UNITED STATES OF AMERICA

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FOOD AND DRUG ADMINISTRATION

CENTER FOR DRUG EVALUATION AND RESEARCH

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PERIPHERAL AND CENTRAL NERVOUS SYSTEM

ADVISORY COMMITTEE

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MONDAY,

NOVEMBER 18, 2002

The Advisory Committee met in the Grand Ballroom of the Holiday Inn, 2
Montgomery Village Avenue, Gaithersburg, Maryland at 8:00 a.m., Claudia
Kawas, M.D., Acting Chair, presiding.

[MEMBERS PRESENT:]{.underline}

CLAUDIA KAWAS, M.D., Acting Chair

THOMAS H. PEREZ, M.P.H., Executive Secretary

ELLA P. LACEY, Ph.D., Consumer Representative

MICHAEL GRUNDMAN, M.D., M.P.H.

RICHARD D. PENN, M.D.

GERALD VAN BELLE, Ph.D.

HOWARD L. WEINER, M.D.

JERRY S. WOLINSKY, M.D.

[MEDICAL IMAGING CONSULTANTS PRESENT:]{.underline}

CRAIG BEAM, M.D.

LEE C. CHIU, M.D.

MARK FOGEL, M.D.

HYUN KWON KIM, Ph.D.

JAMES M. PROVENZALE, M.D.

RUTH RAMSEY, M.D.

[MEDICAL IMAGING CONSULTANTS PRESENT:]{.underline} (cont.)

GREGORY SORENSEN, M.D.

WALTER WOLF, Ph.D.

[ALSO PRESENT:]{.underline}

RUSSELL KATZ, M.D., Director, Neuropharmacological

Drug Products

PATRICIA LOVE, M.D., Director, Medical Imaging and

Radiopharmaceutical Drug Products

ARMANDO OLIVA, M.D., Division of Neuropharmacological

Drugs

ROBERT TEMPLE, M.D., Director, Office of Drug Evaluation I

**I-N-D-E-X**

**Call to Order, Introductions**

Chairperson Kawas 5

**Conflict of Interest Statement**

Ms. Turner, Acting Executive Director 8

**Welcome and Opening Remarks**

Dr. Love 11

**FDA Overview of Issues**

Dr. Katz 14

**Overview of Imaging**

Dr. Charles De Carli 26

**Surrogate Endpoints as Measures of Efficacy: Complexities and
Limitations**

Dr. Michael Hughes 37

**Volumetric MRI and Related Subjects**

**Structural MRI as a Biomarker of Disease Progression**

Dr. Clifford Jack 67

**MRI, Rates of Atrophy and Alzheimer\'s Disease**

Dr. Nick Fox 79

**Quantitative Imaging**

Dr. H. Cecil Charles 90

**MRI as a Potential Surrogate Marker in**

**ADCS MCI Trial**

Dr. Michael Grundman 102

**MR Spectroscopy and PET**

**To Measure Treatment of Neurodegeneration**

Dr. Michael W. Weiner 137

**MR Spectroscopy**

Dr. P. Murali Doraiswamy 148

**Overview of PET**

Dr. William Jagust 163

**PET and Dementia**

Dr. Gary W. Small 176

**Validating Surrogate Endpoints**

Dr. Michael Hughes 199

**Open Public Hearing**

ERIC REIMAN, M.D., University of Arizona and Good Samaritan PET Center
219

**Dr. Mary Pendergast**,

Elam Pharmaceutical Management

Corporation 226

**Discussion of the Issues Presented by the FDA** 234

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

8:00 a.m.

CHAIRPERSON KAWAS: If everyone can find a seat so we can begin.

Good morning. And welcome to the November 18, 2002 meeting of the
Peripheral and Central Nervous System Drugs Advisory Committee of the
FDA.

My name is Claudia Kawas, and the topic for today\'s meeting is the role
of brain imaging as an outcome measure in Phase III drug trials in
Alzheimer\'s Disease.

And we\'d like to start by introducing the people who are sitting around
the table, so perhaps we can start with Dr. Katz.

DR. KATZ: Russ Katz, Neuropharm Drugs, FDA.

DR. LOVE: Patricia Love, Division of Medical Imaging, FDA.

DR. OLIVA: Armando Oliva, team leader, Division of Neuropharm Drugs,
FDA.

DR. FOGEL: Mark Fogel, Medical Imaging, Children Hospital of
Philadelphia.

DR. VAN BELLE: Gerald Van Belle, Department of Biostatistics from the
University of Washington.

DR. PENN: Richard Penn, Professor of Neurosurgery at the University of
Chicago.

EXECUTIVE SECRETARY PEREZ: Tom Perez, Executive Secretary to this
meeting.

DR. GRUNDMAN: Michael Grundman, University of California, San Diego.

DR. WOLINSKY: Jerry Wolinsky, neurology, University of Texas at Houston.

DR. CHIU: Lee Chiu, M.D., MI Imaging Director, California.

DR. RAMSEY: Ruth Ramsey, neuro-radiology and Professor of Radiology at
the University of Illinois.

DR. BEAM: Just in time. Craig Beam, Biostatistician, University of South
Florida, Moffit Cancer Center.

DR. WOLF: Walter Wolf, Professor of Pharmaceutical Sciences and Director
Pharmakinetic Imaging Program, University of Southern California.

DR. KIM: Hyun Kim, Cal State University, Los Angeles. Chemistry 
Biochemistry professor.

CHAIRPERSON KAWAS: We also have our invited speakers sitting off to the
left, and perhaps we can start with introductions there with Dr. Mike
Hughes.

DR. HUGHES: I\'m Michael Hughes, I\'m a Professor of biostatistics at
Harvard University.

DR. FOX: I\'m Nick Fox, senior fellow at the University College London
in London.

DR. De CARLI: Charles De Carli, neurologist, University of California at
Davis.

DR. WEINER: Michael Weiner at the VA Hospital and the University of
California, San Francisco.

DR. CHARLES: Cecil Charles, Duke Image Analysis Laboratory, Duke
University.

DR. DORAISWAMY: Murali Doraiswamy, I\'m a psychiatrist at Duke
University.

DR. JAGUST: Bill Jagust, neurologist, University of California at Davis.

DR. SMALL: Gary Small, psychiatrist, University of California at Los
Angeles.

DR. JACK: Clifford Jack, radiology, Mayo Clinic in Minnesota.

CHAIRPERSON KAWAS: Thank you.

We\'ll now have the conflict of interest statement.

MS. TURNER: Good morning. My name is Tara Turner, I\'m the backup
Executive Secretary. I\'m filling in in the absence of Tom Perez\' voice
this morning.

The following announcement addresses the issue of conflict of interest
with respect to this meeting and is made a part of the record to
preclude even the appearance of such at this meeting.

The topic of today\'s meeting is an issue of broad applicability. Unlike
issues before a committee in which a particular product is discussed,
issues of broader applicability involve many industrial sponsors and
academic institutions.

All special Government employees have been screened for their financial
interests as they may apply to the general topic at hand. Because they
have reported interests in pharmaceutical companies, the Food and Drug
Administration has granted general matters waivers to the following SGEs
which permits them to participate in today\'s discussions: Dr. Michael
Grundman, Dr. Claudia Kawas, Dr. Richard Penn, Dr. Gerald van Belle, Dr.
Jerry Wolinsky and Dr. Howard Weiner.

A copy of the waiver statements may be obtained by submitting a written
request to the Agency\'s Freedom of Information Office, Room 12A-30 of
the Parklawn Building.

Because general topics impact so many institutions, it is not prudent to
recite all potential conflicts of interest as they apply to each member
and consultant.

FDA acknowledges that 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 respect to FDA\'s invited guests, Dr. P. Murali Doraiswamy, Dr.
Michael Weiner, Dr. Nick Fox, Dr. Clifford Jack, Dr. H. Cecil Charles,
and Dr. Gary Small have reported interests which we believe should be
made public to allow the participants to objectively evaluate their
comments.

Dr. Doraiswamy attended a consultants meeting for Berlex several years
ago, has received research grants and/or honoraria from Pfizer,
Novartis, Eisai, Janssen, Merck, Forest, David, Elan, Organon,
GlaxoSmithKline, Wyeth, and Lilly over the past five years. He has also
received grants from the NIH, NARSAD and the American Federation for
Aging Research.

Dr. Weiner has consulted for Pfizer, Aventis, Merck, Synarc and
Novartis.

Dr. Fox has received consultancy fees or honoraria for lectures from
Novartis, Janssen, Elan, Pfizer, Searle, Lundbeck and Pharmacia. His
research has a collaborative research grant from GlaxoSmithKline and has
been contracted to provide image analysis for Novartis, Janssen and
Elan/Wyeth.

Dr. Jack has provided advice to Pfizer and Pharmacia regarding the use
of MRI as a biomarker of disease progression drug trials in Alzheimer\'s
Disease.

Dr. Charles has a professional relationship with Duke University Medical
Center\'s Brain Imaging Analysis Center and the Center for Advanced MR
Development.

Dr. Small is a scientific advisor to CTI and Amersham and has an
involvement in a pending NDA for FDG-PET in Alzheimer\'s Disease.

In the event that the discussions involve any other products or firms
not already on the agenda for which FDA participants have a financial
interest, the participants\' involvement and their exclusion will be
noted for the record.

With respect to all other participants, we ask in the interest of
fairness that they address any current or previous financial involvement
with any firm whose product they may wish to comment upon.

Thank you.

CHAIRPERSON KAWAS: Thank you.

I\'d now like to turn the floor over to Dr. Patricia Love, Director of
Medical Imaging and Radiopharmaceutical Drug Products.

DR. LOVE: Thank you.

Good morning, Dr. Kawas, all members assembled of the Advisory
Committee, all medical imaging consultants, all invited guests. Thank
you very much for coming. This is certainly going to be an exciting day
and in a few moments, Dr. Katz is going to speak with you about the
activities planned. But before then, let me just briefly address some
issues about the status of the Medical Imaging Drug Advisory Committee.

Several of you received a letter over the last few days about the fact
that the Medical Imaging or MIDAC Committee is no longer going to
continue to exist as a standing entity. The basis for is certainly
varied and involves several different aspects, but key among them is the
fact that the agency is only allowed to have 12 advisory committees
existing at any one particular time.

So, with need to add new committees, this is one of the committees that
will no longer be in existence. But that does not mean \-- and let me
please reassure you that that does not mean that we feel that there is
any less need to seek input from advisors and consultants on this
matter. And what you see before you today is a model that we will be
using to seek your input and counsel. It\'s a combination of an advisory
committee, a standing committee with invited guests, obviously, and
that\'s one mode that we can use.

Another option we have is to potentially form an imaging subcommittee.
If we do that, it would be a subcommittee of a standing committee.
Before making that decision, however, because as you know there are
products that are being transferred from the Center for Biologics to
Drugs, we are waiting to determine exactly which products and what types
of areas will be transferred before we make a decision on whether or not
to form a subcommittee of a standing committee, and which committee that
would be.

And, of course, the third option is the option we\'ve been using several
times over the last few years, and that\'s the pubic forums and
workshops that we use for PET and positron emission and tomography
issues, radiopharmaceutical issues that stemmed from the Food and Drug
Modernization Act of 1997. That type of venue allows us to have much
more interactive dialogue with both the advisors and consultants as well
as the public.

So we will continue to use all the methodologies. We certainly as an
agency recognize and value the importance of imaging and this rapidly
advancing technology, its relevance to diagnoses and to treatment. We
will continue to move forward it in that area.

In the meantime, if you do have questions, please you may forward them
to me directly or you may forward them to Linda Skladany, Assistant
Commissioner for External Affairs.

Thank you.

CHAIRPERSON KAWAS: Thank you, Dr. Love.

Now for the FDA overview of issues, Dr. Russell Katz, Director of
Neuropharmacological Drug Products.

DR. KATZ: Thanks, Dr. Kawas.

And I\'d like to welcome you all here this morning. I\'d especially like
to welcome our medical imaging consultants. You\'ve just heard about
their committee, or their old committee. And in particular I\'d like to
welcome our invited experts. Our invited experts will present to the
Committee the state of the art of various brain imaging modalities that
will form the basis for much of what we talk about today. So I want to
thank you all for coming today and for helping us address what we
believe to be a very important issue in the future, development of drugs
to treat patients with Alzheimer\'s Disease.

Finally, also let me welcome Tom Perez, whos filling in and has
graciously agreed to fill in at the last minute as the Executive
Secretary for today\'s meeting. So thanks very much, Tom.

As you know, today we are asking your advice on an issue that\'s become
of considerable interest to manufacturers of treatments for patients
with Alzheimer\'s Disease and a matter of interest to many other parties
as well. And namely, that\'s whether or not we should rely on a drug\'s
effect on a surrogate marker to support to support the marketing of a
treatment for patients with Alzheimer\'s Disease.

Before I go on much more, let me just say what a surrogate marker is.
There are many definitions available, as I\'m sure you know, about what
a surrogate marker is. And I thought since there were many available, I
would take one offered by my boss.

I notice that I\'ve actually neglected to attribute this to him. I
thought maybe I could get it done before he came this morning. But he\'s
here now, so I\'ll have to apologize.

This is from an article that Bob Temple wrote in 1995, and basically it
says a surrogate marker and point of a clinical trial is a laboratory
measurement or a physical sign used as a substitute for a clinically
meaningful end point that measures directly how a patient feels,
functions or survives. Changes induced by therapy in a surrogate end
point are expected to reflect changes in a clinically meaningful end
point.

Another definition that someone gave us of surrogate markers is
something that you measure instead of thing you actually care about.

As these definitions imply, approval of a drug on the basis of an effect
on a surrogate marker presupposes no requirement for a demonstration of
a direct effect on a clinical outcome. And you\'ll recognize that
that\'s unusual.

Obviously, the vast majority of drugs are approved on the basis of a
showing of an effect on a clinically valid or face valid measure of how
the patient is doing, whether it\'s objective or subjective. And, in
fact, in our division all drugs have been approved on the basis of a
finding on a clinical outcome, although on rare occasion a surrogate
marker has been found to be supportive in some of the studies.

In fact, as you know, there are currently four treatments approved for
Alzheimer\'s Disease and all have been approved on the basis of an
effect on clinical outcomes, namely cognitive measures and global
measures, as you know. But now we\'re being asked and we\'re asking you
to consider approving treatments for Alzheimer\'s Disease on the basis
of an effect on a surrogate marker.

Why would rely on the effect on a surrogate marker instead of a clinical
endpoint? Usually two reasons are given. One is that because these
measures are fairly sensitive, one could reduce the sample size
necessary to show an effect and that, of course, makes studies cheaper
and more manageable.

A second aspect of surrogate approvals that is often touted as being
useful is that because surrogates often look at outcomes that may be
very latent in the path of disease, mortality, for example, and it might
be very difficult to study mortality directly, the use of a surrogate
could decrease the sample size and actually make the study actually
practical and more tractable.

And, of course, the Agency has a long history of approving drugs on the
basis of effects on surrogates. For example, the obvious examples, are
anti-hypertensives, drugs which are approved on the basis of a showing
of a decrease in blood pressure, which is a laboratory measurement and
not a clinical outcome in the sense of how the patient feels. Similarly
cholesterol-lowering agents, as the name implies, those drugs are
approved on the basis of a showing of a decrease in serum cholesterol
and not on any specific clinical outcome.

Here, though, these effects on these surrogates has presumably been
shown to correlate with actually a clinical outcome of interests, for
example, decreased cardiovascular outcomes or events. And in that sense
these surrogates can be considered validated; that is to say an effect
on the surrogate has shown to predict an effect on a clinical outcome of
interest.

But in addition to approving drugs on the basis of findings on validated
surrogates, since 1992 the agency has had the explicit authority to
approve drugs on the basis of effects of surrogates that have not been
validated but only that have been reasonably likely to predict the
clinical effect of interest. And I have a definition. This is actually
the language from the so-called accelerated approval regulations, again
adopted in 1992. And I\'ll just read them.

It says that \"The FDA may grant marketing approval for a new drug
product on the basis of adequate and well- controlled clinical trials
establishing that the drug product has its effects on the surrogate
endpoint that is reasonably likely, based on epidemiological
therapeutic, pathophysiological or other evidence, to predict clinical
benefit.\"

It\'s important to note that the regulations anticipated that these
approvals would occur only for treatments for life-threatening or
serious diseases for which there is no other available treatments.

In addition, the regulations also state that, ultimately, these
surrogates would have to be validated, that is to say that the sponsor
would have to demonstrate usually after a drug was approved or
invariably after the drug was improved, that in fact there was a
correlation with the clinical outcome of interest. And, in fact, if that
couldn\'t be shown or if a sponsor didn\'t engage in that sort of
attempt to valid the surrogate, the drug could be removed from the
market more easily than other sorts of drugs.

And, in fact, in 1997 this essentially same standard was introduced into
the Federal Food, Drug and Cosmetic Act under what\'s called the fast
track provisions. So this has been in the regulation since \'92 and in
the Act, the statute, since 1997.

I don\'t want to go into very much detail into the nature of validation
of a surrogate. Dr. Hughes will talk about that, I believe, in a little
while. Let me just point out that validating a surrogate is a
complicated matter, and it ordinarily involves essentially a complete
understanding of all the effects of a drug, positive and negative, as
well as a detailed understanding of the path of physiology and the
biology of the condition being treated. And as you\'ll also recognize,
we usually don\'t have complete information on any of those matters.

So while the regulations permit the Agency to approve a drug on the
basis of an effect on a surrogate that is unvalidated but only
reasonably likely to predict clinical benefit, relying on the effects of
a drug on an effect on an unvalidated surrogate is potentially
problematic. These surrogates, and particularly the surrogates you\'ll
hear about today, imaging modality and Alzheimer\'s Disease, correlate
very well with the untreated condition. In other words, as the
Alzheimer\'s Disease gets worse, we see that the imaging modality gets
\-- modalities, all of them get worse in a highly correlated way, but
it\'s not immediately obvious that a drug- induced effect on that
surrogate necessarily translates into a clinical benefit that we want to
see.

In fact, there are many examples in medicine where a beneficial effect
has been seen on a candidate surrogate, but in fact the clinical effect
of interest has not been shown. And a number of these examples are
explained in various of the publications that you have in your briefing
book.

Now, in the case of putative treatments for Alzheimer\'s Disease,
actually we\'re not being asked by sponsors to rely on effects on
surrogates for the documentation of symptomatic treatments. As I said,
the four treatments that are approved, have all been approved on the
basis of symptomatic effects and typical study designs are fairly good
at picking up or at least capable by design of picking up symptomatic
treatment effects.

What sponsors are generally proposing when they ask us to rely on
surrogates is a showing that the drug has an effect on the underlying
program or path of physiology of Alzheimer\'s Disease. As I say,
typically the study designs that are used now to look at symptomatic
treatments are not capable of documenting such an effect on the
underlying progression. There are clinical trial designs that are
capable of demonstrating this effect, but those trials are very
difficult to do. They involve or would involve large numbers of patients
and would take long periods of time. So in that context, relying on a
surrogate to document progression is very attractive.

In addition, the other reason that imaging modalities in particular
appear to be attractive for this purpose is that they purport to give us
a window into actually looking at the pathology. And so it seems
reasonable to conclude that any effect that one would see on these
imaging modalities in a beneficial way from the drug would necessarily
translate into a clinical benefit that we\'d like to see. I would just
caution that it isn\'t necessarily the case. It\'s a complicated matter,
as I said before. One would have to at least understand what you\'re
looking at in the imaging modality, first of all, in terms of the
pathology and then there are at least three considerations that would
have to be taken into account before we decided that an effect seen on
the modality by the drug actually would translate into a clinical
benefit.

And one is that one would have to ensure that there\'s no interaction
between the drug and the test system itself, the imaging modality that
might give a spurious result. If you get beyond that, it is possible
that a change could be induced by the drug that could appear as a
beneficial effect on the imaging modality but in fact, it might be
entirely irrelevant. An example might be if we\'re looking at total
brain atrophy and if the drug increased brain water, it might be
possible that it would appear as if there is less atrophy when in fact,
the change that was induced was entirely irrelevant.

The other possibility is that a drug may actually have an effect on a
structure that might be relevant or that one might think would be
relevant in the important pathology, but in fact that that effect might
not be what we would think it was. For example, one could show that
there might be less atrophy because, in fact, the treatment preserves
neurons. But in fact that the neurons are not functioning properly, the
beneficial effect on the picture might in fact be spurious with regard
to its clinical concomitant.

So, with as a very brief background into sort of the regulatory
framework in which we need to work and some of the conditions, I just
want to pose to you the two large questions that we\'d really like to
discuss and ultimately vote on.

The first question is whether or not you think any of the imaging
modalities that we\'re going to hear about this morning are in fact, or
have in fact been validated in the sense that I\'ve discussed and in the
sense that Dr. Hughes will elaborate on. Failing that, we would like to
know whether or not you think it\'s appropriate for us at this time to
rely on a drug\'s effect on an unvalidated surrogate to support the
approval of an application for a treatment for patients with
Alzheimer\'s Disease.

So, with that charge, I\'ll turn the microphone back to Dr. Kawas.

CHAIRPERSON KAWAS: Thank you, Dr. Katz.

Well, both the charge and the number of modalities that we\'re going to
be reviewing today, and most notably the number of speakers that we\'re
going to be listening to today, require that we keep this meeting as
much as possible on time, which is already not happening.

For the speakers, I would very much appreciate if you could keep to your
time. There will be a timer up there to warn you shortly before you will
be pulled off of the podium with a hooked cane. And with that, I\'d like
to introduce our first speaker, Dr. Charles De Carli, who is going to
give the overview of imaging.

DR. De CARLI: With that impossible task, I\'ll already start by saying
that I am not going to be able to accomplish it. First off, Ive got to
figure out how this works. There we go.

I want to thank Dr. Mani for inviting me and the individuals of our
Committee here.

To talk about an overview of imaging is beyond this 15 minute time
period that I was allotted. Instead what I\'d like to do, as most of
this will be reviewed more specifically by my other speakers, I want to
talk a little bit about something we don\'t talk about that much, and
that is understanding what is normal in imaging. We tend to focus on
diseases and compare them to specific subgroups, but I would like to
talk, just for a few minutes, about population based imaging and
defining what is normal.

As you all know, data suggests that there\'s a linear change in
cognitive performance with age, particularly in the memory sphere. What
becomes obvious, however, after a careful longitudinal study which was
done by the Chicago group, is that, in fact, what we see are individual
differences in trajectory of performance suggesting that in fact the
aging process is not monotonic descending, if you\'d like to say, but in
fact has quite a bit of variability.

The process of aging involves multiple factors that include both genetic
and environmental factors, and including lifestyle factors that may
either reduce neuronal number in pair brain structure function or
enhance neuronal number. However, ultimately over time with these risk
factors and this balance we can lead to the process of dementia or not.
And it\'s in that regard that I think we have to understand this very
complex interaction in the setting of what is normal aging.

And for this, I would like to use some data from the Framingham study
that I\'ll talk about to assess to certain questions for what is normal
aging based on some cross-sectional differences that we see and rates of
change to ask how do earlier life factors effect risk for later life
dementia. And then the important thing, which we\'re not going to
discuss today but will come up as a derivative of these conversations,
and that is if we have surrogate markers, will we begin to use these
markers very early in life to think about primary prevention strategies
and how to identify these risk factors.

All this data is based from the Framingham Heart study and funded by
both the NIA and NINDS. And it\'s a community-based population study.
The original cohort was begun to study in 1950 and continue about 400 of
them continue to be under observation. Their children began to be
observed in 1971. And this included routine assessment of cardiovascular
risk factors, but MRI and neuropsychology was added in 1999. And we had
the opportunity to cross sectional analysis as well as a repeat analysis
in a subset of these individuals.

The quantitative brain imaging was based on intensity-based mathematical
modeling to define segmentation of brain matter and CSF, white matter
hyperintensity. And then we did some lobar analysis and also evaluated
stroke volume in these individuals.

The cross-sectional data is a little over 2200 individual whose mean age
was 64 years. But, as you can see, it\'s age range across most of the
adult lifespan, 38 to 97 years.

As expected in an older population, there was a slight higher prevalence
of the females in the cohort.

This is example of the cross-sectional data. Individual data points
plotted in men in blue, women in yellow and a regression analysis, a
multi-variant regression analysis that includes looking at gender, age
and age gender interaction, including a squared term. And this is just
from here forward are going to be the regression models themselves. Just
to give you a sense of what the effects are, so this is on total brain
cerebral volume of the hemisphere where we show a strong age effect,
about 47 percent of the variance is ascribed to age with very little age
gender interaction.

A similar relationship can be seen with the temporal lobe volumes. That
is, there\'s a nonlinear decline with aging and no obvious age gender
interaction. However, this seems to be slightly different when we come
to front lobe volumes. In there, it seems to be an accelerated brain
loss among men. And as my wife tends to tell me that if you don\'t use
it, you lose it. And that\'s significantly different than it is among
women.

Ventricular volume can be seem as sort of the inverse of brain aging, in
that the CSF spaces are increased with age in a nonlinear fashion.
Again, there\'s no gender interaction.

And white matter hyperintensity volumes, which are in part an aging
phenomena but also may represent cerebrovascular disease, show the only
gender interaction effect. Of course, there\'s a nonlinear increase with
age, but in about the seventh decade of life, you begin to see a
differentiation between men and women with women showing greater volumes
of white matter hyperintensity then men at a given age. And this has
been shown in other studies.

The aging process is not only associated with degeneration, but also the
appearance of cerebrovascular disease, which is quite common among
individuals as they age. And here\'s an example of silent cerebral
infarcts among this cohort. And you can see a steady age increase in the
prevalence being slightly greater in men than women. In the tenth decade
of life we just didn\'t have enough numbers out here to make this
reliable, so you tend to see this little bit of drop here.

So in the first part of this talk, it becomes clear that age accounts
for approximately 30 to 40 percent of the differences in brain volume.
And that it appears that brain regions change differently with change.
That the frontal lobes, for example, do not appear to atrophy quite as
rapidly as other parts of the brain. And this may, in fact, reflect a
fact that these individuals in this cohort were essentially healthy.
It\'d be interesting to look at these changes in those who do not
successfully age.

Gender differences, at least in this cross-sectional study appear to be
modest. But the important fact that I think is becoming more and more
recognized when we look at the consequence of aging in cognitive
impairment, is that cerebrovascular injury was quite common in this
cohort.

Next, for the final part of this talk I\'d like to turn to the
longitudinal evaluation of a very small subgroup of these individuals,
151, who were divided into five different age groups based primarily on
the fact that we had limited data available from this small study, 38 to
59, and then 60 to 69, 70 to 79, 80 to 84 and 85 to 96. And these two
were chosen because we tend to see dementia beginning much more rapidly
at these two higher age ranges.

We also broke this cohort up into or identified within this cohort 23
individuals age 62 who were at higher risk for dementia based on family
history data; that is because of this, the Framingham study design, we
actually know their parents outcome. That is they either passed away
without Alzheimer\'s Disease after the age of 80 or had it before age
80.

In addition, they may have one or both alleles positive for ApoE 4 and
be at high risk for cerebrovascular disease.

We identified 21 individuals at low risk for cerebrovascular disease or
dementia or having the converse, their parents made it to age 80 without
dementia. They had no ApoE 4 alleles and had less an average
cerebrovascular risk.

Within this longitudinal study design, there are MRIs repeated at about
2 years apart. And these MRIs were analyzed separately and blindly by
different raters.

This is an example of some of the data that we\'re seeing in this very
preliminary analysis. What we find is that if you look at total brain
volume, that there is age related increases in the rate. So this is the
percent difference per year annualized change in MRI. I think this
increase is a little bit spurious because of the small numbers.

Similarly, ventricular rate volume appears to accelerate, the rate of
change seems to be increasing as we get older.

And finally, white matter hyperintensity, again, as possible evidence
for cerebrovascular disease is accelerated in rates of change as the
individual ages.

Now, in this very preliminary look at this data, the low risk offspring
are compared to the high risk offspring. Again, these are people at age
62. And what we see is that they are essentially when you look at the
rate of change of significantly different from a population mean of
zero, there\'s very little change going on in the low risk offspring
with the possible exception of the white matter hyperintensity volume.
However, in the high risk offspring we see a significant difference from
zero and larger than the low risk offspring in rate of temporal volume
atrophy and rate of increase of total ventricular volume.

And this is actual data. Again, these are only two observations so it\'s
linear, and I think more observations will help clarify this. But what
you can see in this hodgepodge of data is that there appear to be
differing trajectories. Again, individuals who appear to be declining
very steeply in terms of total cerebral brain volume; similarly there
are individuals with white matter hyperintensity volume who seem to be
going up much more rapidly. And we\'re even recognizing in small groups
of people this heterogeneity in trajectories associated with apparent
normal aging.

So in summary from the longitudinal data, it appears that the rate of
brain atrophy in white matter hyperintensity accretion increases with
advancing age. And I think that this has to be taken into account when
you start looking at comparative groups of individuals when you\'re
looking at differences between dementia and normal aging. I think that
if your normal aged group is 80 years old, it\'s going to atrophy more
rapidly than, say, a younger group. And that may bring into contrast the
differences between the dementia process.

Most importantly, I think, is we\'re beginning to recognize that
individuals establish different trajectories of aging. And I\'d like to
suggest, although this data is quite preliminary, that this may actually
begin quite early in life. And I think this is an interesting
observation that has impact to using MRI as a surrogate marker, or PET,
for that matter, or imaging in general in the sense that if we identify
these individual differences, then we can study why these individual
differences occur and possibly again to explore ways to modify these
individual differences. Again, with the assumption that these changes
represent a pathological process or an unwanted process.

So in conclusion, with 1 minute and 3 seconds, the understanding of use
of imaging methods as a surrogate markers for disease must include a
clear understanding of what is normal.

And I thank you for your attention.

CHAIRPERSON KAWAS: Thank you Dr. De Carli.

Now Dr. Michael Hughes is going to talk to us about surrogate endpoints
as measures of efficacy, complexities and limitations.

DR. HUGHES: Thank you very much for the invitation to speak here.

I\'m actually a stand-in for Tom Fleming who was going to give this
talk. And I must acknowledge him, because I borrowed a few slides from
him. What I\'m going to talk about is some of the complexities and
limitations in looking at surrogate endpoints in clinical trials.

First of all, I thought it would be useful just to mention a few key
criteria for study endpoints in trials. Clearly they must be measurable
and interpretable in the context of the disease.

They also need to be sensitive to the anticipated actions of the drugs
that you\'re interested in. So, for instance, if you\'re studying an
analgesic in terminally ill patients, you might want to focus on pain
relief and not survival.

And thirdly, in terms of the approval process, I think they should be
clinical relevant.

So here\'s a few examples of the difference between surrogate endpoints
which tend to measure biological activity, some measures which aren\'t
necessarily directly relevant to an individual patient versus those
which measure clinical efficacy which are more directly relevant to
individual patients that are taking the drugs.

You\'ve already seen this definition of a surrogate endpoint. I\'ve
broken it up into two. The first sentence really deals with the idea
that a surrogate is a substitute for one of these clinically meaningful
endpoints.

And then the second really gets to the heart of what we mean by a
surrogate endpoint in terms of drug evaluation. So you want to be sure
that the changes that are induced by a therapy on a surrogate will
reflect or will reliability predict the changes in the clinically
meaningful endpoint. And that\'s the hardest thing to validate in the
context of surrogate endpoints.

Russ already mentioned some of the issues to do with or some of the
interests in why we might want to measure surrogate endpoints focused
particularly on drug approval, accelerated approval and full approval.
But it\'s useful also to bear in mind that surrogate endpoints are
useful for understanding the basic ideas of how the disease works and
how drugs works. And they\'re also really pivotal in terms of Phase II
clinical trials in deciding what drugs to take forward for further
development.

So here are some examples of potential surrogate endpoints that have
been used and the corresponding true endpoints. And this, again, I think
brings out the idea that the surrogates often measures lab measures or
other signs and the true endpoints are very much clinical endpoints
which are relevant to individual patients.

The thing that I\'d really like to stress first of all is this idea of
what\'s the difference between a prognostic marker and a surrogate
endpoint use for drug evaluation. So we can think about a prognostic
marker being any variable that predicts the clinical outcome. As
there\'s no concept in that definition of effects of the drug, so
there\'s no mention of interventions. Whereas, a surrogate endpoint is
really something where the effect of an intervention on the surrogate
reliably predicts the effect of the intervention on the clinical
outcome.

So we\'re bringing in the idea that interventions effect the surrogate
and, hence, effect the clinical outcome. And it\'s really critical to
appreciate that a correlate, in other words a prognostic marker, may not
necessarily be a good surrogate endpoint for drug evaluation. And what
I\'d like to do in this talk is really indicate why that\'s so.

So here\'s a very simple schematic of a disease which acts on or
produces an effect on the true clinical outcome. And it also separately
effects the surrogate endpoint, but the surrogate isn\'t on the causal
pathway between the disease and the true clinical outcome. There\'s
going to be a correlation between these two.

So if you have intervention which effects the surrogate endpoint, it can
have an effect on that endpoint without effecting the true clinical
outcome. So a very common example of this is where the surrogate is a
measure of symptoms of the disease and you can treat the symptoms
without effecting the underlying disease.

So it\'s essential, really, to understand whether you\'ve got just a
correlation or an association, or whether you\'re dealing with a causal
pathway between the disease and the true endpoint which involves the
surrogate. So that involves a lot of basic science, clinical research to
get at the pathways of the disease and also the ways in which drugs work
on those pathways.

And then also empirical evidence about the performance of the potential
surrogate in practice.

Now having said that, even when there\'s an established model for a
causal pathway, you may not have a good surrogate endpoint. So here\'s
what you might think of as the ideal surrogate endpoint. So the disease
effects the true clinical outcome via the surrogate endpoint. So if you
have an intervention which effects the surrogate, you think it will also
effect the true clinical outcome.

So the key thing here is that all mechanisms of action of the
intervention on the true endpoint are mediated through the surrogate.
But even in this setting, the effect of the intervention on the true
outcome could be underestimated if there\'s a lot of measurement error
in the surrogate. That\'s not a varied situation, but it does arise
sometimes.

The other extreme is also very common. You can get overestimation of the
effect on the clinical outcome if the surrogate effect is not of
sufficient size or duration. And the key issue here is whether the
effect of the drug might be transient or whether it will be maintained
long term among the patients taking the drug. So these problems can
arise even when the effect on the surrogate is statistically
significant.

Now in practice, surrogates fail for multiple reasons. And here\'s an
illustration of one situation where it may fail. You\'ve got the
intervention effecting the true outcome via the surrogate. You\'ve also
got important pathways by which the disease effects the true clinical
outcome, which aren\'t effected by the intervention. So the value of
this surrogate in this setting will depend upon the relative importance
of these different pathways, the ones which are affected by the
intervention versus the ones which aren\'t affected by the intervention.

Here\'s another situation where the intervention actually effects the
pathway which doesn\'t involve the surrogate. And in this sort of
circumstance, if you rely upon the surrogate endpoint for evaluating the
drug, you\'ll miss the true value of that drug on the true clinical
outcome.

And here\'s one example of a disease, CGD, where there\'s a high risk of
serious infections, recurrent infections. A clinical trial was
undertaken to evaluate one particular intervention, interferon gamma.
And those the surrogate endpoints or potential surrogate endpoints in
this setting were superoxide production and the ability to kill
bacteria, so things which are relatively easy to measure.

But there was enough uncertainty about the value of these surrogate
endpoints that a large scale clinical outcome study was done where the
recurrence serious infections was the key endpoint in the trial. And in
this particular trial they found a very dramatic effect on the true
clinical outcome, but essentially no effect on the biological markers.

So this would suggest either the markers were just not sensitive to the
effects of the drug or there were other important causal pathways
relating the disease to the true clinical outcome, which the
intervention actually effected.

So a key thing to appreciate is that if regulatory approval is based
upon these surrogates, then that\'s going to focus throughout the
evaluation on those surrogates. And if these surrogates are poor, then
there\'s a possibility that you will miss drugs which have important
effects on the true clinical outcome.

In practice, things are much more complex. You\'ll have not only the
potential effect of the intervention on the pathway mediated by the
surrogate, you may have an effect on other pathways which aren\'t
counted by the surrogate that you\'re measuring. And you may have direct
effects of the intervention on the true clinical outcome.

So in this setting the value of the surrogate is potentially
unpredictable, and here\'s an example from the cholesterol literature.
Here are two clinical trials which both effected cholesterol levels in
roughly the same magnitude, so about just under a ten percent reduction
in cholesterol levels in both trials.

The true clinical outcome of interest was all-cause mortality. You can
see in one trial there was essentially no difference. In the other
trial, there was actually an adverse effect on all-cause mortality
associated with the cholesterol lowering drug.

So, this is despite the fact that if you look at the
cardiovascular-specific mortality, there does seem to be effects of the
active drugs on that subset of deaths. So this would indicate that there
are indeed either direct effects of the active drug on mortality and/or
competing causal pathways which are effected by the intervention in an
unpredictable manner.

Another example of a failed surrogate, and this is the classic one
that\'s often cited, is anti-arrhythmic drugs which were widely used or
prescribed post-M.I. to prevent sudden death. When a clinical trial was
actually undertaken to evaluate these drugs with respect to their effect
on mortality, it was found these anti-arrhythmic drugs actually tripled
the mortality rate relative to placebo. So although they had their
intended effect on arrhythmias, the effect on the clinical endpoint of
greater interest was clearly adverse.

I\'d like to finish with another example from the cardiovascular
literature. And this looks at blockages of the coronary artery leading
to myocardial infarctions. And the idea here was to evaluate the TIMI
flow, which is a measure of flow through the artery. And I\'ve
categorized it simply as complete flow versus no or partial flow.

And here are the results from one particular trial, which compared
streptokinase to TPA. And you can see that the TPA increased the
proportion of patients that have complete flow and also decreased
mortalities, so you might anticipate that it\'s a good surrogate.

Then another trial was done to evaluate RPA, a new drug, versus TPA. And
you see a certain slightly smaller effect on the surrogate. So you might
anticipate that there will be a beneficial effect on mortality. But when
a large trial was done to evaluate the effect on mortality, we found
essentially no difference.

So although the surrogate had been predictive of the clinical effect in
one trial, when it was taken to a different drug comparison it failed.

So in terms of the benefits and risks of using surrogate endpoints, the
benefits have already mentioned. Clearly, we can do smaller and shorter
clinical trials that will make drugs available sooner. The risks of the
drugs that are approved will have unknown effects on significant
patient- relevant clinical outcomes. And the approval focused on the
effects on surrogates could mean that clinically effective drugs are
missed if the causal pathways are not well understood.

And it\'s important to appreciate that ultimately drug approval based
upon the effects on a surrogate involves an extrapolation of experience
with existing drugs to untested new drugs. Non-extrapolation will almost
certainly mean that there will be an increased risk that drugs that are
licensed could have no minimum effects or even potentially adverse
effects on the patient-relevant outcomes.

So minimizing this really requires a very thorough understanding of the
causal pathways for disease effects on the trust clinical outcomes as
well as a similar understanding about the intended and unintended
effects of all of the interventions, not just past interventions but
future ones as well on the surrogate and the clinical outcome.

And you need empirical evidence to support the validity of the
surrogate. And I\'ll talk a bit more about that later.

Thank you.

CHAIRPERSON KAWAS: Thank you, Dr. Hughes.

We now have time for some questions for Dr. Hughes or Dr. De Carli.

DR. WOLF: I would like to ask Dr. De Carli, your studies were devoted to
functional \-- I\'m sorry, to anatomical information. Did you also do
any function studies and cognitive studies in order to correlate to what
extent age was the only variable, and you indicated in your presentation
and I realize it was a very short presentation, but were there also
function studies that you\'re requiring to follow those patients to have
a better view of, not only how the anatomical information is changing,
but how the function information is changing?

DR. De CARLI: Yes, that\'s a very good question. Thank you.

Yes, we did \-- I think the ultimate functional test we did
neuropsychology. And so with brain changes we do see a decrease in some
memory functions and cognitive functions not only across the age
spectrum, but also changing within. So within the very brief period of
time that they were observed there were relationships between brain
volume and general performance in cognition.

So we are seeing that. And, unfortunately, as Dr. Katz is reminding us,
we\'re only see it one direction. If your brain is shrinking, so your
point is going down. And the thought is, of course, is that we want to
modify underlying processes and see if those people with underlying risk
factors\-- for example, cerebrovascular disease, which we know there are
proven treatments for, may alter those changes.

Does that answer your question?

CHAIRPERSON KAWAS: Thank you.

Please?

DR. PROVENZALE: I have a question for Dr. De Carli. Hi. You nicely
pointed out the differences in brain volume changes in young at risk and
young non at risk individuals. It appears from what you\'ve shown us
that if we studied a population comprised of \-- if we didn\'t identify
the individuals who were at risk and combined non at risk individuals
with at risk individuals we might mask or miss the effect of a drug. So
do you advocate as part of drug trials targeting specific populations in
that manner?

DR. De CARLI: Well, I think that, particularly in settings where you
have a drug that you\'re looking at for primary prevention, I think that
would be the focus of your study. You would take high risk individuals
in which you were looking for them to progress onto a particular
endpoint, be that mild cognitive impairment in an aging cohort or
dementia in a cohort with mild cognitive impairment already present. And
so, yes.

But I want to caution you that that data is very preliminary. It does
coincide with some of the PET data that will be discussed, I believe,
today and does support some of that data. But I still would emphasize
that the sum total of number of individual studied, if you combine all
studies available, are less than a 100. And you might gather that I have
a suspicion of anything under 1000.

CHAIRPERSON KAWAS: Dr. Temple?

DR. TEMPLE: This for Dr. Hughes.

Would you distinguish at all between what one might call anatomic
surrogates and functional surrogates? One intuitively feels that if you
really have a good view of the anatomy, if that seems better. But I just
wondered if you had any comments on that.

And then the other question I had \-- or it\'s really a comment \-- that
surrogates fail for two potential reasons which are fundamentally
different. One is that you were wrong about the relationship and the
other is that the drug did something bad in addition to whatever the
good thing was. I\'ve always thought encaidide and flecainide reflected
the latter. They\'re obviously drug were obviously pro-arrhythmic, so
whatever good they might have done was overwhelmed by the fact that they
were lethal. I wondered if you wanted to comment on that.

DR. HUGHES: In terms of the first question that you raised, I don\'t
think there\'s a fundamental difference between how you would approach
the validation of surrogate depending upon whether the type of measure
that it involves.

I think what that may effect is the type of study that you do to
understand the basic science, the clinical rationale for the surrogate.
But in terms of the types of empirical evidence that you would collect
to validate the surrogate, I don\'t think you should have any effect.

CHAIRPERSON KAWAS: Dr. Van Belle?

DR. VAN BELLE: One comment to Dr. Hughes. I tend to agree with your
remarks, and I think you\'ll see that later on. I think the correlation
with the clinical entity is a necessary condition, but not sufficient.
And I think we\'ll get into the sufficiency arguments later on.

I have a question to Dr. De Carli in terms of the measurement error of
the total brain volume. From your graph you had in your presentations, I
tend to see regression towards the mean. In other words, that people
with very high brain volume initially tended to decrease and those with
very low brain volume tended to increase a little bit. Can you give me
some idea as to what the measurement error is in this particular case?
Thank you.

DR. De CARLI: Yes. I think that will be discussed in detail by some of
my other colleagues. But in this very simple separate analysis, and this
wasn\'t very sophisticated, the inter-class correlation for different
raters on repeated measures is less than one percent. But that\'s a
substantial amount when you\'re talking about a brain volume that\'s
1200 cc\'s.

But in a population, I remind you. In a population these effects can be
seen quite easily.

CHAIRPERSON KAWAS: Dr. Fogel?

DR. FOGEL: Yes. This question is for Dr. Hughes.

I wanted to find out in the framework of the surrogate, how is side
effects, for lack of a better term, factored into all of this in terms
of true clinical outcome? And what I mean by that is, say, you have an
anti-hypertensive drug and the endpoint, if you will, is decrease in
cardiovascular disease, yet this anti-hypertensive at the same time
causes depression. Do you consider that a successful surrogate or a
failed surrogate?

DR. HUGHES: I think the cholesterol-lowering examples I gave were a good
example of that. If you looked at the cardiovascular-specific mortality,
then you saw beneficial effects in both trials. When you looked at
all-cause mortality, you see no effect or an adverse effect. So that
suggests that there are adverse mechanisms of action.

My own feeling is that one big issue is what is the true clinical
outcome in drug evaluation. And that may be particularly hard in
Alzheimer\'s Disease to think about. And there may be adverse effects on
that true clinical outcome that could be due to the intervention. And in
that setting clearly any validation of the surrogate endpoint will
capture the potential for adverse effects. But I think there is always
the possibility that there will be significant adverse effects which
aren\'t captured within the true clinical outcome. And I think their
standard approaches is for evaluating those adverse effects needs to go
in parallel with approaches for evaluating the effects of the
intervention of the surrogate endpoint.

DR. FOGEL: Well, I guess my question really is, though, that if the
study is designed to decrease cardiovascular mortality, say, and instead
the study finds that there was a decrease in cardiovascular mortality
but there wasn\'t any change in all-cause mortality, how does one expect
a surrogate endpoint to actually take into account all-cause mortality
when, specifically, it\'s designed for a decrease in cardiovascular
mortality. In other words, it seems that true clinical outcome in this
framework is lumping everything. You know, how does the patient
ultimately do in everything when the clinical trial is really just
designed to decrease \-- and I\'m just using cardiovascular mortality as
an example \-- cardiovascular mortality and that\'s what the surrogate
is basically being designed for, picked for, used for. But yet in this
framework it\'s being considered as a failed surrogate even though it
was not really designed to do all-cause?

DR. HUGHES: Well, I guess my opinion is that when you\'re trying to
design a surrogate endpoint you should focus it on the very specific
true clinical outcome that you\'re interested in, so the disease
specific outcome. And only if you\'re very uncertain about what the true
clinical outcome should be should you consider broader classes of true
clinical outcome.

So in the cardiovascular setting, if you\'re sufficiently uncertain
about the potential mechanisms of action of interventions that you might
be evaluating, then it might be important to understand how a surrogate
fits in with a broader class of true clinical outcomes, including
all-cause mortality. But I think the primary goal should really be to
pick a surrogate which is validated in the context of the clinical
outcomes, which are disease specific and leave the adverse effects of
drugs as a separate issue which is routinely evaluated in clinical
trials.

DR. FOGEL: So the cholesterol example then given that definition would
have been a successful surrogate because it decreased cardiovascular but
was neutral on everything else?

DR. HUGHES: Yes. And I think the reason those large trials were done was
there was sufficient uncertainty about how those drugs worked that
all-cause mortality was thought to be a more appropriate outcome
measure. And, in fact, all-cause mortality is simpler to measure and the
effect to the cardiovascular mortality would be the very dominant
component of all-cause mortality.

DR. FOGEL: Thank you.

DR. VAN BELLE: I have a question for Dr. Katz. We\'ve defined surrogate,
but we haven\'t really defined clinical outcome as to what is desirable.

Does the FDA have, kind of, a catalog of what constitutes clinical
outcomes so that it can know what constitutes the clinical outcome? And
the second part of that question is do surrogates sometimes become
clinical outcomes?

DR. KATZ: Well, there is no catalog of clinical outcomes. But I think as
generally understood, it\'s a measure of direct patient either
functioning or subjective sensation of their symptoms, depending upon
what the condition is that you\'re treating or some objective measure
directly of how the patient is doing.

As someone pointed out, it\'s a measure that\'s relevant to the patient,
him or herself, as opposed to a laboratory measure. Blood pressure is of
no relevance to a patient in terms of how they feel or how they\'re
functioning unless it\'s very, very low or very, very high.

And the second part was, do surrogates every become clinical outcomes?
What are you thinking of specifically?

DR. VAN BELLE: Well, I was thinking on the context of, you know, blood
pressure, where certainly action is taken to lower blood pressure and
clinical action is taken just on the basis of blood pressure readings.

DR. KATZ: You mean in terms of practice? Well, sure obviously sometimes
clinical interventions are employed entirely on the basis of laboratory
\-- drugs are stopped because somebody\'s liver functions are elevated.
So in that sense, I suppose. But for the purposes of a clinical trial,
clinical outcomes I would say are what we typically would use to assess
the drug\'s effect. Clinical outcomes of the sort I defined earlier.

CHAIRPERSON KAWAS: Dr. Temple?

DR. TEMPLE: But the surrogate never becomes the clinical outcome. We use
it freely and comfortably, but it\'s never the clinical outcome for, if
no other reason, that you can never know that the drug doesn\'t have
some unpleasant side effect that you were not able to anticipate.

So if you do a blood pressure trial on 200 people, that doesn\'t tell
you about a risk of one in a 1,000 of something nasty. So it can never
be perfect. We use them, because we don\'t think those effects are
likely.

I guess I want to make one other observation that clinical benefit is
not free of the same risks. One of the great examples sometimes given of
surrogate failures is the failure of several classes of drugs to treat
heart failure to predict favorable outcome. In fact, none of those drugs
were considered useful potentially because of their surrogate effects.
They all improved symptoms of heart failure, but they were nonetheless
lethal because they did something else. And the same problem exists
whenever you use a surrogate; you just have to collect enough data to
reassure yourself on that point. But the surrogate never measures the
other things the drug might do. It can\'t. Because you\'re not looking
at those.

CHAIRPERSON KAWAS: Thank you.

Dr. Wolinsky and Dr. Sorensen, the last two questions.

DR. WOLINSKY: So I don\'t know if this should be directed to Dr. Hughes
or Dr. Temple or Dr. Katz. But if the surrogate is basically to help
facilitate getting answers more quickly that should be predictive of the
clinical outcome, how do we feel comfortable about the safety issues if
the trials are shortened by the effects on the surrogate?

DR. KATZ: Well, that\'s a real question. Were always \-- for drugs to
be given chronic \--

DR. WOLINSKY: I wanted a real answer.

DR. KATZ: That, too, was a fair question.

Well, certainly for drugs to be giving chronically we are interested in
knowing what the long term effects are, the adverse effects of chronic
treatment. And typically we would want that. Now, again, it\'s possible
depending upon the nature of the treatment or the proposed indication
and the importance of it, it\'s possible a drug could be approved
without very much long term adverse event data.

On the other hand, adverse event data can be obtained \-- often is
obtained in the long term in uncontrolled settings, which is easier and
sort of quicker to do. So there are ways to get that data. Your point is
well taken that if we have to do long term studies to get safety data,
why wouldn\'t we want to do the long term effectiveness studies as well
and look at the actual clinical outcome. But there are mechanisms if you
really thought it was important enough to get this drug out there right
away, we could have a minimal amount of long term effectiveness data
with, perhaps, requirements in Phase IV to get more. And, again, you can
get them in settings that are less onerous than long term controlled
trials.

DR. WOLINSKY: Do we have good examples of rigorous Phase IV requirements
for safety, as opposed to appropriate recognition by clinicians in the
field of events that seem to be occurring at too high a frequency and
that type of surveillance?

CHAIRPERSON KAWAS: Dr. Temple?

DR. TEMPLE: Well, sure, there are some wonderful examples.
Cholesterol-lowering drugs, as you just saw, the early ones had great
difficulty showing any benefit, perhaps because of a fluke or perhaps
because they did something bad. But the statens, all of which were
approved on the basis of lowering of cholesterol have, at least in four
out of six cases, shown unequivocal major benefit in long term studies
without much evidence of a downside that was sufficient to outweigh
that. So those are really unequivocal.

There\'s also a massive amount of data on various blood pressure drugs.

I just want to observe that sometimes you can get your safety
information from other settings. So, for example as people have pointed
out, there wasn\'t, maybe until recently, any study that showed that ACE
inhibitors were actually good for you when you took them to lower your
blood pressure. On the other hand, there\'s probably a couple of hundred
thousand people randomized to trials in heart failure and other
conditions. And in all of those there didn\'t seem to be any harm. So
that might reassure you that when you use them to treat blood pressure,
you shouldn\'t worry too much.

So a lot depends on what else you know about the drug from, perhaps,
other sources.

DR. WOLINSKY: I think the question was actually a little bit more
pointed, and that is, I think the studies that you\'ve mentioned, which
are very remarkable studies, were not necessarily Phase IV requirements.

DR. TEMPLE: They were Phase IV agreements in many cases, and it was
before there could be requirements. And there\'s some question about
whether outside of accelerated approval you can have requirements. But
often people have been interested in doing it. So the big cholesterol
studies were done by companies that had agreed to try to do them.

DR. KATZ: There are also examples of drugs which have been approved on
the base of relatively small safety samples, but for which there are
registries in place in which post-marketing through which or in which
additional safety information can be gotten for periods of time. So
there are examples.

CHAIRPERSON KAWAS: Dr. Sorensen, and then we\'ll move on.

DR. SORENSEN: Yes, I have a question for Dr. Hughes. Dr. Hughes, I think
you made the point that if you are looking at unknown, i.e, a surrogate,
there\'s more risk than a known and presumably there\'s some potential
opportunity for benefit or the regulations wouldn\'t have been modified
to allow for that.

My question for you is are there any biostatistical tools to put error
bars around the sizes of those risks? In other words, is there some way
we can get a handle as we listen to these presentations about how good
these surrogates are and to get a sense for the goodness or the badness
of that and what impact that may have on potential benefits or lack of
benefit if we were to use that surrogate?

DR. HUGHES: I guess I\'m going to talk about later about validating
surrogates, but one of the things that you can get out of those
validation procedures is some concept of how reliable the surrogate is,
at least in the previous studies that have been done. I think the big
unknown is whether the future study, future intervention fits in well
with the previous interventions that you\'ve studied.

DR. SORENSEN: It seemed like in the briefing material there some
discussion about statistical tools to actually adjust for the difference
between the surrogates you\'re using and the known outcome. And I don\'t
want a biostatistical lecture, I\'m just trying to figure out if there
are some accepted tools that you might guide us through, maybe in your
talk about that.

DR. HUGHES: Well, certainly you can estimate various measures which
capture surrogacy and you can put confidence intervals on those and you
can use those as a guide to how reliable the surrogate is.

CHAIRPERSON KAWAS: Thank you.

We now have a block of speakers who will be talking about volumetric MRI
and related subjects. And for the first one, Dr. Clifford Jack.

DR. JACK: First, thank you for inviting me to speak. I\'m going to talk
about structural MRI as a biomarker of disease progression.

And I\'d like to begin by returning to a point that was raised initially
by Dr. Katz, and that\'s that it\'s straightforward, but it\'s important
to keep in mind the distinction between validating a marker of
therapeutic efficacy versus validating a marker of disease progression.
In the absence of a positive disease-modifying therapeutic trial, I
don\'t think anyone can come up with evidence validating the efficacy of
an imaging marker of therapeutic efficacy. On the other hand, we can
muster evidence for imaging markers as measures of disease progression.
And most of us in the imaging world, I think, are comfortable with the
idea that indirect measures of disease progression can be validated,
provided that there is a plausible biologic link between change in the
marker and progression of the disease itself and, if enough empirical
studies, independent studies are provided that produce a common result,
i.e., the measured tracks with disease progression.

And what I\'m going to do here in the next 15 minutes is to present four
different studies that I do think provide supporting evidence MRI
markers as reasonable markers of disease progression, the first of which
was published a number of years now. The objectives of this study were
very simple, and that was to measure the annualized rates of volume
change of the hippocampus and temporal horn from serial MRI studies in
cognitively normal elderly subjects and people with Alzheimer\'s Disease
and then to test the hypothesis that the rates were different.

Here are the two structures that we measured. The hippocampus and the
temporal horn. The end was small in this initial study, but patients and
controls were individually matched on age, sex and education, so those
variables should not confound the results.

And here were the results. The annualized rate of atrophy of the
hippocampus was 1.6 percent per year in normal controls, and in cases it
was greater than twice that. The annualized rate of the expansion of the
temporal horn was 6.2 percent in controls and in cases more than twice
that. These numbers are negative, reflecting the shrinkage of the brain;
these numbers are positive representing expansion of the brain \--
expansion of the CSF spaces.

So our conclusion at this point was that this was a reasonable first
step in that we did observe the expected differences in rates between
patients and controls, but it didn\'t prove that, at a more fundamental
level, that changes in imaging tracked or matched changes in clinical
status in these patients.

And that was the topic of this next study, which was to test the
hypothesis that a change on imaging, i.e., in this case rates of change
of hippocampal atrophy over time from serial MRI matched clinical
change. And we used the clinical transition or lack thereof as the gold
standard measure of clinical progression.

And I realize now that I should have put a slide in here describing mild
cognitive impairment. For those of you who aren\'t familiar with the
concept of MCI or mild cognitive impairment, it is an intermediate stage
between cognitive normality and Alzheimer\'s Disease and most are all
patients who eventually develop Alzheimer\'s Disease will go through a
phase of mild cognitive impairment nearly always in memory alone or
memory-isolated type impairment. And we can use, and others have used,
this transition type analysis from normality to the category of mild
cognitive impairment and on to Alzheimer\'s Disease as clinical measures
of disease progression. It eliminates the reliance on a signal cognitive
measure which, as I\'ll show later on, those can go up or down. In this
study we recruited a 129 subjects from our ADRC and ADPR grants which
met criteria at baseline for either normal controls, mild cognitive
impairment or Alzheimer\'s Disease. The controls and MCI patients could
either remain cognitive stable or could decline. And this creates five
clinical groups: individuals who are normal at baseline and who remain
stable; individuals who are normal at baseline but then who decline to
MCI or AD; MCI patients at baseline who are stable or those who decline
to AD.

And one can see that the age and the MMSE scores for each of these key
parallelized comparisons, normal stable versus normal decliner, were
equivalent. Same thing for MCI stable, MCI decliner; age at baseline and
MMSE score were equivalent. So, again, these should not serve as
confounding variables.

And here were the results. One can see that the annualized rate of
hippocampal atrophy of normals who declined to either MCI or AD was
substantially greater than that of normals who remained stable. The
annualized rate of atrophy for MCIs who declined was substantially
greater than that of MCIs who remained stable. And this rate was very
similar to patients who started out with Alzheimer\'s Disease.

So the conclusion from this study was that the rates of hippocampal
atrophy did indeed match the change in cognitive status over time. And
we took this as some measure of validation of the change of MRI volume
as a marker of disease progression.

A next question, one might ask, is what about different techniques or
different rate \-- different brain measures. The question addressed in
this study was, are some techniques better measures than others of
disease progression and is there stage specificity. So the objective of
this study then was to compare the annualized rates of atrophy by
technique, and I\'ll describe different techniques, among six different
groups this time: normal-stable, normal-converter, MCI-stable,
MCI-converter and then AD-slow progressor, versus AD-fast progressor.
This is defined on the annualized rate of change and the Mini-Mental
score.

We measured four structures: hippocampus, entorhinal cortex, whole brain
and ventricle. I\'ll skip over these for the sake of time.

Because this data \-- we were warned not to show anything that hasn\'t
been published yet, so some of the numbers here have been blanked out in
this slide. But, what can you do.

If you look at these comparisons here, normal stable versus normal
converter, and these are the four different measures of interest: whole
brain, ventricle, hippocampus and entorhinal cortex. You can see that
among normal converters the rate of change, annualized rates of change,
are greater than those in the normal-stables for each one of these four
measures.

Come down to this parallelized comparison, MCI-stable versus
MCI-converter; again the annualized rates of atrophy for the converters
are greater for each of the measures and then AD-slow progressor versus
AD-fast progressor, these same results.

A reasonable question to ask then is do some of these measures perform
better than others and is there some stage-specific sensitivity. And to
address this question we use this metric, which is the difference in the
mean rates between this group versus this group, for example, divided by
the pool of variance. And if we then look at these four different
parallelized comparison in rates with respect to the different measures,
one can see that these three do perform better than this one for this
stage, i.e., normal-stable versus normal-converter distinction.

For MCI, again there seems to be a clear winner, and that\'s the
hippocampal measurement. For AD-slow progressor versus fast-progressor
and normal-stable versus AD-fast progressor this measure seems to be the
best performer.

So from this we conclude again that structural MRI rates do seem to
consistently follow expected correlations with clinical transition. And
there does appear to be some stage specificity or difference in the
sensitivity of these different measurements at different stages of the
disease.

The last study I\'ll describe was a multi-site study. The first three
studies I described were all derived from a single site. Any sort of a
clinical trial, however, will be run via a multi-site approach. And so
one can reasonably ask the question, can you get data that makes sense
from multi-sites. And the data that I\'ll describe here was based on the
Milamilene study.

The Milamilene study was originally designed as a 52 week controlled
trial of the this muscarinic receptor agonist. The therapeutic arm of
the trial itself, however, was not completed due to a projected lack of
efficacy on on interim analysis, but the MRI arm of the study was
allowed to continue.

A total of 192 subjects from 38 different centers then ultimately
underwent two different MRI studies separated by one year and we
measured hippocampal and temporal horn volume rates.

This kind of study generates a lot of data, and I will only show 2
slides of actual data. These are the actual change data in five
different measures. The ADAS-Cog was the primary outcome measure. MMSE
and GDS were clinical/behavior ancillary measures. And then these are
the imaging measures that were also used as ancillary measures in this
study.

These are the annual raw change for each of these measures in their
appropriate units, annual percent change and then this column is,
perhaps, the one that\'s the most interest. So this is the proportion of
the group that wound up declining on the measure.

Now, again, all these people had mild to moderate Alzheimer\'s Disease,
and so theoretically everyone of them should have declined because the
disease was indeed progressing over this year period in every individual
in the study. But you can see that the proportion of individuals who
actually declined on the measure, it was only about two-thirds of the
subjects on these measures, particularly this one which is the measure
that\'s used in Alzheimer\'s trial.

Contrast that then with the imaging measures where decline was much more
consistently seen, particularly with the hippocampus where essentially
all people declined. An improvement in performance theoretically
represents or can only represent an error in the measure.

If one does power calculations based on a 50 percent effect size, i.e.,
a 50 percent rate reduction over one year using these data, these are
the data that we got. You can see that the estimated sample size
requirements are substantially greater for the cognitive measures than
they are for the imaging measures. And this is entirely due to the much
greater variance in the clinical/behavioral measures versus the imaging
measures.

So from this study we then concluded that the technical feasibility of
doing a multi-site trial with structural MRI atrophy rate measures was
documented. It was validated.

The decline over time was much more consistently seen with imaging than
with behavioral measures. And finally, due to much greater variance in
rates for behavioral measures versus imaging measures, the sample sizes
required were substantially greater for the behavioral cognitive
measures.

This is the last slide I\'ll show, then, just to conclude by returning
to the original comment that I made. And that is that in the absence of
a positive therapeutic trial, a true disease-modifying trial that
incorporated imaging, the best available evidence that we can muster
supporting the validity of MRI as a biomarker of progression is multiple
natural history studies that consistently demonstrate concordant MRI and
clinical change.

I\'d like to acknowledge the Aging Institute for ongoing support of our
program at Mayo, the members of our ADRC and ADPR grants, particularly
Ron Petersen, my long time colleague and collaborator who was the
principal investigator of both of these grants. These three individuals
of Parke-Davis that allowed the MRI portion of the Milamilene study to
continue, even after the therapeutic trial itself was stopped. And these
individuals from my own laboratory.

Thank you.

CHAIRPERSON KAWAS: Dr. Jack, just for clarification, could I ask a quick
question? What\'s the definition of decline on the ADAS-Cog? If
two-thirds declined, do you mean two-thirds declined one point or more
or two-thirds declined 4 points or more?

DR. JACK: Any decline.

CHAIRPERSON KAWAS: One point was enough?

DR. JACK: Yes.

CHAIRPERSON KAWAS: So only two out of three people in that trial even
declined one point on the ADAS-Cog in 52 weeks?

DR. JACK: One or more. So that statistic just represents a positive
change.

CHAIRPERSON KAWAS: Thanks.

Our next speaker on the series is Dr. Cecil Charles. Oh, I\'m sorry. Dr.
Nick Fox. My apologies.

DR. FOX: Thank you very much. I think it\'s a great honor to be invited
over here, and I do apologize if anyone has any trouble understanding my
accent.

I\'m talking about rates of atrophy and in particular and try and follow
on from some of the other speakers.

Just to give an overview of what I\'m going to talk about, I\'m going to
talk about \-- address the relationship of atrophy rates, the
pathological and clinical progression in treated patients; it\'s the
natural history points that Clifford Jack just made. I\'m going to
address the issue of disease modification versus symptomatic effect and
then try and move on to the crucial question of whether or not it\'s
reasonably likely that atrophy rate change would project clinical
benefit in treated patients. And that is very much related to this final
point, is that the possibility that atrophy rates might be uncoupled
from clinical benefit and how one might protect against that possibility
if you might do so.

Okay. Just going back to the pathology for a moment, Alzheimer\'s
Disease is characterized by the accumulation of tangles, plaques,
synapse loss, dendritic pruning and cell loss and atrophy. And that is
an inexorable, inevitable, characteristic, defining feature of the
disease. And the relationship between atrophy and cell loss has been
documented by several people, not my own work, where one has looked at a
loss of hippocampal neurons and regional atrophy in the hippocampus in
other studies.

Now, what can volumetric MRI address of these, I would argue, core
components of pathology? Well, it can look at atrophy rates, and I\'ll
talk about that.

First of all, what can we understand about the progression of the
disease? Well, what I show here in this first panel is an individual who
has just presented with Alzheimer\'s Disease, clinical probable AD, so
mildly effected. Then there\'s a further MRI scan 18 months later, and
then a third another 18 months later. So a three year interval here. And
what I\'d like to point out, which is really just a pictorial
description of Clifford Jack has just shown, which is this devastating
loss of volume within the hippocampus here. But I\'d also like to point
out that what we see, if you look at the ventricular enlargement here,
the sulcal enlargement, the Sylvian fissures here, that the disease is a
region-specific progression from entorhinal cortexes on to hippocampus
and on to new cortical areas but this process is well established even
when people present to us in the clinic.

The technique which some of the results I\'m now going to show, I\'m not
going to go into the methodological details it relies upon.
Registration, that is positional matching taking a first brain scan, and
super imposing a second brain scan very precisely upon it so you in
effect fuse the two sets of data in the same spacial framework. You can
then automatically subtract those images, produce different images and
create a direct measure of change in volume from those different images.

And just to show that descriptively here, the first scan here, somebody
with mild cognitive impairment. And then what you see there is the
progression over one year. And just go back for a moment, you can see
the ventricular enlargement, the hippocampal loss.

Now, in Alzheimer\'s Disease, again, we can actually visualize the
change. This is addressing the point about whether or not there is a
signal there, is there something that we could measure. I think the
answer is yes both in a region and a global way.

What does this translate to? This is, again, these are old published
data. Now this is looking at early onset Alzheimer\'s Disease showing
the rate of whole brain atrophy in age-matched controls with early
onset. Alzheimer\'s Disease showing a very significant difference
that\'s associated with the disease in terms of rate of brain volume
loss.

Now, I\'ve just struck these slides in while I was waiting, so I\'m
sorry they\'re not in your handout. But to address one of the questions
that was raised about the precision of measurements, this is a test in
reproducibility using this test. It\'s a real test in that it\'s
scan/rescan. So individuals had a first scan, then a year later they had
two scans on the same day. And we looked at what the rate of whole brain
atrophy going from A to B was when compared to A to C, which would
expect to be very similar.

From that you can see there\'s a good correlation, and one can get a
measure of the error in that measurement. And we have unpublished data,
which is why it can\'t be shown here, with larger numbers essentially
showing the same thing; which is remember that the whole brain volume is
about 1200 cc\'s, typically in these patients. This is a .1 to .2
percent error.

Does that correlate with clinical decline? Well, yes it does. Shown
here, rate of brain loss against rate of many mental state examination
scores.

Are these changes consistent over time? Well, this is a group that have
been prospectively followed in a naturalistic way. And this is an
ongoing study with multiple short interval scans.

This is looking at some of the 6 month data showing 2.2 percent in the
Alzheimer\'s group with a standard deviation of 1.4 About .5 plus minus
.8 in the controls. That\'s over 6 months.

The same individuals at one year show a very similar rate, but the
variance is coming down, addressing this issue of feasibility and
measurement of clinical meaningful change.

Now, if we turn to the individual we can show that within the individual
that the measurements are sensitive enough to track change within an
individual and that the changes in normal aging are very different to
that seen in Alzheimer\'s Disease. This is percentage of brain relative
to the initial scan.

Does it predict clinical outcome? Well, one group that we looked at was
with individuals with a family history of Alzheimer\'s Disease. So these
are individuals at risk by virtue by either a known single gene mutation
or strong family history.

This is the normal aging change, which fits with my colleague\'s data,
which he showed a moment ago, which is that whether its physiological
and measurement error, there\'s quite a spread in the changes with just
two time points in terms of rate of atrophy. And this is all based upon
two scans.

I\'d like you to look at this middle column here. These are individuals
column here. These are individuals who are risk, again who just had two
scans, and then who had been followed for 3 or 4 years following the
second of those scans. Those individuals who remained well over that
time period had these rates of atrophy and those who became clinically
effected in the follow-up period a significantly greater rate of
atrophy.

Now, I\'d like you to look at this lowest point here, which I will now
show you in more detail what then happened to that individual. So this
is her serial imaging from 1993 to 1997 registered. In red you\'ll see
the progressive loss of brain tissue or signal on the scans. So the
early rate of atrophy that you saw in that shot was related to this
pre-symptomatic period here, which then slowly accelerates. But I think
what this shows it gives you some sort of measure of some of the errors
within the measurement, but also the inexorable nature of the decline.

That is a regional specific effect, and I won\'t go into the details,
but the bottom pictures here are based on a technique called fluid
registration. And in green you\'ll see areas which have the highest rate
of atrophy on a local basis. So this is that same individual showing the
progression from pre-symptomatic change here effecting the hippocampus,
becoming more profound in the hippocampus, and then becoming more
widespread by the time they\'re clinically effected.

So, just to summarize what I said so far. I think that pathological
evidence shows that atrophy progression untreated AD inexorable
correlates with cell loss. I think MR- based measures are reliable and
sensitive to change, at least at the clinically meaningful level.

Now to address sort of what the meat of what we\'re discussing here. We
can say that rates of cerebral atrophy both from the previous speakers
and my data, I think, are increased in Alzheimer\'s Disease. They do
predict conversion to Alzheimer\'s Disease either from MCI or from
familial cases at risk. Those rates of atrophy correlate with clinical
decline. And I\'d like to suggest that it\'s biologically plausible that
regional specific atrophy reflects pathological and clinical
progression.

Is this a disease-modification effect or a symptomatic benefit? Well, I
think that the issues relating to staggered start or staggered
withdrawal are similar. Is benefit sustained; are all disease effects
modified? And one of the defining features of that is whether or not
you\'re on the causal path, as it\'s been described, which is probably
related to whether you\'re near the causal end of the process as well.

So I think that we suggest that these measures are feasible, they\'re
sensitive, they\'re clinically meaningful arcing and changes seem to be
correlated. But can change in one happen without change in the other?
The issue of a change in the surrogate is both a necessary and a
sufficient condition for the clinical outcome.

Well, can one put forward a model that suggests that that seems
reasonably likely? Well, it would seem to me the destruction of our
neural networks is very closely related to cognitive decline and death.
And that pathological process acts through that destruction.

But could volume change and thereby atrophy rates occur without that
neuronal construction? Well, yes, it could. Because neurons are not the
sole determinative of cerebral volume. Inflammation, hydration, osmotic
effects, protein deposition can all change volume without changing the
number or size of neurons.

For example, we\'ve shown that hemodialysis can be associated with a
three percent cerebral volume change over one day. So that\'s a worry. I
would say, therefore, that neuronal changes are neither necessary nor
sufficient to produce volume change. However, I think progressive volume
loss is more likely, more reasonably likely to be related to progression
neuronal loss. So I would suggest one would require two or more imaging
time points, perhaps many more, including if possible scans of
treatment.

So I\'d like to suggest that it\'s reasonably likely that a measure of
slowed neuronal loss would predict clinical outcome. In fact, my whole
model of how the pathology works in this disease is that if we could
slow loss of brain cells, we would slow the clinical outcome, and that
slowing would constitute disease modification.

I think it is reasonably likely that a slowed rate of neuronal loss
would result in reduced atrophy rates or, the converse, that reduced
atrophy rates in a properly constructed design would probably or
reasonably reflect a slowed rate of neuronal loss. And if that reduction
in atrophy rate followed both the region and time related pattern of
pathology, so not just two scans which could be a purely a drug presence
effect, but several scans in looking both at regional and global
changes, then it would be reasonable to conclude that the clinical
outcome is likely to be improved.

So, that\'s just a summary. Atrophy rates correlate. Causality is
plausible and it may be reasonable again with appropriate study design,
to suggest this predictive power. However, disease modifying drugs are
required to strengthen that link.

Thank you.

CHAIRPERSON KAWAS: Thank you, Dr. Fox.

Now I think our next speaker is Dr. Cecil Charles.

DR. CHARLES: This talk could be called the devil\'s in the details talk.
I\'m not going to show you any images, but talk a little bit about some
of the issues if you\'re going to use imaging.

I\'d like to also thank Dr. Mani for inviting me.

Basically, one of the things that you\'ve already heard some information
and you\'re going to hear other talks talking about imaging, and what
we\'re talking about here is quantitative imaging. And the first thing I
want to do is really kind of talk a little bit about what that means,
particularly in the context of quantitative imaging as opposed to what
we most normally think of as clinical imaging on some of the issues
related to the imaging protocols, issues of how you monitor these things
in the multi-center trial and issues of analysis. And in the information
that was sent out there was some information on how one might cross
validate different analysis techniques.

There\'s sort of an interesting question that arises as we\'ve tried to
do this, is what is quantitative imaging and how is it different from
what\'s done everyday by radiologists in the clinical field? Well, if
you think about clinical imaging, most clinical imaging protocols are
set up to visualize a disease, visualize a lesion or detect it, and it
almost always has a radiologic interpretation. A radiologist looks at
those images either on film or on a computer and has an output which is
generally words to rule in or rule out this diagnosis. And certainly in
this country they\'re going to have a primary, secondary, and tertiary
diagnosis to make sure everything is covered.

What you\'ve been hearing here then is something a little bit different,
and that\'s in quantitative imaging we\'re going to try to extract
tissue characteristics of some kind from some imaging parameter, whether
it\'s MRI, MRS, PET, whatever. And we\'re going to take that information
and using some kind of algorithm, you\'ve already seen some examples of
how information goes from images back to numbers, and we\'re going to
use this algorithm to basically extract numbers.

And the reason we want to extract numbers is because we want to be able
to incorporate it in hypothesis testing. So it\'s the difference between
looking at diagnosis and looking at effect monitoring.

Why does this matter? Well, it matters because the people that build
these imaging devices, basically build them for this stuff over here.
And this is something that\'s not really set up in the initial
specifications or the design criteria for these kinds of systems. And it
requires a little extra care if you\'re going to use them for
quantitative imaging.

Is there a use for clinical imaging in trials? Well, often times in many
of these trials there will be imagings particularly for Alzheimer\'s
Disease to rule out criteria, so there may be an inclusion or exclusion
criteria. And that screening scan may not be done by a quantitative
imaging protocol. So, subsequent imaging sessions are really going to
have to be quantitative.

So what are the things that you can do with quantitative imaging? Well,
you\'ve heard about a lot of them, and one of the things that\'s nice
about these techniques, especially magnetic resonance, is there are a
lot of things that we can test. It\'s kind of the Swiss Army knife of
imaging in that sense.

If we think about quantitative imaging, though, one of the first things
we have to deal with is study protocol design. We\'re now trying to
extract quantitative information so the way that we set up our scan
protocol is going to be intrinsically different. Data quality, as has
already been alluded to in these studies, Dr. Jack talked about the
issues of doing this in multi-center trials, data quality is an issue.
If the scan quality is not very good, then you\'re not going to be able
to analyze the data.

There\'s a lot of practical issues that I won\'t dwell on very long;
data format issues, how data is cleaned rigorously and prospectively
with criteria that are stated at the beginning of the study. Dr. Fox has
already talked about data registration in serial studies. Fortunately we
don\'t have to force people\'s heads into particular positions because
the computer can take care of that after the fact. And there are many
kinds of analytical protocols that can be used. You\'ve already seen
some examples of both tracing techniques, of boundary shift techniques,
fluid mechanical registration, and there are a lot of them for
extracting these quantitative information and ultimately the data\'s got
to archived, which is certainly something that\'s becoming more
interesting.

When we set out to look at these kinds of quantitative studies we try to
work from the endpoint and say if we know how we\'re going to analyze
the data, what can we do in defining the protocol to make it easier for
the analysis algorithm, especially with magnetic resonance because we
can change a lot of parameters to change how the image appears and
minimize certain kinds of artifacts.

We really also want to maximize this sort \-- it should go without
saying, but it doesn\'t always \-- we want to maximize the amount of
information content per unit time. We use in our lab a deterministic
figure of merit where we simply look at the contract to noise ratio per
unit resolution unit time when we\'re comparing different scan
protocols. And the goal is to increase that figure of merit. And then
you use that as, in fact, as a mechanism for testing the variance in
your analysis algorithm. Because at the end of the day we can\'t change
the effect size of a treatment, but we can work to decrease the variance
both in the acquisition of the data and the analysis of the data.

And, of course, because we\'re working with human subjects, patient
comfort and compliance is an issue. If they don\'t come back for the
second scan, then it really doesn\'t work very well.

It\'s already been suggested that you can do this across multiple sites.
Dr. Jack has certainly done a study. If you\'re going to do this across
sites, there\'s a lot of issues in imaging protocol cross-validation.
You may work on different scan protocols, different scanning systems,
you may work with different manufacturers. And even within manufacturers
because there\'s different software and hardware platforms, you\'re
going to have to rationalize the nomenclature so that when you\'re
talking to a technologist running on brand S or brand G or brand P, and
all the other brands, you\'re saying the right thing to the right person
or they won\'t actually know what you\'re talking about.

Your site training needs to be uniform and uniform in the sense that,
again, it has to be tailored to the particular manufacturer. Some people
call a technique one name and other manufacturers call it a different
name, even though it\'s the same physics.

Retrain with upgrades. In long trials people are continually upgrading
these scanners and essentially watching this data as it\'s coming in in
real time to make sure that the protocols are being adhered to and that
the scan quality is good.

Quality assessment\'s got to be quantitative, signal to noise ratio.
Looking at issues like motion artifacts, which are very problematic in
elderly patients, we use quantitative criteria, clutter to noise, to
make that decision. And then these are cut off points for rejection
based on the analysis algorithm. Some algorithms are more or less
sensitive to artifacts. And that\'s something, again, you can define
prospectively.

Protocol adherence, the obvious kinds of things.

And then system performance, and this gets back to the comment I made
earlier that these things are designed to do clinical imaging and issues
of spatial fidelity are a significant issue if you\'re going to measure
the kind of brain volume changes that Dr. Fox and Dr. Jack have talked
about, and you\'ll hear more from other speakers. If you\'re looking at
clinical images and the field of view is 23 centimeters or 24
centimeters, it\'s not going to have a significant impact on a
radiologist\'s interpretation of diagnoses or detection of disease. If
you\'re trying to measure hippocampal volume, on the other hand, it\'s a
serious issue. So adherence to spatial fidelity is something that has to
be looked at and it\'s not something that the manufacturers currently
address at the level that we need in this kind of imaging.

So, incoming data formats. That\'s changing, it\'s getting better with
DICOM formats, but there\'s still a lot of issue with varying media
formats. You can\'t assume that everything\'s just going to be easily
readable when it comes to your lab. There\'s all kinds of proprietary
formats, and then there\'s also a lot of local formats, noncommercial
PAC systems that have been developed. And these are more an irritation
for anyone trying to deal with this data centrally, but it is one of the
things that you have to deal with.

Data storage format, DICOM is really a data transmission standard. It\'s
unfortunately becoming a file format standard and it looks like any file
format standard that was developed in the \'80s and ultimately,
hopefully will improve it a bit. But there are alternate standards that
different labs use, and all kinds of standards that are out there. So
there are many ways to store the data.

Data cleaning really has to be done with some prospective criteria of
QC. You\'d like to get rescans, if possible. If a patient moves and you
get that data, if you can get them back in in a certain time window,
because you want to minimize, again, lost data.

The data rejection has got to be on some quantitative basis, not just
oh, this data looks bad. The clutter to noise exceeded a certain level,
the signal to noise was not high enough and that\'s why you reject the
data. And you notify the site and try to get the person back.

I\'m not going to speak too much about data registration. Dr. Fox has
already addressed it. You want to minimize positioning errors in the
protocol. We always use immobilizers to minimize patient motion and to
make it comfortable for the patient to hold still. Not trying to put
them in any specific orientation, but letting them find a comfortable
position and use an immobilizer to try to keep their head there, and
that tends to work very successfully. And as Dr. Fox pointed out, you
can register serial scans in the computer. So with the computational
algorithms that are there, you can minimize these problems.

And, again, even with on-site training, good technologists and so on
there will be some misalignment and the computer can help us with that.

Analysis, well from the way we think about this, you need some
perspective criteria of what you\'re looking at because the analysis is
going to drive how you define your scan protocol. You want to optimize
some type of figure merit, like I mentioned. You want to optimize your
quality control criteria to match the needs of your algorithm. In some
cases you may find that some algorithms are going to be more sensitive
to these artifacts, and you need to address that.

And, of course, standard operating procedures. If there\'s any user
interaction, if it\'s not a fully automated computational algorithm,
you\'re still going to have to deal with replicate analysis to address
drift and look for an interrater variability. And you also will do this
with computer algorithms, it\'s just easier there.

Archival is really going to be driven by the needs of the sponsoring
agencies, and that\'s sort of an open question.

Central consolidation, replicate archival in different places, and at
the coordinating center. An interesting question here is we\'re getting
to very large data sets in some cases. And even though we think about
things like CD ROMS and DVDs having very good reproducability, when you
start talking about 5 or 6 terabytes of data, errors in one in 50,000
actually become pretty significant when you want to read that. So,
duplicate, triplicate archival is going to be an interesting issue as we
continue to look at these large data sets.

So basically in these kinds of things if there\'s a central coordinating
center, it needs to have a close relationship with the sponsor, with the
imaging sites also to make sure that the imaging sites are really
involved in the study. In many cases the imaging sites simply are seeing
these people come through. They\'re not necessarily part of the
treatment study and getting them sort of cognizant of why they\'re doing
this so it\'s not just pushing the data through can be quite an issue.

Ongoing QC and quality assurance.

Blinded quantitative data analysis and with replicate analysis is
critical. And to the extent that there are regulations that address
this, of course regulatory compliance.

Thank you.

CHAIRPERSON KAWAS: Thank you.

And our final speaker for this sessions is Dr. Michael Grundman.

DR. GRUNDMAN: Thank you.

So I\'d like to address a clinical trial that we\'re doing, the mild
cognitive impairment trial that the Alzheimer\'s Disease Cooperative
Study is doing. And we\'re using MRI as a potential surrogate marker in
that study.

Mild cognitive impairment, as alluded to earlier, is a transitional
phase between normal aging and Alzheimer\'s Disease, and on a typical
cognitive measure such as the MMSE, patients with a mild cognitive
impairment generally perform at around a 27 to 28 range and decline at
less than 1 point per year, compared to Alzheimer\'s patients who
decline at 2 to 3 to 4 points per year.

Again, mild cognitive impairment, the clinical key criteria, in our
study patients need to have a memory complaint which is verified by an
informant with objective memory impairment. They generally have normal
cognition other than memory and generally normal daily function.

Question: Why are we looking at mild cognitive impairment in our
clinical trial? To begin with, part of the motivation for this is
because we\'re trying to do a prevention study so we can delay the onset
of Alzheimer\'s Disease. And previously we\'ve shown in analyses from
our clinical trial sites showing that patients who have MCI decline to
AD at a much faster rate than normal controls, that way we can do this
clinical trial with fewer subjects.

The clinical trial basically is designed as follows: We have 3 treatment
arms, one with Vitamin E, one with Donepezil, one with placebo. We
recruit the patients who have a memory impairment. And the goal of the
study is to see whether or not one of these two agents can delay the
clinical onset of Alzheimer\'s Disease over 3 years.

This is some of the details of the study. The doses of the agents
involved. The study objectives, again, as I mentioned to prevent the
development of Alzheimer\'s Disease, to slow decline on cognition and
function and to see whether or not the agents might reduce the rate of
atrophy on MRI. It\'s a 3 year trial, 769 participants with 69 centers
in the US and Canada.

The baseline ADAS Cog in MCI subjects in our study is around 11 points.
This compares to a normal control group that we\'re similarly following
that has an ADAS Cog score of around 5. In other AD trials that we\'ve
done with our consortium where the ADAS Cog scores typically in the low
20s.

So, we\'re looking at several different potential MRI outcome measures,
but one that we\'ve looked at so far or that we\'re trying to assess is
the role of hippocampal atrophy since earlier studies have shown that it
seems to be affected very early in AD pathology and contributes to the
memory impairment.

So you\'ve seen Cliff Jack\'s data before, which shows that patients who
are normal controls have lower rates of hippocampal atrophy per annum
than patients who are MCI, who are stable, who are decliners and then
who have AD. And you can see that this might similarly parallel the
decline that we saw earlier on the MMSE so that the rates of normal, the
rates in MCI and the rates of AD are all somewhat different and in
parallel to what you might see on a cognitive measure.

So the MRIs that we\'re doing in our study, we\'re doing them at
baseline in a subset of the subjects, so there were 193. And then we\'re
also looking at another scan at the time that the patients develop
Alzheimer\'s Disease or they complete the study if they haven\'t
developed Alzheimer\'s Disease. And we have a number of second scans,
and this number is increasing, but so far it represents only a subset.
So I\'m not going to discuss the follow-up scans yet.

So the specific neuroimaging hypotheses related to the hippocampus are
that the hippocampal volume at baseline will correlate with the
cognitive and functional measures, that it will predict who will develop
AD, that the rate of volume loss may be greater in patients who decline
clinically, and that the therapies might be useful in predicting which
patients are going to decline.

So the first point from the baseline data which I think we\'ve shown and
published, is that the memory scores correlate with the hippocampal
volume. This is just one example of the NYU Delayed Paragraph Recall
Scores. And you can see that in patients with the smallest hippocampal
volumes the scores on there, the number correct, were lower than the
number correct in the patients at baseline who had the highest
hippocampal volumes.

The other thing that seems to be apparent is that the hippocampal volume
at baseline also seems to predict a conversion to AD and changing
clinical measures. The trial isn\'t completed yet, but the preliminary
data that we have thus far suggests that people, if you just divided the
group into hippocampal volume; the top half of the group versus the
hippocampal volume of the lower half of the lower half of the group, you
can see that the people in the top half of the group are declining,
developing AD at a lower rate than the people who were in the bottom
half of the hippocampal volume.

Similarly, you can see that on the clinical dementia rating sum of
boxes, we have a similar effect where the people in the bottom half of
hippocampal volume are increasing at a more rapid \-- I shouldn\'t say
at a more rapid rate, but they\'re \-- over the course of two years
they\'ve reached a higher score, a worse clinical function than the
people in the lower hippocampal volume.

So it was mentioned before what the optimal characteristics of a
surrogate marker might be, specifically that the rate of hippocampal
change correlate with the outcome measures, that it captures its \"net
effect,\" and that it would be really nice if we could get the surrogate
marker to show that it has an effect before the clinical decline or
failure so that we could do shorter studies.

And ideally what we\'d like to see is a close relationship between the
rate of brain atrophy and the rate of clinical decline. And then we\'d
like a treatment that affects both of these things and is tightly linked
to do that.

And so how might we actually do this in our trial? The plan would be to
look at the slopes of decline in hippocampal volume or in whole brain
measures over the period of the study. Take these slopes and then see
whether or not the slopes show less rapid rate of decline in the
nonconverters than in the converters. And then after we\'ve done that,
to see whether or not the treatment can also demonstrate a slower rate
of decline than in the placebo group.

So brain atrophy, I think we\'ve shown so far that the hippocampal
volume is a good predictor of clinical outcome at this point, but it\'s
possible that the brain atrophy may not always be a great surrogate
marker. For example, brain atrophy could occur due to weight loss or
dehydration, and that could contribute to hippocampal atrophy. This was
a clinical study that we did at our Alzheimer\'s Disease Research Center
showing that body mass index correlates with mesial temporal cortex
volume in both men and women. We published this several years ago. So
this is just one indicator that there could be other factors besides
potentially neuronal loss, for example body weight, that might also
contribute to brain volume.

The other possibility is that the intervention may reduce the rate of
brain atrophy and not improve the clinical outcome. So it could be, as
Dr. Katz pointed out earlier, that agents that increase brain water or
had an inflammatory response could lead to some type of brain swelling
but not necessarily alter the clinical symptoms of the disease.

It\'s also possible that you could have an agent which reduced amyloid,
and assuming that amyloid takes up some space, it could actually
accelerate the rate of brain atrophy but at the same time reduce the
toxic effects of the brain amyloid. So at least in the short run it\'s
conceivable, at least to me, that you might actually see an acceleration
of the brain atrophy even though the drug might be doing something good.

What else?

So the other thing is that the intervention, in many cases you could
have a symptomatic agent which could improve the clinical outcome, but
not effect the rate of brain atrophy, And cholinesterase inhibitors are
a good example of that. So if we relied only on a surrogate outcome
measure, we might discard drugs that are good.

And then, of course, adverse events could occur that despite the fact
that we see a beneficial effect on the surrogate which might ultimately
make the drug ineffective.

And so I think hippocampal atrophy and brain atrophy both seem to be
tightly correlated with clinical decline, but if we relied only on brain
atrophy in the absence of clinical data, I think we should be cautious
about that.

On the other hand, if we could show slowing of decline in addition to
decline on clinical measures with a good safety profile, then slowing of
brain atrophy might support disease modification claim.

And then finally, if we could do some clinical trials and we showed that
the MRI data for specific agents correlated with both the clinical
outcome and the rate on hippocampal atrophy, then it\'s possible that we
could use that information in subsequent trials if we were to require
two drug trials for two pivotal trials. For example, maybe we could
accelerate that process and rely on the surrogate data in a subsequent
trial and not necessarily require that all the clinical data be
obtained.

And I\'ll stop there. Thank you.

CHAIRPERSON KAWAS: I want to thank all the speakers for their excellent
presentations and for keeping to time.

The floor is now open for questions. Dr. Fogel?

DR. FOGEL: Yes. This question is for Dr. Jack. Its actually a comment
and a question. The four trials, the four studies that you mentioned, I
guess in Dr. Hughes\' framework would be more along the lines of a
prognostic marker rather than a surrogate since there was no
intervention?

And I guess the other question that I had was in one of the slides you
said \-- summarized in a number of the studies that the decline in
imaging was more consistent than the behavioral cognitive measures. And
the behavioral cognitive measures sounds like it would be the clinical
outcome and the imaging is the surrogate or potential surrogate. So
would that mean that that it wouldn\'t be in your opinion a good
potential surrogate since it\'s out of proportion to the cognitive
behavior?

DR. JACK: Good question. No, I wouldn\'t say \-- out of proportion maybe
is not the right phrase. What\'s really happening is that neurons are
dying and synapses are evaporating in the brains of these patients. The
question is what is the best measure of that pathologic process.

Is it a list learning, or you know a different cognitive test, or is it
a measure of actual brain anatomy. In point of fact, in the studies you
were alluding to, every one of those patients their brains were
shrinking but yet some of them stayed the same or improved on these
cognitive measures. That has to represent test error or retest
variability. It can\'t possibly represent what\'s really going on
pathologically in the brains of these patients.

The imaging measures simply, I wouldn\'t say, were out of proportion to
the clinical measures. They were both trending in the same direction;
downward, but they just did so more consistently across a large group of
people.

DR. FOGEL: I\'m not a neurologist, but couldn\'t you be having the
plasticity of the brain and the neurons forming more efficient synapses
and have the volume decreasing while their cognitive function either
stays the same or improves?

DR. JACK: It\'s possible, but the way the disease works is the cells
die, synapses die away and people\'s cognition in general goes down. I
mean, I suppose it\'s possible for synapses to regrow or whatever, but
\--

DR. FOGEL: Well, I mean as a pediatrician, we see a lot of kids who get
totally devastated when theyre born and we see their brains on MRI and
it\'s incredible how much matter is lost. And yet you see them at 3, 4
years old and they could be nearly developmentally normal. I know that
plasticity is different in children than they are in adults, but I also
know that there are some plasticity that\'s been touted in adults as
well.

DR. JACK: Agreed. I mean, you know, I think the issue of is there
plasticity; if so, how much, how does it work and in adults is maybe not
controversial, but poorly understood. Certainly there\'s much greater
plasticity, no one would argue, in children than there is in adults.
And, you know, I guess the key question here is to the degree to which
there is plasticity, there is the capacity for brain repair in adults,
can that rate keep up with the rate of pathologic progression?

The natural history of the disease would suggest that in most cases it
can\'t. In all cases it can\'t.

DR. FOGEL: Thank you.

CHAIRPERSON KAWAS: Dr. Katz?

DR. KATZ: Yes, I have a question for Dr. Fox.

You suggested that progression in atrophy is reasonably likely to be a
reflection of progressive neuronal loss. But we are here concerned,
obviously, with drug effects and what drug effects on what appears to be
atrophy on an MRI might actually mean. So, would it be your view that a
drug which induced a beneficial change in what appears to be atrophy
could essentially only be due to an action of preserving normally
functional neurons?

DR. FOX: No. The \"only\" is a crucial word in that question. Absolutely
not. It could be due to a multiplicity of completely spurious causes.
But that\'s not \-- only is a different level of proof to reasonably
likely.

CHAIRPERSON KAWAS: Actually, I have a question that maybe is for all of
the speakers, but perhaps Dr. Fox could start out.

I think that we all pretty much are aware and agree with the data that
hippocampal atrophy is associated with AD. We all agree that it seems to
progress with the disease. But the piece of evidence that I\'d love to
hear from each speaker, they think is the best piece of evidence that
suggests that stopping that atrophy will have a clinical effect on the
patient.

It seemed to me when we got to that part of the discussion, then
everyone started talking about reasonably likely and biologically
plausible and seemed to me, and you know hair color and aging tracks.
But dying one\'s hair doesn\'t necessarily do anything for aging.

What is the evidence that doing something to stop hippocampal atrophy
will actually do something for the patient?

DR. FOX: I think since we\'ve never managed to \-- to my knowledge,
since we\'ve never managed to slow disease progression in Alzheimer\'s
Disease, I don\'t think there\'s any way to provide, as you say,
evidence. Isn\'t that the only evidence that you will have?

I don\'t know whether somebody wants to \--

DR. WEINER: I haven\'t been a speaker yet, but I would just add to that
that there is quite a bit of data already correlating hippocampal volume
with neuronal counts in the hippocampus. So I think it\'s fair to say
that there\'s a lot of established evidence that as the hippocampus
shrink it\'s because of neuronal loss. So if you had a treatment that
slowed the rate of hippocampal shrinkage, one could infer that that was
due to slowing of rate of neuronal loss, but it could be do to other
things like \--

CHAIRPERSON KAWAS: But it also infers, though, that the neurons are
working.

DR. WEINER: Correct.

CHAIRPERSON KAWAS: Dr. Penn?

DR. PENN: I was very intrigued by what looked like a statistically
significant effect on a number of your patients actually gaining
hippocampal volume during the study, more than one would likely see just
because of variation in the data. And this brings me back to an example
of a drug that\'s been shown to increase atrophy and then you withdraw
it, and the brain comes back. And that\'s some work I did with Peter
Carland in Canada 15 years ago with alcohol. It\'s very well established
that if you drink a lot, your brain shrinks, and if you stop drinking
your brain grows back, and the cognitive function follows those changes
in brain size. That was done with CT and very primitive compared to the
measurements you\'re now doing. But there are examples of a surrogate
marker where we do have a change that very much goes along with the
pathology.

So, what I\'m wondering is are we obligated to look for those
confounding variables in the patient population; that is nutritional
status, alcohol, other things that might change hippocampal size when
we\'re doing these smaller studies on patients with Alzheimer\'s
Disease?

That\'s a question, sort of.

CHAIRPERSON KAWAS: Anyone in particular? Who do you want to direct the
question to, Dr. Penn?

DR. PENN: Well, Dr. Fox, he looks like he\'s nodding and off to sleep or
something.

CHAIRPERSON KAWAS: Dr. Fox, you\'ve got the floor.

DR. FOX: The nodding is just an unfortunate tremor, I\'m sure.

I didn\'t show data on hippocampal volume change, but I do think that
your example of alcohol is well taken. I think hemodialysis as I showed,
if you changed the gases that people inhale, if you give people
diuretics, all these things can change brain volume. And I think it is
important that any study would look at other factors that might be
confounders, but also I think it\'s very important that you look at the
time course of the progression to try and see whether or not you\'ve got
a continuing effect on progression as opposed to a simple drug effect. I
think that\'s important.

DR. JACK: Let me address two of those. First of all, in natural history
trials the only way any of these alcohol or whatever are going to have
an effect is if there is a bias in your study, and that is that if your
control population has a different rate of alcoholism or different rate
of dialysis patients, or whatever, than your patient population.

With respect to a drug trial, I mean I think everyone here agrees that
it is possible for a drug to dehydrate the brain or hydrate the brain,
and that in turn will produce volume changes that are unrelated to any
functional benefit.

The easy answer to the question, though, I think Nick was alluding this,
is to at the end of the trial take people off drugs and determine if the
drug produced a sustained change in brain volume that was not seen in
the placebo group.

CHAIRPERSON KAWAS: Dr. Love?

DR. LOVE: Yes. Thank you.

This question is for Dr. Charles. You discussed a number of very
practical approaches to an imaging protocol that you would recommend for
inclusion. I would imagine you are speaking prospectively in
developmental studies. Could you identify which ones of those you think
are most critical in an imaging protocol going forward? And looking
retrospectively if you were looking at the literature, which key things
would you look for in those articles to be sure the protocol wouldn\'t
introduce too much noise?

DR. CHARLES: Well, all of them are important. Otherwise you\'re just
adding variance. I mean, to the extent if you\'re looking
retrospectively, I think you have to simply say \-- and it\'ll show in
the data. In other words, if the variance of the measure is higher, then
that\'s likely due to combinations of issues of the analytical algorithm
as well potential issues with site-to-site variance.

For volumetric measures within a single site that\'s well maintained,
that works very well but you also have to check that over time because
we all change our scanners over time. And we\'ve seen in looking at our
scanners at our institution that are well maintained, variations as much
as 3 percent of the field of view in one-year time frames. And if you
don\'t correct for those, again you don\'t have to fix them at the site,
you just have to track them with a phantom so that you can correct the
data after the fact and know what\'s going on.

DR. LOVE: Can I ask one related question to that? Also you mentioned
that the software that\'s currently marketed may or may not be directly
relevant. What approaches would you recommend to validate the software
that\'s used across a multi-center study that\'s an add on?

DR. CHARLES: Well, the software is okay as it is up to a point. It\'s
the combination of the software in the context that the goals of
clinical imaging and the needs of clinical imaging are very different
from what we\'re trying to do. So you have to add some additional
materials like quality control phantoms that maybe the manufacturers
don\'t provide. Particularly if you\'re going to go across
manufacturer\'s boundaries, you cant easily compare a GE phantom to a
Siemen\'s phantom, to a Picker to a Phillips, and all those other names
so that no one will say I said the wrong guys.

But those kinds of things in spectroscopy, you\'ll hear more about MRS
from other speakers, but there where you\'re dealing with very low
signal levels, the way that we do studies is we actually run a phantom
at each setting. Because there\'s a lot of things that cause NMR signals
to vary and you want to be able to track that over time. And you can
remove that variance.

I mean, just as we do repeated measures designs to help minimize the
impact of biologic variance, you\'ve got to do something to deal with
site-to-site and time variance with phantoms.

CHAIRPERSON KAWAS: Dr. Kim and then Dr. Sorensen.

DR. KIM: This question also relates to a little bit with what Dr. Love
has alluded to. This question is going to be for Dr. Fox.

When you do your registration between the images, the pre and the post
or the before and after, when you do correlation could you do some sort
of normalizing before you actually measure your atrophy?

DR. FOX: If I understand you correctly, is the question could you deal
with some of those scan adrift scaling changes or \--

DR. KIM: Not just the drift, but there\'s atrophy overall. How do you
take it out, what is normal and what is disease?

DR. FOX: You can adjust for head size. For example, what is taken to be
a rough measure of premorbid maximal brain size, namely inter-cranial
volume. One certainly can adjust for that. As we all know that women are
more intelligent than men, and yet they have an inter-cranial volume
which is 12 percent smaller than men. So you can adjust for that. And
when you do that, you can for example find that whole brain atrophy
measures are appropriately accounted for and they match. You get rid of
the gender difference by inter-cranial volume correction.

Is that the question that you were asking for?

DR. KIM: Yes. Because I\'m looking for \-- obviously most of us are
looking for small changes. And I just wanted to make sure that those
small changes doesn\'t get covered by the overall change.

DR. FOX: Well, I think most importantly is that the power of following
the individual, the changes within the individual are what matter not
changes between individuals. So what you have with a serial study is the
perfect control, i.e., the person themselves.

DR. KIM: Okay.

DR. SORENSEN: My question is for Drs. Jack, Fox and Grundman.

I mean, it seemed like Dr. Jack presented one slide that indicated that
in three out of the four different groups of patients that he was
looking at, that ventricular volume was more powerful or some way better
than hippocampal volume. And I think I\'ve seen that kind of data in the
literature from other groups as well. And yet the other two speakers
focused primarily, maybe not exclusively, but primarily on hippocampal
volume. Is there a consensus among the AD imaging community as to, you
know, sort of which single volumetric measure is the best one or is
there a hope for that consensus? Or if there were going to be a primary
outcome of a trial, would it be, you know, your five favorite volume
measurements or is there a single one that we would pick, or how would
you guide us there?

DR. JACK: That\'s an excellent question, and the answer is that it\'s
still unknown. And that is an active area of research.

I mean, the reason Michael showed hippocampal data is that \-- for the
trial that Michael showed the data for. The way the software worked is
that you can measure the volume of the hippocampus or the inter-cranial
cortex with a single data point. Our software algorithm is very much \--
actually it was a knock off of Nick\'s boundary shift integral
algorithm, and you need two different time points to put into the front
end of the algorithm. So we won\'t have these other data, the whole
brain regional volumes, et cetera, until both time points have been
acquired.

But your point\'s right on the money; no one really knows. And it\'s
very \-- the point of the data that I was trying to show is that it\'s
quite probable that the best measure will vary with the stage of
disease. So early on in the disease one would suspect that measures of
medial temporal load atrophy rates would be better, more sensitive to
progression of the disease. Later on in the disease measures that were
sensitive to atrophy in neocortical association areas would in turn be
better measures. I mean, that makes sense but I don\'t think anyone has
really worked it all out yet.

DR. FOX: I\'ve got very little to add to what Cliff said, except that
what one\'s trading off is the amount of signal, which may change with
the disease. So namely, if the most in absolute terms change was in an
area, such as the hippocampus or entorhinal cortex at a particular stage
of the disease, that has to be traded off with the measurement error or
noise in that measure, which might be a physiological, it might be a
measurement error of your technique.

And as Cliff said, the answer is not clear yet, but also I think
speaking from a personal perspective, I would suggest if you\'re looking
at probably a range of disease severities, because that\'s very
difficult to characterize anyway, that one should be looking at at least
a combination of measures. For example, a regional and a global measure.
You can choose your region, you can choose your global.

CHAIRPERSON KAWAS: Dr. Katz?

DR. KATZ: Yes, I just wanted to follow-up to Dr. Fox\'s response to my
question. Of course, he\'s right. I asked a loaded question when I used
the word \"only.\" But, of course, our standard is whether it\'s
reasonably likely that an effect seen on a surrogate is going to predict
the useful clinical outcome. And, of course, that\'s going to be a
personal judgment, and everybody\'s going to make that judgment, I would
imagine, on the basis of they would bring different information to bear.

So I would just second something that, Claudia, you said which is that
if and when we get to discussing whether or not people think these
things or other facts we might see on some of these measures are
reasonably likely to predict the clinical outcome of interest, it would
be very helpful for us to know what your individual personal bases were
for deciding that something was or was not reasonably likely.

CHAIRPERSON KAWAS: Thank you. I want to start out then by rephrasing my
question for all the speakers. What piece of evidence makes you feel
that it is reasonably likely that the hippocampal volume changes might
be relevant to the outcome of the disease?

Dr. De Carli?

DR. De CARLI: Well, I think it\'s because we understand the disease
process fairly well. If you accept the amyloid hypothesis that accretion
of amyloid in the interstitial space leads to toxicity, neuronal injury
and shrinkage, damage to neuronal trees and then subsequently loss of
neuronal constituents that include axons; each of these phenomenon
contain space. Okay? They\'re anatomically relevant structures that we
can measure on MRI and that have high correlation. That MRI, the size of
the hippocampus correlates with the anatomy, both increase in the
pathology and loss of the tissue.

Now, it\'s \--

CHAIRPERSON KAWAS: So to your mind what makes it most reasonably likely
is just a strong correlation between volume and disease, is that
correct?

DR. De CARLI: What makes it probable in my mind is that it\'s part of
the pathological \-- it\'s the disease process. It\'s close to the end
stage of the disease process that you have the \-- the pathophysiology
of Alzheimer\'s Disease is neuronal dysfunction followed by cell death.
Now, structural imaging cannot measure neuronal dysfunction, but
functional imaging may.

Second, however, is that that\'s followed by neuronal cell death which
structural imaging measures quite well. So since it\'s part of the
cascade, that if you stop or you interrupt cell death, then you
therefore would stop the atrophy process.

Now, does that mean you couldn\'t have long term improvement
symptomatically without these anatomical changes? Of course.

CHAIRPERSON KAWAS: Well, actually, the question\'s more the opposite.

DR. De CARLI: Right.

CHAIRPERSON KAWAS: Can you not have any real improvement in the person
even if you stopped this change is really the concern? We all agree that
symptomatic therapy shouldn\'t change by definition. But the question I
think we\'re grappling with now is if we stopped this atrophy with
whatever compound in whatever way, how confident do we feel that we will
see that reflected in the outcome?

DR. De CARLI: And my short answer is I feel very confident if it was
involved in the cascade of pathology, and that\'s what I think the
imaging is measuring. So if you have something that you know effects the
cascade of pathology and you see this, then my confidence level would be
extremely high.

CHAIRPERSON KAWAS: Does anyone else in the group want to \--

DR. De CARLI: Anyone else want to stick their neck out? This is a public
hearing, go ahead.

CHAIRPERSON KAWAS: Yes. Yes.

DR. FOX: I\'d like to sort of try and give a sort of more considered or
sort of detailed answer to Dr. \-- not to your observation, but to my
previous one, Charlie, which is Dr. Katz as well, which is your point is
very well taken. And I think for a start one has to reasonably likely
\-- I mean, are we talking about \-- are we all talking about 51 percent
is one issue. I think maybe one should look at what percentage one\'s
looking at for the reasonable likelihood. Obviously, it\'ll be a
guesstimate.

But I think that to any answer about that reasonable likelihood or what
pieces of evidence, would have the caveats that I would want to see
pertinent information about the design of the study. So a sustained
effect. An effect that looks like it\'s both regional and global, an
effect that has maintained beyond withdrawal of the drug and, as much as
possible, of the confounders that are coped with or adjusted for.

CHAIRPERSON KAWAS: Thank you.

Dr. Temple?

DR. TEMPLE: I have what I guess is a practical question. One of the
things that makes the a surrogate start to look really good is a
successful drug. So people stopped worrying too much about blood
pressure once the VA did its studies. Nobody remembers this anymore, but
prior to those studies there was a huge debate about whether lowering
blood pressure was good for you or bad for you. The so called New York
School assured everyone that you\'d have more strokes and more heart
attacks if you lowered blood pressure. That was not tenable anymore once
the VA did its studies.

As a practical matter it\'s hard for me to imagine trials that would not
include a certain number of clinical observations. I mean, we know you
can show effects on cognitive functions in studies of modest size with
drugs that work only a very little bit. So there\'s been success in
there. They\'re not backbreaking trials, you can do them.

So at least early on my thought would be that people would be studying
at least some patients who had observable disease and conceivably
there\'d be some interest in people who weren\'t sick yet. It\'s that
latter group where it would be tempting, I suppose, to rely entirely on
surrogate data. But wouldn\'t much of the support for doing that come
from the fact that you\'d been able to show something in people with
already developed disease, which historically, at least, doesn\'t seem
that hard. Maybe then after that additional claims or things like that
might be based on the surrogate finding.

But are we really going to be looking at case where there are no
clinical data? That seems unusual, except when you\'re trying to maybe
stop people who aren\'t sick yet, that\'s the one case where you might
take a very long time to get real data and might therefore want to rely
on surrogate data only. But just as a practical matter won\'t some of
the confirmation that the surrogate is plausible come from the observed
clinical effects in the people who are already ill?

CHAIRPERSON KAWAS: Go for it.

DR. WEINER: I think you\'ve \-- that\'s the whole point. Nobody is
talking about using imaging as a primary endpoint right now for these
trials. At least, I\'ve not heard any rational person say that we should
right now start using imaging as a primary endpoint.

The role of imaging right now is going to be to provide confirmatory
evidence to the primary endpoints of ADAS Cog and other confirmation.

So when the Phase III clinical trials are done, they will be powered for
ADAS Cog and other clinical endpoints. And I predict that imaging will
be used as perhaps a subset of some of those subjects to provide, first,
confirmatory data which is important in the regulatory process. And,
secondly, the critical issue is whether or not the drugs have a disease
modifying effect. And it\'s very difficult in the Alzheimer\'s area when
you\'re using ADAS Cog to demonstrate disease modification with clinical
measures alone. The only way to do it rigorously is to do a randomized
withdrawal or a randomized trial which requires very large samples,
takes a long time and costs the companies a great deal of money.
There\'s a lot of dropouts in these kinds of studies.

So, if one designed a Phase III trial powered for ADAS Cog to
demonstrate a clinical effect and used imaging to demonstrate disease
modification, that\'s I think the role of imaging. That is, if you show
that a drug slows the rate of cognitive decline, the same time you show
that the drug slowed the rate of hippocampal volume loss, and finally if
you show that there was a correlation between the ADAS Cog effect
together with the effect on atrophy, that would be I think fairly
compelling evidence that your marker is providing evidence for disease
modification. And I think that\'s the current role in Phase III trials.

CHAIRPERSON KAWAS: Dr. Katz?

DR. KATZ: Yes. I think this discussion is very good and very important,
and certainly something we need to hear. I think we may be having it a
little early in the day. I know that Dr. Hughes is going to talk again
about validating surrogates; I think that\'s an important thing for
people to hear, whether or not a single trial with a single drug that
shows the correlation between clinical and imaging is sufficient to
validate even that drug as having an effect on progression is an
outstanding question, let alone for that marker for the field in
general.

So, I hate to cut off discussions, but I think we might profit more from
this after the speakers have been heard.

CHAIRPERSON KAWAS: Excellent. Thank you. Thank you very much.

And in fact on that note, how about a 15 minute break.

(Whereupon, at 10:46 a.m. a recess until 11:08 a.m.)

CHAIRPERSON KAWAS: Thank you, and we\'ll be restarting. And now we will
be moving to a section on MR Spectroscopy and PET. And our first speaker
is Dr. Michael Weiner.

DR. WEINER: Thank you very much.

I\'m going to be talking about the use of MR spectroscopy and MRI to
measure treatment of Alzheimer\'s Disease and neurodegeneration.

So what we need are imaging surrogates which are specific measures of
neurodegeneration. We\'ve been talking a lot about that this morning.
And we also need sensitivity. We want to have maximum statistical power
to determine treatment effects, fundamentally because the clinical
measures have so much variability that huge numbers of patients are
needed in order to determine treatment effects. MR spectroscopy,
perfusion MRI and structural MRI are all candidates here.

Magnetic resonance, spectroscopy measures metabolites in the brain and a
metabolite called N-acetyl aspartate or NAA, which is located almost
solely in neurons has been thought to be a measure of neuronal number or
density, which would be a good measure of neurodegeneration, but it\'s
also sensitive to changes of neuronal metabolism. And you\'ll hear more
about that from other speakers.

Spectroscopy also measure colon metabolites, creatine myo-inositol which
will also tell you something about what\'s going on in the brain.

We have been using a multi-slice magnetic resonance spectroscopic
imaging technique illustrated here where we display images of that
normal marker NAA, creatine and choline, and one gets spectra from
individual regions of interest as shown for example here from white
matter or gray matter. This large PQ represents N-acetyl aspartate, NAA.

An example of the kind of data you get from doing these studies, is this
is a cross sectional study looking at about 40 patients in each group
with healthy controls, Alzheimer\'s, subcortical ischemic vascular
dementia, and patients with cognitive impairment. And what this slide
shows is the NAA concentration in the hippocampus and in the frontal
lobe in these four groups.

Now, in the hippocampus you see the healthy controls have high levels of
NAA. It\'s reduced quite substantially in Alzheimer\'s Disease. It\'s
not reduced as much as ischemic vascular dementia. And on the
cognitively impaired subjects, it\'s reduced about the same amount as
the Alzheimer\'s patients.

On the other hand, if we look at the frontal lobe, note that the
patients with subcortical ischemic vascular dementia have a much lower
NAA compared even with the Alzheimer\'s patients. And this different
pattern contrasts with what we see in the hippocampus where in
Alzheimer\'s Disease the NAA is lower than in subcortical ischemic
vascular dementia.

So you can use spectroscopy to get different patterns of metabolic
change which characterize different diseases.

Now, in treatment trials, of course, we want to do longitudinal studies
to determine treatment effects. And these are some data from a
relatively small sample in our lab showing changes of NAA shown here and
choline shown here in controls cognitively impaired subjects and
patients with Alzheimer\'s Disease. And what you see is the rate of
change of NAA in the Alzheimer\'s patients in both the frontal and
parietal cortex are greater than those in controls and that the
cognitively impaired patients have an intermediate rate of change.

Interestingly, choline is also showing changes in the Alzheimer\'s
patients similar to those seen with NAA.

A number of studies have been published using longitudinal MR
spectroscopy, and you\'re going to hear more about this from the
subsequent speakers, but the number of studies were small and I
personally believe that currently we really have insufficient data
concerning MRS as an outcome measure for longitudinal studies and
Alzheimer\'s Disease. We just don\'t have enough data to say whether or
not spectroscopy is going to be useful.

Now, another candidate is arterial spin labeled perfusion MRI. This is
the technique that measures cerebral blood flow in the brain
quantitatively by magnetically labeling the blood that flows into the
brain and then performing an image of the brain which detects the rate
of blood flow. And these intervals of what these sort of images look
like in healthy elderly controls in patients with Alzheimer\'s Disease,
and this is some early data from our lab showing in elderly control
subjects the rate of cerebral blood flow in the frontal, parietal,
temporal and occipital lobes showing very substantial reductions of
blood flow in Alzheimer\'s Disease giving you the magnitude of the
decreases and the effect size. No one has done to our knowledge
longitudinal studies of arterial spin labeling in Alzheimer\'s and we
have no idea whether or not this is going to be a useful measure in
clinical trials. But this gives us the kind of information you get from
PET scanning. It only takes 12 minutes, so it\'s possible that this
could provide that kind of data within the context of an MRI examine.

Structural MRI, we\'ve been talking about it, it has phase validity as a
measure of neurodegeneration. There are different measures of brain
atrophy. Nick Fox developed the boundary shift interval method and Cliff
Jack has talked to you about the hippocampus. Data has been reported on
different groups of subjects and it\'s been hard to compare methods, as
we pointed out.

We\'ve done a study on 23 elderly controls and 19 Alzheimer\'s patients
who were studied with two scans and with a mean interval of about 2
years. And this gives the rate of change of the entorhinal cortex, the
hippocampus, several different measures of the boundary shift interval,
the cortical measure, the ventricular measure and the total brain
atrophy measure. And this is a measure of the rate of change of the
cortical gray matter measured by segmentation.

You can see that the controls have relatively low rates on the order of
one percent per year. The Alzheimer\'s patients, depending on the
measures, the entorhinal cortex, 7 percent per year, a very high rate of
atrophy. The hippocampus about 6 percent per year. The whole brain
measures have smaller rates of change.

And the coefficient of variation of the Alzheimer\'s patients is shown
here, and the statistical power to measure a treatment effect in
Alzheimer\'s Disease roughly scales with the coefficient of variation.
That is the variants in the Alzheimer\'s population.

This is the beginning of what we need to do for validation, that is this
is the rate of atrophy of entorhinal cortex shown in aqua or hippocampus
in white plotted against the Delayed List Recall score, which is a
measure of memory. And basically what this shows is that patients with
relatively good memory have low rates of atrophy. And the worse the
memory, the higher the rate of atrophy.

This is some rough calculations of sample size for a 20 percent
treatment effect in one year with different amounts of power, 80 percent
power or 90 percent power or using a one tail or two tail statistic
depending on your a priori hypothesis. And this would be sample size per
arm.

So what this shows is that for the entorhinal cortex and hippocampus one
can detect a 20 percent disease modifying effect with something on the
order of 50 to 80 subjects per arm in one year. That\'s a lot more power
than you would have if you did this using ADAS Cog. You\'d need maybe
two or three or four times that number of subjects.

This is a way to analyze the structural imaging data using something
called non-rigid transformations where you take two scans at time point
one and time point two. And then using a computer program which
essentially warps the scan from the second time point back to the first
time point so that every individual pixel in the MRI is coregistered
back to the first time point. And this shows a picture of the shape
change that occurs between time point one and time point two; the blue
showing contraction in the cortex of the brain and the yellow and red
showing expansion of the ventricles and the CSF.

This shows how you could do that sort of warping between time point one
and time point two in a whole series of subjects, and then warp these
change maps to a common space so that one could essentially have a
measure of change for a group of subjects. And this compares the changes
in 55 cognitively normal subjects with two scans versus 17 Alzheimer\'s
patients with two scans.

Note that the scales are quite different because the Alzheimer\'s have
so much greater rates of change. The main point to make is the
Alzheimer\'s patients have much more change in the median temporal lobe
region shown here, shown here and shown here than you see in the
controls. So we do have a pattern of more rapid contraction in the
median temporal lobe in Alzheimer\'s. And the beauty of this approach is
it\'s completely automated. It looks at the whole brain and allows you
to do both hypothesis testing as well as explore studies to look for
regions of contraction.

Another way to display this same data is to look at a surface rendered
image. This is a contraction in controls without lacunae, 37 subjects
with an inner scan interval of about two years. The blue shows
contraction in the cortex. Over here we\'re seeing 21 Alzheimer\'s
patients with a lot of cortical contraction. And these are controls who
have lacunar infarcts who are completely cognitively normal start
showing some increase in the rate of contraction of these subjects.

Another beauty of this kind of warping approach is that you can
correlate cognitive change with shape change. So what this image shows
is a correlation between the change of brain structure over time with
the change of the mini-mental state examination over time in
Alzheimer\'s patients showing those brain regions which had a
significant correlation with the mini-mental state examination.

So in other words, it\'s kind of an image oriented approached towards,
you could say, the beginning of surrogate validation here. Because
we\'re correlating the surrogate, the image, with the primary measure of
the cognition. And it shows that there are certain regions of the brain
that are more correlated with the mini-mental state examination, and
interestingly, more on one side.

So in conclusion, structural MRI has high power to detect longitudinal
changes in Alzheimer\'s Disease. Structural MRI is a relatively specific
measure of neurodegeneration because it\'s probably not very effected by
brain activity or metabolism. In contract, as you\'ll hear from the
subsequent speakers, PET and spectroscopy are sensitive to measures of
brain activity and metabolism; that\'s the power of spectroscopy and PET
scanning. But that\'s also their disadvantage, that because they are
sensitive to state they are less specific measures of neurodegeneration.

Structural MRI does correlate with cognition, as we\'ve shown and Nick
and Cliff Jack have shown, but much, much more work is needed to
correlate structural MRI with cognition.

Certainly this is all useful in Phase II. It\'s currently an unvalidated
surrogate. It\'s not a primary outcome measure for Phase III trials, but
structural MRI is useful to provide confirmatory evidence using that FDA
regulatory language of effect and to provide evidence of disease
modification, which is what we need as this new class of drugs enters
clinical trials.

What is needed are standards for MRI and spectroscopy and PET so studies
can be compared. Because currently different investigators are all doing
it different ways and it\'s really hard to compare data. We need to have
more correlations of imaging data with cognition function and pathology,
and we need data from multiple sites for powering of future trials.

Cliff Jack showed the beginning of that with the Milamilene trial, but
we need more of that.

So in order to get that, what we need is a longitudinal, multi-site
observational nontreatment trial of controls MCI, NAD, using MRI and PET
along with cognition and biomarkers. And, hopefully, a study like this
is ultimately is going to be supported by the National Institute of
Aging with co-funding from the pharmaceutical industry.

Thank you very much.

CHAIRPERSON KAWAS: Thank you.

Our next speaker is Dr. Murali Doraiswamy.

DR. DORAISWAMY: Thank you very much.

I want to thank Dr. Katz and Dr. Mani for inviting me here, as well as
the advisory panel for inviting me.

I\'m going to speak on MR spectroscopy. And many of the studies I\'m
going to be presenting are relatively small sample size studies. And I
want to put that in context. This is an issue that has not been
discussed, which is the cost and the time it takes to do these MR
studies.

A typical MR exam may take about an hour of the patient\'s time, perhaps
a whole day of the experimenter\'s time to plan the protocol and to
analyze and extract the data. They\'re also very expensive.

And a 10,000 patient clinical trial that has two MR scans, one at the
beginning and one at the end, say 20,000 brains, is going to take a very
long time to analyze. Because a typical academic lab processes about two
to five scans a day if they\'re very efficient. So you can see if there
are 20,000 scans, it\'s going to take a very, very long. So it\'s not
the same as doing exams.

And some of the limitations in the longitudinal studies and the sample
sizes we\'re seeing today are really a limitation of the expense and the
time it takes to do these studies. And, hopefully, the NIA initiative
would address that.

So as the previous speaker mentioned, brain MR spectroscopy is a
non-invasive technique that provides a biochemical window into the brain
and it can look at concentrations of metabolites either in whole brain
or in discrete regions. And the size of the discrete region you want to
look at is partly the limitation of the technique.

Now one of the important things to keep in mind is that MRS is usually
acquired along with an anatomical MRI image. So really at perhaps at ten
minutes or more you can get an MR spectroscopy scan in the same sitting
that you get an MRI scan. So really you can get synergistic information.

Now, there are a number of MR spectroscopy markers depending on the type
of MRS study that one undertakes. The type of MRS that I\'m going to
talk about is called proton MR spectroscopy or one hydrogen
spectroscopy. And really the two markers that people are talking about
with regards to Alzheimer\'s Disease is N-acetyl aspartate and
Myo-inositol.

Now the key point to keep in mind here, again this goes to the heart of
whether this constitutes a surrogate marker or not, is we still don\'t
understand fully the function of N-acetyl aspartate in the human brain.
There is increasing evidence that it\'s an acetyl donor involved in
various lipid metabolic pathways, perhaps involved in cell membrane,
neuronal axonal membrane and in other kinds of neuronal functions, but
we still don\'t understand it fully.

So without understanding the function, it\'s hard for me to stand up and
say that it\'s truly involved in the causal pathway of Alzheimer\'s
Disease, even though we don\'t even know all the causal pathways of
Alzheimer\'s as well.

It\'s abundant in the human brain and some data suggests that it\'s the
second most abundant amino acid in the brain. So common sense suggests
that it is involved in a lot of fundamental processes. It increases
during brain development.

There is a variety of postmortem and histochemical studies using
specific antibodies that have shown that N-acetyl aspartate tends to be
concentrated largely in the gray matter regions of the brain. It\'s
primarily present in neurons and not as much in glia cells. And this has
also shown in culture, cell culture studies. So it\'s present in gray
matter to a greater extent than it is in white matter or in CSF.

And that is really what goes to the heart of the postulate that it\'s a
marker of neuronal function or density. And there\'s two kinds of
studies. The earlier studies suggested that it might be a marker of
neuronal density, and these were studies that correlated
histopathological sort of changes and did postmortem MR spectroscopy,
but there\'s more recent clinical evidence suggests that it may be more
a dynamic functional marker rather than a marker of neuronal counts or
density.

Now, the other marker that\'s of emerging interest is Myo-inositol.
Again, we don\'t know exactly what this marker does or what it
represents. There are many theories. Some people say its a constitute
of cell membranes. But really there is recent evidence, at least
suggesting perhaps it\'s a marker of glial activation. And there\'s some
data suggesting that it\'s increased in the prodromal stages of
Alzheimer\'s, such as in patients with MCI or in patients with Down\'s
Syndrome who haven\'t yet developed the Alzheimer\'s.

Now, you have to put these markers in perspective, and this may or may
not be a popular slide, but I think it\'s a slide that everybody on the
Committee needs to be aware of.

Now, the reduction in NAA is not specific for Alzheimer\'s Disease, as
has been referred to by several people who have talked about really body
weight, there\'s a number of other factors, but really there\'s a wide
range of diseases effecting the brain in which NAA has been reported to
be reduced. Now, I\'m not saying that all these studies are very
rigorous good studies. By and large, they\'re small. By and large,
they\'re cross sectional studies. But a number of different conditions.

So, again, suggesting that NAA if it\'s involved at all in the
pathophysiology of Alzheimer\'s Disease is more a downstream marker
rather than something that\'s early and very, very specific for the
disease.

Now all the conditions that I have marked by an asterisk, including some
that I\'ve not indicated, are conditions where potentially reversible
changes in NAA have been reported after either therapy or spontaneous
recovery. And I\'ll give you one example.

In temporal lobe epilepsy, sometimes surgically they take out the
effected seizure focus. And when you look at the contralateral side, NAA
levels increase by about 50 percent after about 6 months after surgery
and up to 100 percent a year after surgery in some studies.

Now, these are all the conditions in which hippocampal volume has been
reported to be reduced. And one of the conditions that\'s very
interesting, Cushing\'s disease characterized by high levels of
cortisol, and there\'s very good animal data suggesting that
hypercortisolemia is associated with hippocampal damage.

And a very recent study from the University of Michigan by Monica
Startman where they took 22 patients with Cushing\'s disease, looked at
hippocampal volumes before and after transfenoidal adenectomy and they
showed that there was up to a 10 percent increase in hippocampal volume
in the same patients after the hypercortisolemia had resolved. So,
again, suggesting that many of these structures are dynamic. So really
depending on the intervals over which you measure the specific disorder
in which you looked at these markers, they have to be interpreted
accordingly.

Now methodologic issues, again, I\'m not going to focus a lot on this
particular slide, but it\'s important to keep in mind that there are
many different techniques available to look at MR spectroscopy as well
as volumetrics. And these have to be standardized across studies and,
really, there\'s very few studies in the literature that have used the
same technique.

For example, the acquisition protocols: What part of the brain are you
looking at; what\'s the voxel size; how big is the volume element that
you\'re looking at, and; really how are the data reported? Are you
reporting them by an internal normalization, by an external
normalization, for example, to a phantom, are you atrophy correcting
these data or are you reporting absolute concentrations? So these all
some things that one needs to bear in mind and really standardize when
you look at these studies.

So I want to summarize for you briefly the MRS literature in the
Alzheimer\'s Disease. Now this slide lists the cross-sectional studies
that have been done in the Alzheimer\'s and really the bulk of the
literature is cross-sectional data. There are at least four postmortem
studies that I could find with a total sample size of about 70
Alzheimer\'s patients, 69 Alzheimer\'s patients and 22 controls, mostly
of the temporal and frontal cortex, and mostly based on per chloric
extracts of postmortem brain. And they found a 20 to a 50 percent
decrease in NAA in the regions of interest and a couple of studies have
correlated this with plaque density. One study with plaque density and
one study with tangles looking at it in adjacent sections.

Now, the in-vivo MRS studies, there\'s about 30 studies or so. The
sample sizes range from very small case series to more than 50 studies,
more than 50 patients. The decrease in NAA in the Alzheimer\'s has
ranged from about 10 to 40 percent, 10 to 37 percent with a couple of
negative studies. In about four or five studies the NAA levels have
correlated with many mental state examines with the Pierson. In small
sample size studies you have a very high Pierson correlation and then
the larger the sample size gets, your correlations tend to be a little
bit lower.

Now there are two studies that have looked at the potential sort of
prognostic role, if you will, of MR spectroscopy, and there may be more.
These are the two studies I\'m presenting today.

One was a study that we published, a pilot study that we did about four
or five years ago where we looked at 12 very mild Alzheimer\'s patients,
we did a baseline spectroscopy scan and then we evaluated them
clinically over the next one year. And what we showed was that there\'s
a correlation between their baseline spectroscopy measures and their
cognitive status one year later. We also found a correlation between the
rate of change in their cognitive status as well, and that was also
presented in this particular study.

Again, the correlation coefficients are high because the sample sizes
are relatively small.

Now, the second study is from the Mayo group. Again Dr. Jack was one of
the investigators in that. A study of 51 patients and the one analysis
I\'m showing you here is a pooled analysis they did where they combined
the MCI and the Alzheimer\'s patients, so really this is the cognitively
impaired group. And they looked at the predicted value of N-acetyl
aspartate over Myo-inositol. Again, the ratios used sometimes because
NAA goes down presumably in the Alzheimer\'s and MI goes up. So really
one would expect this ratio overall to decline.

So this is a step-wise regression with age, education and various MRS
ratios in the model. And this is the correlation that was explained, the
predictive value of that MRS measure looking at various cognitive tests.
This is the auditory verbal learning test and this is the dementia
rating scale. So really there is some predictive value for MRS measures.

If you look at longitudinal MRS studies, I could find only three studies
with a total of 34 Alzheimer\'s patients and 14 controls. The follow-up
in two of the studies was one year long, and in one of the studies was
23 months long. So that\'s the range of follow-up.

The methods varied. To my knowledge these were not controlled in these
studies. I could be wrong, but the paper didn\'t mention it. In all
three studies, in general NAA declined over time. The rate of decline
was about 12 percent per year in the Alzheimer\'s and one percent per
year in controls in the studies that reported a percent change.

The hippocampus decline in one of the studies that concomitantly
measured hippocampal volumes \-- I\'m sorry, this was hippocampal NAA,
this is gray matter NAA. The NAA and hippocampus declined 12 percent per
year in AD, but it was not statistically different from that of controls
in one of the studies.

And in two of the studies the decline in NAA appeared to correlate with
the cognitive decline.

So I want to present to you in the last few minutes a pilot trial that
was done at Duke University, really to look at the effects of a
cholinesterase inhibitor, in this case Donepezil on neuronal markers in
Alzheimer\'s Disease.

Dr. Krishna is the principal investigator. The study, it\'s not yet
published. And it was support by Eisai and Pfizer.

So this was sort of a Phase II study. It was a randomized double-blind
placebo-controlled study of mild to moderate probable Alzheimer\'s
Disease patients. MMSE score ranged 10 to 26. Twenty-four weeks of
therapy with Donepezil or placebo. The Donepezil dose was 5 milligrams
for the first month followed by 10 milligrams subsequently. And then
after 24 weeks there was a 6 week placebo washout.

We obtained spectroscopy measures, MRI measures and the ADAS Cog every 6
weeks during the study.

We measured hippocampal volumes, but sort of this was a post-hoc
analysis. This was not one of our A priori proposed outcomes in the
protocol, but it was done in a blinded fashion, and it was only done at
baseline and week 24. I think I\'m not going to present those data just
because if someone has a question on that, I\'d be happy to talk about
it.

The subjects were recruited at three sites, but all the scans were done
at Duke.

So the trial outcomes, the primary outcome was N-acetyl aspartate, the
secondary outcome was the ADAS Cog and other MRS measures. A post-hoc
outcome was hippocampal volumes.

This is included in your slide set in your handout.

I\'m going to show you some of the baseline characteristics. Really the
baseline characteristics did not differ between the patients. There were
34 patients in the Donepezil group, 33 in the placebo group. You can see
here the mean MMSE score is about 19. And these are the results on the
ADAS Cog. The red line is the Donepezil treated patients, the yellow
line is the placebo treated patients. So these are the 24 weeks of the
trial. This is week 30. And you can see that Donepezil, as expected, was
better than placebo in terms of its effects on the ADAS Cog.

Now, we looked at a number of different regions of the brain, and I\'m
going to present to you the different regions in terms of our N-acetyl
aspartate. Again, you can see here subcortical gray matter. You can see
the red line again is Donepezil. That\'s placebo, the yellow. And,
again, there is some inherent variance in the system and that\'s sort of
reflected perhaps in that.

This is the cortical area, and the red line again is Donepezil. Now, our
technique that we used was particularly bad for looking at cortical NAA
because the voxel we choose cut out the rim of the cortex. So really
there was a lot more noise in the cortex with this particular technique
that we used at that time.

Now, these are the results for the peri/ventricular region. Again, you
can see \-- again, this is Donepezil. At endpoint really there was no
difference in week 24.

Now, this is the white matter. I just have a couple of seconds left, so
I\'m really going to finish with that slide.

And really I think our conclusions were that Donepezil improved
cognition and increased NAA brain levels generally between weeks 6 and
18. However, drug-placebo differences were not significant at weeks 24
or 30. The variance was large. And really I think this was a pilot study
that we did to try to come up with estimates of variant sample size, et
cetera, and at least sort of demonstrates the feasibility, the technical
feasibility of doing a study such as this.

So I want to thank you for your attention.

CHAIRPERSON KAWAS: Thank you, Dr. Doraiswamy.

Our next speaker is Dr. William Jagust.

DR. JAGUST: Well, thank you.

I would like to give you an overview essentially of PET and look at some
of the reasons why PET is certainly interesting in this discussion, and
raise some questions perhaps for consideration.

So why should we consider PET potentially a good surrogate marker in
Alzheimer\'s Disease? And I\'m going to sort of outline my approach and
then give you some examples.

So, PET is a reasonable good assay for tissue biochemistry and also for
physiology that is intimately related to the fundamental disease
processes of interest in Alzheimer\'s Disease. It\'s highly related to
cognitive function. It\'s predictive of cognitive decline, very
similarly to what we\'ve heard about for MR and spectroscopy. And it is
sensitive, reliable and reasonably valid as a marker of the actual
pathology of AD, the amyloid plaques and the neurofibrillary tangles.

And, finally, it is statistically powerful and provides potentially
powerful measures of disease decline.

Now, PET is actually a complicated technology. I think everyone
understands that when we talk about PET, we\'re talking about a method
of mapping in vivo radiotracers and what you label, and the type of
radiotracer we used, depends entirely on what you\'re interested in. I
think for Alzheimer\'s Disease there are three potentially interesting
types of radiotracers.

One are ligands that bind to cholinesterase and that reflect cholinergic
function in the brain.

Another radioligands that bind to amyloid and in the last year we\'ve
hear more and more about this. These are very, very interesting types of
ligands, but as yet I think we have to say they reflect to some extent
unknown characteristics of amyloid and of the amyloid pathology, and
they\'re not completely worked out in a number of ways.

And then what you\'ll hear about most today is fluorodeoxyglucose or
FDG, the glucose metabolic tracer which all evidence points to largely,
though not entirely, reflects synaptic activity.

As far as cholinergic ligands, some of the most elegant work on this was
done by the group at Michigan who used this compound called PMP and
showed that one can actually detect binding a cholinesterase in the
brain. This is a potentially very interesting technique that one could
use to specifically assay the system and also measure effects of drugs
that modulate the cholinergic system. I\'m really not going to talk
anymore about that, other than to point out that it\'s something that
one needs to consider depending on the type of clinical trial you\'re
interested in.

Now, this is an FDG, and the only point I want to make here is to show
you the characteristic signature of Alzheimer\'s Disease on glucose
metabolic studies, a controlled subject and two separate Alzheimer\'s
patients both showing you an area of hypometabolism, posterioral here in
the parietal lobes and also in the temporal lobe. There have been many,
many studies that have replicated this, and many variations on it
showing that it may asymmetric, it may be distributed slightly
differently in different types of Alzheimer\'s patients, but in general
this is the so-called metabolic signature of the disease that also
extends into the posterior cingulate cortex, which in fact may be the
most sensitive region of the brain for detecting early changes in
Alzheimer\'s Disease.

So, what about this in diagnosis? Well, there\'s been, again, in recent
years more and more data gathering on how these metabolic patterns for
glucose metabolism \-- now again I\'m talking about FDG PET \-- relate
to neuropathology, and I just picked two studies here. The first by John
Hoffman and his colleagues showing that compared to pathological
confirmed Alzheimer\'s Disease, this pattern has a sensitivity of about
90 percent and a specificity of about 65 percent for the diagnosis of
Alzheimer\'s.

You\'ll probably hear more from Dr. Small, whos talking after me, about
this study, but this was a substantially larger study showing that PET
was able to both predict progressive dementia in individuals who
presented with cognitive impairment and also pathologically confirmed
Alzheimer\'s Disease, in this case again with a fairly high sensitivity
and specificity.

And so I think there is reasonable evidence from these types of studies
that these metabolic findings are reasonably good markers for the
pathology.

PET also with FDG predicts cognitive decline, and there is really a
plethora of studies that get at that particular issue. One study that we
published a number of years ago shows that a baseline PET scan predicts
a subsequent change in the Mini-mental state in patients with
Alzheimer\'s Disease. Satoshi Minoshima\'s group, again, in Michigan
showed that baseline PET predicts decline for memory impairment, or so
called MCI to dementia, again showing changes in the cingulate were the
most predictive of that type of decline.

More recently the group at UCLA has shown that baseline PET will predict
memory decline in non-demented carriers who have the ApoE 4 gena type.

And finally there\'s been a recent study that suggests that PET may
predict decline in normal individuals who go on to get a mild cognitive
impairment.

So, again, I think ample evidence that PET can predict clinical course.
And this is just an example of the study we published showing that at
baseline glucose metabolic rates predicted the subsequent change in
mini-mental, those with lower metabolism declined more rapidly over the
ensuing two years. And this actually remained significant when one
controlled for a number of demographic factors.

Here showing you that glucose metabolism is related to cognitive
function in the sense that what we see here on the Y axis is a memory
performance and on the X axis a glucose metabolic ratios in the temporal
lobe and in the hippocampus. Just an illustration of another finding
that\'s been fairly widely documented that particular types of cognitive
deficits are correlated with regionally specific patterns of glucose
metabolism.

Now, I want to talk a little bit about progression and change, and
measurement of change over time. And I\'m going to rely on data that was
published by Eric Reiman when he studied a group of individuals who were
asymptomatic who were ApoE 4 heterozygotes with repeated sequential PET
scanning over time. And what you see here is the change in glucose
metabolism or the decline in glucose metabolism over a two year period
in these individuals.

And one can look at this quantitatively and simply look at the
normalized, in this case the region normalized to hold brain glucose
metabolism over time. And one again sees decline over time. And using
these kinds of data one can begin to look at the numbers of subjects one
needs in a clinical trial. And Dr. Reiman published these figures in his
paper.

And one can that depending on the size of the drug treatment effect, and
this here represents the size of the change in glucose metabolism one
was postulating, you would need relatively small numbers of subjects who
are ApoE 4 carriers to detect a change of this magnitude using posterior
cingulate glucose metabolism with 80 percent power.

If one looked at ApoE 4 noncarriers, these numbers got slightly larger,
but still are in the manageable range. And in fact, when one looks at
actual patients with Alzheimer\'s Disease to detect a treatment effect,
one sees that even with a very small treatment effect on patients with
Alzheimer\'s Disease, the number of subjects one needs for a clinical
trial of this sort is actually quite small. This year projected using
frontal glucose metabolism, again with 80 percent power.

So statistically, at least, this is a manageable approach if one is
convinced that measuring this size of reduction in glucose metabolism is
what\'s necessary.

So, let me sort of philosophize about this now. Because this is where
the data meets the road, and maybe we don\'t know how that\'s going to
work out.

So, here are the positives about, I think, FDG PET as a surrogate
marker. And I think that largely relates to the side on linking PET
scanning to clinical declines or to the clinical side of the disease.
And that is, as I showed you, PET predicts clinical decline and
prediction, we understand, does not make a surrogate.

Also PET is biologically plausible. We\'ve heard that word a lot today.
It may well be on the disease pathway, and I think there are several
reasons for believing that it might be.

The first is that it is reasonably sensitive and specific for the
pathology, for the way we define Alzheimer\'s Disease, for plaques and
tangles, that temporal and parietal glucose metabolism seem to reflect
that.

It\'s related to synaptic function. Glucose metabolism, largely related
to that. And we increasingly believe, I think, that synaptic dysfunction
is a key component of the pathological process in Alzheimer\'s Disease,
and it\'s correlated with cognition. And, of course, it\'s statistically
powerful.

But the negative, and the question that\'s been raised, I think, subtly
and really needs to be discussed clearly is what is the link between
using PET and trying to detect an effect on a disease that\'s underlying
modifies its progression. And that relates to the question of whether
PET can distinguish symptomatic therapy or state effects from underlying
disease modifying, drug effects. And there\'s no easy answer to this.

Obviously, the one that\'s been proposed for clinical trials is to use a
randomized start or withdrawal design. Another, I think very important
thing that we need to be considering is the use of PET tracers that
really reflect the basic biology of Alzheimer\'s Disease. And to the
extent that that may be amyloid, the PET amyloid imaging agents really
offer a tremendous option in that direction.

The other point that I want to make is that state effects, as far as we
understand them at least for cognitive states, are relatively small
compared to disease effects. I showed you a PET scan of an Alzheimer\'s
patient, you could see that that Alzheimer\'s patient\'s scan looked
different than the normal control individual.

Individuals performing cognitive tests have metabolic rate changes on
the orders of several percent. You can\'t see that in an individual
image, which is why subjects are averaged over numbers of studies.

So cognitive state effects are relatively small. Disease effects,
cognitive effects, several percent disease effects, 20 to 30 percent.

Drug effects are unknown, and that\'s I think still an unanswered
question.

But sitting and listening to this I was maybe naively wondering if this
isn\'t a subset of the larger problem, which is that when we start to
talk about surrogate endpoints and clinical outcomes being on different
pathways. And really, let\'s take the perhaps trivial but nevertheless
potentially important issue of fluid balance in MR. I mean, that\'s an
effect that\'s going to change a surrogate endpoint, perhaps it has
nothing to do whatsoever with the clinical outcome we\'re interested in.

Suppose we have a drug that has a direct effect on glucose metabolism.
All it does is increases glucose metabolism. An amphetamine, for
example. That\'s the same kind of problem. And I think what this says to
me is that when we\'re thinking about symptomatic or state effects, we
really have to understand the effect of the drug on the surrogate that
we\'re measuring, just the same as we have to understand how a drug
affects fluid balance if we\'re going to make measurements of MR
atrophy. We have to understand how a drug affects glucose metabolism
independent of its effects on Alzheimer\'s Disease if we\'re going to
use glucose metabolism as a surrogate marker.

So just to sort of make a couple of last points about technical issues.
These studies, as are MR studies, can be technically very complicated.
And there many issues that need to be considered in designing a
multi-site acquisition study. That is, of course, subjects state, other
drugs they may be taking and so forth. How one is going to quantify the
image which particularly involves whether one is going to measure the
input of the tracer to the brain, which in a truly quantitative study
requires a catheter in the radial artery, but there are alternatives to
that. And how one is going to measure attenuation and then differences
in instrument resolution across sites.

And then standardization of data analysis is the flip side of all this
where one can quantitate these data with metabolic rates or ratios and
whether one chooses region of interest or atlas or voxel-based
approaches. These are very complicated, but I believe they\'re all
actually manageable.

So to summarize, there is no doubt that PET is not a confirmed
surrogate. That\'s a very easy question to answer. I think some of the
data, some of the data that I showed you and some of the evidence,
suggests that it has a lot of potential in that direction. It\'s
sensitive to decline, statistically powerful, it has strong links to
clinical symptoms, to pathology. But there are real questions about its
relation as a disease modification marker. Any clinical trial, as I
said, has to assess potential state effects on the PET tracer of
interest, no matter what the PET tracer is. And I would submit, no
matter what the imaging modality is.

And really, I think the only way we\'re going to answer many of these
questions is if we begin to cooperate PET in clinical trials when we
finally have disease modifying drugs. That\'s the only way we\'re really
going to get at answers to a lot of these questions.

So, thank you.

CHAIRPERSON KAWAS: Thank you very much.

And our final speaker for this portion of the meeting is Dr. Gary Small.

DR. SMALL: Just getting the technology to communicate here. I\'ll take a
second.

Well, thank you. I\'m delighted to be here and have a chance to expand
on some of the comments that Dr. Jagust just made and throw in a few of
my own in my discussion of positron emission tomography in dementia.

I want to start off with the point that PET is an imaging technique that
provides information not just on brain structure, but also on the
biochemical bases of brain function, which to me is importance since
we\'re looking at in terms of response to drug treatment. And we would
expect that most drugs would have an effect on biochemistry of the
brain.

As we just heard, many of the studies have involved glucose metabolism
using 18-F-fluorodeoxyglucose, which demonstrates the specific patterns
of cerebral metabolic metabolism in various dementias. And there\'s
extensive work in this area. In fact, we have about 25 years of
experience. And I\'ve just listed some of the many studies that have
shown some of the patterns that we\'ve just heard about.

As we\'ve seen in early Alzheimer\'s Disease, parietal regions, temporal
and even frontal regions begin to show this deficit that progresses to
late stage Alzheimer\'s Disease. And interestingly, late stage
Alzheimer\'s Disease has a pattern that looks very much like an immature
brain, as we see in this image.

We also see different patterns in different types of dementia. Here,
again, is an Alzheimer\'s case with the parietal hypometabolism, a
vascular case with both cortical and subcortical deficits, frontal
dementia or Pick\'s Disease with frontal hypometabolism, and the caudate
hypometabolism in Huntington\'s disease.

Now, last year Dan Silverman led an effort, an international effort, to
look at the regional brain metabolism and long-term outcome with PET.
And I\'ve listed all the many collaborators, many of them are here in
this room, and involved centers throughout the United States and Europe.
And we asked questions such as we see in this slide, what is the
accuracy of FDG-PET for assessing the presence or absence of a
neurodegenerative dementia. So we, as in this column, neurodegenerative
disease as seen on PET by blinded reading and then in this column or
this row neurodegenerative dementia present on autopsy. And with these
kinds of numbers one can calculate the sensitivity 94 percent,
specificity 78 percent, and overall accuracy 92 percent.

We can ask the question presence or absence of Alzheimer\'s Disease, we
see similar kinds of sensitivities and specificities. And that was on a
sample of about 130 patients who were followed up to autopsy.

On another group we followed at least two years on an average of about
three years, we asked how does PET predict the progression of dementia.
And we saw similar results in terms of sensitivities and specificities.

So our conclusion from that study was that Alzheimer\'s Disease and
other progressive dementias significantly alter brain metabolism early
relative to the manifestations of clinical symptoms. And the clinical
FDG PET detects this altered metabolism providing an accurate clinical
tool for noninvasive prognostic and diagnostic assessment.

And if one looks at studies where we use conventional clinical
assessments, where we have repeated examinations not using PET, we get
lower sensitivities and specificity. So in the study that has 134
patients with autopsy criteria as the outcome where multiple
examinations were done over the course of several years, we find lower
sensitivities, around 83 to 85 percent and lower specificity is about 50
to 55 percent.

So these data suggest that PET is a reasonably valid marker of clinical
progression and of autopsy findings.

Now, we saw in other material handed out that one of the problems with
the specificity of diagnosis not using PET is in differentiating frontal
temporal dementia from Alzheimer\'s Disease. And at the International
Alzheimer\'s Congress in Stockholm, Norm Foster presented some data that
I thought were quite interesting, specifically looking at this question,
blinded assessments, very well controlled study again, involved several
sites and they had very high inter-rater reliability among the raters
and high diagnostic accuracy, about 80 to 90 percent in just
differentiating frontal, temporal and Alzheimer\'s type dementias.

For the last several years we\'ve been looking at how well PET performs
in detecting very subtle brain changes in people without dementia,
people maybe in their 50\'s or 60\'s who have just minor memory
complaints. So we\'ve been studying middle aged people with the genetic
risk for Alzheimer\'s Disease, apolipoprotein E, or ApoE 4. And back in
1995 in 1995 we first reported that you could see these changes. Eric
Reiman\'s group at the University of Arizona has rated those findings.
And both our groups have in independent samples published additional
data and also data showing how there is change over time. And I\'ll just
show you some of that information.

This is a study we published a couple of years ago in PNAS where we had
54 subjects, half of them had the genetic risk for Alzheimer\'s Disease,
the other half did not. They all had very minor memory complaints. On
the average, they were in their mid-60\'s. And the statistical
parametric map shows you where in the brain there was significantly
lower metabolism in people with the ApoE 4 genetic risk. So the lateral
temporal, parietal, dorsal lateral prefrontal cortex and posterior
cingulate cortex had these changes.

When we followed people with ApoE 4 over a two-year period we found that
these same regions, the parietal and temporal regions, showed decline.
About a 4 to 5 percent decline in these critical brain regions. This is
just in ten subjects, and you can see there\'s no overlap from baseline
to follow-up in this right lateral temporal region in terms of the
metabolic decline.

Now, as we just saw, based on those kinds of data we can begin to make
power estimates of how many subjects we\'d need in a clinical trial to
be able to show a treatment effect. And we saw some of these data just a
moment ago. Just to summarize what the model looks like, instead of
looking at cognitive function, if we looked at metabolic function in
critical brain regions in an ApoE 4 subject on placebo, one would expect
this decline. About 4 or 5 percent over a two-year period. And if the
active drug is working, we would expect a slower decline.

Eric Reiman\'s group has extended these studies to look at patients who
already have clinical dementia or Alzheimer\'s Disease. And these are
figures taken from his article this year showing the significant
differences between patients with Alzheimer\'s Disease and controls. The
areas where there is lower, significantly lower glucose metabolism.
Again, parietal temporal regions, posterior cingulate regions. And he
has followed these patients over time. And here we see the areas where
there is significant decline in these same brain regions.

So to summarize, if one were going to study FDG as a surrogate marker in
brain aging clinical trials, if we had a drug with a 33 percent
treatment effect in the pre-symptomatic cases if we\'re just going to
study ApoE 4 subjects, we only need about 60 subjects per treatment
group. And this would be based on a two-year study. If we\'re looking at
patients with Alzheimer\'s Disease, we\'d need an even smaller number
over a one year period, 36 subjects. And in those studies it\'s best not
to stratify according to genetic risk.

Now, what is the experience thus far with treatment trials looking at
PET changes? And I was able to find in the literature and also just in
press three studies that I think are relevant for this discussion.

And also while I\'m on it, let me mention other conflicts of interests
that weren\'t mentioned. That is that I have been an advisor in the past
for Bayer and have advised Novartis, Eisai and Pfizer as well as
Janssen.

So in this study from our group we looked at the cholinesterase
inhibitor drug Metrifonate. It was a 6 to 12 week treatment period, a
relatively small number of subjects. And we found that there was
cognitive improvement in all of the subject, at least a 2 point increase
on the Mini-Mental State Exam, and also significant increases in glucose
metabolism, particularly in these key regions I\'ve been talking about,
parietal, temporal and frontal.

Steve Potkin at Irvine headed up a study of Rivastigmine, and this was a
26 week double-blind study, placebo controlled, 27 patients in this
study, and they showed very interesting results. 33 percent increased in
hippocampal metabolism in the responders, those who responded to the
drug who had clinical improvement. But the non-responders show a 6
percent decrease in hippocampal metabolism, which was similar to what
was seen, the 4 percent decrease in the placebo treated patients.

Another study that Laurie Tune has presented at several meetings and now
is in press, is a study of Donepezil. Again, a blinded study, 24 weeks
of treatment. And they found that mean glucose brain metabolism remained
stable in the active drug group and declined 10 percent in the placebo
group. And there were significant parietal, temporal and frontal
treatment differences in the study.

Now, here is an image from the study we did with Metrifonate showing
average PET scans before and after with Metrifonate. And here you can
see, particularly in the parietal regions, this is pre-treatment and
post-treatment where there is that increase in metabolism. And at a
lower level in the brain you can see an increase in frontal regions as
well as some of the temporal regions.

In the study with Donepezil, this is showing the Donepezil and placebo
treatment effects on relative average glucose metabolism. This
represents placebo and this is Donepezil at 12 weeks and after 24 weeks
you can see the placebo group declines but there\'s stabilization in the
active drug group.

Now, when we think about PET multi-site trials, I just wanted to cite a
point made from our Alzheimer\'s Association Neuroimaging Work Group,
the PET Research Subcommittee. And we talked about some of these
methodological requirements that we\'ve heard discussion of. Also that
there should be consideration given to trials where we include both PET,
FDG and MRI because of the different kind of information involved. And
also an interest in PET radiotracer methods that will image the
pathologic lesions. So I wanted to spend a few moments talking about
this.

This has been in the news lately and there have been various approaches.
One approach is to alter conventional dyes used at autopsy such as
Chrysamine-G and they\'re effective in vitro, but they don\'t seem to
cross the blood brain barrier.

Now University of Pittsburgh has pushed this approach forward, and
actually did develop a probe that crosses the blood brain barrier. We
saw some of the limited in vivo data in Stockholm. They\'ve scanned
about a dozen subjects. One of the limitations thus far with that, it
uses carbon 11 as a labeling probe and that has a 20 minute half-life,
and is a bit awkward in clinical settings.

At UCLA we\'ve been developing what we call FDDNP and we\'ve shown that
it\'s effective both in vivo and in vitro. We have fluorine 18 labeling,
so it\'s much easier to use clinically. It has 110 minute half-life.

We have information on over 60 human studies that we\'ve completed to
date, and we\'re in the planning stages of multi-site studies. And we
also have postmortem neuropathological validation of our in vivo data.

DDNP is a fluorescent small molecule probe. It\'s neutral and
lipophilic, and it was originally developed for fluorescent microscopy.
And as we\'ll see, it provides excellent visualizations of
neurofibrillary tangles, neuritic plaques and diffused amyloid.

We call it DDNP, it stands for dimethylamino dicyano naphthalenyl
propene and our chemist George Barrio adds fluorine 18 at this end of
the molecule. If one looks at time activity curves and you plot
radioactivity versus time, you can see there\'s very good uptake in the
first 10 minutes. And after about 30 or 40 minutes, one sees the signal
here where in temporal regions there\'s a greater retention or activity
of the molecule compared with other regions. And here you see in the
temporal region the increased activity.

So, if we look at a patient with Alzheimer\'s Disease, this is an MRI
scan, you can see the atrophy by the increased ventricles, this is an
FDG PET scan showing lower activity reflecting lower neuronal activity
in temporal regions and the DDNP scan shows higher activity reflecting
what we think is a greater accumulation of plaques and tangles.

We\'ve plotted the signal against various cognitive measures. And with
the Mini-Mental State Exam and you see a good correlation in controls as
well as patients. And we\'ve done similar studies with more sensitive
memory scores, such as the immediate paragraph recall score and the
delayed paragraph recall score you see very high correlations with the
signal. It separates patients with Alzheimer\'s Disease very well from
controls. And this is the postmortem study I was talking about here. You
can see a coronal section of temporal activity. The patient died 8
months later, and this is autoradiography showing temporal and parietal
activity superimposed on the in vivo scan. And the inset shows you
confocal microscopy of plaques and tangles.

We are just now studying other kinds of dementia. This is a scan of a
patient with a clinical diagnosis of frontal temporal dementia showing
you activity in temporal regions as well as frontal regions. This is the
FDG PET scan showing you a slightly different profile.

And I think the great strategy that we\'ve been alluding to is to
include multiple sources of information in these kinds of studies and
also to ask what kind of question is important. If you want to look at
neuronal function, FDG PET is a good marker. Plaque and tangle load,
DDNP PET. We want to add information about genetic profiles and
neuropsychological functioning, and there are a variety of other
approaches; structural imaging, MRS, functional MRI that can add
additional information.

So, in conclusion I just wanted to mention or review some of the points
made by our neuroimaging work group of the Alzheimer\'s Association,
including myself, Norm Foster, Bill Jagust, Eric Reiman and Moni deLeon.
We thought that PET compliments structural imaging, it can serve an in
vivo biomarker to improve clinical care and research in Alzheimer\'s
Disease. It\'s clearly becoming increasing available. It can confirm the
presence of a neurological disease in mild dementia and assist in
differential diagnosis.

We felt that it should be considered an option for the clinical
diagnosis of Alzheimer\'s Disease. It shows potential for predicting
prognosis in people at risk for dementia and assisting in new treatment
evaluation, increasing efficiency of prevention therapy, testing,
increasing understanding of dementia diseases.

Randomized multi-site clinical trials are needed to further assess
clinical applications and its use as a surrogate marker in drug
development.

Alternate methods of data analysis need to be compared, and the most
effective one standardized.

Development of new PET ligands, we strongly recommended. And we felt
that PET should be included in all clinical trials where Alzheimer\'s
Disease is sought as a pathological substrate for the therapy.

And just to acknowledge some of my many collaborators and many funding
sources.

Thank you very much for your attention.

CHAIRPERSON KAWAS: Thank you Dr. Small. And thank all the speakers in
this section for their informative presentations.

The floor is now open for questions to the presenters on MR spectroscopy
and PET.

I\'ll start then. I\'d like to ask Dr. Doraiswamy, the MR spectroscopy
data that showed NAA wash-out by week 24 and 30, what do you think is
going on there?

DR. DORAISWAMY: Don\'t know the answer to that. It would be nice if we
had another drug. I mean, right now drugs like Donepezil are what we
consider the gold standard for treating Alzheimer\'s. If we had a true
disease modifying drug, for example, an antiamyloid secretinase
inhibitor or something, and if we compared the two, then we would have a
true sense for what would happen with an antiamyloid drug.

The second thing is in this particular study at weeks 24 and 30, you\'re
getting some subject attribution. It\'s a very small sample size study,
so it\'s really hard to tell if what we\'re seeing there is a true lack
of effect or is it really a sample size effect. So I really can\'t
answer that.

At the time we planned the study we didn\'t have any good data to
estimate sample sizes for this kind of a trial and we based it just on
the logistics of, you know, doing a small pilot study.

CHAIRPERSON KAWAS: Thanks.

Actually, I have a second question for anybody who presented.

We saw intriguing data on drugs effect on various modalities. Has
anyone ever tried using a non-AD drug to make sure that we won\'t get
the same effect, a drug that we don\'t believe should be affecting
Alzheimer\'s Disease that we\'re sure it doesn\'t make similar changes?
How specific is the effect, I guess is what I\'m really asking?

DR. JAGUST: Well, I think, for PET any drug that has an effect on
glucose metabolism will affect the results. So, you know, I alluded in
my talk to amphetamines. You know, I mean an amphetamine or
barbiturates, I mean, they don\'t have a fundamental effect on
Alzheimer\'s Disease. They may change patient behavior, but they\'ll
certainly change glucose metabolism.

So I think, you know, my point is that anytime \-- any drug can have an
effect on the kind of signal we\'re looking at in a PET scan, and you
have to understand what the underlying physiology is in order to
interpret the images.

CHAIRPERSON KAWAS: Thanks.

DR. DORAISWAMY: I have a comment. I think in mild to moderate
Alzheimer\'s Disease it may not matter as much as in the advanced stages
where people are taking anti-psychotic drugs and there\'s evidence form
the anti-psychotic literature that some of these drugs could have
potential effects. So at that point, again, that\'s a very good point.
People may need to control for anti-psychotic use.

CHAIRPERSON KAWAS: Yes. Okay.

Okay. Dr. Wolf has a question.

DR. WOLF: Yes. My question probably is directed more generally because
coming from the imaging side and not from the neurological side, I\'m
not that up on what is a mechanism of Alzheimer\'s.

I would like to know to what extent is the amyloid plaque deposition
then reflected in intracellular changes? Because what we see in the case
of N-acetyl aspartate and FDG are all events that happen at the
intracellular level whereas my understanding is, and I stand to be
corrected, is that the amyloid plaque are at the extracellular level and
therefore affect somehow what gets into the neurons or not.

So the question is do we have any measures on changes from the
spectroscopy either from the spectroscopy or from the PET that tell us
the rate of change or the measures of amyloid plaque that could direct
us then to what is happening in that disease?

CHAIRPERSON KAWAS: Does any of our invited speakers want to tackle that
one?

DR. SMALL: Well, I don\'t want to tackle it, but I\'ve got the
microphone, so I\'ll try to address it.

The amyloid plaque correlates with the disease. And, you know, whenever
I talk about the DDNP, plaque and tangle imaging, I try not to get into
debates about the amyloid hypothesis. It may be that the DDNP would be a
great way to track plaque and tangle or plaque deposition. And the good
news at the end of the day to the patient might be you have no plaques
in your brain, your DDNP scan looks great. And the bad news is the
patient wouldn\'t remember the conversation.

So, you know, whether it\'s correlated with the disease or not, I don\'t
\-- whether it\'s actually \-- if you can clean out plaques from a
certain drug, you still may not be able to cure the disease. But I think
the point here, and actually with all these markers, is getting back to
the critical question, is it a good surrogate marker? Does it correlate
with clinical progression? And if it does, is it something we ought to
be measuring just like anything else and leave it up to the drug trials
to prove or disprove a particular underlying path of physiological
mechanism for the disease.

DR. DORAISWAMY: The only thing we know from spectroscopy is that NAA
appears to decline over time in areas that are effected progressively by
amyloid. To my knowledge there\'s no studies in any of the animal models
of Alzheimer\'s Disease, even though there are studies in animal models
of ALS and other conditions. And there\'s only two postmortem studies
that have correlated with amyloid, and they\'re small sample size
studies. So that\'s the amount of the information we have.

DR. JACK: You know, it\'s my understanding that the toxic agent in fact
is oligomeric fragments, so beta amyloid oligomeric fragments. And in
that sense it may be that there is no perfect biomarker for Alzheimer\'s
Disease. The biomarker for the disease in fact is a measure of the
abnormal metabolism that over-produces these oligomeric fragments.

And so every marker, even direct image amyloid burden, in fact may turn
out to be a somewhat of an indirect market. So the same limitations that
apply for markers that everyone admits are indirect, glucose metabolism,
brain atrophy, NAA, et cetera may in fact apply to direct measures of
amyloid load.

DR. SMALL: I just want to clarify one thing and then make another point.
And that is the Alzheimer\'s Association Neuroimaging Work Group
information that I mentioned, this was information that was reported at
the Stockholm meeting. And it is a work in progress. The entire
committee is still going over this information. So if anything, it
reflects the opinions of just the subcommittee, the PET subcommittee,
and it\'s still being edited and worked on, so it\'s not an official
position. I just want to clarify that.

The other thing is in this discussion, you know, it seems to me that
many of the arguments made about MRI also fit with PET. I mean, we\'re
talking about disease modification. The way to determine disease
modification would be these delayed-start study designs and similar
study designs. Because one may find you make \-- for example, you give a
drug to somebody and let\'s say hippocampal volume increases; you take
the drug away, that volume increase may go down. We just heard about the
example with alcohol.

So just the fact that a structural change occurs doesn\'t prove that
it\'s a disease modifying \-- and the same thing is true about FDG PET.
I mean, I showed you some data where we saw increases in metabolism with
cholinesterase inhibitor drugs. I didn\'t give any information what
happens when we withdraw the drug. We\'re presuming that it\'s going to
be symptomatic just as we see with the clinical data. But it is possible
that a drug could produce a disease modifying effect and you could see
that on a functional image. You could withdraw the drug and you could
still see improvement in neuronal function.

CHAIRPERSON KAWAS: Our last speaker for this session is Dr. Michael
Hughes, whos returning to talk to us about validating surrogate
endpoint.

DR. HUGHES: Thank you.

I\'m going to pick up where I left off this morning. I\'m really going
to focus not on biological models, but looking at empirical evidence
from studies to support the validation of a surrogate. And what I\'m
going to do is illustrate the talk a little bit with experience from HIV
where there was a collaborative effort to validate viral loads
surrogate endpoint. And that\'s now actually been incorporated into a
recently released FDA guidance on that issue.

From a statistical perspective, the most commonly cited definition of a
surrogate is really framed in the context of hypothesis testing. And
more importantly, this criterion gives rise to two operational criteria
which are sufficient for validating a surrogate endpoint.

The first one really deals with the issue of correlations, so whether
it\'s a prognostic marker or not. And the second the deals with the idea
that the surrogate must fully capture the net effect. By net effect, it
means combination of adverse and beneficial effects of treatment on the
clinical outcome. And as I mentioned earlier, both are required.
Correlation itself is not sufficient.

This second criteria, it really fits very well with the part of the
Temple definition about establishing that changes induced by therapy on
a surrogate are expected to reflect changes in the clinical endpoint.

So you can develop a framework which might used for establishing
surrogacy. Firstly, the surrogate must be a prognostic marker so you can
deal with that in natural history studies.

Second, is that treatment mediated changes in the surrogate must be
prognostic. And that requires interventional studies.

And the third is whether the effects of treatment on the marker explain
or are associated with the effects of treatments on the true clinical
outcome.

So I\'m going to talk a little bit about the second one, then come back
to the third one.

Here\'s an example which I hope will show you that just looking at early
changes is not \-- treatment mediated changes are not sufficient for
validating a surrogate. So here\'s a typical situation where subjects
are classified as whether they respond to treatment or not, yes or no.
And you can see this is an HIV example that the responders near the
bottom here had a much lower rate of progression to AIDS or death than
the nonresponders. And if you look at this quantitatively, it\'s highly
significant.

But in this particular example I\'ve chosen, this was a placebo
treatment. And it really opens up the possibility that healthier
subjects could respond to the therapy that you\'re studying.

So you cannot establish that a response variable was a good surrogate
using data from an observational study of treatment mediated changes or
a single arm of a clinical trial. However, it\'s important that the
association between the treatment mediated change in the surrogate and
the clinical outcome doesn\'t depend upon the intervention. Clearly, if
it depended upon the intervention, then when you go to a future study
you don\'t know how to interpret the results, the marker results,
surrogate endpoint results.

Here\'s an example of what was done in the collaborative projects. So
this is a plot which shows for a large number of clinical trials and for
a very broad range of treatments within a particular class of
treatments, shows the estimated association for a one log reduction in
viral load and its association with progression to AIDS or death. And
the fact that most of the estimates to the left of the line shows that
reducing viral load using these treatments is associated with better
clinical outcome.

And if you look at the very bottom right hand corner, there\'s a test of
heterogeneity which establishes that from this data there\'s no
significant evidence that association varies between the different
interventions studied.

And here\'s a similar one for a CD4 cell count.

So let\'s go on and think about the third aspect, and that\'s trying to
establish that there really is an association between the changes that
are induced by a surrogate endpoint and the changes in a clinically
meaningful endpoint. And it\'s useful here to remember that the real way
to show that a treatment induces changes in outcome is to use a
randomized trial of that treatment.

And so one must ask the question then how can we use information from
the randomized trial to validate a surrogate? So I\'m considering a
hypothetical trial which is comparing treatments A and B.

A single trial in itself is most useful for providing evidence against
surrogacy, as the case studies this morning showed. Clearly, if you get
the effects on the clinical outcome and the effects on the surrogate
going in opposite directions, then that\'s evidence against surrogacy.

If you have a very well powered trial for the clinical outcome which
shows very similar outcomes, but you find a significant difference in
the effects on the surrogate, and again that\'s useful evidence against
it being a good surrogate.

Having said that, the interpretation of this sort of information from a
single trial really needs to be set in the context of a large number of
clinical trials and assessing whether this happens very rarely or is a
common problem.

So what can be done when you\'ve got effects going in the same
direction, so the effects on the surrogate and the clinical outcome are
pointing in the same direction? Well, the first thing that might be
asked is whether the association between the surrogate and the clinical
outcome varies between the randomized arms. In other words, whether it
varies between the interventions being studied.

And if you find what statisticians call a significant indirection, in
other words the association between the treatment mediated changes and
the clinical outcome varies between the interventions, then that\'s
evidence against surrogacy. It means it\'s not going to be reliable for
future studies.

And clinically what this really means is that the way that you interpret
the different changes for individual patients depends upon the specific
intervention that was used to obtain those changes.

The next thing I would like to talk a little bit about is the idea of
what people call the proportion of treatment effect explained. And this
really came \-- this idea came out of Prentices second criterion that a
perfect surrogate must fully capture the net effect of treatment on the
clinical outcome. And in an imperfect setting we\'re not interested in
fully capturing it, but in partially capturing it. And there is this
concept to proportion of treatment effect explained, which is in the
literature and has been used, but is now largely discredited.

And the reason for this is that the notion of a proportion here is
fallacious; that you can actually obtain values outside of the range of
zero to 1. So finding a proportion of one doesn\'t mean youve
necessarily got a good surrogate. It explains the treatment effect on
the clinical outcome.

So in terms of what you can do in single randomized trials, I think the
most beneficial use is actually providing evidence against surrogacy.

In terms of evidence in favor of surrogacy, generally I think the
opinion is that the framework there is somewhat flawed. And I personally
think it\'s very unlikely that any method will ever be useful in a
single trial because what you\'re trying to do is explain a treatment
difference which generally is imprecisely estimated in the first place.
So your ability to explain it is always going to be weak.

So, the obvious step then is to go into a meta-analysis of randomized
trials. And I think this is the approach which is more broadly accepted
now.

The basic idea here is to evaluate the association between the
difference in effect on the true clinical outcome. So the difference
between randomized arms and the corresponding difference in effect on
the surrogate across multiple trials. And it\'s important to appreciate
this uses information from all trials so you have the standard issues of
meta-analysis about trying to obtain information from all available
trials that address the question of interest.

And this is a schematic of what you\'re trying to get at. So you can
imagine each of these points, the center of the cross, being an
individual randomized comparison. So we\'re asking is there a
correlation between the differences between randomized arms in terms of
the clinical outcome and the differences between the randomized arms in
terms of the marker outcome.

And so we\'ve got a large number of randomized comparisons here. And the
arrow bars are meant to just make the point that within any individual
trial, as in precision and both estimated in the clinical outcome as
well as the marker outcome.

So this would be a schematic for a good surrogate endpoint. So if you
imagine in a future trial you estimate a marker difference up here
between two treatments, and then you can imagine drawing a line down and
then across and you could get an estimate or a prediction for what might
be the likely difference in clinical outcome.

And this is a similar schematic of exactly the same situation I\'ve just
shown where instead of using arrow bars, the size of the circle
represents the amount of information coming from the trial. And it tends
to show the association somewhat more clearly.

Now if you have imperfect surrogates, then the effect of that is usually
to \-- or will be to produce a more diffused association or even no
association.

And if you have an intervention which has an adverse effect on the
clinical outcome, what it will do is produce points either in the upper
left quadrant or the lower right quadrant here reflecting the difference
in direction of effect.

So this collaborative group did this, and obtained data from all
randomized trials of one particular class of treatments in HIV. And the
markers of interest were a measure of viral load and a measure of immune
function. And the true endpoint was what was typically used in clinical
trials at the time, which was progression to AIDS or death.

I think a key thing here is that this was a very successful
collaboration between pharma and academia in obtaining very extensive
data. I don\'t think there was a single trial that was missed in this
meta-analysis.

And this shows the situation for viral load. And I think the most
important thing is in the two quadrants, the top left and the bottom
right, there are essentially no points or points with very, very little
information. So there\'s no real conflict between the viral load results
from these trials and the clinical outcome results. And, in fact,
statistically you can fit a regression line through this and you find
evidence of surrogacy.

CD4, it\'s actually more impressive except that you\'ve got this one
trial which is clearly having inconsistent results between the marker
and the clinical outcome. And this, perhaps, isn\'t particularly
unexpected in that CD4 is a more proximal outcome to the true clinical
outcome than viral load is.

So, I thought I\'d finish just by summarizing what I thought of some of
the issues facing the validation of surrogate endpoints in Alzheimer\'s
Disease. I think there\'s a key issue here about what is the true
clinical outcome that needs to be considered. Clearly you\'re looking at
an association between effects on a surrogate and effects on a clinical
outcome. And if there\'s multiple clinical outcomes, then you want to
look at multiple possible associations.

You really do need some sort of systematic evaluation of the prognostic
value of treatment mediated changes, so that means going into your
trials and looking at whether the changes in the markers really predict
the changes in the clinical outcomes.

And you want to ask yourself does this prognostic value vary much
between populations and more particularly, between different
interventions.

To be honest, I think the biggest challenge of doing anything like this
is just getting people to share data and undertake this sort of
systematic evaluation, whether it\'s done at a qualitative level or at a
very quantitative level. But I think this is a key issue. And,
obviously, the lack of large numbers of longer term trials at the moment
in Alzheimer\'s Disease also limits your ability to do this. But this
collaboration is really the essential facet of being able to validate a
marker.

Thank you.

CHAIRPERSON KAWAS: Thank you, Dr. Hughes. That was very informative.

We do have time for a question or two before lunch break. Dr. Katz?

DR. KATZ: Yes. I think we use the term \"surrogate\" in a number of
different contexts. One important use is whether or not a surrogate has
been validated so that in the next study one could only look at the
surrogate and not have to worry about looking at the clinical.

In the other sense, people have already started to, in a preliminary
way, talk about the utility of using imaging in conjunction with a
clinical outcome in a particular trial of a particular drug and
suggesting that if those are both correlated in a single trial, or maybe
if it was done twice, with the same drug, that that would support a
claim that that drug specifically had an effect on progression. So not
so much interested in using the surrogate in the former sense in which I
just discussed, in other words not so much worrying about whether or not
that that surrogate can then be used with other drugs, but just for that
one drug if there\'s a correlation in a given trial or in two trials
between a clinical outcome and the surrogate.

In your view, would that sort of an outcome support a claim for that
drug for an effect on progression?

DR. HUGHES: People have looked at this issue in other diseases. And the
basic idea and the way it\'s been used in other diseases is to model the
association between the marker and the clinical outcome within each of
the randomized arms of the study. And then use that model to try and
boost your precision in estimating the difference in the true clinical
outcome.

And it\'s been used with, I have to say, very moderate success. The
gains that you get from a statistical perspective, in other words the
gains in precision, are usually quite minimal. And that\'s because the
model describes the association between the marker and the clinical
outcome is often not very precisely estimated.

So, I think you can from a statistical point of view you can use the
joint information to support the licensure of a single drug. But I
don\'t think the gains that you\'ll obtain within a single trial are
going to be particularly marked.

It\'s really driven at the end of the day by the information that
you\'ve got about the difference in clinical outcome between the
randomized arms. And usually that\'s very imprecise.

CHAIRPERSON KAWAS: Dr. Van Belle.

DR. VAN BELLE: Just a question. A lot of the information we heard this
morning deals with non-randomized observational studies. Any role for
observational studies in evaluating the effectiveness of markers or
surrogates?

DR. HUGHES: Well, I certainly think observational studies are very
important. I think they have a definite role in establishing that the
marker predicts the outcome, clinical outcome. I think you can use data
from observational studies to establish that changes in marker levels
that follow the initiation of a treatment also predict changes in
outcome. But there\'s no way that you can use observational data to
really establish that a marker is valid in the sense of drug approval.
In other words, you can never fully establish that the marker effects
explain the clinical effects. You\'ve always got this possibility of an
association in the healthier subjects may be the ones that respond to
the therapy.

So I think you do ultimately have to go into randomized clinical trials
to get the final piece of information that you need.

CHAIRPERSON KAWAS: A final question from Dr. Fogel.

DR. FOGEL: Yes. I have two really quick things.

One was you said on one of your slides that if the surrogate goes in
opposite ways to the clinical outcome, that that is against surrogacy.
But if they reliably go in opposite directions, couldn\'t that actually
be used as a surrogate since you know if it\'s going one way, you know
the clinical outcome is going to go the other way?

And I guess the second question I had was in parentheses, second
sufficient condition that the endpoint must capture the net effect of
the treatment on the clinical outcome, which meant beneficial as well as
adverse effects. Are we in danger of actually throwing away good
surrogates because the adverse effects may be drug specific and that in
other drugs where the adverse effect may not be there, you could still
use it as a surrogate but it\'s just because it had adverse effects that
were specific to that drug that you\'ve thrown it away?

DR. HUGHES: To answer your first question, in theory any marker that
reliably predicts the clinical outcome could be used, even if the marker
goes in the wrong direction. However, that\'s a statistical answer and I
think it\'s really critical, though, that you have an underlying
biological model which associates how the marker should behave with the
clinical outcome.

In terms of your \-- I\'m sorry, I forget the \--

DR. FOGEL: The second question, about second criteria for a surrogate.

DR. HUGHES: And what aspect?

DR. FOGEL: And whether or not you might be in danger of throwing away a
good surrogate because the adverse effects may be specific to the drug
and not because it\'s a bad surrogate.

DR. HUGHES: Sure. I think that\'s a good point. And what it really it
emphasizes is the need not to look at a single study, but to look at
multiple studies involving different drugs. And you\'re really
interested in establishing consistency across a range of interventions.

CHAIRPERSON KAWAS: Dr. Love?

DR. LOVE: Just semantic clarifications for the moment, because the
diagnostic imaging division sometimes uses some of these terms in a
slightly different way.

And my assumption is that your comments are relating to using the
surrogate for licensure of a therapeutic. We tend to also in our
division talk about how we validate an imaging product for approval
perhaps being licensed to be used in this context. So you\'re talking
about the former, using it in a true surrogate sense, reasonably?

DR. HUGHES: Absolutely, yes.

CHAIRPERSON KAWAS: Thank you very much.

I want to thank all of our excellent speakers this morning who were very
informative and stayed to time, which is why they get a whole hour for
lunch.

And I thank all of the Committee members for their excellent questions
and their attention.

The members of the Committee will have a special place in the dining
area where they can eat. And we will plan on reconvening this meeting at
1:45, in an hour.

(Whereupon, the meeting was adjourned at 12:40 p.m., to reconvene this
same day at 1:49 p.m.)

A-F-T-E-R-N-O-O-N S-E-S-S-I-O-N

CHAIRPERSON KAWAS: The meeting of the FDA\'s Advisory Committee for
Peripheral and Central Nervous System Drugs is now reconvened.

We\'ll be beginning with the open public hearing.

I\'d like to remind all the speakers during this portion of the program,
that in the interest of the fairness they address any current or
previous financial involvement with any firm whose product they wish to
comment upon.

And our first speaker for the public hearing is Dr. Eric Reiman,
University of Arizona, Good Samaritan PET Center.

DR. REIMAN: Well thank you very much.

I wanted to offer some personal recommendations on the use of brain
imaging in Phase III clinical trials from the perspective of a brain
imaging researcher whose interested in using these techniques in the
evaluation of drugs for the treatment and prevention of Alzheimer\'s
dementia.

I have no current financial arrangements with industry. I have served as
a consultant to Pfizer, Elan, GlaxoSmithKline, Solvay and Meinse with
regard to the role of these imaging techniques and FDG PET in particular
in early detection and tracking.

In my opinion, brain imaging techniques should provide ancillary
measures of clinical efficacy in Phase III clinical trials, and
information about disease modification in these trials.

To date I believe the published data support the use of volumetric MRI
and FDG PET in the prediction of a drug\'s clinical benefit in that they
are reasonably likely to predict a clinical benefit. I also believe that
they are reasonably likely to determine the extent to which a drug\'s
benefit is related to disease modification.

As you have heard, published studies for both of these modalities have
suggested improved statistical power over traditional outcome measures
and other neuropsychological test measurements, for that matter.

And while I think there is reason to support its use for disease
modification if that support is provided, then those studies when these
imaging techniques are embedded in clinical trials, we will then have
the foundation to validate this surrogate markers.

And of primary interest to our group is that the validation of these
surrogate markers is absolutely critical for their use in the efficient
discovery of prevention therapies. Not only secondary prevention
therapies in patients with mild cognitive impairment, but primary
prevention therapies in cognitively normal persons at risk for the
disorder.

To date it is very hard, in some cases impossible, to test the efficacy
of a promising primary prevention therapy. It is impossible, for
instance, to study a hormone replacement therapy if it was presumed to
be safe soon after menopause and determine the risk of developing mild
cognitive impairment or Alzheimer\'s Disease. And I think these
techniques have special promise in that regard.

Of the imaging techniques that are out there, I believe that volumetric
MRI and FDG PET are the imaging modalities of choice for these trials.
In particular, as you\'ve heard, MRI measurements of hippocampal
entorhinal cortex and whole brain volume, and FDG PET measurements of
posterior cingulate parietal, temporal and pre-frontal glucose
metabolism, published studies have supported their potential role in
predicting a drug\'s clinical benefits and determining the extent to
which the changes reflect disease modification.

As you\'ve heard, for each of these measurements cross-sectional studies
have shown a correlation with dementia severity, studies for most of
these measurements have shown prediction of subsequent clinical decline
and also prediction of the histopathological diagnosis of Alzheimer\'s
dementia.

And there are longitudinal data for each of these measurements now that
indicate that the changes are progressive, provide data for preliminary
power estimates and suggest greater statistical power than traditional
outcome measurements. As you\'ve also heard, these declines precede the
onset of dementia. For the MRI measurements, we have good data showing
the parallel on decline with memory concern prior to dementia. For the
FDG PET measurements we have good data showing these declines precede
the onset of any cognitive impairment in carriers of a common
Alzheimer\'s susceptibility gene that one out of four of us have,
providing a great promise in the study of prevention therapies once
these markers are better validated. And to validate these markers, we
have to have these imaging techniques embedded in clinical trials.

I strongly believe that two imaging modalities are better than one. That
the use of both volumetric MRI and FDG PET in Phase III clinical trials
can largely address most of the surreptitious effects that have been
discussed to a large extent in addition to randomized start or
withdrawal trials. These complimentary measures of brain function and
brain structure together can provide converging evidence in support of a
drug\'s therapeutic effects. And when used together are very likely to
provide information about outcome and disease modification.

Together they increase the certainty that the effects would predict
outcome and reflect disease modification. This is less relevant for
Phase III clinical trials, but for proof of concept studies, one could
also imagine an unlikely confounding effect on one imaging modality that
minimizes one\'s ability to detect disease modification effect. If, for
instance, in the unlikely effect that the removal of plaques shrinks the
brain, one would still have another measure for proof of concept
studies. Less relevant for this issue, but there are numerous benefits
to the use of both measurements in increasing our certainty that our
findings will be relevant to predicting clinical outcome and disease
modification.

I believe that the combination of these techniques will provide the best
foundation for the development of these likely surrogate markers in
providing information about at least one\'s a valid surrogate marker in
the future. And I believe that they provide the best foundation for
establishing their relative roles in the efficient discovery of
prevention therapies.

I believe that the use of the combination of these techniques for the
reasons I\'ve described, that the additional cost is more than
justified. Both imaging techniques are widely available. And I believe
that the logistical challenges can be readily addressed in performing
both studies using both modalities in these subjects.

So in conclusion I\'d like to suggest that volumetric MRI and FDG PET
should provide ancillary measures of efficacy in Phase III clinical
trials, that they are likely to predict outcome, that there\'s reason to
give industry the incentive to get a label for a disease modifying
effect because of that reasonably likely criterion. And that once
that\'s done and these studies are used, we\'ll have several additional
long term benefits of their use.

I believe, as I\'ve mentioned, that the combination of MRI and PET is
justified at this time and could help address some of those lingering
uncertainties. And I believe that the long term benefits of using these
techniques in Phase III clinical trials are extremely important, the
further validation of these surrogate markers and the development of a
way to discover prevention therapies, including primary prevention
therapies without losing a generation along the way.

Thank you.

CHAIRPERSON KAWAS: Thank you, Dr. Reiman.

Our next speaker is Dr. Mary Pendergast from Elan Pharmaceutical
Management Corporation.

DR. PENDERGAST: Good afternoon, and thank you for allowing me the
opportunity to present to you this afternoon.

I am Mary Pendergast, Executive Vice President of Elan Corporation, Elan
Pharmaceutical Management Corporation, the holding company.

Part of Elan develops and sells genetic and other tests for Alzheimer\'s
Disease. And another part of Elan is working to develop therapeutics for
Alzheimer\'s Disease. Elan does not have an interest in any brain
imaging modality or technology.

In my written statement to the Advisory Committee I explained why a
surrogate marker does not need to be validated before it is used in drug
development as a primary endpoint. Rather, any surrogate marker that is
reasonably likely to predict clinical benefit can be used to approve a
therapy so long as trials studying clinical endpoints are carried out
later. On that point I think there is an agreement between the agency
and myself.

I also think that if there is a surrogate endpoint that is reasonably
likely to predict clinical endpoint, the FDA must permit its use, even
when the agency might prefer to wait for validation of the surrogate
endpoint or the agency might wish that there were trials using
well-defined clinical endpoints. The agency may not agree with that. It
probably does not want to have its discretion curtailed, but I think
that that interpretation is the only way to give meaning to the
congressional directive that FDA must facilitate the development of fast
track drugs.

In any event, any surrogate marker that is reasonably likely to predict
clinical benefit, I would argue in that circumstance the FDA should and
would want to use the surrogate marker because Alzheimer\'s Disease is a
serious public health problem.

If you look at the slide, I\'m sure you\'ve all seen the billboards on
the buses around Washington and other cities. 40 million persons
infected with AIDS, zero million cured.

The same is true for Alzheimer\'s Disease. 15 million infected are
infected, zero million are cured. And if you look at this slide, you\'ll
see that in the United States there are four times as many people that
have Alzheimer\'s Disease than have AIDS in this country.

Surrogate markers have made it possible to develop disease modifying
therapies for HIV infection. There are no disease modifying therapies
for Alzheimer\'s, and I think one of the reasons why is because we
haven\'t started using surrogate markers yet for drug development in
Alzheimer\'s.

We need to use surrogate markers to develop drugs for Alzheimer\'s
because by the time the patients have full-blown Alzheimer\'s Disease,
or even the inappropriately named Mild Cognitive Impairment and they are
showing symptoms, they have lost a significant amount of their power.
They have suffered probably irreversible neuropathology. While drugs
that might have symptomatic effects might be relatively straightforward
to study using clinical endpoints, that may not be the case for disease
modifying drugs. Based on animal studies, disease modifying drugs may
not show immediate symptomatic relief, but rather by attacking the
underlying pathological cascade, they might slow the rate of
neurodegeneration.

Given the variable course of Alzheimer\'s Disease, trials showing a
change in the slope over time, even in MCI or AD patients, will be large
and long and a surrogate endpoint might tell us more quickly whether the
treatment is working or failing. We will be able to learn whether the
drug is having an impact before the trial participant dies or becomes
yet more demented.

Perhaps more importantly, surrogate markers will permit us to study
drugs at earlier phases of the neurodegeneration, before the
neuropathology becomes severe enough to be manifested by clinical signs
and symptoms. We want to be able to study and ultimately treat patients
in that 15-year period when the neurodegeneration is taking place, but
the symptoms are not yet troublesome.

We should also remember that the clinical endpoints currently used are
somewhat crude. For example, ADAS Cog has a huge standard deviation. Ten
years from now we will probably think of our current clinical endpoints
the same way we now think of the earliest definition of Acquired Immune
Deficiency Syndrome, which was a rigid definition based on clinical
symptoms, that turned out to miss many patients with HIV infection who
needed therapy.

As with HIV, in Alzheimer\'s Disease the more valuable endpoints will
probably be the surrogates.

In summary, there are several types of brain imaging modalities: MRI,
MRS, PET. If they are well established or validated as Dr. Hughes has
described, they can be used as primary endpoints for traditional
approval. But even if they are not well established, even if they are
not validated, they can still be used to support approval of fast track
drugs with confirmatory trials to follow.

I\'d like to point out that there was five years between the time the
FDA first approved a drug for HIV infection based on HIV PCR and the
time HIV PCR was validated as a surrogate endpoint. That\'s five years
of patients that received treatment, that\'s five years that patients
got a treatment that could keep them alive long enough for the next
therapy to come down the pike.

There are examples other than cardiology that can be used with respect
to surrogate markers. I\'ve mentioned HIV. There are other diseases as
well. In my written statement I point out the analogies that could be
made between rheumatoid arthritis and Alzheimer\'s Disease, diseases
where you have both endpoints based on the signs and symptoms of the
disease and measurements based on the structural damage that the disease
causes.

I urge you to think of the drug development landscape broadly and find,
as with HIV, rheumatoid arthritis, cancer and other diseases that
surrogate markers have an essential role to play.

The questions you need to ask yourselves are not difficult. They are a
question of risk. Is slowing the rate of cerebral atrophy reasonably
likely to correlate with clinical benefit? Is slowing the rate of
accumulated tangles and plaques reasonably likely to predict clinical
benefit? Is slowing the rate of decay from normal metabolism to
hypometabolism reasonably likely to benefit the patient? I mean, ask
yourselves the question: If this was your brain, would you want it to
shrink, get plaques and tangles and become hypometabolic? I wouldn\'t.

I think one more point I would like to make is that this is a question
of risk. And one of the ways risk comes up is in a question with respect
to safety. Because it is definitely true that if you approve a drug on
the end of a couple of Phase II trials using a surrogate marker, which
has been done many times before by the agency, you will not have the
same large safety database that you otherwise would have had. But both
the agency and its accelerated approval regs in 1992, and Congress when
it passed the Fast Track legislation in 1997 had solutions to that
problem. The solutions are several-fold.

First, they require the companies to continue to study the drug out to
their clinical endpoint and out to a large safety database. And those
trials can be compelled and they have been compelled.

Second, the agency can demand additional safety reporting and monitoring
by the drug company during this period when the drug is approved on a
surrogate and when the final clinical trials are finished.

Third, the agency can restrict the distribution of the drug to
practitioners with certain academic degrees, to tertiary medical
centers, to whatever they feel they need to do for the safe use of the
drug. And they can limit and in fact completely exclude the ability of
the companies to advertise about the drug.

And finally, Congress recognized that the agency will make mistakes with
surrogate markers. It\'s inevitable. And so there are very easy ways of
getting these drugs off the market.

When we first invented this system when I was at the agency, we called
it \"easy on/easy off.\"

So, with that, I\'ll answer any questions you might have.

Thank you.

CHAIRPERSON KAWAS: Thank you.

Is there anybody else in the room who would like to make a comment
during this time. This is the last chance anybody in the audience or
otherwise may have. Okay.

This concludes the public hearing portion of this meeting, which takes
us to what I consider to be the hard part, although it seems like a lot
of people in the room don\'t think it\'s going to be nearly as hard as I
do; the discussion of the issues presented by the FDA.

So, I\'m going to open the floor for the discussion in a moment. I want
to remind everybody that in addition to discussing the presentations
that we\'ve heard in general and in specific, that we also were given
several questions that we\'re supposed to be focusing our thoughts on.
And those questions have been provided to all of the Committee members,
the first one of which is: How is the surrogate imaging modality best
validated?

So, with that I open the floor for the discussion of the Committee on
how is the surrogate imaging modality best validated?

DR. SORENSEN: I was wondering if I could ask a question of, I think it
was Dr. Hughes. As I was listening to your presentation and thinking
about it over lunch, I wondered if any surrogate endpoints could ever be
considered valid? I mean, you showed some data of where cholesterol
failed, and I guess there was some discussion, Dr. Temple mentioned,
about hypertension before the definitive studies were in. And I just
wonder how \-- and yet we have some that have been used by the agency.
I\'m trying to figure out how we get over that \-- we make that
decision.

It seems like you described that there were a lot of ways to show that a
surrogate didn\'t work if you have single trials that show that there is
a discrepancy. Other than meta-analysis, it seems like that\'s probably
the way. And even those, you had exceptions to all of your
meta-analysis.

Is there a time when somebody can say, you know, the Cochran report is
out, we\'re done, or how do you actually kind of make that decision?

DR. HUGHES: I think you have to put validity in the context of risks of
using a surrogate. And I think you can reasonably say based upon both
clinical trial data and epidemiological information that
anti-hypertensive effects, cholesterol lowering effects, effects on
viral load, possibly effects on CD4 count in HIV are good surrogates.
And that if you base decisions about the effectiveness of a drug on the
markers based upon past experience, you\'re very unlikely to make an
error.

DR. SORENSEN: And so is that a database of 10,000 patients, of 50,000
patients or what?

DR. HUGHES: I would say in each of those areas you\'re probably talking
about a database of 20 or 25 large, randomized trials and in each of
those cases I think there\'s epidemiological evidence after the sort of
validation process is being conducted to show that you were right.

In other words, if you take HIV as an example, the effect on the marker
is dramatic and the effect on clinical outcomes has been dramatic and
you can see it in surveillance data in the U.S.

DR. SORENSEN: Yes.

DR. HUGHES: So I think in those contexts for the types of interventions
that were being studied or evaluated, the risk of inappropriate approval
is probably minimal.

Now, having said that and if I take HIV as an example, that\'s been
demonstrated for antiviral drugs within certain classes. That doesn\'t
mean that those same markers would work well for, say, immune based
therapies.

And I don\'t think, for instance, the FDA would necessarily advocate the
use of those markers for immune based therapies.

DR. SORENSEN: Sure.

DR. HUGHES: So I think in some areas I would consider the markers have
been validated in the sense that the risks of using those markers for
certain classes of interventions has been minimized.

DR. SORENSEN: Well, so then just to finish my comment, I guess I wasn\'t
counting exactly how many studies were presented today, but I don\'t
think there were 25 randomized trials of this. At least it doesn\'t look
like we\'ve got a validated surrogate endpoint for brain imaging in
Alzheimer\'s as of yet.

CHAIRPERSON KAWAS: I think that probably a large number of people in the
room would agree with that statement.

I have a question for any of the invited imagers or anyone else.

We\'ve talked about human studies today and we\'ve seen some interesting
human data, although not 25. Has there been any work done with animal
models to show that these interventions and these measurements may be
relevant? And if so, can someone share some of that with us?

DR. De CARLI: It\'s not my own data, but there\'s mouse models showing
that there is brain atrophy accompanying the progression of the disease.
They were abstracts presented at Stockholm. I don\'t know that they\'ve
become full papers, but I think they will be shortly. But there is some
preliminary animal data that shows atrophy associated with the
progression of the disease.

CHAIRPERSON KAWAS: And is there any data that shows that interventions,
for example the vaccination mice, has anyone imaged their hippocampal
volume or \--

DR. De CARLI: I haven\'t seen that data yet.

CHAIRPERSON KAWAS: Or their NAA?

DR. De CARLI: I haven\'t seen it.

MS. ROBERTS: Having applied it to therapeutic modality, but rather than
using FDG PET, we used FDG autoradiography and PDAPP transgenic mice and
found that they had a preferential and progressive decline in posterior
cingulate glucose metabolism, the one brain region that is homologous to
that in the humans suggesting that dysfunctional brain imaging measure
might provide a way to track disease progression in the animals and
screen candidate treatments. But that needs to be extended to other
mouse strains and confirmed in other studies.

CHAIRPERSON KAWAS: Dr. Small?

DR. SMALL: We\'ve also done studies with transgenic Alzheimer\'s mice
with FDDNP with autoradiography and found increased cortical signal
compared to control mice.

One of the challenges with micro-PET is that the mouse head tends to be
a bit too small to pick up the signal. So if we could get some good
transgenic rats, we might be able to get a little bit farther, they tend
have bigger brains.

DR. FOX: With your permission, I\'d like just to make a comment on the
question from Dr. Sorensen. Would that be all right?

The example given of hypertension is perhaps the \-- in terms of number
of patients, perhaps the most validated surrogate. I think it would be
worthwhile to think about some of the hypothetical possibilities that
we\'ve drawn up about brain imaging as a surrogate, for which I accept
there are lot of possibilities where you could alter the surrogate
without altering the outcome.

But if we take hypertension, venesection, massive venesection would
probably reduce your blood pressure but might not alter the outcome in
the way you would hope.

So I think it\'s always possible to put out some possible hypothetical
example of where the surrogate would fail, and it\'s all down to
understanding or trying to understand the pathological cascade. So I
think, you know, a massive venesection might not be a good effect on
clinical outcome, but would effect blood pressure.

CHAIRPERSON KAWAS: Thank you.

Let me try and summarize and then everyone can tell me how I
mis-summarized.

I mean, it sounds to me from the discussion that we\'ve heard so far and
the invited speakers who showed us data that overall the general
sentiment is the best way to validate a surrogate marker would be in
human studies by combining multiple studies. And I seem to have heard a
lot of calls for putting imaging into ongoing clinical trials in order
to be able to do that.

Is the answer potentially to our question that the best way to validate
a marker is to continue on doing human studies as opposed to trying any
other alternative approach?

Dr. Grundman?

DR. GRUNDMAN: Yes, I agree. I think we need to do human studies. But I
think what we really need to do is a large multi-center type study where
we look at serial PET and MRI in conjunction with the cognitive and
clinical outcomes and see how they predict the clinical outcomes in a
really rigorous prospective fashion. I think that would give a lot more
credence and credibility to the field in terms of using them as a
marker.

CHAIRPERSON KAWAS: Which is basically how the clinical trials for the
most part are being done now, at multiple sites. So superimposing it on
the trials would be a strategy as long as it met those requirements?

DR. GRUNDMAN: Yes. I mean, the other problem, you know, in terms of
validating a marker, in terms of requiring that a drug actually modify
the surrogate and modify the disease outcome is that, you know, this may
not be possible. We don\'t have drugs right now that can do that. So I
would say in the absence of a drug effect that we can definitively state
has an effect on the outcome, even an observational study, a large
observational study that could make the correlations between the PET and
the MRI and the clinical outcomes would be a reasonable approach right
now.

CHAIRPERSON KAWAS: I have another question, actually, for our invited
speakers.

Dr. Fogel, did you have \--

DR. FOGEL: Yes. It seems that I agree with Dr. Sorensen that we don\'t
really have a specific surrogate at the present time that looks like we
can use. And I was wondering if maybe Dr. Hughes might be able to
address it.

Even though we don\'t have just one singular surrogate, I guess I\'m
wondering in terms of using a, for lack of a better word, a composite
surrogate? We\'ve heard of a number of candidate surrogates that one
might be able to use. But I\'m wondering from a statistical standpoint,
and I guess from a study design standpoint, would it be better to \-- or
how hard would it be to combine some of these surrogates to make a
composite surrogate, if you will, weighted or unweighted and then use
that composite surrogate to be able to tell whether or not that goes
towards the clinical outcome? Because it doesn\'t seem from looking at
all the data that any one will do it. But if we combine two or three and
weighted it, or however one wanted to do it, whether or not that might
be a useful approach to using surrogacy for clinical outcome.

DR. HUGHES: I think that\'s an excellent point. I think the way that you
would validate a composite would be exactly the same as you would
validate any individual measure. So I don\'t think it changes the
validation process. And you\'ve got the same problems with lack of
information.

DR. FOGEL: Although if you have a number of different, you know each one
has a certain percentage to correlate, for lack of a better word, with
the clinical outcome. And I guess I\'m just wondering if each one has
that certain small percentage, the intersection of all three might be
more specific than either one together. And I think the crux of the
problem here is that it\'s not specific. These things can go awry in
many different ways.

I believe somebody talked about using amphetamines to increase glucose
uptake as opposed to just being due to Alzheimer\'s. And I guess the
question is if we meet at the intersection of 3 or 4, or however many
people eventually decide might be a good thing, that that intersection
point might be the goal that we want to reach rather than any individual
one.

DR. HUGHES: No, I think you\'re quite right that you could create a
composite which would be much more specific. And I guess the way that
you would start going about that would be in, for instance, natural
history studies to create a prognostic indicator based upon several
measures which would be a better predictor of ultimate outcome. And then
having done that, validate it in much the same way as you would validate
an individual measure within a clinical trial.

DR. FOGEL: I mean, there\'s precedence in the congestive heart failure
world about using composite endpoints to look at the efficacy of drugs.
And I\'m just wondering whether or not a similar framework might be
useful in Alzheimer\'s Disease as well. So it isn\'t like there\'s no
precedent for it; there is.

CHAIRPERSON KAWAS: Dr. Provenzale, and then Dr. Wolf.

DR. PROVENZALE: Thank you.

I think one of the fundamental issues we\'re trying to grapple with here
is how to make the jump from a prognostic marker, even a very good
prognostic marker, to a surrogate marker. And I think part of the
discussion has gone along the lines of, well, maybe if we combined a
number of prognostic markers, does that make a surrogate marker.

And I\'d like to, you know, ask the question of the group what is it
that is presently or what do people feel is presently lacking that would
make the difference, that would push us over the hump, as I think Craig
was getting at? And I don\'t think the answer is simply just tacking on
more and more prognostic markers. But as Dr. Grundman kind of pointed
out, the problem is that we don\'t have a drug that effectively treats
this disease, so how do we somehow pull surrogacy out of this? I\'m sure
it\'s possible, but I think that\'s where we\'re stuck.

CHAIRPERSON KAWAS: Do you feel the need to respond?

DR. GRUNDMAN: No. I was just going to say, basically that\'s the
problem, we\'re in a catch-22. You\'re saying we can\'t have a surrogate
unless we have an effective drug. So once we have an effective drug, we
won\'t need a surrogate anymore.

DR. PROVENZALE: You have to pull yourself up by your bootstraps, and how
do we do that?

DR. SORENSEN: And you need 25 studies.

CHAIRPERSON KAWAS: Dr. Katz?

DR. KATZ: Well, I guess it matters what you mean by all of this. Again,
there are several concepts. One is how do you validate a surrogate or
the question I probably would be more interested in hearing responses
from the Committee on is whether or not anybody thinks that any of the
surrogates proposed today, or any other surrogates for that matter,
candidate surrogates have actually been validated in the ways that Dr.
Hughes talked about, and a I talked a little bit about? I think I know
the answer to that question, but I still think it would be useful to
hear people talk about that.

As far as, you know, sort of this catch-22, of course if we don\'t have
a drug that has an effect even on the surrogate, let alone whether we
know it has a clinical effect, of course we couldn\'t approve such a
drug. But as you\'ve heard from various people, we do have a standard,
an alternative standard for the approval of drugs, so called fast track
drugs, which we can impose.

Now, Mary Pendergast suggests that we must impose it. I\'m not sure.
I\'m not sure that there\'s that much difference, quite frankly, in our
views.

But nonetheless, there is a standard. And that standard says reasonably
likely to predict. So at some point if you think that no candidate
surrogates actually have been validated, we have to discuss whether or
not anybody thinks that in the absence of any clinical finding for a
particular drug, whether or not an effect on one of these potential
candidate surrogates is reasonably likely to predict. And then if we
pick a surrogate, let\'s say MRS or NAA or whichever one you might pick
\-- you might pick none of course \-- but if you picked one, we could
use that as the standard to test the next drug that comes down the pike.
And it either will have an effect on the surrogate or it won\'t.

So I don\'t think we have \-- I think you have to worry about having a
drug that does this when you talk about validating a surrogate. But if
you want to impose the standard of reasonably likely which permits the
approval without expressly and explicitly without validation, then
you\'d pick one, and we\'d use it.

So, I don\'t think we have to worry so much about the so-called catch-22
or the absence so far of such a drug.

CHAIRPERSON KAWAS: I\'d like to give Dr. Wolf a chance to speak. But
then would it be helpful to you, Dr. Katz, if we sort went around the
table and let each person express whether or not they think any of the
images that they\'ve seen today have been validated for use as a
surrogate and if so, which ones?

DR. KATZ: Yes.

CHAIRPERSON KAWAS: Okay. Dr. Wolf?

DR. WOLF: Well, my question will address this partially. Because one of
the problems we have with many of the imaging modalities we have seen is
that the techniques and procedures that were used for many of them were
quite different. And although we have a number of studies that use, for
example, MRI, because they use different protocols they are not strictly
comparable.

So one of the problems we need in developing the prospective studies is
to have a uniform, well thought out protocol so that we can compare
studies across multi-centers. And right now we have a number of common
studies that use the technology, but which are done in a different
manner and therefore, are not necessarily strictly comparable.

So, for example, we have seen some data where study X got positive
results, study Y got negative results. And they\'re probably both done
correctly. But because they were doing things in a slightly different
manner, their results came out differently.

So this is one of the problems that we have to face. I mean, what is the
time resolution, what is the spatial resolution, what\'s the degree of
localization; what are a lot of these parameters we use in imaging
modalities and how comparable are they from one side to another.

CHAIRPERSON KAWAS: Thank you, Dr. Wolf.

I think that Dr. Katz actually gave us two separate questions, and I\'d
like to sort of sort them out as we go around the table and let
everybody give their opinion.

So the first question is have any of these markers been validated for
use as a surrogate, at least on the level that the individuals believe
it should be. The second question, which we will take up later, is the
reasonably likely possibility that any of these markers may be useful,
and which of those markers have met that level of standard.

So, to begin with, can we start with the right side of the table and we
will give everyone an opportunity to answer the question whether or not
they think any of these modalities have been validated as a surrogate
marker in the disease of Alzheimer\'s.

Dr. Provenzale, I think \--

DR. PROVENZALE: It\'s my opinion that none of these have been validated
at present as a surrogate marker.

DR. FOGEL: No, I don\'t think any one of them have been validated
either, although I would like to at some point get back to the concept
of whether or not by a meta-analysis one could use a composite, and the
data may even be there it would be valid but we don\'t know it because
the analysis hasn\'t been done, a combination of various surrogates.

DR. VAN BELLE: I don\'t think any evidence has been presented yet that
would convince me. And especially because I think as we heard this
morning, and I think this makes sense, you would only be able to
establish effectiveness if you had a series of randomized clinical
trials.

On that point, given the claim for improved power, it should be
relatively easy to incorporate these candidate surrogate endpoints into
clinical trials because presumably they\'re going to have bigger power
than some of the other clinical endpoints.

CHAIRPERSON KAWAS: Good point.

Dr. Penn?

DR. PENN: I think for all effective purposes the MRI quantitative
measurements of atrophy have been validated for being quantitative
measures of atrophy; just that. And that it is reasonable to use those
as a surrogate. And that it does, in fact, measure disease and that
within 10 or 15 years we will be looking at the disease that way rather
than looking at clinical manifestations. And it\'s going to be a painful
thing to go through this transition, but I think that it\'s very likely
that it\'ll happen in the same way it\'s happening in MS now for using
MRI to show the disease itself.

CHAIRPERSON KAWAS: Okay. I think my opinion is that none of the markers
to date have been adequately validated for use as surrogate in studies
of AD.

DR. GRUNDMAN: I would very much like to have a valid surrogate marker,
but I agree with you. I don\'t think that we actually have one. I think,
you know, if you look say just at the MR measures and you wanted to have
some sort of standard outcome in the clinical trial that we could use as
a standardized measure, I wouldn\'t know right now whether or not we
should look at the hippocampus, or we should look at whole brain, or
look at gray matter or look at some other measure. What\'s the best
measure of clinical progression as it relates to a standard clinical
outcome measure that we would look at in the clinical trial. And I
don\'t know the answer to that. And that\'s why I think we need to do
those sorts of prospective large scale studies to make those
correlations in a sort of definitive way and figure out what the best
measures of brain atrophy actually are.

And I can say a similar thing about PET. You know, there were different
regions of the PET scan that showed decreased metabolic rates and, you
know, posterior cingulate frontal, temporal, you know. I\'m not sure
which, was it the whole scan, is it part of the scan? What particular
segment would you be looking that? We saw some measures that correlated
on the left side of the brain that correlated better with the MSSE than
others.

I think at this point we just don\'t know which measures even of the PET
scan are the best or most closely associated with the outcome measures
that we\'re interested in in a clinical trial.

CHAIRPERSON KAWAS: Dr. Wolinsky?

DR. WOLINSKY: So the short answer is no, these are not validated
surrogates. The longer answer, which I feel compelled to give, is that
there is very intriguing data that\'s been presented here and outside of
the room that says that quantitative image analysis and functional
imaging of various types is our only portal to the pathology of brain
disease. And I\'m not at all comfortable that any of our current
\"clinical outcomes\" are more reliable than these portals will be in
the long run. And, in fact, I\'m very discouraged that at least some of
the things that I deal with are not telling us a good picture of what
goes on.

The issue, though, is a little bit different and comes back to the first
answer, which is, no, these are not proven surrogates, they can\'t be in
the definitions that we\'ve been given to work under. And maybe after we
get around the table if there\'s time for other things, we could maybe
think about more novel ways to use these kinds of critical tools in
trial design that might be more useful in dissecting what happens in
trials. But that\'s a longer statement.

DR. CHIU: Up to now, the clinical imaging \-- because I\'m stating from
an MI point of view, and we read the MI and then we do a profusion, we
do a PET scan. And we do see it. Actually, today it\'s listening to
lectures. We lack of understanding what MI can do. So we should have
more education in this regard. Because MI up to now, it\'s throughout
whole United States. It\'s only you have 1.5 test results. We don\'t
have to go any \-- you know \-- good scanner. And we can perform it, we
can really see the cortical atrophy. We do see a hippocampus
abnormality, even though not specific for AD. But we do see the changes.

So I believe in the MI image and PET and the fusion image.

CHAIRPERSON KAWAS: So are you saying they have been validated or they
just have potential promise, need further study?

DR. CHIU: From our center\'s point of view, the clinicians who believe
in that, we continue to \--

CHAIRPERSON KAWAS: So you believe that they have been adequately
validated as surrogate markers for use in drug trials?

DR. CHIU: That\'s correct.

CHAIRPERSON KAWAS: Dr. Ramsey?

DR. RAMSEY: I would agree with Drs. Grundman and Wolinsky, and for the
same reasons that they gave, but I\'ll withhold my opinion from PET
since I don\'t know enough about it.

I hesitate a little bit because I think there are a lot of people behind
me who are probably a lot smarter than I am who think that they are
valid markers. So that bothers me a little bit. But even as they were
presenting their information, I kept feeling, as Dr. Chiu did, that I
want to see more images, can I see a little more about that. What about
the T2 weighted images, are there a lot of hyper increased signal
intensity areas, is that what\'s really depressing the NAA? What else is
going on? Do they have seizures, is that what\'s really affecting the
temporal lobe?

So all of those concerns. And maybe it\'s just because in this short
period of time we can\'t present all the data that\'s out there, but I
feel that from what we saw and what\'s available, it hasn\'t really
validated as an acceptable surrogate.

CHAIRPERSON KAWAS: Dr. Beam?

DR. BEAM: My short answer is simply that I don\'t know. I wish I could
say yes or no at this point in time, but given the data that I\'ve seen
in the short period that we\'ve been here, I just can\'t make this
determination right now.

I would like to abstain from the question on the basis of simple
ignorance. I would like to have more discussion about this and perhaps a
longer presentation of the existing data might lead us to a different
conclusion in the future.

DR. WOLF: My concern is that I\'m not sure how valid and how meaningful
the clinical data are and to what extent they are definitive and they
are truly a gold standard.

If we go to the basis that the current clinical procedures are an
absolute gold standard, then I\'m not sure the imaging modalities are
yet proven to be equivalent. If on the other side, we have concern that
the clinical measurements are also fraught with a lot of uncertainty,
then probably the imaging modalities are close in uncertainty. And under
the circumstances I\'m not quite sure to say yes we can discard them. I
think we need to consider them. I think we need to for each drug we need
to consider the weight of the evidence.

And if imaging modalities provide enough supporting evidence that
reinforces and supports some of the clinical data, then they must be
considered as part of the package.

As single systems because they are not part of the traditional
standards, I don\'t think we want to go for that. But at the same time,
we need to continue looking at them because, like Dr. Wolinsky said, I
don\'t think we have a good measurement at the present with the clinical
outcome.

CHAIRPERSON KAWAS: Okay. Dr. Sorensen?

DR. SORENSEN: Yes, it\'s also my opinion that the standard for
validation of surrogate endpoint has not been met by any of the data
we\'ve seen so far.

CHAIRPERSON KAWAS: Thank you.

Dr. Kim?

DR. KIM: From the data presented today and some of the literatures that
are available, I see changes but I\'m yet to make the connection between
the changes that we see here on the data and what we see it as a
connection between that change and the AD. So I\'m still yet to be
convinced with that.

CHAIRPERSON KAWAS: Thank you.

Did you want to \--

DR. VAN BELLE: No. I think we got a pretty clear view of whether or not
people in general think that any of these have been validated in the
sense that we\'ve been talking about. And that\'s very helpful.

If I can move to the next question, which is, given that the consensus
is that none of these have been validated, the question then arises
whether or not we should rely on the drug\'s effect on a surrogate \--
I\'ll leave for the moment which one or which ones \-- whether we should
rely on the effect on the surrogate solely in the absence of clinical
changes to support the approval of a treatment for Alzheimer\'s Disease.

As I pointed out earlier, and as Mary Pendergast pointed out, we have
language both in the regulations and in the statute, in the Act, the
law, that say that we at the very least can approve a drug on the basis
of an effect on a surrogate that is reasonably likely to predict a
clinical benefit or to represent the clinical benefit like, for example,
progression.

I know you\'ve heard the term all morning and afternoon \"reasonably
likely.\" And, of course, I can\'t give you a lot of guidance as to what
that means, although the language in the regulations talk about
epidemiologic, pathophysiologic or other sorts of evidence. That\'s not
very helpful. But the question now given that you believe that no
surrogate is validated is should we rely on the effect on a surrogate in
the absence of a clinical change at this point, at this time, to approve
a drug for Alzheimer\'s Disease, which if you say yes, you have had to
have concluded that it was reasonably likely to predict or to represent
a clinical change. And if you do say yes, I\'d be very interested to
know how you\'ve come to that decision.

But that\'s the question: Can we approve a drug on the basis of an
effect on an unvalidated surrogate in the absence of a showing of a
clinical effect?

And again, I would ask you when you think about that to take into
consideration Mary\'s point, which was that there is a belief, anyway,
that a drug that would have an effect on progression may not have an
effect that can be seen clinically very early. Right now the symptomatic
treatments can show effects in 6 weeks, 3 months, 6 months certainly.
But there is a question as to whether or not a drug that has an effect
on the underlying progression as represented, perhaps, by a surrogate
will show clinical benefit early.

So, anyway, that\'s the question we critically need you to discuss.

CHAIRPERSON KAWAS: Dr. Van Belle?

DR. VAN BELLE: Apropos to that point, I\'ve been pondering a graph that
Dr. De Carli showed from the Framingham study where he has data relating
the brain volume from age 30 to age 95, basically I don\'t know whether
you remember that graph or not. But it\'s clear that there was a very
steady progression of decline in brain volume from age 30 on.

I wonder if he had superimposed on that, say, the MMSE that might have
been estimated at the same time, whether that would have shown a decline
as well or whether that\'s pretty standard?

The point I\'m trying to make is that I\'m not convinced yet that
changes in brain volume are necessarily associated with changes in
cognition. And I think that\'s really a prerequisite for dealing with
one of these imaging surrogates as a possible modality for a clinical
endpoint.

CHAIRPERSON KAWAS: Thank you.

Dr. Sorensen?

DR. SORENSEN: Yes. I\'d like to respond to Dr. Katz.

I think \-- I was hoping you were trying to set up a kind of a straw man
by saying is there any chance that one would be happy with an agent that
didn\'t have a clinical benefit but did have an MRI benefit. And
certainly if one had the option to have both a clinical benefit and an
imaging benefit, you would certainly take that option.

And so my initial response was of course not, that wouldn\'t be
feasible. But then I got to thinking about kind of potential scenarios
where the answer \-- where I could try to find a way to say yes to that.
And the best analogy I could come up with in the few moments is the
coded stems that we\'ve seen such dramatic results from
angiographically, and yet it might take, you know, years to provide that
their clinical outcome had some meaningful benefit in, say, survival of
patients.

And so I guess I can imagine an scenario where someone might have a
complete cessation of atrophy that had been, you know, documented before
they were on the drug and then they stopped. And that the MMSEs or the
ADAS Cog tests were trending towards a positive impact, but they hadn\'t
actually reached a positive impact. And so you\'d have to say there is
no evidence that statistically that there was a clinical benefit.

You know, would I want to at that point say to patients that this drug
couldn\'t be approved? I think at that point I\'d probably go squishy
and say \"Well, let\'s look at the safety profile, let\'s look at some
of the other mitigating factors.\" Because we don\'t really have good
tools to understand what\'s going on in the brain. And if this one
marker, whether it was the ADAS Cog score that we knows it has some \--
some challenges or whether it was an imaging score showed a lot of
benefits. Even if the other ones didn\'t. I would hate to close the door
on that.

So, I think that the challenge of prospectively defining what that is,
what that reasonableness is, I think is very hard. But to say there\'s
no scenario at all under which I could come up a situation where I
didn\'t have a clinical benefit, but I did have an imaging benefit,
would I never allow that to lead to an approval? I don\'t think I\'m
ready to quite close that off completely.

DR. KATZ: I\'m not \-- if I can respond.

Yes, I\'m not asking whether or not there is \-- it\'s possible at some
point or there is some imaging marker that at some point maybe, you
know, I\'m not asking if we should close the door forever and all time,
or even if we should close it now. I\'m asking should we open it now,
really.

I\'m saying right now do you think that there is an imaging modality, a
surrogate marker if a drug was shown to affect it beneficially but have
no clinical effect in a trial of some reasonable duration; whether or
not those sets of facts should allow us or should force us to approve a
drug for Alzheimer\'s Disease now.

DR. SORENSEN: Okay. So you\'re not closing it quite \-- sorry. I\'ll
just finish the point if that\'s all right.

I see. I thought you were going to try to take \-- peel away from one
extreme down to sort of the reasonableness issue. And I guess I would
still say that those markers that could lead to success that would I
think be compelling evidence that a drug might have benefit, could even
be the ones we\'ve seen today as unvalidated as they were. If somebody
came to me with a set of data that was large and the imaging was done
well, and they had a logical scientific argument, and they just barely
missed by their clinical performance scores, I\'d certainly be very
tempted to seriously consider that, and would have to weigh in on other
aspects. I\'d have to look carefully at it and I wouldn\'t want to close
the door to that.

DR. WOLF: I would like to support what Dr. Sorensen just said and expand
it a little bit.

If the imaging modality shows definite positive results and the clinical
outcome is not deteriorating, does not show a significant deterioration,
then that drug may be considered seriously.

If on the other side there is a positive imaging outcome but clinically
the patients continue deteriorating, then obviously the imaging modality
cannot be weighed over the clinical arena. But the question is is when
we have the borderline situation, whether there\'s no significant
deterioration from the clinical point of view.

One of the things we don\'t know is what is a temporal relationship of
what we measure. Are the imaging modalities giving us information that
is earlier or later that with what we manifested clinically? And if in
the case that the imaging modalities give information that manifests
itself at an earlier stage, then shorter trials may reveal something
that the clinical trial just have not caught up with.

So, again, it\'s something that needs to be left open depending on the
correlation between the clinical and the nonvalidated imaging modality.

CHAIRPERSON KAWAS: Well, I agree with both of the previous speakers, but
actually I would like to contract the discussion back down a little bit.
And if I understand Dr. Katz\' question, I would like to respond.

As a person who works in Alzheimer\'s Disease and sees these patients
and understands what the images look like in these patients, I really am
absolutely \-- I mean, I completely understand the correlation between
the imaging and the patient\'s status. But what I don\'t find
convincing, I think, and I\'ve been trying to find all day, and I do
think in the future we might have but I really believe very strongly we
don\'t at the moment, is any evidence that makes me think that it is
\"reasonably likely\" that altering these markers would necessarily have
an effect on the disease.

I\'m not convinced that we\'ve seen anything here that couldn\'t just
turn out to be hair color, and that aging would still go on and death
would still happen, and the hair would be black or the hippocampus would
be bigger.

I think that superficially it sounds very tantalizing to assume that
these things track very strongly with disease state, but I don\'t think
I\'ve been shown any evidence that makes me feel confident in saying
it\'s reasonably likely that altering these parameters would have that
effect.

I\'d especially \-- I want everybody around the table to try and give
their thoughts. So, can I try going around again?

DR. PROVENZALE: Well, I was asked for my short answer to the last
question, and I gave just a short answer.

But it\'s clear to me that probably one of the imaging techniques or a
combination of imaging techniques that were presented today will prove
very valuable in assessment of therapies for this disease. And so to get
to the question when or under what circumstances should one feel
comfortable relying on one of these imaging techniques as a reasonably
likely to be successful surrogate marker, I would say that there are
probably a number of venues under which if well controlled prospective
randomized trials in, let\'s say, that involved multiple sites all using
the same techniques, the same pulse sequences, let\'s say, or the same
PET imaging sequence, if they showed overwhelming evidence that one of
these markers \-- let\'s use hippocampal volume as an example.

Obviously, if we\'re talking about volumetrics, we could be talking
about the whole temporal lobe, we could be talking about the whole
brain, we could be just talking about small areas of the brain.

But, for instance, if there were a study in which a therapeutic agent
was in a randomized controlled trial given subjects who were at high
risk for developing AD but who at the beginning of the study all had
normal hippocampal volumes, and if the study were executed properly and
if a big difference were seen in the rates of change of decrease in
hippocampal volumes, that would be to me fairly compelling evidence.

Obviously, as Dr. Wolf pointed out, we\'d have to take the clinical into
consideration; that goes without saying. If the hippocampal volumes
remain stable but the MMSE were deteriorating, that would be a different
situation. But to me that would be very provocative and promising
information.

Unfortunately, I think that we\'re stuck with a disease that progresses
relatively slowly over time. And so we would not expect to see a
dramatic change, a stabilization or improvement in MMSEs over months or
a year or two. And so we have to, I think, more or less rely on markers
such as this.

So although they\'re not validated, I think they offer as someone put
it, that\'s our window into looking at this disease. I don\'t know which
one it is. I think it\'s quite possible that a combination of the two,
let\'s say thin section MR imaging for volumetric analysis with
coregistered PET imaging or coregistered MR spectroscopy and PET
imaging, or all three techniques together.

Although I don\'t think these are validated, I think we have to somehow
figure out how we\'re going to use them to advance the field.

DR. FOGEL: I thank you.

Well, you know, because a surrogate by our definition means that we have
to have an intervention that reliability predicts the clinical outcome,
we obviously don\'t have in my opinion. So what all these great tests
that we\'ve been talking about falls into the realm of prognostic marker
than surrogate. And so when we talk about reasonably likely to help the
disease, we\'re really talking about reasonably likely using prognostic
markers rather than surrogate markers.

And I guess I have a question for Dr. Katz, and that is we\'ve heard a
number of times already that these \"surrogate markers\" can be used in
Phase II trials as drug picks to go on to further evaluation in Phase
III trials. And we\'re essentially we\'re being asked is do we want to
take this out of the realm of Phase II trials and enlarge this to Phase
IV trials be unleashing it on the public. And so I guess I\'m wondering
how comfortable the FDA feels about taking stuff from Phase II to Phase
IV on the basis of these prognostic markers?

DR. KATZ: Well, I think that\'s the question we\'re asking you folks.
What we want to know, and I don\'t really \-- that\'s post-marketing.
But Phase II, Phase III, people have their own idiosyncratic definitions
of what those mean.

My question to the Committee is do you think it\'s appropriate at this
time to base an approval of a treatment for Alzheimer\'s Disease on the
basis of a change on one of these candidate surrogate markers in the
absence of any clinical change? I\'m talking about the definitive trials
on which approval would be based. So that\'s the question I\'m asking.

DR. FOGEL: Under the most likely scenario?

DR. KATZ: Whether or not an effect on any of these surrogates. And by
the way, for those who think that these surrogates are reasonably
likely, it would be very useful to hear which ones do you think are.

But, yes, under the reasonably likely standard, whatever that means. Do
you think it\'s reasonably likely that an effect on the surrogate in the
absence of a clinical finding is reasonably likely to predict a useful
clinical outcome.

DR. FOGEL: And I guess in my opinion it falls, again, back to that
we\'re dealing with do we have prognostic markers. Because those are the
ones that would be reasonably likely to effect the disease. And we have
a number of them that have been prognostic \-- have been shown by data
to be prognostic, meaning that they don\'t have an intervention but that
have been shown that the marker itself has shown to change or to
differentiate normal from disease state.

And I guess in my opinion from listening to all the data and reviewing
some of the literature that we were given, I would vote for hippocampal
volume and FDG. But, again, that would be under the reasonably likely
scenario and not necessarily as a surrogate.

CHAIRPERSON KAWAS: But just so I make sure I understand your position,
Dr. Fogel. If a study was brought forth today that showed that by giving
somebody a compound you could alter their hippocampal volume and their
FDG PET, say both of them, but no clinical change, would you be in favor
of approving that drug for the treatment of Alzheimer\'s Disease?

DR. FOGEL: Under the reasonably likely phrase, the answer would be yes
you would do that because it would fall strictly under that definition,
because it would be a prognostic marker.

CHAIRPERSON KAWAS: Yes. But whatever suggests that altering that
prognostic markers makes a difference, is guess what I \--

DR. FOGEL: Right. See, the point is that if you saw an alteration that
would then take it into the realm of surrogate rather than prognostic
marker. And the fact that \-- you\'re saying that this compound actually
changes the \--

CHAIRPERSON KAWAS: I\'m saying, we give you the drug, you\'ve got
Alzheimer\'s Disease, you get the drug, your hippocampus gets bigger now
on imaging.

DR. FOGEL: Right. But there\'s no correlation between the intervention
and the outcome relative to the marker. So it still leaves it in the
realm of the prognostic marker. And if it leaves it in the realm of the
prognostic marker, then under the reasonably likely phraseology that
we\'re being charged with, the answer is yes, I would vote for that.

CHAIRPERSON KAWAS: Okay. Dr. Van Belle?

DR. VAN BELLE: I would be reluctant to approve because of the two
requirements that we need, namely some linkage between the imaging
modality and the clinical outcome, and then some information about the
imaging modality and the disease progression or disease state. And so at
this time I think the imaging work is clearly crucial to studying
disease state and disease process. But I don\'t think we\'re there yet
at the clinical level.

While I have the floor, may I make one small additional comment?
Somebody earlier mentioned the situation where the imaging modality
would have been significant in a clinical trial and the clinical
evidence borderlined. I think it was one of the speakers on the other
side of the table mentioned that.

Some kind of analysis of co-variants with the co-variant being the
imaging modality might have been one way that the precision might have
been improved and would be based on the assumption that cognition or
change in cognition was related to the imaging characteristics. So there
are actually ways to deal with this statistically. It\'s a small point,
but nevertheless it might make a trial a little bit more sensitive.

Thank you.

DR. PENN: I think we\'re seeing here is a shift in a general opinion
about where we should take the risks and benefits for Alzheimer\'s and
general neurodegeneration diseases, and that\'s what the law asks us to
do, which is be willing to make a shift towards the risk of putting out
drugs that are worse, don\'t work, don\'t correlate with the eventual
clinical outcomes that we\'d like to have them have.

And I think the whole question of what\'s a reasonable situation in
which we would approve such a drug depends upon whether or not we can
really find out about that drug in the next X number of years with a
Phase IV study that works. Because if we release a drug that has
marginal clinical benefit that shows results with a surrogate that
we\'re using, it seems to me that the only safe way to find out whether
that drug really is good is to do what we\'ve been doing, which is the
standard type of thing what we\'ve been doing all along, which is
require efficacy and safety data over a fairly long period of time. And
then we\'ll have safer drugs. But we\'re going to miss a number of drugs
that we could have approved earlier and found out in a Phase IV whether
they worked or not.

So if we have the machinery to check up on what is actually happening in
the field after a drug is released, that\'s fine. But I have my doubts
as to whether we have that machinery in hand now to do that. We can
require certain things of drug companies and so forth, but try and get
somebody not to take that drug or to follow up a double-blind study
after it\'s been released with the impression that it was released
because we think it works; it\'s going to be practically very hard to
do. And that, I think, is the real bind that we\'re having here.

And I think everybody says correctly that we don\'t a surrogate marker
that\'s been proven to be a gold standard or that morphs into, as I
said, the real thing, which is representing the disease. And that\'s
obvious. But the question is where along the line do we make the
reasonable judgment that we can go ahead with safety releasing this drug
knowing that this hypothetical drug that works on the \"disease
process,\" whether it\'s worth that risk. And that\'s a practical
question whether the FDA can later enforce the proper studies to be done
and be willing to quickly put on an quickly take off something.

DR. KATZ: I\'d just say that those mechanism exist technically. In fact,
they\'re requirements if we would approve a drug on the basis of a
surrogate that\'s reasonably likely to predict the clinical benefit,
there is a requirement that, as Mary pointed out, that the sponsors
perform studies to validate the surrogate in Phase IV.

Now, it may be that from a practical point of view that\'s very
difficult to do in any given case. I think when the regulations were
written, it sort of anticipated that those validation studies were well
on their way towards being completed at the time of approval.

Here, at least some people it sounds like believe that those studies
need not be underway at the time of approval and whether or not they
could actually be done, practically, for the reasons you suggest, I
don\'t know. But there are regulatory mechanisms to require them,
technically.

DR. PENN: But if I were a company and I had ten years more to go on my
patent, I would take at least 10 years to figure out whether it worked.
And I \--

DR. KATZ: Well, I think we\'d have something to say about that.

DR. PENN: Yes, I know. But, I mean it\'s not an easy environment in
which to deal with. And that becomes \-- so it becomes a question of the
real specific facts with a real drug as opposed to just sort of
generally saying, well we\'ll accept this or we won\'t accept this. I
think everybody\'s going to have trouble turning down a drug that makes
the hippocampus a lot bigger and clearly in a clean study does, and we
have data that the patients certainly aren\'t getting worse and it\'s
looking like it\'s coming into significance. But, clearly, no one wants
to just go with a hippocampal drug with no clinical \-- what we\'ve
classified in the past clinical outcome data.

So it depends on the specific case. And I think until you get a case
like that, we can\'t answer the question in a reasonable fashion.

CHAIRPERSON KAWAS: I guess I want to make a couple more comments.

I mean, I actually think I could not be overly impressed by a drug that
makes a hippocampus bigger. Because this disease does not just effect
the hippocampus.

I think that in considering this I\'ve been contrasting it with a
disease where I actually feel like imaging has a role as a surrogate
marker potentially, and that\'s multiple sclerosis. And there, at least
in my concept of the disease, the number and size of lesions can be
important in a way that I can more easily see.

Although I know that the hippocampus is part of Alzheimer\'s Disease,
it\'s not the only part of Alzheimer\'s Disease. And so I can very
easily imagine that particularly when we\'re talking about a single
marker, that it actually might have the possibility of being actually
reasonably unlikely that effecting any single marker will necessarily
have a major effect on the disease. Because this is a disorder that
effects the entire brain, extracellular, intercellular, almost all the
parts, neurochemistry and otherwise.

So, that\'s the nature of my concerns in approving anything that only
effected a single marker.

DR. GRUNDMAN: I\'d sort of echo I think that opinion.

I think also in the realm of Alzheimer\'s Disease clinical trials for
demonstrating progression, I don\'t think that they\'d be overly onerous
to show some clinical efficacy. I think, you know, we have instruments.
We have the CDR sum of boxes. We have the CGIC. We have the ADAS Cog. We
know what their rates of progression are over, say, one year or 18
months. And I think we can design trials to demonstrate a one-third
decline or some clinically relevant efficacy outcome measure and see
whether or not the imaging supports that clinical conclusion without
using the imaging marker as the sole criterion.

Now, it gets more complicated when you move to prevention. Because
there, I think, it requires much larger sample sizes and smaller
effects.

I\'ll stop there for the time being.

DR. WOLINSKY: I think it depends \-- I\'ve got to be careful with words.
So we\'re not talking about surrogates anymore. We\'re just talking
about anatomical measures of disease or biochemical measures of disease?

DR. KATZ: Well, I\'d call it \--

DR. WOLINSKY: Because we had to throw the surrogates off the table?

DR. KATZ: No, no. I\'d call them unvalidated surrogates.

DR. WOLINSKY: Okay. Okay. Became semantics could really get us into
trouble here if we\'re not careful.

I would say I actually wouldn\'t \-- if I was in your seat and somebody
came to me and said okay, I want to use one of these markers as a
primary outcome measure and I want you to be able to tell me in advance
if I won on that, that you\'ll give me approval even if I lose on my
clinicals, I would say fine. Pick atrophy because I don\'t think we\'re
going to get a brain Viagra that\'s going to blow up the size of the
brain in about 15 minutes. So that you would actually have a shorter
term study that would not allow you to actually get a clinical
correlate.

Now, it might be a little bit more uncomfortable if you pick something
like PET scanning, depending on what the law again was, or if you picked
spectroscopic marker. Because there may be other things that could begun
to normalize or reverse those kinds of changes. And in a short term
study you might see a change in that that might or might not have a
clinical correlate.

So I think, again, it becomes the practicality of what that marker is,
what the expected time course is, how long it\'s going to show and how
likely is it to be linked with the clinical outcome at least to a near
miss level.

So I don\'t think that this is as scary as it sounds, just because of
the nature of the technology right now.

DR. CHIU: I still strongly believe you measure modality, you know \--
better than MRI or PET scan combined. You cannot use CT, you cannot use
x-ray. And we don\'t treat patient AD just based on the MRI findings. We
need clinical, you know.

CHAIRPERSON KAWAS: Actually, I think that that\'s Dr. Katz\' question;
Should we consider approval a drug that effects the imaging of whatever
sort or however many different image modalities you choose, but does not
have a clinical outcome.

DR. CHIU: Okay. Single MRI imaging, you cannot judge from that. You need
to see with examination. You cannot just judge one. If you do blind
study and you treat a patient as AD, but you have it today, you have a
\-\-- 6 months, you\'re able to see the effect of the medication. You
can just from one study.

So to me I think what else can you do? You don\'t have any other
modality, imaging modality. So I do believe this the most powerful
imaging modality.

CHAIRPERSON KAWAS: So you would support a drug approval based on only
longitudinal imaging studies without clinical demonstration of any
clinical changes?

DR. CHIU: Definitely. What I need to see is a study, not just single.

CHAIRPERSON KAWAS: Okay. Mr. Ramsey?

DR. RAMSEY: I\'ll give the short answer. I would not approve a drug.
That would be too much of a leap of faith for me to say that it would be
likely to have an effect when, in fact, you can\'t see any effect. And I
would defer to Jack Welsh, who I know is a little bit out of favor. But
one of his sayings was \"to accept reality as it is, not as you wish it
was.\" And that\'s what I would say.

So, no.

CHAIRPERSON KAWAS: Thank you, Dr. Ramsey. Your brevity especially is
appreciated.

Dr. Beam?

DR. BEAM: I guess my answer is a question for Dr. Katz.

Dr. Katz, if this were to happen, what would go on the label as that
drug as far as a claim for what the drug does?

DR. KATZ: Well, I have no personal experience with approving drugs on
the basis of surrogates. It might say something like decreases
hippocampal atrophy, or something along those lines. But we wouldn\'t
approve it in the first place if we didn\'t think that that meant
something clinically. In other words, reasonably likely at the least to
predict a clinical outcome.

So what exactly the language would be, I don\'t know. But we wouldn\'t
approve it, as I say, in the first place unless we thought that language
was reasonably interpreted to mean it had an important clinical effect.

DR. BEAM: Well, if the language said simply that this maintains volume,
for example, and doctors believe that this is associated with positive
outcome for Alzheimer\'s patients, something like that, I think I could
go along with that approval process.

DR. KATZ: Again, the specific language is \-- it\'s certainly important,
obviously. But the fundamental question is whether or not we ought to
approve it in the first place.

Drugs can do lots of things and effect lots of outcomes that we can
measure, but we would not necessarily approve a drug on the basis of its
effect on some serum marker or something if it didn\'t mean anything
clinically. So we still have to bite the bullet, in effect.

DR. BEAM: Right.

DR. KATZ: I mean, we have to decide first and foremost fundamentally
whether or not the effect is, again, reasonably likely to predict
something important clinically. I mean, that\'s \-- we can\'t get out of
the conundrum by just describing exactly what the drug did, let me just
say that. We have to believe that that meant something.

DR. BEAM: Then my answer would be no to that question.

CHAIRPERSON KAWAS: Thank you, Dr. Beam.

Dr. Wolf.

DR. WOLF: I sort of had addressed this question before. The answer is
that if there is a positive indication from the imaging and while there
is no conclusive clinical indication of progression, there\'s no
regression, there\'s no deterioration, then the drug should be
considered.

DR. SORENSEN: Okay. So I\'d like to give my answer by telling a little
story that I think I found in literature that seems to support a
scenario under which I could answer this question.

And that\'s the drug called Etanercept. As I understand it, it\'s a drug
that acts on rheumatoid arthritis. And it was originally compared to a
standard treatment for rheumatoid arthritis called methotrexate. And it
had both the clinical outcome and it had some imaging outcomes.

And the clinical outcome was to measure some kind of rheumatology score
every month and the imaging outcome was to image the joint space
narrowing and erosions at 6 and 12 months.

It took off and looked really good in the first few months, but its
primary endpoint at 12 months it just barely missed statistical
significance. The joint space narrowing didn\'t work, but the erosion
scores showed a really dramatic improvement in erosion score reduction.
So it looked like the knee joint was looking a lot better. And it was
approved. I don\'t know exactly how, I wasn\'t privy, but it did get
approved and they had to do some follow-up studies. And the follow-up
study was published this year, and that was some 2 year data which was
basically the same protocol. And in 2 years the drug did work. It showed
that the rheumatology score, the clinical outcome score was better and
the erosions continued to be improved and overall the imaging endpoint
just lead the clinical endpoint.

And this was a scenario where I think people understood the pathology.
Not all of the imaging endpoints worked. The joint space narrowing was
clearly not working. So that biomarker failed. But one that people did
understand and seemed to fit was reasonably, and I think in hindsight I
would say, and I think most people would say, that the agency made the
right decision there. They got a drug that was effective onto the market
sooner.

And I would say that if a similar situation came up with Alzheimer\'s,
that I would be in favor of the same course of action. If you just
barely missed your clinical endpoint but there was some earlier data
that suggested that it had worked, and the imaging was compelling. Maybe
not all of the imaging endpoints, but at least some that made a lot of
sense. Then I would say, yes, the biological link between \-- maybe not
just one endpoint like, you know, the hippocampus, but if the whole
brain or if it were the ventricles, or if it were glucose. You asked us
which one of those, I know, and I\'m waffling a little on that. But I
would probably say whole brain volume for me and probably glucose
metabolism.

If those were to improve, I\'d say that that would be a compelling story
and I would seriously consider it.

CHAIRPERSON KAWAS: So is \-- let me understand your answer. Is your
answer that if the imaging is positive and the clinical is borderline,
you\'d go with the imaging.

DR. SORENSEN: That\'s correct.

CHAIRPERSON KAWAS: If the imaging is positive and the clinical was
negative, what would you say?

DR. SORENSEN: If the imaging was positive and the clinical was clearly
negative, say, that there were no indication that \-- or that the
patients \-- well, let me break it down.

If the patients did no worse than the placebo, I would probably still go
with the imaging. IF the patients did no \-- did worse than the placebo,
I would certainly not go with the imaging. I don\'t know if that answers
your question.

It\'S a little a bit like one of the earlier respondents said. It\'s
tough to argue this in the abstract. You\'d like to actually see a case
in front of you.

In the case of Etanercept, it was a tough call because the primary
endpoint wasn\'t made, but it was borderline, and that made it easier.

How borderline is borderline before you\'d call it? Well, I\'d have to
see the case or I have to see the situation.

If it were far from borderline, I probably wouldn\'t go with the
imaging.

CHAIRPERSON KAWAS: So maybe the best way to phrase your answer is that
you think imaging should be an ancillary, but do you think it should be
primary?

DR. SORENSEN: I mean, it would be more than ancillary. It was more \-- I
mean this drug got approved on the basis of the imaging and not on the
bases of the clinical. The clinical didn\'t work.

CHAIRPERSON KAWAS: So does that mean imaging should be used as a primary
outcome then in your opinion?

DR. SORENSEN: So in my opinion I think they made the right decision here
by using imaging as a primary outcome. They did. I don\'t know whether
it was accelerated approval or not. Like I said, I wasn\'t closely
involved, but I just read the literature. But I think they did get
approved and they did have to do some follow-up studies, so maybe that
means they were given accelerated approval with these conditions.

And that kind of scenario is one that I would endorse for Alzheimer\'s
as well.

CHAIRPERSON KAWAS: Okay. Dr. Kim?

DR. KIM: I think my hangup is more of making the connections, more
definite answers. I see evidences and yet that evidence doesn\'t really
show me whether that has a direct correlation with the AD at this point.

With that said, I think what I\'d really like to see with the present
technology would be more of a Phase IV studies which also give us a
little more for us to understand, for the technology to catch up even
better. And have a lot more data.

I think one of the problems that I have is that we don\'t have enough
data in whether it is one modality or multiple modality, or even the
normal ways that we haven\'t even thought of yet.

So I think it\'s more of which came first or which comes first. But I
would like to see this in one of the Phase IV and come and revisit this
portion here. So even with the present data, I don\'t agree that I would
approve based on this.

CHAIRPERSON KAWAS: Okay. Thank you very much.

Mr. Perez is keeping a tally of the votes of some sort, and I\'m glad
it\'s him and not me. Because I\'m not sure how some people voted.

But, Dr. Katz, did we help in any way with these answers?

DR. KATZ: I, too, am having a similar difficulty.

The no\'s were pretty clear. I\'d have to add up how many I had. But
maybe it would be useful to further probe the people who felt that a
drug currently could be approved.

There are a few questions that I have that would help clarify people\'s
thinking for me. I mean, I think I understand when people said no I
don\'t think we\'re ready; I understand that. It would be useful to have
the deeper understanding of exactly why the people who said they might
or they would, thought that way. It would just be helpful to us to
understand that.

So there are a couple of things I\'d like to ask. I don\'t know if you
want to do that now or \-- okay.

The first is, the one that I did raise, which is the question of which
marker. The folks who think we\'re in a position now to approve a drug
on the basis of an effect solely on a surrogate that\'s reasonably
likely to predict clinical outcome, which marker or which modality or
modalities, which imaging modalities.

The other is, there are two other considerations I think we ought to
talk about. Again, the purpose of using a surrogate in this case would
be to do shorter studies, smaller but also shorter studies. So I have
two questions.

One is what about the possibility that an effect that you might see,
let\'s say at 3 months on whatever surrogate you choose, what about the
possibility that that might be transient? In other words, the
understanding here is that if an effect was seen, it would presumably
persist and ultimately translate into a clinically meaningful difference
to the patient. But suppose that in 3 months you see an effect on the
surrogate, but at 5, 6 months it\'s not there anymore. I mean, we
wouldn\'t have that information at the time of approval on the basis of,
let\'s say, short term studies. So I wonder what people think about
that.

The other reason that people like surrogates is because they can be very
sensitive, so you need fewer patients. But what about the possibility
that a statistically significant difference on whichever marker you
choose is seen in a relatively short term study; what about the
possibility that even if that effect persists it might not get any
larger and how do we know that that size of a change actually ultimately
will translate into a clinically meaningful outcome?

So, which marker or markers, which surrogate or surrogates, how do we
account for the possibility that the effect that you see in a short term
study may just be transient, and do you worry about that? And the
possibility that the very sensitive measure will pick up very small
changes, let\'s say, in hippocampal volume and the possibility that that
in fact might have no clinical meaning?

So, it\'s a lot, but it would be very useful for us to hear what people
think about that.

CHAIRPERSON KAWAS: Okay.

DR. KATZ: Those of you who said no, relax.

CHAIRPERSON KAWAS: Although we have opinions on those things, too.

Now I\'m going to let the people who said yes start self-identifying. My
take on it is it\'s more of this side of the room, but if I\'m ignoring
anyone over here, I\'ll make sure.

Oh, all of a sudden \-- I got to tell you, Dr. Katz, all of a sudden the
vote to me looks like it shifted. A lot of people want to talk now. But
let\'s start over this way.

DR. WOLF: Okay. Let me start here.

First of all, any imaging modalities we have, we would be using, we
would have by now long term studies available so that we know that we
would have some feeling whether something seen at 3 months maintains
itself at 6 months, 9 months, etcetera. These are part of the studies
that are ongoing, so we would have some background on what those changes
represent.

CHAIRPERSON KAWAS: Well, maybe the question that he would like you to
answer then is how long do we need to follow before we should feel
confident that it\'s not transient?

DR. WOLF: I am happy I don\'t have to make that decision. But let me
give you an example of the kind of area I work in, which is oncology.
And in the case of tumors, the gold standard is reduction in tumor, in
solid tumors reduction in tumor volume.

Now, you can have a tumor that is stable which is composed of dead
tissue. And in those cases functional measurements of metabolism and
perfusion give you much more valid information and much earlier
information, the changes in volume of the tumors.

So that it depends very much what is the biological parameter we are
measuring and what is a sequential basis of those biological changes
appearing.

So I don\'t know enough about Alzheimer\'s Disease to know how it
progresses, what determines what, what are the different biological
steps that will cause neurodegeneration, etcetera. But the question is
if we have a marker, an unvalidated surrogate marker that gives us some
information that suggests that a positive change is occurring and if
there is no clinical indication that is contrary to that, then we have a
reasonable probability that something positive may be happening and it
is worthwhile considering.

CHAIRPERSON KAWAS: Dr. Wolf, perhaps you could speak directly to Dr.
Katz\' question; which marker do you favor, if any, right now is the
first one. I mean, is there any particular one that grabbed your
attention or just the idea of markers in general in neuroimaging?

DR. WOLF: I would say at this moment the idea of markers, because I am
not \-- I have listened to some of the information we have, all of them
give a limited information of what\'s happening at the tissue level, but
they don\'t give a good comprehensive idea. So I think it would be a
combination of different markers that would have to be considered.

CHAIRPERSON KAWAS: Okay. And you hedged your bet sort of on the issue
about transient effect that may washout over time. But how about his
final question; the possibility that these images may be so sensitive
that they\'re detecting small changes that may not be clinically
relevant?

DR. WOLF: If they detect small changes, then they\'re likely to be
washed out by comparing a number of different patients. Because you
would have\-- I mean, you would not have the same \-- if you had exactly
the same degree of every comparable level of small changes, then that
would be meaningful. Just statistically I would suspect small changes
would disappear in the analysis if they\'re compared over a sufficient
number of patients.

CHAIRPERSON KAWAS: Okay. Thank you.

Who wants to \-- Dr. Sorensen?

DR. SORENSEN: I think \-- I\'m sorry.

CHAIRPERSON KAWAS: No, I was just going to sort of remind the questions;
which marker, the possibility that the effect might be transient and the
size of the effect.

DR. SORENSEN: Right.

I think that none of the studies \-- or none of the markers that I\'ve
seen have been \-- have followed the rigor that the FDA or any good
scientist would require them to follow in collecting and analyzing them.

I mean, I\'m familiar with Dr. Love\'s draft guide document on medical
imaging, and as far as I can tell, maybe there\'s one MRI study but none
of the PET studies and none of the MRS studies have followed that level
of rigor in using centralized readers, in standardizing their protocols
and all of that.

So, as a result, I\'m not sure that any of these in their current state
today are actually acceptable as an endpoint. And my earlier positive,
you know, views were assuming that somebody could come and pull that
level of rigor together and actually follow the rules, you know, the
scientific rules that exist for doing science well.

And so as a result, that\'s influential because the things that are
closest to that I think are the MRI volume measures. They seem to be the
ones that have been used a couple, or at least one multi-center trial
with a prospectively defined analysis algorithm and endpoint.

And so I would say that the volumetric markers are probably at the top
of my list just because they seem to have the best track record for
being analyzed.

The sensitivity thing, I think most people like it\'s not just the
sensitivity, it\'s the lower variance that makes these interesting. But
I think the point\'s the same, that you might \-- if it\'s a low
variance whether it\'s sensitivity, you see things that might not be
clinical relevant. And I think that is a very relevant issue if you saw
a \-- you know, an effect size that was too small or that seemed, you
know, within one standard deviation of the noise of your measurement,
that wouldn\'t be very interesting to me. And I think each of them have
different denominators, so I don\'t know the right language to use to
describe how much of an effect I\'d expect to see. But even with the
lower variance if I saw two standard deviations of an effect, I think
that would make me feel more comfortable even if that was small relative
to what I would expect the clinical outcome.

The transient thing I think is also a very important point. And I think
that that has to be measured against the background of the natural
history of the disease and it\'s variation. And from looking at the
graphs we saw today, I would guess that at least a year you would have
to see this effect. That if it were less than that, it would be suspect.

CHAIRPERSON KAWAS: Thank you.

Dr. Chiu, do you want to \-- I think you actually have already told us
which markers. I think you told us MRI and \--

DR. CHIU: Depend on from the common more point of view, you know, those
are static image you\'re able to see the hippocampus, the size of the
ventricle. Now we have a new pulse sequence called a T1 flare image.
You\'re able to clearly see the structures of the \-- and so forth.

So the MIs getting, you know, more and more to more powerful \--

CHAIRPERSON KAWAS: So any particular MRI measurement that you think or
multiple measurements of anatomical MRI?

DR. CHIU: I think it\'s all individual. We have a computerized, you can
use it. I\'m talking about really talk about the imaging of the grading
signal and noise, it come with T1 flare. It really give you very crispy
gray and white matter. You really can see it.

Earlier mentioned about the T2. We can do T2, just make sure we\'re not
dealing with a multiple\-- dementia, you know, that kind of \-- it\'s
helpful.

MRS spectroscopy, not many of these center you have that MRS. But
that\'s another powerful tool. It take longer time. Usually you have to
take good time to measure that.

Come to the PET scans, I don\'t know if its approved or not. We do a PET
scan, but that\'s more expensive.

So I\'m talking about standard imaging, T1 weighted coronal view plus
volumetrics that\'s can be done. I think that\'s probably the more \--

CHAIRPERSON KAWAS: And do you have any concern that some of these
effects may be transient and/or too small to be clinically meaningful?

DR. CHIU: Yes. Back to the MS, I don\'t know, to way back to ten years
ago we have a MS study. We use a standard T1 and \-- T2, we\'re able to
make diagnoses. But as you said, it\'s come and go, not that specific.

Then we have this MP \-- and you can pick up more earlier, more definite
MS. Now we\'re using this as a gold standard. A lot of patient come in
clinical and questionable MS. And we\'re able to make a diagnoses window
there. Do all kind of spinal tap I\'m able to make a diagnoses. We are
the one who give them more definite diagnoses.

So come to transient, I think clinical doubt you probably have to do
every 3 months to see that how that changes. They might \-- patient
maybe dehydrated or alcohol taken \-- show the changes. But at least
over a year and every 3 months for \-- the symptoms, and that\'s how we
do now.

CHAIRPERSON KAWAS: Thank you.

I think there was some interest on talking at this side of the table,
too. Dr. Fogel?

DR. FOGEL: Yes. Well, I agree with Dr. Sorensen that none of the data
that we\'ve been shown today reaches the rigor that we need. And when I
had mentioned the MRI volumetric analysis and the FDG, I meant it in
combination with prospective future trials that would be multi-center
and wide scale trials targeted things like MRI volumetric information as
well as functional information like FDG.

In terms of whether or not the effective short term and transient, I
agree about the year definition. I mean, in the accelerated language it
says that the surrogate has to be reasonably likely to predict clinical
benefit. And I don\'t think anything that\'s going to be transient or
short is going to be reasonably likely to translate into clinical
benefit unless it\'s present for a long period of time. So, a year is
better than 3 months, so I would pick a year although I don\'t have data
on that.

CHAIRPERSON KAWAS: And what would you say about the data that we saw
today, the MR spectroscopy data in the randomized trial that showed an
effect on the imaging that washed out before the study was done, which
was 24 weeks, as I recall?

DR. FOGEL: That was transient, I would think, because it did wash out.
And since we don\'t have it \--

CHAIRPERSON KAWAS: And that, by the way, was in a study where the drug
did show clinical benefit, by the way.

DR. FOGEL: That\'s right. That\'s right.

CHAIRPERSON KAWAS: So in this case the clinical benefit continued to the
24 week and beyond mark, apparently, but the MRI spectroscopy which
looked like it was correlating with clinical benefit disappeared by 24
weeks. I mean, how would you interpret that sort of data?

DR. FOGEL: Not really clear how you would interpret it with the
exception that it might have done the \-- I\'m not really sure how I
would interpret it.

DR. SORENSEN: The graphic showed how the ADAS Cog score is the same at
week 24 for placebo and Donepezil. So I think they had equilibrated by
the end.

DR. FOGEL: So you\'re saying that it didn\'t show clinical benefit.

DR. SORENSEN: It did early on.

DR. FOGEL: No, no, no. But I\'m saying it was transient because it
didn\'t show it for a sustained period of time?

DR. SORENSEN: It did for 24 weeks, but not for 30, at least the graph
I\'m seeing.

DR. FOGEL: Right. And if we use the definition of \--

CHAIRPERSON KAWAS: But the MRI did not show it at 24 weeks.

DR. KATZ: But that was after washout.

CHAIRPERSON KAWAS: Thirty is washout, so that doesn\'t count.

DR. KATZ: Right. But it does suggest that \-- and I think we ought to
talk about this later for the people who said no, so you can\'t relax
completely. I still have some questions, but the fact that that
surrogate after washout, after discontinuation of the drug, was back to
where the placebo patients were suggests\-- again, there\'s a suggestion
that if you \-- one way, one operational way to show that a drug has an
effect on progression independent of imaging is to take the drug away.
And if the effect still persists, that\'s pretty good evidence that you
had an effect on progression.

In this case, the effect on the surrogate didn\'t persist. The drug was
discontinued.

CHAIRPERSON KAWAS: It didn\'t even persist while the person was still on
drug in this case.

DR. KATZ: Well, okay. Even worse, right. But even if it had and you took
away and it goes back after a drug is discontinued suggests that that\'s
also documenting just a temporary symptomatic effect and not a
structural effect.

DR. FOGEL: Right. So it seems that there\'s some debate about that. But,
I mean, I would think that you would need a year to actually show a
sustained clinical \-- a benefit that wasn\'t shown before but that
might have actually had a clinical benefit.

And in terms of the imaging modality being too sensitive to small
changes to make a clinical difference, I think that that\'s a very
important point. And the reasonably likely phraseology, again, says that
it needs to predict clinical benefit. And if one feels that the changes
are too small to predict clinical benefit, then it shouldn\'t go into
the approval process.

But the other thing I want to just bring your attention to the fact that
there are \-- we might have other surrogates that we hold to a higher
standard that you need larger \-- that aren\'t as sensitive that you
need larger changes and we risk missing efficacy because we need to see
those large changes. Because those surrogates aren\'t sensitive. And so
we basically balance in our equation everyday in other surrogates that
are less sensitive than imaging studies \-- danger of missing efficacy.

CHAIRPERSON KAWAS: Thank you.

Dr. Penn wants the last word before the break, so take it away.

DR. PENN: At least a year, probably 2 years overwhelming evidence that
the surrogate moves, not equivocal evidence. And no deterioration
clinical state during that period of time. So the criteria for a fast
release drug without the usual clinical benefit shown yet has to be
enough so everybody nods, yes, that really is the right thing to do. So
it has to be very strong evidence.

DR. GRUNDMAN: Just a question. You know, if you\'re going to do a study
for 2 years on Alzheimer\'s Disease and require that the surrogate
effect persists for that length of time, wouldn\'t you expect to see a
clinical outcome measure showing the same effect over that period of
time if you powered the study adequately? It\'s really \-- I think it\'s
a practical question and if over that length of time you didn\'t
actually see a clinical benefit, even though you saw something on the
MRI, I\'m not sure how you would interpret that.

CHAIRPERSON KAWAS: Okay. I mean, I think what the last two speakers have
both said was essentially that we should do the studies as we normally
do them and look at the surrogate markers in addition to the clinical
markers? Isn\'t that more or less what I heard?

I mean, I think a lot of interest in doing these surrogate markers is
the idea to not have to do two year studies. It\'s the idea that if I
can give something to someone and make their hippocampus go up 20
percent in size, there\'s something that maybe that can happen quickly
and that can give me information quickly that I can then use to shorten
the studies, not only in terms of numbers but certainly in terms of
time. But at least some of the people who I couldn\'t tell how they
voted the first round, really are saying that they want to see them
together before they would feel comfortable.

Is that true?

DR. PENN: That\'s what I said, I\'d be satisfied in a year or two. What
I said was that I\'d be satisfied by overwhelming evidence maybe in a
year without definite clinical changes for releasing it with a Phase IV
coming on after that.

CHAIRPERSON KAWAS: Well, I guess what I meant, though, is you would want
the clinical markers also being measured at the time? You don\'t want
just the \--

DR. PENN: Oh, yes. I don\'t think anybody\'s saying we just have to go
to surrogates and forget about clinical results.

CHAIRPERSON KAWAS: Can we quote him on that?

DR. KATZ: Well, no, I think there\'s a difference between measuring the
clinical outcomes and requiring that they be positive by whatever
definition.

DR. LOVE: What I am hearing is several different approaches which tend
to be leaning towards what you\'re saying. Look at the clinical, make
sure that it\'s not deteriorating or at least that there\'s some level
of static, and then look at the image.

What I\'d like to know, one thing I\'ve heard and whether you want to
take the break and come back and address this, it has more to do with
question one, is related the how would you validate or at least the how
you would be comfortable that it is meeting a reasonable standard for
surrogacy.

And that is, to go back to your discussion earlier, you mentioned
composites. Several people said anatomic plus some type of functional
measure. I would like to hear some discussion on how you would look at
that. Would you be at this point ready to make some type of assessment
on what you want to see for the anatomic, what you\'d want to see for
the functional; does one have to lead the other in relationship to the
clinical or not. Is there enough information to move to that yet. Are
you just looking at coprimary, meaning you\'d have to see a change in
both measures or one of the other. Just some discussion on that maybe
after the break.

CHAIRPERSON KAWAS: And with that, I think we will take a 15 minute
break. We\'ll be back shortly after 4:00.

(Whereupon, at 3:47 p.m. a recess until 4:07 p.m.)

CHAIRPERSON KAWAS: Okay. We need to pick this meeting with Dr. Love\'s
questions. And just to sort of refresh where we were, I think overall
the Committee has a lot of enthusiasm and positive feelings about the
potential for neuroimaging to be used as a marker in studies of
Alzheimer\'s Disease. And Dr. Love would like to know if we were to go
about getting the data or the kinds of studies that need to be done in
order to allow us to use neuroimaging effectively. And some of her
specific questions included do we need an anatomic measurement only. Do
we need a functional measurement also. Do both of them have to be
positive. Does one of them have to lead the improvement or not.

And so the table is now open for the questions that Dr. Love posed to
us. And who would like to start with this challenge?

Dr. Van Belle, a statistician.

DR. VAN BELLE: Oh, I just want to say that I have no opinion on which
modality should be used. But I think what I would like to say up front
is that we would agree, I think, that the context has to be that of
randomized controlled clinical trials. That observational studies won\'t
do it. I just want to make sure that we understand the game plan before
we go talking about modality.

Thank you.

DR. LOVE: Just maybe to clarify my question. My thought was that in the
context of that randomized trial what I\'m hearing is that there are
persons around the table who are interested in some one or more and the
idea or a theme of a composite has come up several times. So I\'m
curious how you would go about working that into the study, what
specifics would you be thinking about.

CHAIRPERSON KAWAS: Does Dr. Fogel want to suggest a composite?

DR. FOGEL: No.

CHAIRPERSON KAWAS: I mean, I\'ll start by saying I think that most of
the discussion seems to me to coalesce around the idea that two
instruments would probably be better than one, and that most of the
people who have suggested combining have generally suggested one
anatomic and one functional measure.

And it seems to me that overall on the table the most common anatomic
measurement has either been hippocampal volume followed by total brain
volume. And the most common functional measure, I believe, that seems to
be surfacing is PET scanning.

Now, is there anybody who agrees with any of those or disagrees and
wants to \-- Dr. Fogel?

DR. FOGEL: No, I agree that one would need \-- I think that you would
need both, and that I think that Dr. Love had questioned whether or not
we needed one positive or both positive. And I would hold that you would
need both positive and, as you mentioned, that there should be one
anatomic like hippocampal volume and one functional like PET scanning.
And they\'d compliment each other because as people had rightly
suggested, if you give a drug and it causes inflammation and edema and
doesn\'t show a shrinkage, that you would anticipate wouldn\'t show any
functional benefit yet the volume would be there. So, I mean, you\'d
need both, if you will, positives to give you a more reasonable
likeliness, if you will, to show a clinical benefit if the prognostic
marker is not transient and stays there for a long period of time.

DR. WOLINSKY: If I might, I guess I need some help from the experts over
here that might be doing those PET and anatomic studies together.
Because I would like to know before I built a composite that the
measures of the composite were at least somewhat independent of each
other so that I wouldn\'t be just measuring the same thing twice. And
I\'m not exactly sure that I heard from the experts who were presenting
what the interdependence of those measures are where the groups have
tried to look for that.

So that would be very important, especially since in my \-- as best as I
can remember, back to neurobiology 101, the number of cells there
determines the amount of metabolic need determines the amount of
cerebral blood flow determines the FDG. And all of that could just be a
function of atrophy.

DR. JAGUST: Well, you know, so the empiric answer to that is that one
can correct PET images for atrophy using the MR data. And you can do it
in a actually quite sophisticated way taking into account the thickness
of the cortical gray matter and so forth. And when you do that you still
find substantial differences between Alzheimer\'s patients and controls,
although those difference are attenuated.

Now, I think you can always push that question further and further down
stream to the fundamental disease mechanisms and ask are the same
fundamental processes causing the changes in metabolism and the changes
in brain structure. And, of course, you can only conjuncture about that.

DR. WOLINSKY: The problem is I\'m not asking whether or not you still
see fundamental differences between controls and patients with
Alzheimer\'s Disease. I\'m asking whether we\'re in a longitudinal study
those move in an identical fashion or whether they move in a
differential fashion. And that question is critically important if
we\'re talking about using one or two measures in a therapeutic trial as
either a supportive for a surrogate marker.

DR. De CARLI: Yes. I guess that Cliff\'s not here.

The one thing I think that has been done where we see differentiation of
those two effects is in vulnerable populations where to date MRI data,
structural imaging has not shown changes where metabolic imaging has
shown changes. The problem is that like the E4 carrier, is that \-- I\'m
going to turn it over to my colleague \-- about what the outcome of
those individuals has been. I mean, do they progress on to dementia
without structural imaging or is this \-- and so how it relates to as an
early marker. But other that, that\'s the only evidence that I know
about where the two are disconnected. Most the other evidence suggests
that they\'re connected. But almost all of it\'s cross-sectional. We
have some longitudinal data, but we haven\'t analyzed it completely yet.

DR. REIMAN: I would concur with the idea that the PET changes one sees
cross-sectionally are not entirely attributable to atrophy and that the
data is available but hasn\'t been looked at to determine the extent to
which atrophy accounts for those changes.

And I would also concur with the idea that we can see these PET changes
at the moment in distinguishing people at risk for Alzheimer\'s Disease
from those who aren\'t prior to the onset of memory impairment, with a
little bit more power with imaging with PET than with MRI.

I think the rationale for using the complimentary measures is that it is
very unlikely that a confound, say, on brain swelling will effect a
change in neuronal activity and vice versa. And the advantage of using
both is that you can reduce the chance that you\'re going to see that
kind of confound.

CHAIRPERSON KAWAS: Thank you.

I think that, you know, what has been addressed the table so far is the
idea the two studies is better than one; you want them both positive and
you want them both independent.

But I\'d like to suggest the possibility that I can easily imagine a
legitimate marker and a therapy influencing only one of those markers
and not both of them. And I wonder if we\'re not mixing up our apples
and oranges here if we pick out two or more markers and insist it be
positive on all of them.

I mean, you can change with drug the metabolism of the brain potentially
and change your functional outcome measure, i.e, your PET or whatever,
and potentially not change your anatomic and still have something
that\'s very efficacious.

So do we really want to insist the two measures which are absolutely
necessary and that one should be functional and one should be anatomic?

DR. WOLINSKY: Well, I thought the question was a question of a
composite. And so I was constructing a composite and if they were all
doing the same thing, then I have no reason to waste my time, effort and
money on a composite when I can just measure one.

CHAIRPERSON KAWAS: If you knew which one worked?

DR. FOGEL: Well, just like any other test, the composite has a
sensitivity and specificity. And because it\'s not perfect, you\'re
going to miss some efficacious drugs and you\'re going to let in drugs
that aren\'t efficacious. And so just like any other test that is
sensitivity and specificity, but the bottom line is that the accelerated
phraseology wants us to have a reasonably likelihood to predict clinical
outcome and so we want to error on the side of being able to let a drug
out that we\'re sure that to the best of our ability it can predict and
to a high likelihood that it might predict a clinical benefit. And to do
that, it would seem logical that one would want to have both an anatomic
parameter as well as a functional parameter to do that. And we\'re
trying not to let drugs in to the general population that may not prove
clinical benefit by doing so. So we\'re erring on the side of leaning
towards the clinical benefit at the cost of not letting a drug out into
the general population that won\'t have a clinical benefit and may cause
adverse effects that we really shouldn\'t have let out in the first. So
we\'re really trying to increase that likelihood as much as possible,
which is why you would want two at the exact same time, two
simultaneously having a positive result in this composite, which is why
it was suggested in the first place.

DR. SORENSEN: Dr. Kawas, I don\'t think you need both. I like Dr.
Penn\'s point. One compelling story is good enough. And I agree with you
that we, as much data as we\'ve seen today, I think everyone would agree
we\'re still at a fairly early stage of our understanding of these
markers. And I wouldn\'t want to insist that a drug had to succeed at
both this early in the game.

I think the biggest challenge around all of this is that we still don\'t
have really enough data to speculate about specific details. So to be
explicit with Dr. Love\'s questions, my own sense is that given the
numerous single center studies of both PET and MRI, I feel like when
someone gets around to doing the multi-center trial the right way, that
there will be a link between the pathology and the imaging. But those
links have not been established today, to my knowledge, in a well
designed, well controlled prospective trial. That data just isn\'t
there.

And so it\'s hard to say which one would be first or which one is
better. I think we\'d be speculating and you\'re hearing some
speculation at this point, but it\'s speculation.

If the rumors about the NIA sponsored study or these others, maybe
they\'re included in commercially sponsored trials are true, maybe
within a year or so maybe we\'ll have enough data, hopefully well before
somebody would come to you or at least concomitant with when somebody
came to you, and then we could use that to help guide this.

I\'d be nervous offering guidance to somebody right now when a well
designed study could change that guidance.

DR. PROVENZALE: Comment. I\'m in agreement with Greg. I think we\'re
missing a lot of the basic data. It\'s similar to feeling an elephant
from many different angles.

The data that we\'ve seen and that we\'ve read in the literature is very
promising. When I mentioned correlation of, let\'s say, PET and
volumetric MR imaging or PET and spectroscopy, I was basically pointing
out that we don\'t know what the correlates, we don\'t know what the
glucose metabolic rate change is in areas \-- I mean, I don\'t think we
do. In areas of hippocampal shrinkage accounting for volume loss or in,
you know, what\'s the correlate of NAA decline with glucose metabolic
changes on a pixel by pixel basis? We don\'t have that information.

I think a lot of what we\'re basically talking about here is we\'re not
really answering the questions of how would you design this from the FDA
perspective. We\'re really kind of outlining a wish list of the
necessary, from our standpoint, perquisites for moving forward. And
there\'s time to do this before, like Greg said, a drug comes to market.
But these are the things that we\'d be interested in learning more about
before we could answer your question.

CHAIRPERSON KAWAS: Dr. Wolf?

DR. WOLF: I think we have three levels we have to worry about;
biochemical changes, physiological changes and anatomical changes. And I
think one of the areas, again adding to the wish list you just
mentioned, that we need a lot more information is markers of molecular
changes that occur relatively early and that can be measured and that
can be indicative of what\'s likely to happen later on in the
physiological and in the anatomical phase.

So the question really \-- the answer to your question is we don\'t know
at this moment which one of these measurements is the most efficacious
one, but I think we need to accumulate the data and see which one
correlates best, and hopefully try to develop some additional markers
that are more specific and go more to the mechanism of the disease
process in order to really have a good handle.

DR. OLIVA: I would like to hear from the Committee members who earlier
said that, no, that there are no markers that are reasonably likely to
predict clinical effect. I\'d like to hear your answer to the following
question: Dr. Fox earlier this morning suggested to me, anyway, that
quantitative MRI imaging might be reasonably likely to predict a
positive clinical outcome if that effect persisted. So what would you
think of a clinical trial design that would incorporate quantitative MRI
imaging as a primary outcome that also incorporated a randomized
withdrawal design that showed persistent effect?

CHAIRPERSON KAWAS: I\'ll start, because I think I\'m one of the people
who suggested \-- well, your words were no markers likely to predict
outcome, and actually that is not what I think.

I, in fact, think that it is very likely that some of these markers
would be relevant for outcome. The problem is I\'ve not seen the data to
make me know that. So I think there\'s a bit of a difference between
those two things.

The second part of your question was then what would I think about a
design that withdrew. And here I think it really depends on the
mechanism that we\'re trying to get at with a particular drug.

So although I understand why everyone\'s suggesting a composite, I think
that\'s too stringent a test. I think a drug could easily work in a way
that would show up on a functional surrogate marker and not show up on
an anatomical surrogate marker and still be absolutely relevant to the
outcome. So I actually don\'t particularly like the idea of a composite
anything, because all you\'re doing is putting together a bunch of
things in the hopes that somehow that makes you closer to right.

But the part that\'s missing with the single is the information that
would make me feel confident that if I make that change on the PET scan,
that I\'ve also made a change in the patient down the line. And in that
sense, the design wouldn\'t help me at all I don\'t think.

Dr. Fogel?

DR. FOGEL: No, a composite doesn\'t actually do that in the hopes that
you\'re going to be right. What a composite does is it takes the
probability that you\'re right on two of them and gives you a higher
probability that together if when they intersect that you will be more
reasonably likely to eventually predict an outcome. So it\'s not that
one is hopeful. You\'re trying to be more specific because when you\'re
saying \-- if you don\'t have data to show that the unvalidated
surrogate is going to have a clinically relevant outcome, then you have
to hold it to a higher standard. And to hold it to a higher standard,
you have to be more specific. And to be more specific, you need to have
more than \-- it seems to me from the data that has been presented, that
you need to have more than just one unvalidated surrogate to be positive
simply because it will be more specific in terms of effecting the
disease and more likely to actually have a relevant clinical outcome.

DR. KATZ: Yes. I\'d like to just expand on Armando\'s question. But I
think it\'s the next critical series of question or series of questions
that I think the no voters should address, which is what if anything at
the moment should be part of the design of a study that would allow us
to conclude that a drug has an effect on the underlying progression of
the disease?

Dr. Fox had talked about including a withdrawal phase, as Armando
pointed out. You know, obviously, people have talked about the so-called
randomized withdrawal or randomized start clinical trial without
surrogates, which involves at some point withdrawing the drug or putting
people who had not been on the drug on the drug and seeing whether or
not any effect persists between the drug and placebo patients. So that
intrinsically has a randomized withdrawal phase. And we believe that
study is interpretable as having an effect on progression if the effect
at the end of the randomized period persists during the withdrawal
phase.

So I\'d like to hear what again, if anything, elements ought to be
included in the clinical trial that will allow us now to conclude, if
anything. If you think this is even doable now, that if a drug doesn\'t
effect on progression.

I mean, Michael talked about a study in which simply just correlated the
clinical with the surrogate at some point down the road, and that might
be sufficient.

So, I think we really need to hear from those of you who voted no.
We\'re not ready to rely solely on a surrogate Are we ready to rely on
any combination of elements in a trial and what ought those elements to
be to support, in effect, a claim for an effect on progression?

DR. VAN BELLE: Well, as the most no of the no\'s, I suppose.

First of all, let me say that I would set very high hurdles for
surrogates. I think there\'s enough evidence and there are enough bad
cases that I think the agency should move very carefully with respect to
accepting surrogates.

Secondly, if I were in charge, what I would do is I would still take a
randomized clinical trial with the endpoint as the primary outcome and a
surrogate as the secondary outcome. And whether I did this in terms of a
randomized withdrawal or one or the other designs, I\'m not sure.

One intermediate point which you made is, of course, you could also try
to do some kind of a dose response in the sense where the level of the
surrogate is correlated with the clinical response. If there is a
treatment effect and if the surrogate is doing what it should be doing,
then there should be some kind of a correlation between the surrogate
level, if you wish and perhaps the change in volume or the lack of
change in volume, with the change in cognition, say. That would be
simpler trial probably, then some kind of a randomized withdrawal which
has practical as well as ethical problems, maybe.

But that\'s what I would start with. And it\'s pretty humdrum and pretty
traditional, but that\'s where I think I would put it at this time.

The other thing we were talking about trials of a year. These are very
hard trials to do with older patients. You know, there\'s death dropout.
The patients that are deteriorating the most rapidly are most likely to
withdraw. You really are going to have a hard time with any kind of a
trial to prove the efficacy of a surrogate, if you wish, if you\'re
going to take that long a time, yet that\'s what the people here around
the table suggest.

The other thing, the final thing I\'ll mention is that how tolerable are
these procedures, again, for the reasonably advanced Alzheimer\'s
patient in terms of agitation in terms of where they\'re at? You know,
somebody saying an MRI takes an hour. That, I assume, is not just
sitting under the instrument for an hour. But what kind of a time line
are we talking about and are we bordering on patient abuse just to
satisfy a clinical question? I just raise that as an issue.

DR. FOX: I just wonder whether I could both make comment on the
tolerability issue and perhaps also since my comments on what I felt in
terms of sustainability or effect would have on terms of reasonably
likelihood, if I may make comment?

Firstly, on terms of patient\'s tolerability, I can really only speak
for volumetric MRI and sequence takes that we use for the images you saw
takes 10 minutes. We\'re nearly completing a study of 50 Alzheimer\'s
mild to moderate, meaning mini-mental of 19 where they have 9 scans over
a year. We\'ve had 5 percent dropout and 5 percent missed scans. And one
of the people who had a mild cortical infarction was keen to come back
and complete the rest of his scans.

So, the care and attention of the investigators, the people find very
supportive in this extremely distressive disease. And having MRIs does
not, in my opinion, have major contribution. Yes, some people will be
claustrophobic. In my experience we have a higher number of
claustrophobics in the controls than in the Alzheimer\'s patients. And,
yes, some people can\'t stay still. So that\'s, as far as I\'m
concerned, about that issue. And people are desperate for that care and
regular attention.

And as far as the sustained effect is concerned, I was trying to make
two points that in my very brief presentation. One, yes, withdrawal I
think adds support. But the other point I was trying to make is we set
up lots of hypothetical situations here. And I\'d like to put one which
makes the point about the sustainability, having watched my at-risk
cohort see hippocampal atrophy progress inexorably and their whole
brain, and then seen the clinical progression follow that. And it was
always very compelling to me if that \-- at least the natural history
component.

But the sustainability, I have two parts. One withdrawal, which I think
adds confidence, and the second is I think there is a different level of
confidence one has if, for example, if I scanned you monthly for 6 or 12
months and saw that the rate at each of that time was being consistently
reduced; I have a greater level of confidence in that and a greater
likelihood that a reasonable person would think that was associated with
clinical outcome than if I just had a first and a last scan.

That was the point I wanted to make. I think both add in my opinion a
level of reasonableness, if that\'s a word.

DR. GRUNDMAN: I basically agree with that. I just think that so in terms
of Dr. Katz\' question about what would you include in a trial to, I
guess, try to make it more likely that you would accept a surrogate as
reasonably likely that it\'s going to improve the clinical outcome, is
that the question?

DR. KATZ: No. Again, the use to which I think we\'re mostly talking
about these imaging modalities would be put would be to support a claim
for an effective drug on a progression, on the underlying progression of
the illness.

A number of you said that we\'re not ready to come to that conclusion on
the basis of an effect solely on the surrogate. So what I\'m asking is,
is there a trial design that we could do now that would support an
effect, a claim for an effect on progression? Would it combine the
imaging plus a clinical? Would it combine imaging plus clinical plus a
withdrawn phase? I\'m just trying to get a sense of what, if any, design
you think we\'re ready to employ to address the question of an effect on
the underlying progression of the disease. It doesn\'t even have to
include a surrogate.

DR. GRUNDMAN: Okay. So I would say two things.

One, if you were to do a clinical trial \-- first of all, you know,
again it always depends on the clinical group that\'s involved in the
trial. Because, you know, your sample sizes are going to be smaller and
the trials are going to be a shorter if you\'re doing an AD trial than
if you\'re doing an MCI trial, or if you\'re doing some sort of a
prevention trial.

So if we\'re just talking about AD trials here, I think that you could
do a clinical trial with clinical outcome measures that we\'re used to,
CDR, CGIC, ADAS Cog, classical outcome measures and measure MRI. And if
they are both consistent with one another, then I think that that would
support, you know, a disease progression modification type claim. I
think it would support that. Obviously, it doesn\'t prove it, but it
would at least be consistent with that notion if you saw, you know, half
the rate of decline on the clinical and cognitive measures and you saw
some diminution in the rate of decline on the MRI atrophy; that would at
least be consistent with that conclusion.

DR. KATZ: Any minimum duration?

DR. GRUNDMAN: For the trial?

DR. KATZ: Yes.

DR. GRUNDMAN: You know, I think it sort of depends on how many people
you have in your trial. Because you can show an effect with a larger
number of people over a shorter period of time.

DR. KATZ: Right. But \-- perhaps. But what I\'m asking you is there any
minimum duration below which you would say well, I see they both go in
the right direction, but that doesn\'t prove to me that there\'s an
effect?

DR. GRUNDMAN: I think probably \-- you know, I would say just
practically for the clinical measures you might need a year to show that
with several 100 people in each group.

I think for the MR measures, I\'m not sure how long it would take
because I\'m not sure it\'s quite as well worked out.

But I think one other point is that if MR measures were conducted in the
context of a clinical trial and you were collecting them in a sequential
fashion, say every 6 months, and you did, say, an 18 month or 2 year
study and you found that the effect on the MRI occurs at, say, 6 months
and the effect on the clinical occurred at 18 months, that would help
give me confidence that if they did another trial with that agent, that
if you found an effect over that short of period of time, that might
support an accelerated approval.

DR. PROVENZALE: Comment?

CHAIRPERSON KAWAS: Yes. I\'m not sure it\'s an ideal situation, but I
would suggest that a withdrawal design probably needs to be incorporated
to convincingly make the case for disease modifying.

DR. KATZ: Including an imaging surrogate or just clinical, or just
imaging?

DR. WOLINSKY: Yes. I don\'t think it\'s very practical to think about
withdrawal designs. I\'d rather be an optimist. I\'d rather believe that
the treatments are going to work, and I\'d rather deal with the issues
of the so called randomized start or delayed start of therapy.

Well, when you start using something like atrophy as the endpoint of
measure if these curves never catch up, that\'s telling us something
very important. And that\'s what we would expect to see. And certainly
for some of the studies we\'ve done in MS where we\'ve had randomized
starts of a sort, these are exactly the kind of curves we see.

DR. KATZ: No, that\'s fine. Again, I\'m just asking what the elements of
such a trial would be. We can refer to them sort of generically as a
switching maneuver or whatever you want \-- let\'s say randomized start.

DR. WOLINSKY: Yes, but one loses patients and the other one keeps them.

DR. KATZ: Well, no, fine. Again, I\'m just trying to understand whether
or not you need some sort of a phase like that at all or whether just a
simple parallel study which shows a correlation at some point down the
road between clinical and surrogate is sufficient. I\'m just trying to
get a sense of what people think.

DR. PROVENZALE: Comment. With regard to design of the length of the
study, that is I think largely governed by what change you\'re hoping to
see, what would be statistically significant. I mean, you know, let\'s
say going back to what Dr. Jack showed about the hippocampal volumes.
You know, a statistician would basically have to decide, you know, would
a difference between 1.5 percent decrease and 3.5 decrease; that\'s the
annual rate of change, I believe, that he gave us. You know, would that
be long enough or would you have to have 2 years at those different
rates in order to see a statistically significant difference between the
two? If you remember, the standard deviation there was relatively high
for those rates of change.

So, I mean, I don\'t think that this is a question that we an answer
without a calculator, basically.

DR. KATZ: Well, I take your point. But it\'s true that depending upon
what the treatment effects size is, and you know, and the rate of change
you might need \-- if you enrolled a lot of patients, you might be able
to do a shorter study. But I\'m trying to find out whether or not as
part of the element of this theoretical study I\'m trying to decide
whether or not even if you could show an effect with 500 people at 3
months, for example, is that satisfying. The fact that you could show an
effect doesn\'t necessarily mean that you would believe that that is an
effect that is structural and would persist.

So I\'m just trying to get a sense if people think, well even if I could
show it at 3 months, it still wouldn\'t convince me it\'s a structural
effect. I still want to see at least 6 months or at least 12 months.

I recognize that everything we\'re asking you today is hypothetical.

CHAIRPERSON KAWAS: Well, hypothetically, I would like at least 6 months.
And I would like a combination of cognitive and imaging. And you take
the GCIC, substitute the imaging.

DR. GRUNDMAN: I would say you can take the CGIC, but I\'d still like to
see some sort of measure of clinical change as opposed to just simply
cognitive change in the trial.

CHAIRPERSON KAWAS: Like what?

DR. GRUNDMAN: Like the CDR sum of boxes, some sort of measure of
function.

DR. PROVENZALE: It depends on what. Are you talking about an AD study or
an MCI study.

DR. GRUNDMAN: We\'re talking about an AD study.

DR. PROVENZALE: It totally depends on \-- the problem with a CGIC, I
think there is a problem with that because the longer the trial, the
harder it becomes to remember what\'s going on at the baseline. So you
do need some sort of functional severity measure which can be assessed
serially over time which doesn\'t depend on the person\'s recollection
of their baseline status.

CHAIRPERSON KAWAS: Any other takers for Dr. Katz\' questions? I\'ve
never seen a committee so quiet.

Dr. Love, how about the questions that you posed for us? Have we
approached them in anyway helpful or could you like guide a little?

DR. LOVE: I think, yes, I think they\'ve been helpful.

Obviously we are speculating at this point in time and looking for
approaches to use in designing these trials and not just for Rusty\'s
purpose of what are we going to do for a drug when it comes for
approval. Because these questions are being asked now and these studies
would need to be designed at this point in time, but also these studies
may be useful to help establish the reasonable likelihood aspect of
this.

So, there would be probably features that we want to think about based
on things that you\'ve all mentioned. Maybe if you\'re looking for
clinical effect at 3 months, does that means that the functional imaging
measure should be timed along with that if you think the functional
imaging may change before the anatomic image does?

So, just those kinds of thoughts would need to go into the design of the
trial. But I think we\'ve heard a variety of comments and issues we\'ll
need to continue to think about.

Yes?

DR. GRUNDMAN: Well, I was just going to say, you know, I think that the
functional measures or the functional and the anatomic and the clinical
measures should be done simultaneously. But I think they should be done
serially over a period of time so that you can compare the simultaneous
measures with each other, and then you can also compare the imaging
measures with their ability to predict the ultimate outcome that you\'re
looking for in the trial.

CHAIRPERSON KAWAS: Dr. Van Belle?

DR. VAN BELLE: Just one final comment. I was somewhat negative today,
but I\'m actually quite excited about imaging, and I do think it\'s a
very useful technique. Some of my best friends do imaging.

I just think that should it be done in a proper scientific context. And
I think the rules for that are actually fairly straightforward and are
known to the FDA as well as to the industry. It\'s just a matter of
doing it.

CHAIRPERSON KAWAS: I\'ll ditto everything Dr. Van Belle just said.

And also in follow-up to your comment, Dr. Love, I can easily imagine a
surrogate marker like one of these images being positive long before the
clinical outcome. It just doesn\'t have to be, wouldn\'t necessarily be.
I mean, I can se how both of them in some cases depending on what the
drug is doing, could become positive together. But I can easily see, I
mean the imaging becoming positive before the clinical, whereas the
opposite is quite hard to imagine.

DR. LOVE: Right. But that\'s the type of information that would be
useful in the long run to determine that this is truly a surrogate or at
least reasonably \--

CHAIRPERSON KAWAS: Right.

DR. WOLINSKY: Okay. I would sorry about that a little bit just from
experience in a different field. Because some of the metrics that we
measure in MS seem to be dependent upon events that may occur a year or
two earlier; that the dye is then cast so that if there\'s an effect of
the drug it may take the third or fourth year until you begin to see the
effect of that drug.

So I\'m not sure until you know, and I don\'t know about MS, but I do
get the feeling that we may know a little bit more about that than
Alzheimer\'s, I\'m not sure that we actually can make these predictions.
And, therefore, you have to be very careful that you cast long enough
nets for your data.

DR. LOVE: And that probably goes to one of Rusty\'s questions, how long
\-- how long should the studies be. There\'s the short term how long \--

DR. WOLINSKY: But assuming that we see in an Alzheimer\'s Disease is
actually locked in some kind of a way, and I\'m not sure that it is, it
looks to me that the data that Dr. Fox had presented and some of the
other data would say that these studies have to be as a minimum to
rigorously detect atrophy about a year and probably 2 years. And that
means that if you\'re doing at the late start, you\'re into a fairly
long trial.

CHAIRPERSON KAWAS: Well, does anyone else from the Committee have any
comments, issues they want to bring up? Any discussions? Any of our
invited speakers? Dr. Doraiswamy?

DR. DORAISWAMY: One of the comments that was made was the time course of
whether your clinical outcome would turn out to be positive before the
imaging outcome or vice versa. I mean, we know from our clinical trials
already that the ADAS Cog becomes positive around 6 weeks in many of the
drug trials. I\'m not sure that most people expect a hippocampal or a
brain atrophy volume to become positive at 6 weeks. So in most trials
the brain volume changes are probably going to occur after the clinical
outcome sort of changes. That depends on what outcomes you\'re looking
at. Obviously, if you\'re looking at survival or nursing home placement,
etcetera, then probably your imaging outcomes will predict those. But
certainly not the ADAS Cog outcome, because we already know that from
the clinical trials.

So, I just thought I\'d throw that out.

CHAIRPERSON KAWAS: Well, I just want to comment that we know that from
trials that were symptomatic trials. But if we\'re talking about disease
modifying trials, which I think is actually one of the interests, and
we\'re talking about \-- then that\'s where I think we\'re going to see
the imaging positive before we\'re able to detect a difference in the
rate of cognitive decline. But in symptomatic trials, absolutely. I
mean, you fix the symptoms before you fix the underlying disease in
terms of time always.

Yeah. Dr. Fox?

DR. FOX: I was just going to agree with what you said in that the power
calculations for disease modification suggest that you\'d be likely to
see \-- if you had a purely disease modification effect, you\'d be
likely to see the effect on your surrogate before your clinical. And
it\'s quite probable that you have both the symptom \-- you may well
have a symptomatic and a disease modification or one without the other.
It\'s possible.

DR. DORAISWAMY: And you may never see a cognitive effect. Because in the
Vitamin E trial, for example, Vitamin E did not have any effects on the
ADAS Cog at all. And it\'s possible, I mean no one\'s looked at brain
changes in relation to Vitamin E therapy, but it\'s hypothetically also
possible as you and some others indicated that you could get a disease
modifying agent that effects brain structure without effecting cognition
at all. That\'s a theoretical possibility.

CHAIRPERSON KAWAS: I mean, I think the disease modifying trial is really
the important thing in the field as well as the discussion that\'s
happening today. I don\'t think most of us are that worried about
whether or not these are useful for symptomatic \-- drugs that give a
symptomatic effect only.

Dr. Katz?

DR. KATZ: Just to respond to what Dr. Doraiswamy said, if you had a drug
that effected a surrogate but had never had an effect on cognition, I\'m
not sure what you\'d have. In fact, I think it would suggest that the
surrogate\'s a failed surrogate.

CHAIRPERSON KAWAS: Does anyone disagree with that statement? I mean, I
think that\'s exactly right. The only issue is that if it takes a longer
time to show disease modification on a cognitive outcome, but the
assumption would be that eventually you would be able to demonstrate it
if the surrogate was a valid one.

DR. GRUNDMAN: I was just going to make one point about Dr. Katz\'
question about whether or not to do randomized start designs with
imaging and so forth. And what was the point I was going to make?

I think that \--

CHAIRPERSON KAWAS: What\'s the volume of your \--

DR. GRUNDMAN: Oh, the point was that, you know, a lot of these drugs
could have both symptomatic and disease modifying effects. And so the
waters get sort of muddied when you do those designs and the curves go
back but not completely. So then do you power your study to show that
residual difference or, you know, I think those types of designs become
pretty complex when you have to deal with them in reality and not just,
you know, in a theoretical construct where the differences are
maintained at the same level that they were when the randomized portion
of the trial ended.

DR. KATZ: Well, I think things have the potential to get extremely murky
if you had a drug that had an effect on the surrogate \-- I won\'t say
in progression yet, but on the surrogate and also had a symptomatic
effect. Because if you did a short term study with a drug like that, you
might see an effect on the surrogate and you\'d see your clinical
effect, and you want to conclude that this must have an effect on
progression because there\'s a correlation. But, in fact, it may have
two actions. And the effect on the surrogate may actually translate into
absolutely nothing clinically.

So, it\'s very complicated. Although I suspect in a case like that, if
you did a randomized withdrawal and you maintained \-- I don\'t know.
But if you maintained a difference on the surrogate but perhaps the
clinical outcome went back to where it was, you might argue there still
was some effect on the underlying structure. What that meant clinically
you still wouldn\'t know.

CHAIRPERSON KAWAS: Okay. Well, it\'s been a very long and interesting
day. And I want to thank all of the members of the Committee, all of the
invited speakers, the FDA, the audience, and I think Dr. Katz has some
comments for us.

DR. KATZ: Well, I just also want to thank everybody. It\'s been a long
day, an interesting. It\'s a lot of complicated issues.

I appreciate our invited speakers coming, the imaging consultants and
the neuro committee.

And I neglected to thank one person when I spoke earlier this morning
who really is largely responsible for the meeting at all, that\'s Dr.
Ranji Mani who is a senior reviewer in the neurology group in the
division who identified the experts and really wrote our briefing
documents, and really put together the whole meeting. So he deserves our
great thanks.

(Whereupon, at 4:54 p.m. the meeting was adjourned.)