients, across animals, across times. You need a relational
> database to help you with that, to identify changes and the function
> of what the proteins are doing.
>
> \[Slide\]
>
> I am going to spend some time, as I mentioned, and not just talk about
> the nonclinical but I am going to talk about some clinical. I think
> this really kind of excites the imagination and I think it tells you
> that the future is now. Okay? We are being challenged presently with
> some very exciting data that is out there in the peer-reviewed public
> literature.
>
> So, very quickly I am going to go over the next six or eight slides
> just to impress upon you the reproducibility of this information. This
> is a publication, a collaboration of two groups, The Netherlands
> Cancer Institute and researchers at Rosetta Inpharmatics, who have
> recently been bought out by Merck. The focus, as you can see from the
> title of the publication in [The New England Journal]{.underline} is
> on gene expression signature as a predictor of survival in breast
> cancer. There was also a paper in [Nature]{.underline} and I am going
> to actually present data from both of those.
>
> \[Slide\]
>
> So, the question that these investigators are asking is can gene
> expression profiling be used to improve prediction of clinical outcome
> as it relates to breast cancer? The specific aims were to identify
> patients at risk to develop distant metastases and to accurately
> select for adjuvant therapy\--who to treat; can we determine better
> who to treat, who not to treat or who to avoid over-treating?
>
> \[Slide\]
>
> Proof of concept in a sense here is this first unsupervised cluster
> analysis of breast cancer expression profiles. What we mean by
> \"unsupervised\" is we are looking at correlation between and among
> gene transcript changes as a function of the samples they derived
> from. You can see across the top, there, we are looking at 5000 genes.
> Which genes behaved similarly; which ones were not together; which
> ones went down together; which ones correlated, in a sense, as a
> function of the tumor samples. Of course, on the left side you can see
> which tumors are behaving similarly as a function of the gene
> expression profile that they exhibit using a microarray.
>
> Red means up and green means down. The transcripts are up or down and
> you can see that there is a pattern there. It isn\'t just random
> noise; there is a pattern there. In fact, there are 98 tumor samples
> there. The 20 at the bottom are derived from women with germ line
> mutation in BRCA1; the 70 above are not. You can clearly see that
> pattern in a totally unsupervised approach.
>
> \[Slide\]
>
> So, you dig down deeper and you look at those 78 tumors to which the
> women did not have the BRCA1 mutations. You can whittle those 5000
> genes down to 70 significant prognosis genes using a standard
> T-statistic based approach. This is a supervised approach, so you look
> at phenotypic outcome. These are tumors that were derived from women
> over a period of 10 years, starting 20 years ago. So, you had outcome
> on these women in terms of whether they developed distant metastases
> and also whether some of them died. I will show you some of the plots
> of that data. But you can clearly see patients with what we call good
> signature, with that red section of genes up in the right-hand corner,
> there, as opposed to women with a bad signature with a poor outcome.
>
> \[Slide\]
>
> These are patients that are lymph node negative, the same 70 prognosis
> genes lymph node negative. I am not a clinician. I don\'t know a lot
> about the practice of medicine but I have a sister-in-law and she was
> diagnosed with breast cancer and she was lymph node negative and I
> breathed a sigh of relief. I think a lot of people in the family
> breathed a sigh of relief. What this is saying, however, is that even
> amongst those women that are lymph node negative, 14 of 40 of those
> women went on to develop metastases and they had a poor signature. Of
> the ones with the good signature, only one of 33 went on to develop
> metastases. So, it is looking like we have something even better than
> current clinical practice.
>
> \[Slide\]
>
> Patients lymph node positive, sort of the flip side of the
> coin\--women with poor signature, yes, the majority of the ones that
> had metastases had poor signature but there are some that had a good
> signature there even though they were lymph node positive, and 42 out
> of those 44 had good outcome.
>
> \[Slide\]
>
> Again, just putting it all together and looking at the outcome, good
> signature; bad signature\--I don\'t want to spend a lot of time on the
> study; I don\'t have a lot of time to spend on this study but you can
> see that same red/green segregation of what is going on.
>
> \[Slide\]
>
> Here are the Kaplan-Meier plots, again showing that for prognosis the
> good signature versus poor signature outperforms current medicine.
> That is just an example. That is an example I spent a lot of time on.
>
> \[Slide\]
>
> There is prostate cancer; there is brain cancer.
>
> \[Slide\]
>
> Here we have GI cancer of some sort.
>
> \[Slide\]
>
> We have kidney cancer. There are a lot of different cancers these
> things are being applied to. I believe Brian is going to show an
> example not of cancer but psoriasis where, again, you can use this
> approach.
>
> \[Slide\]
>
> So, what is the approach? Thematically, this is the approach. Based on
> phenotype, you can visually or by some objective accepted method
> determine two different ends of the spectrum, green and blue,
> metastases, no metastases within a defined framework, and then look
> for differences. Can we define differences using expression profile?
>
> \[Slide\]
>
> What you find is that when you do those expression profiles, yes, you
> can define differences. Not only do you see these global differences
> in just those two broad phenotypes, but you find subclasses within
> those observable phenotypes as well. This is a process that goes on in
> an iterative way getting better, and better, and better at
> distinguishing what look like the same disease or the same people
> using the technology that we have now. So, this is saying we have a
> very powerful technology and we can do things better.
>
> \[Slide\]
>
> This is just a visual graphic that says everybody might look the same
> but we can clearly classify them in different categories using some of
> the new technologies, genomics, proteomics.
>
> \[Slide\]
>
> Let\'s think about what we just did. We took 5000 genes we applied in
> the clinical scenario and we reduced that down to 70 genes, which
> really did all the work for us. That was looking at outcome over a
> ten-year period.
>
> What if we could do that same thing in toxicology? We don\'t have to
> wait ten years. We can treat animals with or without drug and look at
> differences that occur in a much faster time with a lot faster
> throughput, and we can go through that same process of turning 5000
> genes down to what are the critical genes that actually serve as the
> business end to help us make these kinds of decisions or classifiers.
> I hate to use the term prediction at this point in time. Drug
> developers have to predict; clinicians have to predict but toxicology
> reviewers I think classify and integrate data.
>
> \[Slide\]
>
> This is shown on a slide where we are looking at signature projection
> scores. It allows accurate clustering of compounds and allows
> identification of subparts of a compound\'s effect. One more point,
> 5000 genes, whittled down to 70 left 4930 genes that didn\'t
> contribute a whole lot there. I think that is where a lot of angst
> comes when we start talking about toxicology. There are a lot of genes
> that are changing and a lot of signals there, and we have to do a lot
> of good science to figure out where the signals are that we need to
> pay attention to and what we shouldn\'t get into an uproar about.
>
> But we have the capability of doing that to a certain extent already,
> and I think this slide from our collaborators and Iconix makes this
> point very clearly. This class of compounds, statins\--you can see all
> the statins along the bottom line. Each one of those squares does not
> represent one gene but represents a signature of 6-20 genes and they
> have an overlapping redundancy, some overlapping genes which go into
> that signature and some which that are not totally overlapping. So it
> is like that 70 gene score, only now we have different sets and we are
> talking about 6-20. But you can see the consistency with the statins.
>
> Here we are looking at pharmacology. The statins are doing something
> desirable. If you put another compound in there and it performs like a
> statin you should see that signature come up and that tells you
> something about the pharmacology. So, we have proof of concept
> established here. With the NSAIDs you can see performing signatures
> there. Clearly, the dose defines the toxin. You can push these things.
> That is the purpose of a toxicology experiment, you have to push the
> toxicology. We won\'t be happy in the FDA unless you tell us what the
> toxicity is. With the statins, if you push them high enough, you also
> see a hepatotoxicity signature.
>
> You can see an estrogen signal over there, and then you can see with
> DNA cross-linkers carboplatin, cisplatin and you can see the
> cross-linkers significant there. So, the signatures are relating
> expression changes to pharmacology, toxicology, pathology, clinical
> chemistry, the chemical structure of the compound and the biology.
>
> How can we do this? This is somewhat simplistic but it makes the point
> how can we come up with a signature of a statin? Here we are showing
> the biochemical pathway for how a statin works. Statin works
> pharmacologically because it inhibits H and G CoA reductase. We
> learned in biochemistry that there are feedback loops, substrates,
> downstream factors that will regulate a biochemical pathway. If you
> inhibit H and G CoA reductase all those enzymes downstream don\'t see
> the same substrate concentration. They are trying to drive and
> up-regulate expression of genes to compensate for that. So, we are
> seeing an attempt on the part of the animal, a compensatory response,
> to compensate for the inhibition of H and G CoA reductase.
>
> So, you see a pattern. all those red spots indicate that with
> simvastatin all those genes went up, and it makes sense. It makes
> biochemical sense that is telling you there is some pharmacology going
> on. And, it wasn\'t just one transcript. There was a combination of
> transcripts which gave you the confidence in what you are seeing here.
>
> \[Slide\]
>
> Here, making the point, are a lot of different platforms and there are
> a lot of different steps you have to go through to get knowledge out
> of that whole process, that data generation process. You have to worry
> about the RNA. Sampling integrity is critical. There are a whole lot
> of enzymatic processing steps\--amplification, labeling, array
> hybridization, washing, array scanning, a lot of steps in the process
> there.
>
> You turn that data into information through statistical processes,
> data transformation. There is normalization, scaling and, again,
> statistical processes you apply to select the key transcripts.
>
> Finally, there is the new and exciting, burgeoning and very much in
> need of field of bioinformatics. If someone can put bioinformatics
> behind their name I think they are on a road to success. People are
> hungry for these people to explain all this information, to tell you
> about all these cellular processes, biochemical pathway analyses
> referencing previous experiences. Database comparisons enter into that
> and there is no substitute for just downright good homework, getting
> into the literature and trying to figure out what is going on.
> Knowledge of biological truth is what we all want here, and this is
> where the decision-making level is in terms of the regulator. This is
> where it has to occur.
>
> \[Slide\]
>
> So, what concerns and initiatives have been raised about that triad,
> drug development, regulatory oversight, patient care? At the technical
> level, I have alluded to this already, with so many variables and
> options in data capture\--I didn\'t get into the details about the
> probes; probes can be at different ends of the genes on these
> different platforms but it is an issue. RNA sample processing,
> hybridization processing, data analysis\--what is the true signal? Are
> we measuring true biology reproducibly and accurately? Or, is there
> too much systematic experimental error to reasonably contend with? Are
> the data misleading us? Can we get the same true answer from data sets
> on different platforms and different laboratories? Can universal
> reference standards help us?
>
> These are all concerns that were raised in the May workshop that we
> had. It was an open public workshop. What is a reasonably detailed and
> practically useful relational data set to constitute a regulatory
> submission that defeats healthy skepticism concerning the integrity of
> the data? We need to define that. Will these data be appropriate, ever
> be appropriate for an FDA database to draw from and develop
> institutional knowledge against?
>
> \[Slide\]
>
> What about the category of biological concerns and issues? How would
> the agency react if an oncogene was activated? I am finally going to
> answer that question for you. I am going to have fun with this
> presentation; I am really enjoying this.
>
> Would the sponsor have to notify the FDA, clinical investigators and
> IRBs? This is a question that was posed to us in May. To be fair, that
> question got some answers and all those answers weren\'t all that
> pleasant for drug sponsors to hear at this workshop in May, and it
> raised a lot of concerns and we have to hit these concerns head-on and
> that is what we are going to try to do here, or at least get started
> with that today.
>
> Will they be\--and I put this in quotes\--\"more sensitive\" because I
> am not really convinced yet that they are more sensitive; they are
> earlier clearly but are they more sensitive in terms of dose response?
> Will \"more sensitive\" gene expression changes drive drastically
> lower clinical trial starting doses and prolong Phase I clinical
> trials?
>
> Which expression alterations\--and this is a difficult one and, again,
> this is a critical biological question\--are reliable and biologically
> relevant classifiers or biomarkers of\--and I have to separate these
> out\--which are desirable effects, which are undesirable but clearly
> tolerable, which are healthy and fully compensatory responses to the
> exposure that we can adapt to and not be concerned about, and which
> are the special category of intolerable drug actions that are going to
> lead to irreversible outcomes? How are they all biologically relevant?
>
> \[Slide\]
>
> I think Janet has a very good plan and she is going to get this out to
> you today to get some feedback from you in terms of some concerns and
> issues raised that we can maybe deal with procedurally. Would all
> these tasks have to be done under GLP conditions? Would they all be
> interpreted by FDA as relevant to human safety? Is a research
> information package approach feasible? What data are appropriate for
> that mechanism, what data are inappropriate? These are extremely
> important questions that we need to resolve.
>
> How will the FDA prepare itself to work with these huge data sets in a
> timely manner? That is an internal process that we have to deal with
> and we are dealing with that, talking about that. How are we going to
> ensure that individual reviewers do not \"prematurely\" interpret,
> generate their own hypotheses, or over-interpret those expression
> alterations whose biological significance have not been scientifically
> established?
>
> How will the FDA communicate which expression alterations have reached
> a scientifically mature level of understanding and can rationally be
> considered relevant safety biomarkers?
>
> \[Slide\]
>
> Here is the sort of traditional drug development process. You think
> you understand something about disease, the biology of disease and you
> feel you have a validated target. You put a compound there and you see
> if you can alter that target, if your drug is doing what you wanted to
> target. Then, you develop a lead compound to inhibit that process and
> you come up with a better and better lead compound.
>
> Well, there is a line sort of between that discovery process which
> Janet alluded to and then the process which FDA really focuses in on.
> But in my own discussions with individuals in drug development and
> industry, I think it is not always clear now that some of the stuff
> they do in those early phases of development do not need to be shared
> with the FDA. That needs to be clarified. There are some grey areas in
> there. Is it a no line; is it a dotted line; or is it a solid line
> here? I think that is something that we need to work out and be very
> transparent and clear to the industry about because it doesn\'t quite
> seem to be a totally level playing field.
>
> \[Slide\]
>
> This kind of captures in a sense if that dotted line appears between
> that solid green box\--if there isn\'t a solid line here, then why not
> allow a lot of imprecision? Why not allow a lot of hypothesis
> generating? Maybe the platform doesn\'t need to be very validated.
> And, let the scientific researcher generate the hypothesis, look at
> the consistency and keep things wide open. As opposed to, as you
> progress the concept here being this is what we envision, that there
> is going to be more and more fine tuning and more of a demand
> eventually on the precision and maturity of the signals and
> interpretability of the biology as you move closer and closer to the
> clinic.
>
> \[Slide\]
>
> What is CDER doing to address the issues? First, building the internal
> capacity infrastructure. One thing we did early on is establish a core
> expertise within the CDER laboratory. I like to think of that as an
> effective process. Having that core expertise within the CDER
> laboratory, we have contaminated the rest of the Center and we are
> bringing our knowledge, our experience and our skills and getting more
> people excited about establishing committees. You know, we have a neat
> toy. We had some money and with that money we are able to buy a cDNA
> array scanner. We got some cDNA arrays and just went to town, and
> through a beautiful collaboration with International Life Sciences
> Institute we were able to get up to speed very quickly and to be able
> to compare and learn a lot through that process.
>
> What did this do? This enabled us in a sense to sort of establish
> credibility. We were able to leverage the Affymetrix GeneChip system
> and we have a wonderful research agreement with them. We are working
> very closely with them on a more profound biological issue, and also
> working with Rosetta on a research agreement so we can look across
> platforms with the same samples and try to understand the biology.
>
> We were also very fortunate, and I want to thank the Office of the
> Commissioner, we have an Office of Science grant from the Office of
> the Commissioner and we are using that to develop consensus toward RNA
> standards development initiative. This involves all the medical
> centers within the FDA in a collaborative mode and involves the NCTR
> as well, to look at can you establish a benchmark data output across
> platforms using, for example, a mixed tissue standard. We are focusing
> on the rat as the fundamental toxicology model. We had a tremendous
> workshop, working through NIST, to bring this out and to get more
> collaboration. NIH is going to join us as well.
>
> Expanding core expertise for CDER review enhancement\--we have
> established a nonclinical pharmacogenomics subcommittee. The focus
> there is on regulatory decision-making practices, procedures and
> policies as nonclinical expression data come to us. the database I
> referred to through Iconix, the DrugMatrix database, leveraged that.
> Again, a concept the previous commissioner brought to us and pointed
> to this as a way we are going to be operating in the future. But if
> you think of it, you know, how are we going to get up to speed very
> quickly?
>
> This database all of a sudden gave us access to 550 pharmaceuticals
> and how they perform. These are things in the PDR so we can turn the
> clock of time in a sense and say what if we had this capability 10,
> 15, 20 years ago and 550 pharmaceuticals which are now in the PDR?
> What would we have done if we had seen that microarray data? So, we
> have something to compare it to. This is stuff we have experience with
> that is being used, and we feel comfortable with these things. We can
> ask about oncogenes.
>
> \[Slide\]
>
> Very briefly, the point I want to make here is that the same sample
> was run at two different sites on the same platform, Affymetrix,
> FDA\--blue is Affy. They are showing that you don\'t always get the
> exact same genes identified as statistically significant. But what
> this is saying is, okay, if you don\'t get the exact same ones sort of
> surfacing above a p value, what happened on the other platform when it
> was significant on one platform? Well, this shows you there is
> tremendous correlation. It behaved pretty much the same way, p less
> than 0.001; 8000 transcripts; 0.001 times 8000 may be 8-10-fold. Over
> here, you see in quadrant 2 and 4 there are about 8 or 10
> spots\--amazing statistics that kind of play out there.
>
> \[Slide\]
>
> If you lower the p value, 0.01, you have a lot more genes to work with
> if you are in the hypothesis-generating mode. Look at all the genes in
> quadrant one and three. You have more in two and four \"false
> positives\" maybe to contend with but if you are in the
> hypothesis-generating mode why not go down to this level?
>
> \[Slide\]
>
> You don\'t have this in your slides, sorry. This is saying another
> story, and that is if you use a slightly different statistical
> approach to identify the genes of interest\--Affy has a unique thing,
> increased/decreased calls where you look at exact probes across
> results from controls to treated animals and I don\'t want to go into
> details of that but look at this correlation. It is amazing; a much
> better correlation. So, the statistics that you use to identify those
> transcripts are equally important in terms of how you define, and this
> is something we need to work together on. Each platform sort of has
> their own approach to identifying transcripts of interest and this is
> something we need to work toward together.
>
> \[Slide\]
>
> Here is the answer to this question. What if a reviewer sees an
> increased expression of oncogenes in a product submission package? We
> used the Iconix database to ask this very simple question. You have 72
> genes on the left-hand side. They all have the term \"oncogene\" in
> them. We are really talking about proto-oncogenes here; these are not
> mutated genes. They have oncogenic potential if they are mutated. The
> list of oncogenes is growing every day. [Nature]{.underline} came out
> with an issue a month ago with, you know, this list of what could be
> oncogenic if it is mutated. It is going on and on.
>
> But here you can see diphenhydramine clustering. We have 9 known human
> and rodent genotoxic carcinogens; 5 human and rodent non-genotoxic
> carcinogens. There are 14 carcinogens proven to be carcinogenic in man
> and we have 14 non-carcinogens, at least tested in rodent 2-year
> bioassay. Do we see oncogenes going up with these rodent
> non-carcinogens, and do they cluster themselves all the way from all
> the carcinogens? There is no pattern here. There is no clustering of
> the non-carcinogens away from the carcinogens when you just look at
> oncogenes.
>
> You can see mannitol and up-regulation of oncogenes there. You have
> aspirin. You have diphenhydramine. Oncogenes are going up. This is a
> message more to our reviewers than anything else. You see an oncogene
> go up, no single transcript is going to derail and identify a safety
> issue.
>
> \[Slide\]
>
> I mentioned that efforts have been initiated by all the medically
> related product centers, along with NCTR, along with NIEHS and several
> microarray stakeholders, Affymetrix, Rosetta using Agilent-based
> platform, Iconix using the Amersham platform to develop standards
> useful for evaluating platform features and, as well, experimental
> performance if you want proficiency of the end user. Ultimately,
> bottom line, biologic conclusions that are independent of platform and
> represent biological truth is what we want to help these standards do
> for us. That project is ongoing.
>
> \[Slide\]
>
> I mentioned we have a PTCC nonclinical pharmacogenomic subcommittee
> and you can see here what the various goals are of that group. We want
> to effect an appropriate infrastructure for pharmacogenomic data
> review and integration. That is our initial and primary goal right
> now.
>
> A question came up during one of the discussions, what are we going to
> do to explain to sponsors how we want the data submitted to us? That
> was a question that was posed to Larry. This is our first goal, to get
> that out and open to them if they choose to submit microarray data
> right now, at this point in time, if they choose to generate it. If
> they choose to generate it and want to submit it to us, how should
> they submit that data? And, this is what we want to be transparent
> with them about.
>
> \[Slide\]
>
> What else are we doing? Establishing a network to assimilate
> reasonable consensus; develop mechanisms to communicate and deliver
> our needs. I mentioned working with NIST to develop RNA standards. A
> workshop has been held; an initiative has begun.
>
> We have a number of collaborations with NCTR, a tremendous resource to
> the agency down there. FDA inter-center communications group, white
> paper. There is a National Research Council committee, a government
> liaison group, again, to explain to them what our needs are. This is
> really something that NIEHS has thrust and tried to develop to get
> input from a lot of different regulatory centers in terms of how they
> can help us as well, and we are involved with that. MIAME-tox, minimal
> information about a microarray experiment as related to tox,
> specifically working with MAHS on that and also with ILSI. There have
> been FDA-PhRMA workshops.
>
> In sort of a non-collaborative mode, engaging external expertise. On
> June 10 we are going to convene that group for the first time that we
> are going to ask to help us with what should the data submission be
> that we receive from sponsors. What is workable? What is reasonable?
> What is compatible with current practice within the drug development
> industry that doesn\'t add a lot of burden and can work with the
> current systems that are currently being used? Then, what is
> reasonable for experts in the field in terms of how broad does the
> data have to be? Do we have to see images? Can we have scaled
> normalized data? So, these are all things we need to work out.
>
> We are going to practice a mock data submission. We need to develop
> our prowess at this. We can ask and maybe be careful what you ask for,
> but we are going to see how this whole process works and try to work
> out some of the kinks.
>
> \[Slide\]
>
> I am doing pretty well. I am only three or four minutes over and I am
> at the end. CDER shares the vision that applications of
> pharmacogenomic technologies will improve recognition, understanding
> and assessment of pharmaceuticals by recognition; easier, quicker,
> more accurate identification of efficacy, efficacy potential
> toxicities and toxicity potentials. By understanding, we mean modes or
> mechanisms of drug actions; by assessment, the significance and
> relevance of the findings to humans.
>
> \[Slide\]
>
> CDER has a responsibility to enable, not force nor impede evolution,
> vision, desired state, preferred future, whatever the best term is
> there. So, if we think about the drug development process and where it
> interfaces with FDA, lead compounds going into analytical trials and
> perhaps animal trials can be reduced and shortened if we work together
> and we get to the crux of the biological value of this approach.
> Perhaps our clinical trials will have a lot more options to pursue and
> we will have potentially shorter, more focused kind of trials with
> clearer indication of efficacy going into them, and potentially more
> product approvals.
>
> Just one last quote struck me and I am going to change it a little
> bit. This is a quote from a very brief piece that appeared in [Nature
> Genetics]{.underline}. The individual is Chris van Ingen: As the
> impact of new technology on the quality of human life increases, so
> too do the responsibilities of appropriate and reasonable regulation.
> I think we have a good plan to work with people on that and I hope you
> give Janet a lot of good feedback. Thanks.
>
> DR. DOYLE: Thank you very much, Dr. Sistare. Next we are going to
> bring Dr. Larry Lesko back and he is going to address Pharmacogenomics
> from the perspective of drug metabolism and dosage.
>
> **Pharmacogenomics\--Drug Metabolism/Dosage**
>
> DR. LESKO: Thank you and hello again.
>
> \[Slide\]
>
> As you see in the binder that you have there, I have more slides than
> Frank had but I promise not to show them all. I also promise not to
> show any microarray data.
>
> What I will talk about, however, is pharmacogenetics, which many
> people think is a subset of pharmacogenomics. I will be speaking about
> it from a fairly narrow perspective, that of drug metabolism and
> dosage.
>
> As you have heard already today, there has been a paradox in the
> modern drug development process. Clinical trials provide evidence of
> efficacy and safety at usual doses in populations. On the other hand,
> physicians treat individual patients who can vary widely in their
> response to drug therapy at the usual dose.
>
> Well, in clinical pharmacology we try to take care of this paradox in
> part by looking at subgroups of patients defined by demographics,
> disease or other types of factors. While that is not quite dealing
> with the individual patient, it does deal with individual subgroups of
> patients with respect to drug dosing and drug therapy.
>
> \[Slide\]
>
> You will see in any discussion of pharmacogenomics many definitions.
> This is the practical definition that I will provide for our
> discussion: systemic genomic analysis in populations of treated
> subjects to identify variants that predict drug response including the
> occurrence of adverse events.
>
> \[Slide\]
>
> Leading from that definition are three broad areas of application. You
> will hear more about this, but drug discovery is obviously an area
> where genomics can be used to better understand the cause of disease
> and, therefore, identify new drug targets. On the far right is drug
> selection. Drug selection results from using genomics to
> differentially diagnose something that we call hypertension or
> diabetes and then align a specific drug in a class with that subtype
> of the disease.
>
> I am not speaking about either one of those. I am speaking about the
> middle channel, talking about drug therapy and how drug therapy and
> the knowledge of genomics can be used to tailor the dosing to an
> individual or to a population.
>
> \[Slide\]
>
> This is a cartoon that illustrates the pharmacogenomic strategy as
> applied to practice of medicine. It is a bit of a simplistic
> presentation because it takes all patients with the same diagnosis.
> Based on a single nucleopolymorphism, or SNP, the idea is to remove
> non-responders or toxic responders in advance and then, based on other
> SNPs identify responders or patients not predisposed to toxicity whom
> we can treat. It is idealistic in that it treats non-responders and
> responders as two different groups when, in practice, it has actually
> turned out that it is more probabilistic than that and there is
> probably an overlap in terms of response between the responders and
> the non-responders.
>
> \[Slide\]
>
> In any case, in terms of pharmacogenetics we know already that there
> is a pharmacokinetic basis for differences in drug response that
> define those two groups I just showed you. A way of categorizing those
> differences is based on extrinsic factors such as the environment,
> smoking, diet, alcohol, drug interactions Rx, OTC and herbal products
> taken together. Also intrinsic factors and intrinsic factors can be
> demographics; could be disease states; or the subject of our
> discussion today, pharmacogenetics.
>
> An analogy would be just like we define subgroups of patients based on
> renal disease or hepatic disease which is phenotype, the thought is
> that we can also define subgroups of patients based on their phenotype
> by looking at polymorphisms in the genes that encode metabolic
> enzymes.
>
> \[Slide\]
>
> So, I am going to change that cartoon a little bit and talk about the
> pharmacogenomic strategy applied to drug metabolism and dosing.
> Notice, on this slide I don\'t have responders and non-responders.
> Rather, I have a group, group A, that has a genetic profile for
> toxicity with the usual dose of the drug. It isn\'t indicated that
> this group not receive the drug, but it is indicated that this group
> receive the drug but perhaps at a lower dose. That is the implication
> of what I am referring to in pharmacogenetics and drug metabolism.
>
> As you can see, groups 2 and 3 continue with the theme of responders
> but link the responders more to the drug selection process than to
> identifying a drug target. So, I am really operating under conditions
> of number 1 in the presentation that I am making today.
>
> \[Slide\]
>
> This illustrates what I am referring to when we talk about
> pharmacogenetics and drug metabolism. This is the case where I have
> the usual dose given to a population of patients, but those patients
> between them have different plasma concentrations. They may have those
> different plasma concentrations for many reasons, as I indicated,
> environmental or intrinsic factors. But I show on this slide patient A
> with a certain wild type sequence in their DNA that encodes for the
> activity of the enzyme. Patient B has a polymorphism that creates a
> mutation in the variant.
>
> The net result, if you go to the far right, is a plasma concentration
> time profile in the wild type patient that is right within the
> boundaries of that therapeutic range. When you go below that, because
> of the slower metabolism imparted by this polymorphism, the exposure
> goes above the therapeutic range and that patient, or patient
> subgroup, is predisposed to toxicity.
>
> \[Slide\]
>
> We know from history, probably twenty years of history, that many of
> the cytochrome enzymes are, in fact, polymorphic. I could pick several
> of them but let me use as an example one of the most popular, and that
> is cytochrome 2D6. On the left I show sort of the portfolio of
> cytochrome enzymes. I could pick for this example 2C9, 2C19 or many
> others that are polymorphic, but I picked on 2D6 to illustrate the
> example because this is an important enzyme in terms of prescription
> drugs. It is responsible for metabolism of about 40 percent of
> prescription drugs. That means over 300 million prescriptions for
> drugs with polymorphism in the 2D6 per year.
>
> The example on the bottom represents the terminology in the cytochrome
> enzyme literature. I have a family called CYP2. I have a subfamily
> called 2D6, and then I have a gene defined by, in this case, a
> deletion at 5 2D6\*3. If you happen to have that 2D6\*3 you are going
> to be characterized as a phenotype called poor metabolizer.
>
> \[Slide\]
>
> What are the clinical implications of that? Well, this shows a
> theoretical but somewhat real dose-response curve for nortriptyline,
> and on the left-hand side I have indicated the therapeutic window
> which is somewhat well defined. You can see that there is an overlap
> between the exposure response curve for therapeutic effect. That would
> be the therapeutic response curve. There is also an overlapping curve
> for toxicity and that window isn\'t very wide. It is about three-fold
> in practice and, in fact, nortriptyline levels are monitored in
> therapeutic drug monitoring labs quite often.
>
> If you think about nortriptyline and the implications of having a wide
> range of exposure from the usual dose, you may be moving into that
> efficacy world of reduction in anxiety and symptoms of depression, or
> you may be moving into that safety world of tachycardia, arrhythmias
> and drowsiness. That is all possible from the usual dose without
> taking into account metabolic patterns of a patient.
>
> \[Slide\]
>
> This shows a little bit more. On the left are illustrated
> nortriptyline plasma levels as a function of frequency. It shows the
> bell-shaped curve. The majority of patients are called intermediate
> metabolizers. Their plasma levels are somewhere around 60. The poor
> metabolizers may be on of those 2D6\*3 on the right hand side and you
> can see their exposure is significantly higher. Then you have people
> with extra activity that are on extensive metabolizers side who have
> lower blood levels and, in fact, may require a lower dose to achieve a
> therapeutic response. Translated into functional dosing, on the right,
> the dose in the PDR for nortriptyline is 25-300 mg. You can see the
> wide range and that wide range is a result of the wide range of
> requirements that patients have based on their metabolism.
>
> But you could ask what is the equivalent dose if I were to achieve the
> same exposure in all patients or the patients who are a mix of poor
> metabolizers, intermediate and extensive? That is what the graph on
> the right shows. In order to achieve equivalent exposure I would have
> to give, for example, to the extensive metabolizer 120 mg. To get the
> same systemic exposure the dose would have to be reduced in the poor
> metabolizer to 25 mg. That is the implication that we have for
> therapeutics, the wide dose requirements defined by the genotype of
> the patient.
>
> \[Slide\]
>
> Well, if this is a wonderful part of science, one might ask the
> question what has been accomplished over the last twenty years with
> the integration of this at the bedside. I did a search of the
> electronic 2003 version of the PDR which had 2000 entries and
> identified 51 labels that contain pharmacogenomic information. That
> doesn\'t seem like a lot of advancement over the course of time in
> looking at the field. In most cases, in fact, of the 51 labels the
> information about genomics in the label was not easily translated into
> clinical practice.
>
> \[Slide\]
>
> Here is an example of one label that came up in the search. It was
> thioridazine, Mellaril. In that label I thought the information was
> fairly informative in terms of communicating the risk. In the
> contraindication section of the label thioridazine was contraindicated
> in a subgroup of patients which are seven percent of the Caucasian
> population who are known to have a genetic defect leading to reduced
> levels of CYP 2D6. In the warning section, to further provide
> information to the prescriber, there are certain circumstances that
> may increase the risk of torsade because of long QT in patients with
> reduced activity of 2D6.
>
> So, here is a factual piece of information, no different than other
> factual pieces of information in the label. It does not go on to say
> what to do or how to identify those patients in terms of dose
> reduction or in terms of laboratory tests.
>
> \[Slide\]
>
> A recent example that illustrates the inclusion of genomic information
> in a label is that of atomoxetine or Straterra, which was approved in
> January of 2003. This goes a step further in terms of informative
> information in the label. For example, it includes in the human PK a
> statement that a fraction of the population are poor metabolizers
> resulting in\...and the label goes on to describe that.
>
> There is good news here. The inhibitors of 2D6 in extensive
> metabolizers increase exposure, however, if you are a poor metabolizer
> you are not prone to that interaction. In the adverse reaction section
> the following ADRs were either twice as frequent or statistically
> significantly more frequent in PMs compared to EMs. Finally, in the
> label the laboratory tests are available to identify CYP 2D6 poor
> metabolizers and, in fact, this is a relatively new development in the
> field in terms of the widespread availability of laboratory tests to
> identify poor metabolizers. So, this is probably the most informative
> label that we have at the moment.
>
> \[Slide\]
>
> Brian is going to talk a little bit more about pharmacogenomics during
> drug development. Talking to Brian and other people in the industry,
> we have been told that 80 percent of new clinical trials include
> collection of samples for DNA analyses. We haven\'t been told what
> those analyses are or how they are going to be applied.
>
> This graph kind of illustrates the number of clinical trials under
> way. So, if you multiply those clinical trials by the number of
> samples collected you can see why we have so many gene banks springing
> up.
>
> \[Slide\]
>
> But what is going on from our experience at FDA, we undertook a
> survey, a very informal survey that doesn\'t claim to be 100 percent
> accurate, of the appearance of pharmacogenomics in INDs and NDAs. We
> started this very recently, and you can see the time course of this
> survey. Over that short period of time the number of INDs and NDAs
> that contain genomic information has gone up very dramatically. It has
> gone from 5 to 70. I expect this is an underestimation of the actual
> utilization of genomics in the drug development process as perceived
> from the IND/NDA standpoint, but it gives one a sense of what is going
> on.
>
> \[Slide\]
>
> What do these 70 applications represent? They represent predominantly
> genotyping of the cytochrome enzymes that are responsible for drug
> metabolism. You can see the breakdown in the pie chart. By and large,
> most of the genotyping is related to the enzyme I picked as the
> example, CYP 2D6.
>
> On the bottom of that slide are some bullets that represent other
> things that are being genotyped that are much more exploratory than
> the confirmatory data on the cytochrome enzymes. For example, we are
> beginning to see genomics as applied to receptors in an attempt to
> understand differences in drug response. This is very preliminary. It
> is very exciting to see it but it is not in the confirmatory world
> like the metabolite enzymes are in. We just don\'t understand that as
> much. So, there is a lot of activity going on, as evidenced by the
> trend in the data that we are seeing.
>
> \[Slide\]
>
> On a lot of people\'s minds is the issue of drug safety and the fact
> that adverse events are a major public health issue, as well as a
> major pharmacoeconomic issue. So, the question is logical, how does
> this relate to pharmacogenetics?
>
> We have asked the same question and we were piqued by the article that
> appeared recently in the literature that really linked several
> databases but provided us with some interesting information that the
> top 27 drugs frequently cited in adverse drug reaction reports,
> including nortriptyline, 59 percent of those drugs are metabolized by
> at least one enzyme having poor metabolizer genotype; 38 percent of
> those drugs in the top 27 drugs are metabolized by 2D6, mainly
> cardiovascular and so on. So, this is inconclusive but it is
> circumstantial evidence that polymorphism in drug metabolism matters
> when it comes to drug safety.
>
> \[Slide\]
>
> It raised an issue for us about how pharmacogenetics can improve
> existing therapy. The emphasis, by and large, when people talk about
> genomics is the future. How is this going to help us develop better
> drugs, more targeted drugs and reduce adverse effects?
>
> According to Dr. Collins, at NIH, mainstream genomics is still eight
> years, ten years away. But I think we are on the verge of possibly
> doing something sooner that can benefit public health. Thus, I ask
> this question, how can pharmacogenetics improve existing therapies,
> which I will define as all medicines that have been approved by the
> FDA for prevention or treatment of any disease in humans under patent
> or not?
>
> \[Slide\]
>
> As an example of one case study we can look at thiopurines and more
> specifically 6MP. This is a drug that has been approved for acute
> lymphocytic leukemia mainstay therapy for over 50 years, and clinical
> studies over the last 20 years have focused on various phenotypical as
> well as genotypical methods of refining optimal dose.
>
> To look over the history of 6MP to the present time, there have been
> incremental advances in using pharmacogenetics. Efficacy is fairly
> high for an oncolytic application. Adverse effects are also associated
> with that. In the case of 6MP, the risk of myelosuppression is the
> primary limitation to drug dosing. Blood counts have been
> traditionally used to monitor therapy, however, there is evidence to
> suggest that pharmacogenetic testing can significantly reduce,
> although certainly not eliminate, this risk.
>
> \[Slide\]
>
> In addition to its approved use, there are many off-label uses of 6MP,
> inflammatory bowel disease, various autoimmune disorders. If one were
> to look at the prescription use of this drug, it wouldn\'t be
> surprising if the use off-label exceeds the use on-label.
> Nevertheless, it is a mainstay of therapeutics in today\'s practice of
> medicine.
>
> \[Slide\]
>
> What we know about the genetic basis of 6MP is fairly well
> established. This is not exploratory but, rather, confirmatory that a
> certain enzyme TPMT, thiopurine methyl transferase, catalyzes the
> inactivation of the compound. If you happen to be a deficient patient
> you accumulate excess of thioguanine nucleotides in your hematopoietic
> tissues and this accumulation in RBCs leads to an increased risk in
> all patients of severe and possibly fatal myelosuppression.
>
> \[Slide\]
>
> Let\'s look at integration of genomics via this flowchart. What this
> flowchart shows is the prevalence of genotypes for TPMT in the
> population. I start at the top with all newly diagnosed ALL patients
> per year. That is 30,000 and 3000 of those are pediatric; the rest are
> adult. By and large, that population can be subdivided on their
> genotype into three broad categories. On the far right is the wild
> type. Most patients are of the high activity type. In the middle is
> the heterozygote deficient, intermediate activity, and on the left,
> homozygote deficient, the one that we probably would be most concerned
> about. That individual would have two mutant alleles.
>
> It is further well-known that three major SNPs, or single gene
> polymorphisms, define these mutant alleles. It is quite common to find
> in the population \*3A, 3C and \*2. I have shown in parentheses the
> prevalence of these alleles. There is a rare \*3B, 120,000. There is
> also one more that I don\'t have on there, \*3D but it usually travels
> in association with \*3A so one doesn\'t have to measure it
> specifically. If you are in that at-risk category on the homozygotes,
> low activity, the prevalence is 1/300; for the heterozygotes it is
> about 11 percent of the population.
>
> \[Slide\]
>
> In order to think about a genetic test to guide therapy and guide
> dosing, one has to look at clinical utility. We have tried to do this
> by looking in the literature at the incidence of toxicity. What this
> graph shows on the left-hand side is the cumulative incidence of dose
> reduction due to toxicity in a patient population receiving the usual
> doses of 6MP.
>
> Notice in the homozygote deficient, the v/v patients, 100 percent of
> the patients need a dose reduction because of toxicity because they
> can\'t metabolize the drug. The middle category, heterozygotes need a
> dose reduction in 35 percent of the cases because of toxicity. As one
> would expect, the inherent toxicity of the drug in the high
> metabolizers is around 7 percent.
>
> What is very interesting though, if you move to the right-hand side,
> is you can see the different dose requirements that these patients
> require. These are the average final weekly 6MP doses. If you are a
> wild type allele, on the far right, the dose is pretty standard.
> However, if you are on the far left, homozygote deficient, you can see
> the dramatic reduction in dose necessary to avoid toxicity.
>
> The problem in dealing with this drug dosing of 6MP is that you have
> to interrupt therapy based on the toxicity, and interruption of
> therapy lessens the intensity of treatment which is very important in
> the ALL patients. Furthermore, if one has multiple drugs in the
> regimen, as is typical for an ALL patient, reduction of the 6MP dose
> allows for full dosages of the other chemotherapeutic agents. In fact,
> many of the drugs in a regimen for an ALL patient have overlapping
> toxicities so for a physician to figure out which of the drugs is
> causing toxicity a test can assist that.
>
> \[Slide\]
>
> In order to construct the framework of how to think about the
> integration of a genomic test into existing therapies, I have borrowed
> some of the guidelines from the Secretary\'s advisory committee on
> genetic testing from their report. I think these are very reasonable
> questions to ask. Is the phenotype relatively common? Is the impact of
> the phenotype serious? Does early detection of a genotype alter
> therapy, in this case drug dose? Are there accurate, reliable tests
> available and, once those tests are performed, is there counseling
> available for the physician or the patient as necessary?
>
> In going through this questions one by one as they apply to 6MP or as
> they apply to a drug metabolized by 2D6, I would hope we could come up
> with a rational answer as to how to move forward in improving existing
> therapies using pharmacogenomics.
>
> \[Slide\]
>
> You heard about many working groups within FDA, well, here is yet
> another one. We are going to have a summit one of these days to talk
> to each other about working groups, but this is the one I have been
> chairing since June of 2001. This group had, as you can see, a
> cross-section of individuals from all of our centers. It also has a
> cross-section of disciplines represented on the group.
>
> \[Slide\]
>
> Our goals are listed on this slide. We came together originally in
> 2001 to organize a public workshop to at least get on the table a
> discussion with the industry and others about the issues of
> pharmacogenomics. That occurred in May, 2002 and the workshop report
> was released this month in [The Journal of Clinical
> Pharmacology]{.underline}, in the April, 2003 edition. It makes for
> some interesting reading, if you haven\'t seen it.
>
> We also used the working group to make presentations at public
> meetings, primarily professional associations, to begin to provide
> some regulatory perspective, which audiences generally want, and to
> begin to engage audiences from academia and industry in discussions of
> issues.
>
> We are developing a draft guidance for industry on pharmacogenomics
> that will emphasize primarily the clinical side of pharmacogenomics,
> the efficacy and safety trials. It will discuss statistical issues. It
> will discuss labeling recommendations and it will discuss in part some
> of the issues revolving around diagnostic tests and nonclinical
> pharmacogenomics.
>
> What it will eventually discuss at the end of the day depends in part
> upon coordinating the activities of this working group with the
> others, and we are still some time away from that guidance.
>
> We have also used this working group as a forum to discuss the
> scientific details of a submission requirement for PG data that Dr.
> Woodcock will talk about in the next set of remarks that she is going
> to make. We have tried to develop a number of case studies that would
> represent data that would be typically in a standard review stream,
> versus that data that would perhaps be outside that review stream in
> terms of submission to the FDA.
>
> With that, I will conclude and turn it back over to the Chairman, Dr.
> Doyle. Thank you.
>
> DR. DOYLE: Thank you very much, Dr. Lesko. We will move on. Next we
> are going to hear from Dr. Brian Spear who is Director of
> Pharmacogenomics, Global Pharmaceutical Research and Development, at
> Abbott Laboratories. Dr. Spear is going to share with us a perspective
> of the industry\'s use of pharmacogenomics and regulatory issues.
>
> **Industry Use of Pharmacogenomics and**
>
> **Regulatory Issues**
>
> DR. SPEAR: Thank you very much.
>
> \[Slide\]
>
> I appreciate the opportunity to participate today and to put forward
> an industry perspective on the use of pharmacogenomics in drug
> development. Now, what is this industry perspective that I have? Good
> question. Part of it comes from my experience at Abbott Laboratories
> where I am responsible for pharmacogenomics, including
> pharmacogenetics and cellular molecular toxicology. It also comes from
> active interaction with a number of people in the industry, primarily
> through either formal or informal groups such as the industry
> pharmacogenetics working group which has been working closely with
> FDA, for instance on last May\'s workshop, through PhRMA, through FPIA
> which is the European equivalent, and other groups that involve
> industry, regulators and academics who are focusing on scientific and,
> to some degree, regulatory issues related to pharmacogenetics.
>
> But the views that I am going to put forward are my own. I think that
> they correspond with those, and I have had feedback saying they
> correspond with those of other people in the industry but they are
> still my own and I will take full responsibility for them.
>
> I should say that within the industry the participation among the
> major pharmaceutical companies is 100 percent. All of the companies
> are involved in pharmacogenomics. This is not something that a company
> here and a company there have taken a chance on. This is now a
> standard part of the drug discovery and development process in every
> one of the drug discovery and research companies.
>
> \[Slide\]
>
> What I would like to do is two things. One is to just go over, in very
> brief detail and by example, some of what the major activities are
> within the drug industry in pharmacogenomics to give you a sense of
> where we are putting our resources and what we think is important.
> Then I would like to raise some issues that are important to us in
> terms of how our work is done, how the results are going to be used,
> and what the regulatory consequences are to try to lay a bit of
> groundwork of where we would like to see the interaction between the
> industry and the FDA.
>
> \[Slide\]
>
> The primary uses of pharmacogenomics within industry relate to
> clinical trial results, to data quality, to study design, to
> biomarkers. Some of the results here will not be reported; some of the
> results will. Some of them will eventually go on to drug labeling.
> Some of them may lead to specific labeling to direct a genetically
> defined group for therapy. However, targeting drugs at genetically
> defined groups, that is a drug strategy where you are going to make a
> special drug for a special genetic group, is not a primary focus in
> industry. You hear this from time to time. You seldom hear it from
> industry. Rather, we are still engaged in conventional drug
> development but now we have a new set of tools that we can use to
> direct the drug development or interpret the results in new ways.
>
> What I would like to do is to describe three areas which seem to
> involve the greatest degree of effort within the pharmaceutical
> industry: clinical genotyping, pharmacogenetics, preclinical gene
> expression and clinical gene expression.
>
> \[Slide\]
>
> First of all, clinical pharmacogenetics\--I am not going to go into a
> lot of detail, but there are areas where understanding patients\'
> phenotypes can help considerably in interpreting or designing Phase I
> studies. First of all, there are outliers that appear in Phase I
> studies and when you are looking at 6, 8, 12 patients one outlier can
> have a considerable effect. In some instances, understanding patient
> genetics can help you understand the pharmacokinetics much better, and
> I will show an example in just a moment.
>
> Secondly, you can use this to exclude certain patients or you can use
> it to include certain patients, depending on what you are trying to
> tease out in a particular Phase I study on healthy volunteers.
>
> You can use it to normalize genotypes. What do I mean by that? Well,
> as Dr. Lesko pointed out, poor metabolizers of 2D6 appear somewhere
> between six and eight percent of the American population. In a small
> trial you would hope that you will have genotype frequencies which
> represent that so that you are not, because of small sample size,
> missing the boat completely or overloading on the wrong patients.
>
> Finally, bridging the other populations. We have had no discussion so
> far about ethnic or racial differences in allele frequencies. But some
> racial groups or ethnic groups have high frequency of poor
> metabolizers for enzymes, low frequency of poor metabolizers, high
> frequency of hypermetabolizers. Because of that, if you have not
> allowed for these frequencies in your trials, you may find it
> difficult to translate data, let\'s say, from the United States to
> Japan or China, or vice versa. By normalizing the allele frequencies
> to account for these differences in different national populations,
> you may make it possible to bridge much more easily.
>
> \[Slide\]
>
> Let me just show you an example of a Phase I trial. This is a study
> with investigational drug. We are looking at the effect of the
> investigational drug on the pharmacokinetics of desipramine. This is a
> fairly straightforward trial that one would run. But in this
> particular case, because desipramine is metabolized almost entirely by
> 2D6, as is nortriptyline, patients who are poor metabolizers do not
> metabolize at a rate which will appear normal in your Phase I trial.
>
> What we did in this case is we genotyped people ahead of time. We
> removed those who were poor metabolizers. I think there were 3 in a
> population of 36. Then we carried out the interaction study only on
> those who had active genes to metabolize desipramine.
>
> There are two major reasons to do that. First of all, patients who are
> poor metabolizers do not typically tolerate desipramine. So, they
> would be at risk for injury, especially at the dose levels in the
> trial protocol, and they would probably drop out. If we did get PK
> data from those patients, since they don\'t metabolize it, it is going
> to be confounding whether you are looking at the genotype effect or
> the drug-drug interaction effect. Therefore, we excluded those
> patients. It is a safer trial and it is a trial that is going to have
> a cleaner outcome.
>
> But when you look at the results you can see that there is still an
> outlier. If you look at the rate of elimination you have one with a
> long half-time. It is being metabolized slowly. Why is that? Here
> genotyping helped us again. This individual turned out to have the
> unusual phenotype \*6, which is one of those nonfunctional null
> alleles, and then \*9, which is a partially functional mutation. This
> is an extremely rare genotype. Nevertheless, we just happened to get
> one.
>
> So, you can see that most of the patients cluster with a consistent,
> readily interpretable PK, and then we have one outlier. We have
> interpretation of that outlier and we can go on to say that, due to
> studies we have done in our ethnic diversity panel, and published
> figures, to say that this \*6/\*9 occurs about 1/250 in the
> population. We can account for it. We have the mechanism. Therefore,
> we don\'t have to concern ourselves significantly with that anymore.
>
> \[Slide\]
>
> What about pharmacokinetics in Phase II and Phase III studies? First
> of all, I am going to use pharmacogenetics here not quite in Dr.
> Woodcock\'s way but we are using DNA sequence variants as they relate
> to drug response. There are genetically identifiable groups whose
> disease can be differentiated by genotype. These may be more rapid
> progressors. They may have more pronounced disease. These are very
> good populations that can be used to carry out targeted small clinical
> trials, as has been done with celecoxib with patients with colon
> cancer.
>
> Secondly, we can include or we can exclude patients from these trials,
> include if we would like to carry out a proof of concept study where
> we would like to be able to demonstrate that it really does what it is
> supposed to do in a likely population; exclude if we are concerned
> about at-risk populations and we want to prove the concept before we
> go and expose those patients. We can stratify studies according to
> genotype, either by clinical response or risk of adverse events. In
> some cases we may wish to proceed with drug development for a
> genetically defined group.
>
> \[Slide\]
>
> Also, in Phase II and III studies it is possible, when you have a
> large enough number of patients, to use those patient samples to
> discover new genetic markers related to disease outcomes. This is an
> example I have taken from Alan Roses at GlaxoSmithKline. These are
> patients, in this case, with Alzheimer\'s disease who have been
> scanned using SNP markers in the genome to determine where in the
> genome there is association of a genetic variant with the condition.
> This is able to demonstrate the effect of APOe alleles on Alzheimer\'s
> disease. The same approach, using large number of patients and large
> number of genotypes, can be used to discover genes associated with
> drug effect or with drug side effects.
>
> \[Slide\]
>
> Going on to preclinical gene expression, there are two ways primarily
> that people are interested right now in preclinical gene expression.
> One is toxicogenomics, which I will come back to. Briefly,
> toxicogenomics is attempting to predict toxicity of candidate
> compounds or identify mechanisms of toxicity. The second is to
> identify potential biomarkers that reflect drug toxicity or drug
> efficacy, and these are biomarkers that one can use later in clinical
> studies.
>
> \[Slide\]
>
> I would like to go just a little bit more into toxicogenomics. This is
> the \"everything on one slide\" result. This is a very broad
> toxicogenomic study that was carried out between Abbott and Rosetta.
> This is the same sort of heat map or clustering analysis that Dr.
> Sistare showed, where across the top you see 3500 different genes. The
> lines there in the dendrogram indicate how similar those genes are in
> terms of response of rat liver to those genes. The longer the line the
> more disparate they are; the shorter the line the more similarly they
> respond. Down the right side are 52 different hepatotoxic chemicals or
> pharmaceuticals in multiple doses. On the left you see another
> dendrogram which indicates how similarly they respond to each other.
> At every intersection of a vertical and horizontal line there is a
> result, and that shows whether gene expression was increased, or
> decreased, or remained the same when a rat was exposed to that
> compound and then was analyzed for that gene. You can see a wealth of
> information on this slide. You can especially see it if you are not
> red/green color blind, as I am.
>
> \[Laughter\]
>
> But even with that, it is apparent that there are some genes that
> respond very similarly to each other. There are some drugs that
> respond similarly to each other. From this, you can determine patterns
> that are indicative of hepatotoxicity or particular types of
> hepatotoxicity.
>
> \[Slide\]
>
> This is now taking results from one drug on that graph on one page of
> 28 pages of results. This is just put up here to give you an idea of
> the wealth of information that comes out of one of these studies. You
> can see in that magnified section that there are some genes\--and I
> don\'t even know what these genes are; with 3500 of them I am not even
> going to try to\--but you can see that there are some whose variation
> is at a significantly great level and some where it is not. This is a
> treasure-trove of data mining. There are any number of post hoc
> experiments that one could do with this data. When we talk about the
> concern we have about interpretation of this data, it is this exact
> post hoc data mining which first comes to mind.
>
> \[Slide\]
>
> Let me briefly describe some work going on in clinical gene expression
> now taking samples from patients in clinical trials and looking at
> gene expression responses. The primary effort here is to find
> biomarkers that we can use to indicate drug response or to indicate
> early signs of drug toxicity. It is also very useful, if you have been
> doing the sort of rat or mouse studies that I showed in
> toxicogenomics, to be able to compare human responses to the animal
> model responses to see if we are getting similar effects or different
> effects.
>
> We can use this to identify genes that seem to be key for particular
> responses, which may then become genetic markers if we find that there
> are polymorphisms in there. Since each one of these genes that is
> expressed indicates a protein which is being expressed, this now may
> be an indicator that there is an important protein which has an effect
> in the drug response or as part of the pathogenesis of the disease.
>
> \[Slide\]
>
> Here are some results from Andy Dorner, at Wyeth, looking at gene
> expression in patients with psoriasis who were treated with
> cyclosporin or IL11. They looked at 7000 different genes\--much the
> same sort of analysis that we looked at in rat liver. They have access
> to a human sample, which is skin. They found that there are 159 genes
> whose expression is related to the incidence of psoriasis. Of those,
> they found 142 which respond to treatment with psoriasis. You can see
> in the two graphs here, starting at 1, which is the incident level,
> that with these particular genes the expression level decreased when
> they were exposed to the drugs, and they responded well. Whereas,
> patients who are not responders, whose psoriasis did not get better,
> as you can see on the right panel, there is no improvement in the gene
> expression pattern. So, we have a very clear indicator from these
> genes that there is a correlation of these genes with drug response.
>
> \[Slide\]
>
> Using these high density approaches, human clinical gene expression
> raises some challenges. The greatest, as I already mentioned, is we
> generate huge amounts of data, more than can be readily interpreted.
> As I said, it is open to many different types of interpretation. The
> statistical methods that we are using now themselves are experimental
> methods. We are still developing methods that can generate true p
> values, that can show true association of patterns with outcomes.
>
> Once we reach a conclusion, we have yet to work out good methods to
> confirm those conclusions. We can do the same thing again and use the
> same interpretation but that doesn\'t necessarily confirm it. So,
> these are still in process. As I said, there are multiple
> interpretations. Two people can take the same data, use different
> interpretation algorithms and reach different conclusions as to which
> genes are important. But at the bottom of all of this is, yes, we have
> the human genomes sequenced. We have a sense of how many genes there
> are. We have names for many of them. But the correlation between our
> knowledge of the genome and our knowledge of clinical outcomes is
> still rudimentary and we are not at a point yet where we can clearly
> say if this gene does this, the clinical outcome will be that, and
> that is at the bottom of much of the work that is going on and much of
> the uneasiness we have with people who would want to draw facile
> conclusions from the data.
>
> \[Slide\]
>
> There are proposals being considered for ways in which this data could
> be submitted to the FDA so that the FDA can learn from the data, can
> develop methods and test methods on the data which would, at the same
> time, not jeopardize us to this post hoc analysis concern. So, what I
> would like to do is just reflect on some of the comments that have
> come from people in industry to the suggested proposals and what we
> are looking forward to in proposals for how this could happen.
>
> Because of the concern about post hoc analysis, and you have heard
> already from Dr. Sistare, there is a reluctance by pharmaceutical
> companies to submit data on investigational drugs to the FDA. In many
> cases that is because we are reluctant even to carry out the
> experiments on investigational compounds because we recognize that in
> IND updates it would be appropriate that you would ask for that
> information. This is hampering our research in the same way it is
> hampering the transfer of information.
>
> Why is that? Well, the same issues\--we are using analytical methods
> that haven\'t been validated. There are multiple interpretations. The
> reviewers may not be trained in any way related to this data but may,
> nonetheless, recognize the names of some key genes. It is possible the
> results could be over-interpreted; they could be misinterpreted. Even
> without those problems, the sheer analysis of it could lead to
> lengthening of timeliness while people work through what it all means.
>
> The perception on our side then is that submitting this data could
> jeopardize a drug development program and, as a consequence, we don\'t
> want to do that. The risk may be low but the consequences may be high.
>
> \[Slide\]
>
> In considering a process by which these types of results might be
> exempted from normal review, and we will hear from Dr. Woodcock
> shortly what that proposal is, there are certain things we would be
> looking for. One of them is to lower our risk. We would like to be
> able to do the work without the risk that we are going to jeopardize
> our programs. That is the bottom line.
>
> We would want data to be evaluated by experts. We would like an
> evaluation that is consistent from drug to drug, and we would like to
> find out what those results are. We would hope that the review would
> be independent of the drug review timeline. That is, the drug review
> is not going to be held up while people are trying to figure out what
> to do with the genomic data. We would hope to work closely with the
> FDA in determining what is the best process to represent the science
> and get the best regulatory approach, continuing the type of work that
> we have been doing with the FDA so far.
>
> \[Slide\]
>
> There are some things that seem to be up in the air, and I think they
> can be worked out. Just what would fall under this research exemption?
> How would it be defined? There will be a definition but as yet we are
> not sure what it is, and we hope that there is some input to that.
>
> There would certainly be situations, and it was mentioned at some
> point this morning, where there may be a public health issue that
> would overrule this exemption. We would like to know what that public
> health issue is. Certainly, if it is a real public health issue we
> would be certainly in agreement but this needs to be made clear.
>
> It is also not clear what sort of feedback would be, the process by
> which we would get information back from the FDA once they have looked
> at data from one company and perhaps compared it to data from others.
>
> There are also things that we would find unfavorable in a process like
> this. One that comes up repeatedly is, well, what if we have this
> program for three years and then we decide to rescind it? And,
> everyone is concerned about that. But I think perhaps a more real
> concern is will this process then lead naturally to a request for more
> and more data? If there is a process by which pharmacogenomic data may
> be submitted, does that lead to the assumption that pharmacogenetic
> data should be submitted? That is a decision that we believe should be
> made based on the specifics of the drug and the drug responses, not
> simply because there is a process by which it can be carried out.
>
> \[Slide\]
>
> This matter of submitting high density data is important. It is one of
> many issues that are facing industry right now with respect to
> pharmacogenomics. I would just like to bring up some of the other
> issues that are important to us in this field. Of course, the biggest
> thing is not industry-FDA, it is nature which is not easy. Biological
> systems are very complex and genetics is only part of it, and even
> when it is, that is not a clear yes/no answer. This is something we
> are always dealing with.
>
> From a drug development standpoint, in carrying out a pharmacogenomic
> study generally you have no idea what the result is going to be. You
> don\'t know the result and you don\'t know its value until you have
> finished. If these get to be expensive and you are trying to convince
> people that you are going to carry out a study but you don\'t know
> what the result will mean, this is a natural impediment to conducting
> these sorts of studies.
>
> There is also no clear regulatory pathway for what to do with the
> data. If you look at drug labels where genetics is found in drug
> labels, it is in all different sections from one drug to another. So,
> it is not clear to people who are designing drug development programs
> that there is a role for genetics in all of this. It seems to be an ad
> hoc thing.
>
> In industry, where we have significant financial constraints, a
> program which requires up front investment with uncertain outcome,
> uncertain even as to whether the results will help the drug or hurt
> the drug, those programs are not going to be looked on favorably when
> competing for resources. So, these are built-in impediments.
>
> \[Slide\]
>
> Let me just bring up some of the issues which have been surfaced by
> people within industry that we think are questions that we would like
> the industry and FDA be able to discuss more fully.
>
> First of all, what is the reasonable expectation of the role of
> genetics in drug responses? Dr. Lesko was saying, well, it may be
> large; it may be small. If the expectation is that most drugs have a
> genetic component, then that tells us one thing that we need to do in
> terms of conducting our studies. If the assumption is that few drugs
> have a genetic component, that tells us something else. It is not that
> I know the answer to this, although we have been trying to address it,
> but we need some clarification of what those expectations are, and
> those expectations have to be consistent with the best scientific
> information.
>
> In a practical sense of conducting clinical trials, in the
> hypothetical case where a drug in its pivotal studies shows a benefit
> and has a reasonable safety profile, such that that drug might
> reasonably be expected to be approved, if we had carried out a genetic
> study and found that maybe 30 percent of the people don\'t respond
> and, yet, the remaining 70 percent do, are we now going to have to
> have a restrictive label that says don\'t treat the 30 percent
> non-responders? That is, are we going to be penalized in our label
> because we did the extra work? This is a question that comes up all
> the time from drug development people.
>
> This next question I have here sounds almost trivial but it is also
> very important to a lot of people. If we collect DNA, is the FDA going
> to expect us to do something with the DNA? Because if the answer is,
> since you have the DNA, then why don\'t you come in and genotype
> something, the response is very simple, fine, we don\'t collect DNA.
>
> \[Slide\]
>
> If we look at different genotypes in the population\--let\'s say we
> have three different genetically identifiable groups, homozygous wild
> type, homozygous mutant, heterozygous, and we want to evaluate the
> drug, does that mean we now have to redesign our trial where we power
> it to the smallest group? That can make for a very, very large
> clinical trial and that also is an impediment to carrying out that
> sort of study.
>
> We would like to know what the regulatory requirements are for a test
> that might accompany a drug. Let\'s say we have a drug for which we
> want to exclude a particular genetic group, what are the regulatory
> requirements for that test? Does it need to be an FDA approved [in
> vitro]{.underline} diagnostic? Can it be a home brew? What about the
> tests that are used just for conducting clinical trials? What are the
> regulatory requirements for those? In order to do this we need some
> clarity.
>
> Finally, if we only test a drug in a certain genetically defined group
> of people, what is that going to do to the label? Again, there are
> many answers. We just need clarity on that.
>
> \[Slide\]
>
> Just to end this, three points. First of all, pharmacogenomics is now
> an integral part of drug development. It is an emerging part but it is
> already taking place within all the drug companies. Secondly, we have
> had excellent cooperation with the FDA in trying to develop the
> scientific basis for using these procedures and for regulating drugs.
> Thirdly, the greater the clarity in FDA\'s expectations of
> pharmacogenomics, the greater will the progress be in use of these
> technologies. Thank you very much.
>
> DR. DOYLE: Let\'s take a ten-minute break.
>
> \[Brief recess\]
>
> DR. DOYLE: Next we are going to hear from Dr. Woodcock, and she is
> going to share with us the FDA proposal on pharmacogenomics. So, Dr.
> Woodcock?
>
> **Fostering Technology Development\--Pharmacogenomics**
>
> **(continued)**
>
> DR. WOODCOCK: Thank you. I think you have gotten a very good overview
> of some of the issues that we are facing and why we are here, before
> you, today to discuss how to move forward on introducing the
> technology into the regulatory arena and actually making this come to
> pass.
>
> \[Slide\]
>
> Drug regulators, according to the framework that has been set up, have
> basically two kinds of obligations. We have to determine if drugs are
> safe and effective for marketing, and if they remain safe and
> effectiveness. We also have to protect human subjects who are enrolled
> in trials. That is the regulatory framework that has been set up for
> us and we have to fit this new information into this framework.
>
> \[Slide\]
>
> I am not going to talk about the PHS Act. It is slightly different for
> the biologicals but the Food, Drug and Cosmetic Act defines safety for
> a new drug application as the FDA will evaluate reports of all methods
> reasonably applicable to show whether or not such drug is safe under
> the conditions in the proposed labeling. That is the requirement for
> safety.
>
> Effectiveness is even more vague in a way. We need adequate and
> well-controlled trials to show that the drug will have the effect it
> purports to have under the conditions of use.
>
> \[Slide\]
>
> These obligations and framework have been translated into regulations.
> For an IND the sponsors have to submit pharmacology and toxicology
> information, and here we are talking about the animal data, on the
> basis of which the sponsor has concluded that it is reasonably safe to
> conduct the proposed clinical investigations.
>
> \[Slide\]
>
> For the new drug application for nonclinical studies, it says submit
> the studies that are pertinent to possible adverse events. Those that
> are pertinent. For the clinical information for a new drug
> application, submit data or information relevant to an evaluation of
> the safety and effectiveness of the drug product. That is the legal
> and regulatory framework.
>
> \[Slide\]
>
> The question that is before us and that we want to have a discussion
> on is when and how to use this developing pharmacogenomic information
> in regulatory decisions. That is kind of the crux of when is the
> information reasonably applicable to safety. It is a statutory
> standard. Under what circumstances is this information required,
> either by regulation or statute, to be submitted to the FDA? These are
> the questions we would like you to talk about.
>
> \[Slide\]
>
> But we have a proposal. We are not just asking you to come up with
> something. This proposal is that FDA will establish policies on
> categories of pharmacogenomic studies, broad categories. This will be
> hard. Let me give you some examples.
>
> We will have a submission requirement, an explicit submission
> requirement as part of the policy and the submission requirement will
> be of several types. A submission might not be required for some types
> of data that are being generated. I think the various presenters
> presented some kind of data that wouldn\'t be required to be submitted
> to the FDA\--early drug development data and so forth.
>
> A submission may be required under the current statute and regulations
> right now for some other types. We would sort that out in this policy.
> But some of the submissions would not have regulatory decision impact.
> This is the proposal.
>
> \[Slide\]
>
> What do I mean by that? We would say in our policy related to the
> categories what the regulatory impact of the study type would be.
> Results from some study categories would not have regulatory impact.
> In other words, they wouldn\'t influence the decision making. We
> wouldn\'t say to Abbott you have to do ten more clinical trials or
> five more animal studies, or whatever.
>
> Other study results though will have to be utilized as part of the
> safety and effectiveness evaluation. That is clear right now and I
> think that is clear going into the future. The question is about the
> difference between these two types of categories.
>
> \[Slide\]
>
> A possible threshold determination or a proposed one that we could
> use: does the genomic information represent a valid biomarker with
> known predictive characteristics? That is an important threshold that
> I want you to think about. I know Dr. Spear said, well, maybe if this
> information were to be submitted and it was analyzed by the FDA, it
> doesn\'t mean necessarily we would require that a drug would have to
> be used in a subset, or something, to respond to your hypothetical,
> where you did a trial in everyone and some subset seemed to respond
> better but you would have to submit that information. That is the
> proposal. But that wouldn\'t mean that you could only market that drug
> for that subset. So, those things aren\'t linked necessarily.
>
> The FDA is proposing we would develop this threshold and this set of
> policies using a public and transparent process with advisory
> committee insight, and we are starting this process now.
>
> \[Slide\]
>
> Possible procedures\--procedures are important in the transparency. We
> would establish an interdisciplinary pharmacogenomic review group
> probably across the centers. There is expertise in different places.
> We would also categorize these studies and develop internal procedures
> and publish them in a guidance so they would be publicly available.
> Our guidance procedures call for us to publish a draft, get public
> comment and then go to a final guidance. So, we would have a lot of
> opportunity for public discussion.
>
> We could propose that the results would be submitted to the IND or the
> NDA as a research information package for review by this group. These
> are the ones who are not going to have regulatory impact. This would
> allow FDA to get this information where we have determined it is not a
> valid biomarker, but it is useful information that we need to learn
> about, and that Frank and his group needs to learn about, and Larry\'s
> people need to learn about. We could get this; we could review it
> centrally and develop our knowledge base without negatively impacting
> on the clinical development programs or the review process.
>
> We would have to have periodic public re-evaluation of this decision
> tree that we would construct to allow all this to happen, to make sure
> you are still on the same track, which gets to the issue of would we
> renege on this at some point. We would have to have a public
> discussion.
>
> \[Slide\]
>
> Examples I think that are fairly clear that do have some impact in the
> sense that you would need to submit doesn\'t mean, as I said, that
> there would be impact on the label necessarily in some way but trial
> enrollment by genotype, if you are going to stratify people in your
> trial by genotype, you want to enrich for responders, you want to
> avoid bad outcomes in a trial so you are going to eliminate certain
> people from a trial. That is something that needs to be submitted as a
> part of your clinical development plan in this proposal.
>
> Selection of dosing based on metabolizer genotype, I think we would
> all encourage these type of activities to be done during drug
> development where there is a hypothesis, say, that a metabolizer
> genotype may make a big difference.
>
> \[Slide\]
>
> Where there is a safety rationale based on animal genomic data, say,
> explaining why a toxic finding in animals is unique to that species
> and is not expected in humans, other species, and so on, in general,
> we think that results intended to influence the course of clinical
> development process should be included in the safety and efficacy
> evaluation. Does everybody follow that? So, if you are randomizing
> patients based on these data, if you are modifying their dose, if you
> are doing those sorts of things, this should be included. This has
> gone into mainstream drug development as part of the safety and
> efficacy evaluation.
>
> \[Slide\]
>
> Potential results that could be done and not have regulatory impact,
> and I am not saying whether or not these would have to be submitted;
> that is going to depend on the interpretation of the regulations that
> I showed you, but what if you found a new transporter gene and your
> drug is metabolized by it and you want to look at the diversity of
> this gene versus the response, the clinical response in clinical
> subjects? We really wouldn\'t know anything about what this gene means
> or is, and so forth. So, that type of study would be one that
> wouldn\'t have regulatory impact, decision-making impact. Something
> that is being done frequently now is what I call sort of fishing
> expeditions where you are looking at markers in clinical trial
> subjects during the trial. That is hypothesis generating. That would
> have no regulatory impact. The same with microarray screening in trial
> subjects where you are simply trying to find out correlations. The
> same with gene expression microarray screening in animal toxicology
> studies.
>
> Again, I am not saying whether or not these would have to be
> submitted. I am saying until these have been established as far as the
> validity of information from these studies, they would not be figured
> into regulatory decision making. We already have good tools, animal
> toxicology, traditional nonclinical development. We have good tools to
> ensure the safety of clinical subjects, and so on.
>
> \[Slide\]
>
> We are going to have one more presentation but then these are the
> questions that I would like maybe the discussion, if possible, to
> focus on. Is this approach reasonable? We have to move forward. You
> have heard the horns of the dilemma that we are on. These studies are
> being widely done and they may have tremendous promise. There is
> extreme concern about how they are going to be used, and that is
> legitimate because they are not really ready for prime time, many of
> them, for regulatory decision making. We need to find a way to get the
> information in, develop our policies, develop a regulatory framework
> for this information, and help to move this field along. So, is what
> we have proposed reasonable and feasible, or should we go back to the
> drawing board?
>
> Will it achieve its objectives? Obviously, we need to achieve the
> objectives via policy. We need a free exchange of data, and this is
> how I started the afternoon although that was a long time ago and you
> probably don\'t remember. We need to have free exchange of data. We
> need to have the ability of the FDA scientists to begin developing the
> framework for the new findings so they can be integrated into
> regulation. We need to advance the use of the new science, not impede
> the use of a new science in drug development.
>
> With that, I will turn it over to the last speaker and then will come
> back.
>
> DR. DOYLE: Thank you, Dr. Woodcock. For our last presentation we have
> Dr. Benjamin Wilfond, who is with the Medical Genetics Branch of the
> National Human Genome Research Institute at the National Institutes of
> Health. Dr. Wilfond is going to address ethical issues associated with
> regulatory review of pharmacogenomic data.
>
> **Ethical Issues with Regulatory Review**
>
> **of Pharmacogenomic Data**
>
> DR. WILFOND: Thank you.
>
> \[Slide\]
>
> It is a pleasure to be here. I certainly enjoyed hearing the prior
> presentations. There are a lot of questions which I wish I had time to
> answer but I won\'t address. What I will do is try to focus on Dr.
> Woodcock\'s specific proposal and make some comments about some of the
> ethical issues related with that.
>
> For those of you who aren\'t familiar with ethicists, basically all we
> do is ask more questions\--
>
> \[Laughter\]
>
> \--but on a more serious note, we try to refine the questions to give
> the questions greater clarity. So, that is what I hope to do today so
> it will be a way of beginning the discussion in a more earnest way.
>
> \[Slide\]
>
> We have heard from all the speakers that the ultimate progression from
> pharmacogenomic research is to improve clearance applications. But it
> is a challenge to get to that place. The question that is being asked
> from a regulatory perspective is when is it appropriate to use the
> data, either for the drug approval process or for clinical decision
> making.
>
> \[Slide\]
>
> The FDA\'s proposal is to divide this into two current approaches. The
> first is the regulatory approach which, as Dr. Woodcock described,
> would be based upon when a sponsor intends to use the data either to
> demonstrate safety or efficacy from clinical trials, or if they would
> recommend specific clinical applications of the data. However, it
> would then put aside other categories as research uses which might
> include unique genes or gene expression profiles that would not be
> part of the regulatory process.
>
> \[Slide\]
>
> One of her slides had this quote which I wanted to discuss because I
> wasn\'t quite sure exactly what it meant, which is develop thresholds
> and policies using public and transparent process with advisory
> committee oversight. I understand all the words. I think the challenge
> is to figure out what exactly does that mean, or what exactly would
> that do.
>
> I don\'t mean to say this facetiously. I think it is actually a very
> difficult question. What was mentioned is that we need to have some
> threshold for these issues of biologic validity and clinical
> relevance, but the challenge is what process will we use to decide in
> a particular instance when this threshold has been reached. I think
> that is going to be the hard question.
>
> \[Slide\]
>
> One of the ways we can think about this is to think about two
> different types of decisions. One I will call policy decisions, which
> would be thinking about categories of studies, types of data, and
> whether we would use of not use those types of data. We can also think
> about thresholds for inclusion from a conceptual point of view in
> terms of biological relevance or clinical significance. But again, as
> I pointed out, the challenge will be the application specific
> decisions. Those are the ones that I think will be the most
> challenging, and those will be the ones that either will be used for
> making further requests for more data or to be used for evaluations
> for decisions about regulatory approval.
>
> Both of these categories would benefit from review of ongoing research
> data. So, the general idea of trying to ask industry to provide data
> to the FDA could be useful in either of those two processes.
>
> \[Slide\]
>
> From my quick understanding of what is being proposed for the
> pharmacogenomic review group, it is going to be primarily focused on
> policy. So, the question is will this group, or should this group also
> consider specific drug application decisions itself or might it, as
> part of its activities, say that a decision needs to be made and a
> second group ought to do it? I think that is one question that I think
> is important to get some feedback on about what the role of this
> pharmacogenomic group should be. Dr. Woodcock may have a particular
> answer for that, I don\'t know, but that is a question I would like to
> know about.
>
> The second issue, and this is the second main point that I want to
> make, as we think about this group or another group, is the procedural
> options we have for how this group should practice. I wanted to
> identify three specific categories of options that we would need to
> consider. One is going to be the issue of the confidentiality of the
> group. Would this be a public group or a private group? The second
> would be is this independence? Is this a group that is internal to the
> FDA or external to the FDA? The third is authority. Is this an
> advisory group or is this a determinative group?
>
> \[Slide\]
>
> I think these are very important questions and there is not one clear
> answer to them but it might depend upon what you wanted this group to
> do. To illustrate this point, I have this slide that shows IRBs, data
> monitoring committees and FDA advisory committees along these three
> points. You can see that each of these has different features among
> these characteristics. All of these are appropriate for what they do,
> but the question will be, as we think about this group, where might it
> fit in to these categories?
>
> So, with regards to confidentiality, clearly a public group will have
> greater accountability and transparency. With a private group there
> will be a greater willingness of industry to share what otherwise
> would be thought of as confidential data. An internal group will have
> greater familiarity and expertise with all of the issues related to
> the drug approval process, but an external group might have greater
> objectivity and not feel so constrained. With regards to authority,
> groups that provide recommendations are often much more acceptable,
> except that it still would require the designation of who ultimately
> would be making the decision.
>
> \[Slide\]
>
> I think I would like to end by saying that a choice between these
> procedural options includes ethical considerations of these three
> categories and the tradeoffs between them, and that this information
> could be used to decide whether or not this review group would decide
> about specific applications or refer to a specific group and,
> secondly, to decide about these issues of confidentiality,
> independence and authority.
>
> What is missing from the slide is perhaps the hardest question, which
> is what criteria ought this group, whether it is the review group or
> second group, use for making decisions in specific cases because, as
> much as we would like to say that this will only be for research, we
> have to sort of think down the line and realize that at some point we
> will cross that threshold. Thank you.
>
> **Questions and Discussion with the Board/Presenters**
>
> DR. DOYLE: Thank you very much. Now it is in the panel\'s bailiwick.
> It is now time for us to respond. The proposal is in front of us.
> Anyone have any comments or suggestions? Yes, Dr. Laurencin?
>
> DR. LAURENCIN: First I want to thank the speakers today. This was
> really a great session and great information.
>
> My comments are that before we consider the world of pharmacogenomics
> we should consider a group that we can identify right now that are
> suffering healthcare disparities possibly due to drug effects, and a
> group that could possibly benefit from the monitoring or higher level
> monitoring in terms of drug effects, and that is the African American
> population in the United States.
>
> If we think about pharmacogenomics, it is interesting that
> pharmacogenomics as a discipline probably has, as part of its roots,
> the observation that certain antimalarials when given to African
> Americans cause hemolysis, which led people to uncover problems with
> G6PD deficiency and, again, led to the realization that different
> drugs have different effects on people. We know now in the United
> States that there are a number of drugs that are given to the African
> American community for problems that have high prevalence in that
> community, yet, the numbers of patients that are tested in terms of
> clinical trials, those numbers are low, leading to questions about the
> effects.
>
> Now, with more and more acceptance of data from European trials, one
> would expect that the numbers of African Americans that suffer from
> some of these diseases, in terms of clinical trials, will be
> decreased.
>
> I know the Commissioner is sensitive to this. I have spoken to him
> about it and he is very sensitive to this issue, but I think that
> before we explore the world of pharmacogenomics and get committees and
> councils set up to start to explore this or subgroups, I think that we
> need to really take a step back and also look at what is going on in
> populations in America right now whose healthcare disparities may be
> affected by the manner in which drugs are regulated right now in the
> United States.
>
> DR. DOYLE: Dr. Shine?
>
> DR. SHINE: Can I ask Janet some questions?
>
> DR. DOYLE: Sure, absolutely.
>
> DR. SHINE: First of all, just for clarification, Janet, when you made
> the statement \"free exchange of data\" I presume that means between
> the FDA and industry, and that it doesn\'t mean that it goes on the
> web.
>
> DR. WOODCOCK: In principle, it is most desirable that this information
> all be published but I believe that a fear of regulatory action is
> inhibiting publication, although also inhibiting publication,
> obviously, is the proprietary nature of some of the data and its use
> in drug development. To answer your question, no, I meant free
> exchange between the regulators and the regulated.
>
> DR. SHINE: Thank you. Again, I understand that you are proposing a
> plan but it just would be helpful to be a little bit more explicit.
> Under the proposal, there would be a threshold determination of
> genomic information which represented valid biomarkers with known
> predictive characteristics. First, does that imply then that those
> biomarkers would be then obligatory in terms of all the testing that
> is done, or are we talking about biomarkers that sponsors happen to
> collect as a consequence of it? I am trying to understand how
> prescriptive this would be.
>
> Secondly, some sense of what you mean by valid biomarkers. I can
> easily see a requirement, for example, for looking at genetic evidence
> with regard to metabolism. I could see that even ultimately being a
> requirement in terms of how one does it. On the other hand\--and this
> goes to Cato\'s comments\--will there be an obligatory requirement
> that studies be done in such a way as to determine biomarkers for
> particular populations? So, some help with regard to that?
>
> DR. WOODCOCK: What we are talking about right now is studies that
> companies are doing for their own purposes, and whether or not that
> data needs to be submitted, number one, and if it is submitted whether
> it would be used in regulatory decision making. We are not talking at
> all about requiring companies to do any kind of genetic testing during
> drug development.
>
> As Larry Lesko showed, we are seeing a lot of the drug metabolism
> polymorphism data right now, and that is something that clearly is a
> valid marker, a predictive marker for many systems, many enzyme
> systems of what is going to happen to metabolism of that drug. So,
> that is kind of one category. But we are not requiring that people be
> genotyped. We have other ways of getting that information through
> pharmacokinetic data, phenotypic data, all sorts of ways of
> determining how people metabolize drugs.
>
> So, no, we are not talking about establishing a system whereby we
> would develop new requirements. We are talking about all this
> information being generated out there and what should be submitted to
> the FDA, and what of that universe that is submitted to the FDA, which
> part of that universe should be used for regulatory decision making.
> The threshold that is being proposed is the universe where we
> understand what the information means.
>
> DR. SHINE: For example, if they have information about a genotype
> related to metabolism, that might be required whereas something else
> might not be required.
>
> DR. WOODCOCK: Required? You mean in a submission?
>
> DR. SHINE: Yes, if they had the information.
>
> DR. WOODCOCK: Yes, that is correct.
>
> DR. SHINE: Finally, I am curious, you talk about the research
> information package for review by IPGRG. Now, information package\--I
> would imagine, from what I understand about the discovery process and
> so forth, there is a huge range of research that may go on with
> relation in a company, a laboratory or whatever, some of which bears
> directly on that study; some of whiich is interpreted or inferred for
> the purposes of that study. It seems to me there is a huge potential
> spectrum there. What kinds of criteria would you use for defining what
> this package would look like?
>
> DR. WOODCOCK: Well, I think Frank Sistare was trying to get to some of
> that in his talk. We have to work with industry in that particular
> instance to define the parameters of what would be submitted because,
> obviously, these results are platform dependent. So, we would work
> with industry on the specifics of a submission.
>
> DR. SHINE: But what would be the principles that underlie that
> discussion? I mean, what boundaries would one want to place around
> that in terms of what we would be talking about? I am trying to
> understand what the underlying thesis would be in terms of the scope
> of the activity. I understand you would negotiate the contents but
> there needs to be at least some definition of the area.
>
> DR. WOODCOCK: Well, the area would be at some point during drug
> development, when you have a drug that is going into people and you
> are submitting that information in the IND, say, to the FDA or you are
> going through clinical development with a drug and you are going to do
> genetic testing on animals or people, that type of testing might be
> submitted to the FDA.
>
> DR. SHINE: But it would be specific for that drug?
>
> DR. WOODCOCK: Absolutely. Yes, everything we do is more or less
> product specific, yes. So, it would be specific tests in animal or
> human or maybe cellular systems in some cases on that drug that is
> actually in an IND, is actually in drug development. Other submissions
> might be voluntary. People were telling me at the break that their
> folks are doing natural history studies\--you know, there is a lot of
> other information. Hopefully, that will be published and we would also
> like to see it as well.
>
> DR. SHINE: The last question goes to the discussion by our ethicist.
> As I understood your presentation, you in fact do separate the role of
> the IPGRG from the regulatory process. As I understand it, they would
> have nothing to do with the regulatory process per se?
>
> DR. WOODCOCK: They probably would talk to the folks making the
> regulatory decisions but they would be separate. It would be a
> separate review. We do that in other cases, such as animal
> carcinogenicity studies. These are centrally reviewed at CDER by a
> committee of expert toxicologists and statisticians. They give a
> recommendation back to the division about what that study means. In
> this case probably very little would go back to the reviewing
> division, although they might talk to them.
>
> DR. SHINE: But you used two words, \"talk to them\" or
> \"recommendations.\" Which would it be? Is it a discussion or does
> this group make any kind of recommendations?
>
> DR. WOODCOCK: The current proposal is that we would establish the
> categories up front. That is very important. Once the categories are
> established up front, that submission comes in and if it is not a
> regulatory impact submission it goes to the multi-disciplinary review
> group to look at. They don\'t make recommendations about regulatory
> action. They do not.
>
> DR. SHINE: Thank you. Thank you, Mr. Chairman.
>
> DR. DOYLE: Dr. Thomas?
>
> DR. THOMAS: I would just like to ask the experts if they feel
> comfortable with platform uniformity and standardization,
> harmonization, as well as the assimilation in terms of a repository or
> library for this technology based on the state of the science before
> we get down to that next step.
>
> DR. DOYLE: Any response? Frank?
>
> DR. SISTARE: I am not an expert so that is why I don\'t feel
> comfortable answering that. I don\'t know who is an expert on that
> topic, but I can speak from some level of experience.
>
> One of the points I tried to make in the slides is that platform
> comparisons, even the same platform, the same sample, in the hands of
> different investigators, if you are looking at a venn diagram, is not
> going to perfectly overlap. But the question is what does overlap or
> even what doesn\'t overlap, does that represent biological truth? Is
> it accurate information? That is the key. So, what we are actually
> trying to assemble is what actually is out there in the literature to
> try to address that question. When people for the most part have tried
> to confirm the results of transcript alterations using alternative
> methods, say RT-PCR, there is very good agreement. So, that suggests
> that when you do see changes, they are real. Very few publications
> have looked and asked when you don\'t see changes, are those
> non-changes real. In the few that have been published, again, there is
> very good agreement.
>
> Now, let me answer this question from another perspective. If I look
> at one platform and look at another platform, again, do I get perfect
> overlap in those transcripts? The answer is no. A venn diagram won\'t
> be perfect. If you do the correlation coefficient, like I did,
> quadrant 1, 2, 3 and 4, you are going to see some things in quadrants
> 3 and 4 where one platform says they went up and another platform says
> they went down. Is one wrong? Maybe not because there may be spike
> variants and you may see real changes there. They may both be giving
> you accurate information.
>
> We are actually involved in a very nice collaboration right now with
> the same samples being looked at on two different platforms. The
> bottom line right now that we are seeing is you can come to the same
> biological conclusion\--you can come to the same biological
> conclusion, and that is the bottom line, can you get to the same
> knowledge level when you look across platforms. A lot has been said, a
> lot of nay-sayers have said confusion, havoc, a lot of craziness by
> looking across platforms, but the bottom line is, by careful
> investigators following established protocols, making sure they are
> doing everything right\--new tools in the hands of fools, you are
> going to get garbage out\--but if you have careful investigators
> looking very carefully, really testing what they are doing and
> validating it, you are going to get similar biological conclusions at
> the end of the day.
>
> But it still is a burning question and we need to address that. We are
> addressing that exactly. We have this Iconix database that we have
> access to, 550 compounds. We are generating data on different
> platforms and asking the question if I take this different platform
> data and I relate it to this database or for the Amersham Motorola
> platform can I get the same biological answer? So, those experiments
> are being done and they do need to be done. There is a lot that needs
> to be done to calibrate platforms across different laboratories. We
> are hoping a universal RNA reference standard will help that process.
> We are not at the point where we can rely on this for regulatory
> decision making, but if we envision the day when we can use this
> information and obviate the need for some of the very lengthy
> experiments we do right now using very traditional toxicity testing,
> we have to go through this process of making sure we get accurate
> answers.
>
> DR. THOMAS: Thank you.
>
> DR. DOYLE: Go ahead, Dan.
>
> DR. CASCIANO: Another response to the question, I don\'t think the
> mystery is any different than with any other assays that we have
> developed over the last 25 years. Even so-called standardized assays
> like the Ames assay provides us data that is not relevant to so-called
> gold standard until you go deeper into the analysis and understand
> well what may be true for a mouse may not be true for a rat. So, it
> depends upon what you mean by standardizing and what is the gold
> standard. So, I don\'t think we are that much different.
>
> DR. LAURENCIN: I am not sure I would use the Ames assay as an example.
>
> DR. CASCIANO: I only used it because it is a standardized assay.
>
> DR. LAURENCIN: Yes, okay.
>
> DR. DOYLE: We have a few other questions here. Dr. Rosenberg?
>
> DR. ROSENBERG: First of all, Frank, I agree with you about gene
> expression data. That is why I want to concentrate maybe on something
> that is a little more relevant in real-time today, which might be the
> genotyping data, and talk a little bit about threshold determination
> and criteria that the agency may want to consider.
>
> It would seem to me that in trying to relate genotype what is going to
> be very important for you to consider is a term that geneticists use
> called penetrance. Where penetrance is very high, and the threshold of
> penetrance is probably going to have to be thought about differently
> for toxic phenotypes versus efficacious phenotypes, and you will
> probably have to break them down differently, but normally what one
> would consider, of course, is that if a genotype is highly penetrant
> with its phenotype, then it meets the kind of criteria that the agency
> would want to consider as being meaningful because if genotype and
> phenotype correlate, then you can get decent data.
>
> The problem, of course, is that for very few loci is that the case,
> and when you are dealing with the human population and you have things
> that have lower penetrance where many people, or a number of people
> are carrying genotypes but express different phenotypes from that, you
> have to be very careful about making regulatory decisions about who
> should or who shouldn\'t be taking drugs because their phenotypes have
> to be correlated with the background of perhaps many other genes that
> are being expressed against a background of the genotype you are
> measuring.
>
> Therefore, genetic penetrance I think is a very good criterion and
> threshold concept that you should be looking at in order to kind of
> start to make or think about decision making in terms of which of
> these genotypic changes are relevant to the agency starting to make
> real decisions about.
>
> DR. DOYLE: Dr. Woodcock?
>
> DR. WOODCOCK: Thanks. I think that is a very good point. I think we
> have one other step that we have to go. It can\'t be just phenotypic
> expression. It actually needs to influence drug response or we have to
> think it is predictive in some way. So, there are kind of two
> thresholds that genetic information has to make. I mean, many of the
> drug metabolism polymorphs, sure, affect drug metabolism because
> metabolism has multiple routes and they aren\'t that predictive of one
> level, for example, so you know, we don\'t make a big deal out of
> that. So, I think your point is extremely well taken.
>
> DR. DOYLE: Yes, Dr. Pickett?
>
> DR. PICKETT: Yes, I also would like to just compliment the group on
> the presentations today and to also hear about some of the thinking
> here, at the FDA, on pharmacogenomic approaches.
>
> I think the procedures Janet has outlined, to me, make a lot of sense
> on the path forward. What concerns me are examples that implied
> regulatory impact versus those that do not, and how one can really
> clearly define that. To me, it is obvious in terms of metabolizer data
> to regard to 2D6 polymorphism, etc. and that that should be an
> important component. For example, if you are developing a drug against
> HIV and against the C\*5 receptor it would be wise to genotype
> patients that actually express that receptor before you initiate
> therapy.
>
> What is unclear to me is transporter gene diversity versus response in
> clinical subjects. You said that that would be pharmacogenomic data
> without regulatory impact. To me, it would depend upon the data that
> obviously comes out from that type of study before one can make that
> determination. So, going into the process I think it will be a
> challenge to clearly define pharmacogenomic data that will be subject
> to regulatory impact versus that which is not subject to regulatory
> impact.
>
> DR. WOODCOCK: Could I make a comment about that maybe to stimulate
> some more discussion? I think we agree with you. There is a difference
> though. Say, a firm is studying a transporter gene and wishes to use
> it, that is a completely different situation than being compelled to
> use that information that you collect. In any case, if a firm wishes
> to use genetic data as a biomarker to shape the design of a clinical
> program, then that is completely fine and we welcome that. That is a
> challenge obviously, but that is not the subject of this. It is where
> this kind of data is being collected for research purposes to see
> perhaps, if you wish to do that, but you are not doing it. Does that
> make sense?
>
> DR. PICKETT: Just one other comment. Because pharmacogenomics DNA
> arrays are only part of the overall biological picture, it is not
> clear to me how other technologies, such are proteomics\--for example,
> I mean, if I was focusing on oncology and trying to understand
> signaling pathways, more than likely I would look at phosphorylation
> of certain proteins in a signaling pathway to give me more information
> versus a DNA array experiment. So, it is not clear to me how the
> agency is thinking about utilizing that data to generate really a
> total picture of biological response.
>
> DR. WOODCOCK: Is it fair, I will ask the assembled people here from
> the FDA, all the different people in the back there? I think it might
> be fair to say we are looking at all of this in a similar way. This is
> a spectrum of markers and folks will be collecting these data, and so
> forth, so it is very similar. I mean, proteomic data is, in my mind,
> more empirical than the genetic data because it is more of an
> observational empirical pattern recognition type of thing. But
> conceptually, as far as this scheme we are proposing, it is very
> similar.
>
> DR. SISTARE: Yes, I would add that I agree 100 percent. Again, one of
> the points I was trying to make is that the RNA is a response to what
> the proteins are really doing there. It is an indirect measure because
> we can measure it so tremendously right now. It is much more
> difficult\--we don\'t have the protein arrays to the point where we
> can get fidelity of those kinds of measurements right now. It is
> happening; it is developing; and definitely we need to be on board
> with that. There is some extremely provocative data coming out of some
> of the CBER labs right now. For example, the HER2 NU story and that is
> only part of the story. Part of the downstream of that is from HER2 NU
> receptor signaling. Looking at phosphorylation of proteins and
> signaling pathways is adding another level of information in terms of
> who to treat; how to measure responding. So, I agree with you 100
> percent. We need to be prepared for these kinds of input data as well.
>
> DR. WOODCOCK: Can I just say one more thing?
>
> DR. DOYLE: Sure.
>
> DR. WOODCOCK: But once the developer makes the decision to segregate
> patients based on one of these markers, then we are in a different
> game. That is part of the development program and we all have to look
> at that and consider it.
>
> DR. DOYLE: Thank you, Dr. Sistare and Dr. Woodcock. Dr. Riviere?
>
> DR. RIVIERE: As time is running out here, I just want to make one
> comment. I think you are going along well. You just really need to be
> very careful as to what you actually require. I think you have brought
> up all the cases. The point is that that data is existing and it is
> going to be generated, and really you have to start developing a
> framework upon which you can even accept the data into the system.
> Right now you can\'t handle any of this data. As you work on this just
> keep it very, very flexible because, again, I would think that the
> proteomic data might actually show more information in some cases than
> the genomic data.
>
> I also agree with you that if the genomics data is actually being used
> to design a clinical trial, then obviously that data somehow has to be
> presented. If it is not, and if it is being interpreted to, say, an
> animal toxicology study or to rule out a response in another species,
> you need to at least have some mechanism of bringing that data into
> the picture because right now you don\'t. I think it is a process. In
> a year or two a lot of the outstanding questions right now will not be
> questions or we are going to have a bunch of new questions. I think
> you are doing fine; just keep going.
>
> DR. DOYLE: Dr. Davis?
>
> DR. DAVIS: As someone who has watched this and been to most of the
> meetings that Frank and PhRMA have had together, I would like to
> applaud the effort. You know, we started out with a great deal of fear
> that the agency was going to try to force something down industry\'s
> throat that we weren\'t ready to present and how it was going to be
> interpreted.
>
> Clearly, Janet has presented, to me, a well thought out proposal. I
> think there is still a long way to go but, clearly, there is a sense
> of recognition that there are these two camps of data, one being the
> research grade data that we were very much concerned about that, all
> of a sudden, lead optimization data was going to need to be dumped
> into a regulatory package. I think most of us are aware, I believe,
> that if this is data that safety decisions are being made around, then
> you have to share that data and we will have to debate what it means.
> But if we are making decisions with it, we are drawing some
> conclusions from it so it would be crazy not to think that we would
> have to share it with our FDA colleagues.
>
> But the question will be what are the grey areas, and what to submit
> with no impact versus submit with regulatory impact and I think we
> just have to work that out. But I think the groups are in place to
> work on that, the ILSI, PhRMA groups, some of the workshops that are
> going on. So, I am quite pleased with the proposal. Thanks.
>
> DR. DOYLE: Any other comments regarding this proposal?
>
> DR. SHINE: I thought the representative from Abbott was going to say
> something.
>
> DR. DOYLE: Brian?
>
> DR. SPEAR: I will take the opportunity. I was intending to respond to
> the question about having a standardized platform. It is very
> important that we have sufficient standardization that we can have
> data from one experiment to another and compare them. The concern I
> would have would be if standardization involved something that would
> inhibit technology development. That is, saying that one device
> procedure or method was the acceptable device procedure or method
> could significantly stop development of new devices procedures,
> methods and so on and that would be very antithetical to what we would
> like to see in research.
>
> DR. DOYLE: Dr. Sistare?
>
> DR. SISTARE: We totally agree with that. Maybe the best way to
> characterize what we would like to do is to provide universal
> calibrators as opposed to standardizing a procedure, but offer
> universal calibrators so people could see that we are on the same
> page.
>
> I wanted to also, if I can, come back to something that Dr. Rosenberg
> started with when he started talking about penetrance, focusing on
> DNA. I want to bring that discussion to RNA. One thing I think I
> failed to make clear\--you know I don\'t think I heard one work of Dr.
> Lesko\'s presentation because I was going through my presentation and
> what I didn\'t say and what I did say but, anyway, I have heard it
> before and I have the notes\--but one of the things I didn\'t make
> clear and I kick myself for not making it clear, is the data that you
> saw at The Netherlands Cancer Institute to day, and from this day
> forward, that information from those arrays is being used to make
> clinical treatment decisions. Okay? They have made the decision the
> data are powerful enough now to decide who does and who doesn\'t get
> treatment based on expression array.
>
> I would ask our ethicist, in a sense, to ask the question, posed with
> data like that is it ethical to not use that information to treat a
> lymph node negative patient with chemotherapy in a situation like that
> when you have data that look that compelling. So, when I said the
> future is now, it is now. This is happening; they are using expression
> patterns to decide who does and doesn\'t get treatment.
>
> DR. ROSENBERG: Frank, help me then because in the data you showed
> clearly, again, there wasn\'t 100 percent separation in those groups.
> Therefore, if there are people who are in the not treated group but
> they still are at risk, and you showed they are at risk, to deny them
> treatment because they fall in some RNA expression group I would say
> is just as damning. You can\'t deny such a person treatment because
> you have shown, in fact, that they have a statistically significant
> probability of having the same problems as people in the high risk
> group and, therefore, I think it is very hard to use that data to make
> good clinical decisions.
>
> DR. SISTARE: There is no doubt about it, it is hard to use the data.
> There are two ends of the spectrum where I think some decisions are
> easier to make. There are grey areas where it is very difficult to
> make decisions. And, even at those ends where you will see that people
> don\'t conform, it means that there are other variables. If you want
> to talk about penetrance, there is not complete penetrance, if you
> will, for RNA expression patterns which means we have to do more work
> to define what is different about those individuals. We are not
> perfect yet, but it is better than what we are doing now in terms
> of\--again, I am not a clinician. The question you are asking, I agree
> with you 100 percent and I don\'t know what they are doing in The
> Netherlands in terms of deciding not to treat patients. That is a much
> more difficult question I think than to treat someone that otherwise
> looks clean.
>
> DR. DOYLE: Thank you Dr. Rosenberg and Dr. Sistare. Dr. Shine?
>
> DR. SHINE: I would just comment that ultimately it is going to be a
> decision made by a patient with a doctor, given the relative risk, the
> options and whether they want to be treated and what the complications
> are. So, I agree entirely with Marty that we have to be very careful
> from this point of view.
>
> I do think that the pharmacokinetic data becomes critical in terms of
> what you are talking about. So, I am comfortable that if you do good
> pharmacokinetic data, that will provide a lot of the information with
> regard to the genotype.
>
> Mr. Chairman, do you want a sense of the committee on this matter?
>
> DR. DOYLE: Yes, I think that would be a good thing to do.
>
> DR. SHINE: Well, I would just comment that the devil is in the details
> and seeing how, in fact, this is articulated and elaborated is the
> critical thing. But I think the sense of the committee, which they can
> express in any ways they want, is that the approach that is being
> proposed is sensible. It is well thought out. It offers some promise
> both to improve the regulatory process and the educational process for
> the agencies, and I would support moving forward with this proposal.
>
> **Closing Remarks/Future Directions**
>
> DR. DOYLE: I sense that is exactly what the committee is thinking
> right now. So, there is general agreement so thank you for
> articulating that, Dr. Shine. Does the committee have any further
> comments? If not, let me move ahead and try to summarize these
> conversations.
>
> First of all, relative to the quality systems presentations, I think
> the Board applauds the FDA\'s efforts in the quality systems area. The
> Board encourages FDA to emphasize metric measurable outcomes when
> approaching what one can do to improve the systems.
>
> With respect to FDA personnel, the Science Board encourages the
> worker\--I can\'t read my writing\--that the worker provide these
> outcome measures and needs to consider not only total buy-in from the
> internal work force but also consider increasing head count and/or
> outside expertise. We encourage the FDA to continue strategic
> succession planning not only for attrition of personnel over time, but
> planning for what type of science you will have in the future and what
> type of expertise you will have on hand.
>
> The Board applauds FDA\'s efforts in getting involved with sponsors
> early in clinical development but asks the agency to emphasize careful
> consistency.
>
> Relative to pharmaceutical manufacturing, the Board is interested in
> hearing about similar efforts with regards to vaccine manufacture. The
> Board encourages the use of case reports as a learning/training tool.
> Thirdly, the Board encourages measuring outcomes and benefits of PAT.
>
> Relative to the patient safety initiative, the Board encourages FDA to
> facilitate feedback and communication. Are there any other comments or
> follow-up that I have missed in that regard?
>
> DR. SHINE: Pursuing the ambulatory data.
>
> DR. DOYLE: Yes, pursuing the ambulatory data. Thank you.
>
> Relative to pharmacogenomics, the Board applauds the FDA\'s efforts in
> this area. It encourages the FDA to step back and look at specific
> populations, that is minority populations, as we look at
> pharmacogenetic populations.
>
> FDA needs to explore and consider what needs to be done about the
> standardization of different microarray platforms. They need to
> consider penetrance and phenotyping and genotyping. Marty, would you
> like to further clarify that?
>
> DR. ROSENBERG: No, I think Janet made exactly the correct point, that
> it is good criteria to use and that you have to pick the right
> phenotype because it has to be the medically relevant phenotype to be
> measuring.
>
> DR. DOYLE: Thank you, Dr. Rosenberg. We need to clearly define which
> examples determine a regulatory impact and which do not. We need
> approaches that are sensible and offer promise to improve the
> regulatory process and encourage FDA to move forward on this issue.
>
> Does anyone have any disagreements with that or any additions?
>
> DR. NEREM: There was the statement that Ken made, and I think we
> should emphasize that the devil is in the details.
>
> DR. DOYLE: All right, good point. Dr. Shine?
>
> DR. SHINE: Before we adjourn, to come back to the Commissioner\'s
> initial talk this morning when he talked about the substantial decline
> in the number of new drugs coming through, the new drug applications
> and, at the same time, we have been talking about pharmacogenetics and
> pharmacogenomics. I am just curious given that there are people here
> from very diverse backgrounds, including industry, as to how much of
> this reflects the fact that although we may know lots about the genome
> and lots of genes, we don\'t know what the targets are and we don\'t
> know how to attack those targets in a predictable way given the
> multiple genes that regulate a variety of processes. We have the
> problem of polygenic involvement in many of the common illnesses, and
> so forth, and I am wondering\--you know, it reminds me a little bit of
> the computer revolution. For 20, 25 years in the \'60s and \'70s we
> were going to see this huge revolution in computing and it wasn\'t
> until the mid to late \'80s and the early \'90s that it just exploded
> and you began to see real differences in the way companies operated,
> middle management being laid off, and so forth. I am wondering whether
> there isn\'t going to have to be a very prolonged period of
> development in this area, and then an inflection point when some of
> the fundamental questions about these targets are addressed. I am
> worrying about whether the movement from chemistry to genetics may, in
> fact, account for some of this lag. I am just curious, since we have
> some very knowledgeable people here, whether they would have any
> comments about the Commissioner\'s observations which I found very
> interesting.
>
> DR. DOYLE: Any thoughts.
>
> DR. DAVIS: Well, it is too bad Cecil left given the position that
> Cecil holds with Schering. I think you are absolutely right. I think
> everything you elucidated is part of the issue or the perspective.
> Genomics has given us a lot of targets that we don\'t know what they
> do, and also even after you have a target, being able to put a drug on
> that target and have it have an effect is not always an easy thing.
> Some of those targets are not as easy to get to.
>
> So, I think we are in a place where there is a plethora of information
> but we aren\'t sure yet what to do with some of that. On top of that,
> as the Commissioner mentioned, there is the increasing review time.
> So, I think it is a combination of a whole lot of stuff that has
> really got us in the position that we are in. I am optimistic, being
> in industry, that in a couple of years there is going to be this
> deluge. We sure hope there is, anyway and I hope we don\'t have to lay
> off a lot of people, like the computer people did.
>
> DR. DOYLE: Just a few last comments. First of all, I want to thank
> Commissioner McClellan. He just did a super job in sharing with us his
> vision for the FDA and I think, by all standards, at least I feel very
> comfortable that the FDA is in good hands.
>
> Secondly, I want to thank all the presenters for the excellent
> presentations that they have given and the forward thinking that was
> provided. I want to also in particular thank Dr. Woodcock and her team
> for the very informative overview that we received of their research
> program yesterday. That was very well done. Finally, I want to thank
> Susan Bond for assisting this group for the last three years, and wish
> her all the best.
>
> With that, happy trails and we look forward to seeing you in the fall.
> Excuse me, one last thing, I want to let either Dr. McClellan or Dr.
> Crawford have the last word here. So, do you have anything you would
> like to add?
>
> DR. MCCLELLAN: I don\'t have anything to add. This has been a terrific
> discussion of a whole set of complex issues. Thank you for your
> comments about where we seem to be headed. To the extent that we have
> coordinated vision and are actually making progress in getting there,
> it has nothing to do with me and everything to do with the very
> talented and dedicated staff in this agency, and I am pleased to see
> that the Science Board is able to have some impact on the direction in
> which we are heading. We are going to need that help more than ever so
> thank you very much for what you are doing.
>
> \[Whereupon, at 4:17 p.m., the proceedings were adjourned.\]

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