UNITED STATES OF AMERICA

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FOOD AND DRUG ADMINISTRATION

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CENTER FOR DRUG EVALUATION AND RESEARCH

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CARDIOVASCULAR AND RENAL DRUGS

ADVISORY COMMITTEE

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94th MEETING

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THURSDAY,

OCTOBER 11, 2001

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The Advisory Committee met in Building 10, Clinical Center, Jack Masur
Auditorium, National Institutes of Health, 9000 Rockville Pike,
Bethesda, Maryland, at 9:00 a.m., Jeffrey Borer, M.D., Acting Chairman,
presiding.

PRESENT:

JEFFREY BORER, M.D., Acting Chairman

JOAN C. STANDAERT, Executive Secretary

PAUL ARMSTRONG, M.D., Member

MICHAEL F. ARTMAN, M.D., Member

PRESENT: (cont.)

THOMAS FLEMING, Ph.D., Member

ALAN T. HIRSCH, M.D., Member

JoANN LINDENFELD, M.D., Member

STEVEN NISSEN, M.D., F.A.C.C., Member

GLORIA ANDERSON, Ph.D., Voting SGE Consultant

RAYMOND LIPICKY, FDA

ALSO PRESENT:

PETER CARSON, M.D. (Novartis)

JAY N. COHN, M.D. (Novartis)

LLOYD FISHER, Ph.D. (Novartis)

ROBERT GLAZER, M.D. (Novartis)

MATHIAS HUKKELHOVEN, Ph.D. (Novartis)

JAMES HUNG (FDA)

MALCOLM MACNAB (Novartis)

SHERI TARGUM (FDA)

C-O-N-T-E-N-T-S

PAGE

Conflict of Interest Statement 4

Introduction, Acting Chairman Borer 6

NDA 20-665/S-016 and NDA 21-283/S-001, Diovan

(valsartan), Novartis Pharmaceuticals Corporation:

Company Presentation:

Introduction, Mathias Hukkelhoven, Ph.D. 7

Clinical Efficacy Data, John N. Cohn,

M.D. 10

Safety Data, Robert Glazer, M.D. 112

Benefit-Risk Discussion, Jay N. Cohn, M.D. 130

Committee Reviewer, Thomas Fleming, Ph.D. 158

Committee Review and Discussion

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

(8:58 a.m.)

ACTING CHAIRMAN BORER: I\'d like to call this meeting to order.

This is the 94th meeting of the Cardiovascular and Renal Drugs Advisory
Committee.

We have a conflict of interest statement to be presented by Joan
Standaert, and then I have a couple of opening comments about the format
today.

Joan.

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

Based on the submitted agenda for the meeting and all financial
interests reported by the committee participants, it has been determined
that all interests in firms regulated by the Center for Drug Evaluation
and Research present no potential for an appearance of a conflict of
interest at this meeting with the following exceptions.

In accordance with 18 USC 208(b)(3), a full waiver has been granted to
Dr. Thomas R. Fleming. A copy of this waiver statement may be obtained
by submitting a written request to the agency\'s Freedom of Information
Office, Room 12A-30, Parklawn Building.

In addition, we would like to disclose for the record that Dr. Paul W.
Armstrong has an interest which does not constitute a financial interest
within the meaning of 18 USC 208(a), but which could create the
appearance of a conflict.

The agency has determined notwithstanding this interest that the
interest of the government in his participation outweighs the concern
that the integrity of the agency\'s programs and operations may be
compromised.

In the event that the discussions involve any other products or firms
not already on the agenda for which an FDA participant has a financial
interest, the participants are aware of the need to exclude themselves
from such involvement, and their exclusion will be noted for the record.

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

That concludes the conflict of interest statement for October the 11th.

ACTING CHAIRMAN BORER: Okay. I\'m going to first ask if there are any
comments from the public. The meeting is open for public comment.

(No response.)

ACTING CHAIRMAN BORER: Okay. If there is no comment, we\'ll move on.

I want to point out that the schedule as denoted here on the agenda
shows a 3:00 p.m. adjournment time. We\'re going to try to move ahead
reasonably efficiently to meet that adjournment time because of the
extraordinary problems that now exist with regard to air travel and the
extended time that some of our committee members need to be able to
reach their planes in order that they don\'t have to stay an extra
night.

It shouldn\'t be a problem if we stick to the schedule. So it may be
that at some point I\'ll cut off discussion not arbitrarily, but only so
that we can stay within our agenda.

In addition, you\'ll notice that there\'s a change in the alignment of
the end table here. The only reason for that is so that I as the
Chairman can see all of the committee members and not exclude them from
commenting in the appropriate way at the appropriate time.

With that having been said, we\'ll begin the discussion of Diovan
(valsartan) for the indication of treatment of patients with congestive
heart failure. The sponsor is Novartis Pharmaceuticals Corporation, and
the presentations will be introduced by Novartis by Dr. Mathias
Hukkelhoven, Vice President for Regulatory Affairs.

DR. HUKKELHOVEN: Dr. Borer, Dr. Lipicky, members of the Advisory
Committee, FDA, and guests, good morning. My name is Mat Hukkelhoven. I
am Vice President of Regulatory Affairs for Novartis Pharmaceuticals
Corporation.

On behalf of Novartis, I would like to thank you for this opportunity to
present and review Diovan data for a new indication, the treatment of
heart failure.

Diovan or valsartan is an angiotensin receptor blocking agent acting on
the AT-1 receptor subtype. It was approved in 1996 for the treatment of
hypertension, and it has been widely prescribed since that time. It is
now available in over 80 countries.

We are pleased that we are able to present data which demonstrates
clinical benefit with Diovan in treating patients with heart failure.
Diovan is the first angiotensin receptor blocking agent to achieve such
results. These beneficial results were achieved on top of a background
regimen that included an assortment of approved drugs for each
participating patient as prescribed by their physician.

Our development program for the new heart failure indication consists of
several studies. Val-HeFT or Protocol 107 is a key morbidity/mortality
trial involving approximately 5,000 patients, and it was conducted at
302 centers in 16 countries.

In addition, we also conducted four shorter term control studies,
Protocols 103, 104, 106, and 110. These studies evaluated a variety of
endpoints other than morbidity/mortality, including quality of life.

Our clinical program was developed in consultation with the FDA.
Importantly it was agreed that the Val-HeFT study could employ two
primary endpoints, and a positive outcome for either would support an
application. The two primary endpoints are all cause mortality and the
combined endpoint of morbidity and mortality.

Based on our clinical results, the following profile emerges. Diovan
improves morbidity since it reduced hospitalizations for heart failure.
It slows the progression of heart failure. It improves the New York
Heart Association functional class rating and ejection fraction. It
improves signs and symptoms of heart failure, and it improves quality of
life versus placebo.

The most common adverse experiences were dizziness and hypotension.

We propose the following draft indication statement based on our data.
Diovan is indicated for the treatment of heart failure, NYHA Class II to
IV, in patients receiving usual therapy, such as diuretics, digitalis
and either ACE inhibitors or beta blockers. Presence of all these
standard therapies is not mandatory.

Our discussions this morning pertain solely to a new indication for
congestive heart failure. Dr. Jay Cohn will discuss the efficacy of
Diovan in treating patients with heart failure. Dr. Cohn is Professor of
Medicine at the University of Minnesota, and he serves as the study
chairman for our Val-HeFT trial.

Dr. Robert Glazer will then summarize the safety of Diovan in heart
failure patients. Dr. Glazer is Director of Cardiovascular Clinical
Research at Novartis.

Dr. Cohn will then return to summarize our perspectives of risk-benefit
in this indication.

In addition to the speakers this morning, we have the following advisors
who are available to answer specific questions the committee may have:
Dr. Peter Carson, Associate Professor of Medicine at Georgetown
University and Chairman of the Endpoint Committee, if and when he
arrives; and Dr. lloyd Fisher, Professor Emeritus at the University of
Washington.

I would now like to ask Dr. Cohn to the podium.

Thank you.

DR. COHN: Thank you, Mathias.

Dr. Borer, Dr. Lipicky, members of the committee, it\'s a pleasure for
me to be able to share with you this morning the data supporting the use
of valsartan in the management of heart failure.

Let me provide you a little background for a moment on why we are here
today. The management of heart failure has undergone considerable
changes in recent years.

The pointer is where? There should be a pointer here somewhere, but
that\'s all right.

As you can see from this first slide, there has been quite a development
of drugs for the management of heart failure over the last \-- thank you
\-- over the last 15 years or so, beginning with the first demonstration
that nitrate and hydralazine could alter the course of heart failure.
That was the first clinical trial carried out in heart failure.

And then subsequently the ACE inhibitors were assessed initially in
Class IV heart failure and subsequently in more moderate heart failure,
Class II and Class III, demonstrating efficacy on the long-term outcome.

Subsequently there were data to support the use of beta blockers
beginning in around 1996. In 1999, a single study demonstrated that
spironolactone had a favorable effect in very severe heart failure. That
has never been submitted to the FDA for evaluation.

And most recently, the demonstration that one could achieve benefit with
biventricular pacing.

So there has been a considerable expansion of our therapeutic
armamentarium over these years.

Now, the rationale for an angiotensin receptor blocker is, I think, well
known certainly to the committee. It\'s widely appreciated that
angiotensin exerts a variety of adverse effects both on the vasculature
and on the heart and on neural hormonal system that may contribute to
progression of heart failure.

Traditionally, we have used ACE inhibitors in an effort to inhibit the
formation of angiotensin II, and that was, indeed, the concept over the
years that we\'ve been widely using ACE inhibitors to treat heart
failure, but it\'s becoming increasingly apparent in recent years that
ACE inhibitors given certainly in the doses that are currently used
clinically does not suppress very effectively the formation of
angiotensin II, and that a good deal of the efficacy of ACE inhibitors
may be related to its preservation of bradykinin by inhibition of the
breakdown of bradykinin, and that bradykinin nitric oxide system may be
an important contributor to the long-term benefits of ACE inhibitors.

Thus, if angiotensin II still persists, and it may well also persist
because of the activity of alternate pathways to formation, particularly
the

chymase (phonetic) system which is active in tissues, then we still may
have circulating in tissue levels of angiotensin II which interact with
the AT-1 receptor to subserve vasoconstriction, vascular and cardiac
growth, and adverse consequences in the syndrome of heart failure, and
this, of course, is where the angiotensin receptor blockers, such as
valsartan, which are specific inhibitors of the AT-1 receptor, might
further block the renin angiotensin system which we believe has
deleterious effects in heart failure.

Now, what I\'m going to present to you today is the clinical development
program for valsartan in heart failure, and as Mathias has already
described to you, there are four preliminary studies that were done that
led to the major outcome trial called Val-HeFT that will spend most of
the time this morning discussing.

Study 103 and 104 were sort of proof of concept studies, that is, can
one get a hemodynamic effect when one administers valsartan in patients
with heart failure.

Hemodynamics do not serve as an adequate surrogate for long-term
efficacy of drugs in heart failure. They may well serve as a target for
acute interventions because there is an acute response to hemodynamic
response, which will influence acute symptoms. But the long-term course
of the disease cannot really be predicted by hemodynamic effects of the
drug.

Nonetheless, it is often important, particularly if you\'re using your
drug with a known hemodynamic effect, that we demonstrate that the drug
is exerting this predicted effect in patients with heart failure.

So two trials were done. Study 103 was a trial in patients receiving
neither ACE inhibitors nor beta blockers, and this was carried out in
Russia, and it was placebo controlled and also lisinopril controlled,
and there were \-- and I\'ll show you the data on valsartan dosing \--
there were 116 patients in this trial. It was of four weeks\' duration,
and the major primary endpoint was hemodynamic effects from right heart
catheterization.

Study 104 was carried out in the United States and Veterans Affairs
hospitals. The patients were all mandated to be on ACE inhibitor, and
they were on ACE inhibitor in doses that are recommended from the large
scale trials.

They could not be on beta blockers, and there was a placebo controlled
assessment of four weeks\' duration of administration of valsartan in
two doses in 83 patients.

Study 106 was an exercise tolerance treadmill exercise study, and as
I\'ll suggest to you in a moment, exercise, again, is not a very useful
surrogate marker for long-term efficacy, but it\'s one of the kinds of
endpoints that one often carries \-- attempts to study.

These patients were allowed to be on ACE inhibitors and beta blockers,
and the vast majority were receiving ACE inhibitors, and about a third
of them were receiving beta blockers. There were 770 patients in this
trial. It was a 16 week study, and the primary endpoint was exercise
tolerance.

Study 110 was a study in which patients were not allowed to be on ACE
inhibitors during the trial, but they had been on ACE inhibitors, most
of them, until the randomization date. They were also allowed to be on
beta blockers, and about 30 percent of them were on beta blockers.

And it was a comparison between enalapril and valsartan in patients who
had been on an ACE inhibitor up until the day of randomization. So it\'s
a protocol that basically asks the question: will valsartan exert the
same benefit as continuing an ACE inhibitor in patients with heart
failure?

And it was a six minute walk test, a 12 week duration study.

And then Val-HeFT, which will spend most of the time on, was carried out
in 5,010 patients, and it was a morbidity/mortality trial, but in
addition, there was a substudy in which a walk test was assessed.

Now, this is the study design of the two hemodynamic trials, Studies 103
and 104. This is the study in Russia. This is the study in the United
States.

The Russian study examined three different dose levels of valsartan, 40,
80, and 160 milligrams twice daily, and they compared that to
lisinopril, which was titrated up to ten milligrams a day, and there was
a placebo group, and after a run-in the patients were randomly assigned
to these five different treatment groups and followed for 28 days. They
were catheterized at day zero and again at day 28 to assess hemodynamic
effects.

Study 104 used two different doses, 80 and 160 twice daily versus
placebo, and these patients, once again reminding you, were all on ACE
inhibitor. None of these patients were on an ACE inhibitor prior to
randomization.

These are the hemodynamic data in Study 103. The placebo group is in
green. At the left are the bars at day zero, four to eight hours after
administration of the drug. The values were meaned over that 48 hour
period.

On day 28 assessments were carried out at zero time. That is, before
drug was administered the patients had been on chronic therapy, and then
the third set of bars is 12 hours after the dose was administered on day
28.

In green is the placebo group, and you can see very little change in
pulmonary capillary wedge pressure during the follow-up period, and the
little increase here.

The three valsartan doses are shown here, 40, 80 and 160 milligrams
twice daily, and this is lisinopril, and you can see there is a clear
hemodynamic effect of valsartan and probably also of lisinopril compared
to placebo. These are least mean squares and some of the values are
statistically significant and some not.

This is Study 104, and this again is the pulmonary capillary wedge
pressure. Now the unique feature of Study 104 is that at zero time,
patients were given a dose of lisinopril to maintain full ACE inhibitor
effect throughout the study duration. So they got lisinopril here, and
they got lisinopril again here before a dose was administered on day
zero and day 28.

So the placebo effect in green is really a lisinopril effect in patients
on chronic ACE inhibitor therapy, and then in addition to lisinopril,
the valsartan patients were given valsartan 80 or valsartan 160, and
what one sees is a trend for a dose response to valsartan, that is, a
great reduction in pulmonary capillary wedge pressure here on day zero,
here on day 28, and again on day 28, hour 12, there isn\'t much
difference between lisinopril and the drug.

We also looked at diastolic pulmonary artery pressure because some
patients didn\'t get their wedge pressure measured, and once again, a
dose dependent reduction of pulmonary \-- PA diastolic pressure.

This is systemic blood pressure, systolic blood pressure. Again, the
appearance of a dose dependent reduction of blood pressure.

Now, we also measured hormones in Study 104, and this was plasma
aldosterone levels. Plasma aldosterone levels were strikingly reduced
with both doses of valsartan compared to lisinopril and appeared to be a
bit of a dose dependent effect here.

And plasma norepinephrine also exhibited some decline in perhaps a dose
dependent fashion, and in all of these studies, the one 60 milligram
twice daily dose of valsartan exerted the greater hemodynamic effect. So
that was the dose that was selected to be introduced into Val-HeFT when
we designed Val-HeFT.

Now, the exercise studies I will review briefly for you. As I\'ve
already suggested, exercise tolerance, that is, treadmill exercise and
six minute walk tests, have not served as a very reliable guide to
efficacy and heart failure, but these studies were carried out just to
determine if there was any demonstrable effect from valsartan.

This is the trial, 106 trial with treadmill exercise, and there were
three different doses of valsartan studied in that trial versus placebo.

Remember most of these patients were on an ACE inhibitor, and somewhere
over a third of them were on a beta blocker. There was no demonstrable
difference among the four treatment groups on change in exercise
performance and the p values were not significant. So no demonstrable
additional exercise improvement when valsartan was added to background
therapy.

And these are the two six minute walk tests. This is a substudy from
Val-HeFT, and I put it in here because it was an exercise study. There
were 633 patients in that substudy. There was really no striking change
in six minute walk tests, essentially equal between the placebo and
valsartan groups.

And this is Study 110, which was a six minute walk test. Now, this was a
positive control study because it was valsartan versus enalapril in
patients previously on an ACE inhibitor. There were 134 patients in this
trial, and there was no difference at least between enalapril and
valsartan. In fact, the trend was for a little greater improvement with
valsartan than enalapril, but nowhere near statistically significant. So
those studies are basically a wash.

Well, now let me go into the Val-HeFT protocol with you to review what
we have done. This was the design of Val-HeFT. Entrance criteria,
patients with chronic stable heart failure, and they had to have
ventricular enlargement both by transverse diameter of the left
ventricle at end diastole that is greater than 2.9 centimeters per meter
squared, and by an echo ejection fraction of less than 40 percent, and
they all had to be in New York Heart Class II to IV for eligibility in
the trial.

Now, each of the echo labs that participated in this study were
validated for their ability to both perform and read an echo, and it was
a monstrous undertaking, I can assure you. Many of the centers were
offended by the fact that they had to send three echoes in and
demonstrate that they could do it right.

And I can assure you that most of them did not do it right, and the core
laboratories that oversaw the echo quality had to go back and reeducate
echo technicians and readers as to the importance of precision in the
performance of the test.

They all eventually met the criteria, and we also monitored the quality
control throughout by randomly collecting echoes and submitting them
through our core laboratory. So we did the best we could do in a
multi-center study without having all the echoes read in a single core
lab, which would have been an unbelievable burden.

So these were the entrance criteria by echo, and then the patients were
randomized. They stayed on their prescribed therapy, and we encouraged
all of the physicians to get patients on optimal therapy for heart
failure, and then they were randomized to receive valsartan 40
milligrams twice daily, which was titrated at two week intervals. It was
a forced titration to 160 twice daily unless there were adverse events
along the way that inhibited progressive titration, and I\'ll show you
the data on that.

And then double blind placebo therapy in the other group, and we
followed the patients. All of the patients were followed until 906
deaths were reported, and I\'ll show you how that figure was arrived at.

There were two primary efficacy endpoints as Mathias has already
discussed to you. One was mortality, that is, the time to death,
survival curves, and the other was a combined endpoint which we called
morbidity, but it included mortality, all cause mortality. Plus episodes
of sudden death with resuscitation were called an endpoint.

Patients who were not hospitalized, but needed therapeutic doses of
intravenous inotropic or vasodilating agents for four hours out of
hospital, that was equivalent we thought to a hospitalization, and that
was counted as an endpoint. And the other one was hospitalization for
heart failure.

In all of these events, the death and all the other primary endpoint
events, were adjudicated by an endpoint committee, and they reviewed
every one of the hospitalizations until a patient was identified to have
had a hospitalization for heart failure, which gave them an endpoint for
the trial. So this was a burdensome effort as well, but we thought it
was very important to do.

Now, here were the statistical considerations in Val-HeFT. Since we had
two endpoints, we divided our alpha into two and, therefore, assigned an
alpha of a .025 to both morbidity and mortality.

The mortality alpha was further reduced by the interim analysis carried
out by the Data Safety and Monitoring Board using the O\'Brien-Fleming
method, and that then came down to .02 as the level of significance.

The assumption of the sample size was based on a predicted placebo death
rate of 12 percent per year. We didn\'t achieve that. It was nine
percent. So in a way, we were under powered from the very beginning
because the mortality rate was lower than we had predicted.

We were trying to identify a reduction in mortality of 20 percent with a
90 percent power and a two-sided significance of .025, and that\'s how
we came up with the need for 906 events in order to achieve our target.

All patients were followed to the study end. They were censored
obviously at the end of the study. At the time of loss to follow-up, and
I can tell you that you\'ll see in a moment that there were very few of
those, and at the time of heart transplant. So those were the censoring
criteria.

There was an endpoints committee, as I pointed out that, reviewed all of
the endpoints, and there was semi-annual interim analysis by the DSMB.

Patients were all over 18 years of age. They had chronic stable heart
failure, and as I pointed out, their ejection fraction less than 40
percent and their left ventricle larger than 2.9 centimeters per meter
squared.

For those of you who aren\'t used to the index, LVIDD, this means left
ventricle well over 5.5, and usually over six centimeters in the trial.

And they all had to be on a stable regimen of prescribed heart failure
therapy for at least four to six weeks prior to randomization.

The usual exclusion criteria, patients with significant valvular,
obstructive valvular disease, patients with recent ischemic episodes or
CVAs or recent reperfusion therapy, patients likely to need bypass or
reperfusion in the near future. People with rapidly deteriorating heart
failure were excluded. Those on the transplant list were excluded. Those
with predominant right heart failure due to pulmonary disease were
excluded, and those on drugs which we felt were contraindicated, such as
Class IC anti-arrhythmics, patients who required IV inotropes or IV
vasodilators in the previous three months.

There were 302 centers in 16 countries that participated in the trial,
and this is the breakdown of the number of patients entered from each of
these various sites. The United States entered a little less than 50
percent of the patients, and you can see the big contributors outside
the U.S., Italy, the Netherlands, Germany, et cetera.

The follow-up averaged 699 days in both treatment groups. The mean daily
dose of valsartan administered was 254 milligrams. Remember 320 would
have been the target dose so that we came pretty close.

The mean dose of placebo was just slightly higher, 283.

The duration of treatment in days was somewhere over 600 days on average
between the two groups, and 84 percent of the valsartan patients and
92.7 percent of the placebo patients achieved the target dose, which is,
I think, pretty remarkable. This is a high dose of valsartan, and yet
the vast majority of patients achieved that target dose.

Here\'s the disposition of the patients. Ninety-nine percent of them
completed the trial either to death or a trial endpoint. Premature trial
termination in the absence of death occurred in only a small number of
patients, one percent in both groups; a small number of heart
transplants, 18 and 23; loss to follow-up. I think this is a tribute to
the quality of the performance of this trial. Three and four patients
lost to follow-up out of 5,010. A few withdrew consent.

Four hundred and 48 or 18 percent of the valsartan patients and 14
percent of the placebo patients stayed in the trial, but discontinued
trial treatment. Again, I think a very acceptable number.

The majority of those, the difference between the two was related to
intolerable adverse experience, nine percent in valsartan, five percent
in placebo.

Here are the baseline characteristics of the patients. There were 2,511
in the valsartan treated group and 2,499 in placebo. They averaged about
63 years in age. Eighty percent were male. Ninety percent were white.

We have an unfortunately small representation of black patients. Some of
these are South African blacks, and a slightly larger fraction are
African Americans. It is too small a group to make many conclusions
about, and there was a small number of other racial groups identified.

Coronary disease was the etiology of the heart failure in about 57
percent of the patients. Thirty-one percent were identified as having
idiopathic cardiomyopathy, and the causes are shown here.

Sixty-two percent of the patients were in Class II heart failure, 36
percent in Class III, and a small number of Class IV patients.

The ejection fraction averaged about 27 percent. The left ventricle was
3.6 centimeters per meter squared body surface area. So these are large
ventricles.

The blood pressure, 124 over 76, and here was their background therapy,
and this is going to become of some importance as we go through these
data. Eighty-six percent of the patients were on a diuretic. Two thirds
were taking digoxin. Thirty-five percent were on a beta blocker, and 93
percent on an ACE inhibitor.

Now, this was far higher than we see in the practice of medicine in the
community today, and remember one reason for that is the patients who
are on an ARB were excluded from participation, and even though there
are no data yet, there are many physicians who are substituting ARBs for
ACE inhibitors because their patients coughed once.

And consequently those patients were excluded. So these are patients who
are largely on ACE inhibitors, and only seven percent were not on an ACE
inhibitor, and that\'s an important group, too.

Their quality of life was assessed by the Minnesota living with heart
failure questionnaire, and the overall average score was about 32, which
by the criteria of that questionnaire puts them in the moderate heart
failure range, not severe, but not mild either, and that\'s broken down
by the emotional and physical component of that Minnesota form.

Well, what kind of doses of ACE inhibitors were they on at baseline? And
this is the list of ACE inhibitors that were being used. Remember these
are physician choice.

The three biggest ACE inhibitors in use were enalapril, lisinopril, and
captopril. The doses of these drugs are very close to the recommended
doses. For enalapril and lisinopril, very close to the 20 milligram dose
which is generally recommended daily dose. Captopril, probably lower
than one would have chosen to use in clinical trials, but obviously this
drug is often used more than once a day, and the mean dose was about 80
milligrams, and the other is incomparable doses.

What about the beta blockers being used? Well, the two most commonly
employed beta blockers were carvedilol and metoprolol, and they were
used in doses which are probably lower than one would choose based upon
clinical trial data, but this is the real world, and I see patients
coming referred to me on beta blockers, and for the most part, they\'re
on low doses.

There\'s some concern about titrating up to the doses that have been
used in most clinical trials, and then the other beta blocker uses are
shown below.

Well, this was the primary endpoint of Val-HeFT. These are the two
primary endpoints. Mortality was identical in the two treatment groups,
a hazard ratio of 1.02.

The morbidity endpoint, which of course includes mortality, exhibited a
striking reduction in the valsartan group compared to the placebo group,
a hazard ratio of .87, a p value of .009.

So the study achieved its target endpoint with one of the two primary
endpoints.

Here is the Kaplan Meier survival curve exhibiting superimposition of
the placebo and valsartan arms over 30 months of follow-up.

And what about mechanism of death? These were all adjudicated by our
endpoint committee, and you will see there\'s very little difference
between the valsartan and placebo groups. Sudden death here, pump
failure death here, sudden death with per monitory worsening of
symptoms, other vascular causes, non-cardiovascular deaths very similar.

So there appeared to be no mechanistic difference in what led to death
in the two treatment arms.

Here is the Kaplan-Meier curve for morbidity, which exhibits separation
beginning at about three months and then widening over time. Once again,
this was a 13.2 percent risk reduction, and the p value was .00852.

Now, in the morbidity endpoint, obviously we had four different possible
contributors to morbidity, and here is the breakdown of those four
contributors. The biggest difference was heart failure hospitalization
occurring at 18.2 percent of the placebo group and 13.8 percent of the
valsartan group, and that leads us to do an analysis of hospitalizations
in more detail.

Here were the cardiovascular deaths, which are very similar. Here\'s the
first nonfatal morbid event exhibiting a striking reduction in the
valsartan arm, first heart failure hospitalization, a similar reduction
of hazard ratio to .725. The first sudden death with resuscitation, very
small numbers so not too meaningful.

This is the curve for incidence of worsening heart failure. Now, one has
to censor deaths when one does this kind of an analysis, but it gives
you some idea about the frequency in which patients are hospitalized for
heart failure as an initial event, and you can see that the curves begin
to separate at about three months ago, and they widen over time.

This is a 27.5 percent risk reduction, and the p value for that is
.00001.

Now, the agency had raised some issues about overall cause
hospitalizations, and let me just provide you that data because I think
there\'s been a bit of a confusion about that.

Heart failure hospitalizations, now these are investigator assessed
because all the endpoint committee did was to adjudicate the first heart
failure hospitalization. Once a patient had been hospitalized for heart
failure, they no longer adjudicated hospitalizations.

But of course, hospitalizations occurred, and the investigator was busy
assessing hospitalizations; the investigators were doing this on their
own.

Well, what did the investigators find about heart failure
hospitalizations? Well, the investigators identified 266 fewer
hospitalizations in the valsartan group compared to the placebo group.
This was all cause hospitalizations, a similar reduction. So this is
statistically significant. This is obviously not because it is
influenced by a large number of non-heart failure hospitalizations which
were equal in the two groups.

So this is a tribute in our decision to adjudicate heart failure
hospitalizations as an endpoint for the trial. They had no reason to
think that valsartan would reduce the number of other hospitalizations,
but we hoped it would reduce heart failure hospitalizations and not
increase non-heart failure hospitalizations, and that\'s indeed what we
found.

Now, there\'s also been some question raised about the days in the
hospital. Not only are we interested in how many patients get
hospitalized or how frequently they\'re hospitalized, but what about the
number of days in the hospital?

Well, highly significant reduction of days in hospital, mean days in
hospital during the trial, 3.5 for valsartan compared to 4.8 for
placebo. All cause hospitalizations also tend to be reduced, not quite
significant, and non-heart failure hospitalizations once again,
identical.

Well, what about days alive and out of the hospital? Well, this is an
attempt to get at that. This is not an easy number to get at, but once
again, as you might expect, there\'s more days out of the hospital and
alive in the valsartan treated group than in the placebo group. And
these are, again, the data I showed on the previous slide.

Well, you can\'t do a statistic on that very easily because the number
of days out of the hospital, alive and out of the hospital, varies
tremendously based on when the patients were entered into the trial
because there\'s a wide range of duration of follow-up. So you get this
wide standard deviation, and then you get a p value which, of course, is
nowhere near significant.

So it\'s important to correct the number of days alive and out of
hospital for the number of years of follow-up or months of follow-up,
and we\'ve done that on this slide. And this is the mean days per year
alive and out of hospital, and now, of course, the standard deviation
gets much lower, and the heart failure days in hospital is highly
significant. All cause is not, and of course, non-heart failure remains
essentially identical.

Well, we monitored a number of secondary endpoints. Signs and symptoms
of heart failure were assessed by the investigator, and new York heart
class was assessed, and this is the change from baseline to endpoint in
each patient of these measurements.

Here\'s New York heart class. More patients with valsartan improved and
fewer worsened than in the placebo group, and that was highly
significant, .001, and that\'s true also of jugular venous distention,
of edema, of rales, not quite of third heart sound, of paroxysmal
nocturnal dyspnea, of dyspnea at rest, dyspnea on effort, fatigue, and
not quite orthopnea.

This is actually a remarkable demonstration of efficacy. I\'ve never
seen a trial before in which all of these secondary endpoints exhibited
a benefit from a drug. It\'s hard to come up with this kind of data, and
so this was remarkably congruent.

DR. FLEMING: Could I just \-- you\'re making a key point. If we could go
back, Jay, if I could just interrupt you for a second.

DR. COHN: Sure, by all means. I love to be interrupted, Tom. So don\'t
hesitate.

DR. FLEMING: When you point out how remarkably congruent it is, I look
at that and wonder as a statistician if it\'s even more congruent than
random chance would anticipate. Yes, these are all significant. They all
show about a one to three percent more favorable result in the percent
that improve and a one to three percent more favorable result in the
number that worsen, almost exactly the same across all of these
endpoints.

There must be a lot of correlation between these endpoints.

DR. COHN: Well, sure, there are. New York heart class and symptoms all
go together. Jugular venous distension is an observation which
shouldn\'t really relate to such things as fatigue or dyspnea, but they
are all going to go sort of together, and your point being then what?

DR. FLEMING: My point being addressing your point that it was remarkable
that they were all significant. Well, if in fact you have a general
quality of life phenomenon and you have many variations of measuring the
same phenomenon, then I would expect a consistency and significance
across those results. It\'s not as though we have 13 independent
assessments all of which \--

DR. COHN: Oh, no.

DR. FLEMING: \-- achieve a p of .001.

DR. COHN: I would agree. I was just commenting on the fact that in other
trials, and I\'ve been involved in a lot of other trials, it\'s been
very hard to actually demonstrate any clinical benefit on these kinds of
clinical measurements, and we were able to do it in this study on all of
them.

But your point is well taken that they are mutually dependent in many
respects. So you might expect them to go together.

This is the Minnesota living with heart failure questionnaire. Now, this
is filled out by the patient. All the other data are obtained by the
physician, the physician\'s assessment. This is the patient filling out
a form. This form is completed when the patient walks in the door before
they meet with the health care provider.

So they sit down and they fill out this 21 question form. So it\'s very
independent kind of assessment before they\'ve been influenced by
meeting with the nurse or the doctor.

And the primary endpoint was the change from baseline to endpoint,
whenever the endpoint occurred, and the patients on placebo exhibited a
progressive worsening of their quality of life. A rise in score means
quality of life has become worse. The patients on valsartan did not
exhibit that worsening. So the overall score was highly significantly
favorable for valsartan.

Now, this score is traditionally broken down into two components. I must
point out to you that this overall score has been heavily validated in a
number of trials. The breakdown into emotional and physical is not as
well validated, but nonetheless, there are a group of questions which
are defined as the physical score and another group defined as the
emotional score, and there was a similarity in the benefit of valsartan,
perhaps more dramatically with the physical score which you might have
expected.

This is the time course of quality of life over the 30 months of the
trial. Assessments were made at four, 12, 18, 24, and 30 months, and you
can see that in the first few months there was a tendency for an
improvement in quality of life, not really different between the two,
and at 12 months not really much different.

But by two years, there was a significant difference. At the endpoint,
again, out here, the trend was even greater. None of these were quite
independently statistically significant, but the endpoint was, and that
was the prescribed endpoint for the trial, was baseline to endpoint, and
whenever the patients ended, they were assessed.

Now, ejection fraction was monitored by echo, and these are the ejection
fraction data from baseline to endpoint. The valsartan group exhibited a
rise of about four units of ejection fraction. The placebo group, a rise
of about three units. It\'s a small difference, but highly significant.

And this is the sequential changes in ejection fraction over time. By
four months there was already a difference; 12 month, 18, 24, and 30
months. In all of these time frames, the valsartan group exhibited a
greater rise in ejection fraction than the placebo group.

Now, I must tell you that this increase in ejection fraction in the
placebo arm is not consistent with previous data. We\'ve monitored in
the Val-HeFT trials. The placebo group goes down.

Yeah, Jeff.

ACTING CHAIRMAN BORER: Well, I wondered about this, too, but all of
these people are on background therapy.

DR. COHN: Exactly.

ACTING CHAIRMAN BORER: That affects EF over time.

DR. COHN: And, in fact, as you will probably see later on \-- I think I
have a slide that shows it \-- the group on beta blocker, we did not
prescribe that they had to be on beta blocker for, say, more than six
months before they\'re randomized. So what you will see in the beta
blocker treatment is a tendency for an ejection fraction to go up during
the course of the trial from the beta blocker itself. So this is a very
well treated group, and I suspect the rise in EF reflects the effect of
other drugs.

But that\'s the way the data came out, and it was certainly a
significant benefit of valsartan.

Now, we also did ejection fraction Study 106 just to show congruence
because here we have three different doses of valsartan, 40, 80, and
160, and here was the placebo group, and you can see in Study 106, all
three doses seem to improve EF more than the placebo.

Once again, it does look like 160 was the more effective dose. So we
were reassured that we probably did use the right dose in Val-HeFT.

Now, we also monitored left ventricular chamber dimension by echo, and
this is the change from baseline to endpoint of left ventricular
internal dimension in diastole. It went down in the valsartan group,
less reduction in the placebo group, the difference very highly
significant.

And here is the sequential changes in LVIDD. The reduction was apparent
by four months and persisted throughout the trial. So clear evidence for
benefit on left ventricular dimensions or remodeling, which I would call
that, from valsartan, but modest, not gigantic, but highly significant,
a tribute to the large numbers of patients that we were able to monitor.

Now, we also measured neural hormones. This was norepinephrine and BNP
levels monitored over time, and the endpoint was baseline; the secondary
endpoint was baseline to the endpoint assessment, and here you can see
valsartan prevented the increase in norepinephrine over time that was
observed in the placebo group. This is the valsartan group, highly
significant difference.

And with BNP, the placebo group rose. The valsartan group fell, and once
again, a highly significant difference.

Here are the sequential changes in norepinephrine. They were measured at
four months, 12 months, and 24 months, and you can see at each time
frame norepinephrine was rising in the placebo group, not in the
valsartan group.

And here is BNP once again, a decline in the valsartan group and a
progressive increase in the placebo group, again, highly significant
differences.

So what can we say from this overall summary of the Val-HeFT data? We
can say that valsartan clearly reduced morbidity in patients receiving
prescribed therapy for heart failure by 13.2 percent. This was the p
value.

It decreased the risk for first heart failure hospitalization by 27.5
percent, and here the p value gave us four zeros before the one.

It improved signs and symptoms of heart failure. It improved ejection
fraction, reduced left ventricular dimension, improved quality of life,
and had a favorable effect on norepinephrine and BNP, albeit with
similar mortality between the two groups.

Now, let me then just put all of this together with the three placebo
controlled preliminary studies, as well as Val-HeFT and review what we
have learned about valsartan.

Hemodynamics, I think clear evidence that valsartan is effective on
hemodynamics both in the absence of ACE inhibitor and in the presence of
ACE inhibitor.

We\'ve determined that valsartan cannot produce further improvement in
exercise performance, at least in the modest size studies that we have
carried out, in addition to background therapy. In the patients in whom
we have monitored signs and symptoms, the Val-HeFT study, they were
improved.

Quality of life was improved in the Val-HeFT. Neural hormones were
demonstrated to be improved in actually all three of these studies, not
determined in 106.

Left ventricular function was monitored in two studies, Val-HeFT and
106, and in both of them the effect was favorable, and of course, the
morbidity trial and mortality trial, Val-HeFT showing a favorable effect
on morbidity, but no demonstrable benefit on mortality.

So let me stop there in terms of the overall study, and then we\'re
going to get into subgroup analysis in a few minutes, but I\'ll be
delighted to take any questions from the committee at this time.

ACTING CHAIRMAN BORER: Let\'s keep the questions to clarification of the
data at this point because Jay has the risk-benefit discussion later
when we can get into philosophical issues if there are any.

I have one question while everybody is sort of gathering their stuff. If
you go back to slide EC-14 \--

DR. COHN: EC-14.

ACTING CHAIRMAN BORER: \-- yes, this is really a secondary issue, and
it\'s just for clarification purposes because you already made the
point, I think, quite correctly that short-term exercise studies don\'t
predict long-term benefit.

But my reading of what we were sent was that an imputation of a zero
value was made for people who died when exercise time was determined for
this study, and that the determination that the zero value should be
imputed was made after the study was completed.

I don\'t know when it was made relative to unblinding. I\'m sure it was
before unblinding, but we weren\'t told that, and that if you didn\'t
impute the zero value, in fact, the patients on enalapril did better
nominally, not significantly, but nominally, than patients on valsartan.

So although this is not the big, burning issue of the day, I\'d like a
little clarification about that, if you would.

DR. COHN: Yes. I wasn\'t involved in one of those two studies and the
analysis of the substudy. Who can address that?

Tom. Tom is our biostatistician.

MR. CHIANG: Tom Chiang, Novartis.

Yes, the zero imputation has been defined and decided and documented
prior to unblinding for analysis, and real data imputation obviously is
done after unblinding.

ACTING CHAIRMAN BORER: Well, don\'t go away yet.

Why did you do that? I mean, I\'m just speaking post hoc here. I mean,
you have no evidence of a difference in mortality in these groups. So
after the fact one might expect that maybe, you know, you\'re not
affecting death, but we\'re suggesting that we are affecting heart
failure.

Why would you impute a zero value to people who died rather than last
value carried forward? I mean, what was the reasoning behind that?

MR. CHIANG: Before unblinding, we don\'t know mortality would be equal.
So we plan a lot of sensitivity analysis, and you know, imputation for
patient died, you know, or could not work due to heart failure, you
know, was defined.

ACTING CHAIRMAN BORER: Thank you.

Are there any other questions, issues of clarification from the
committee? Paul.

DR. ARMSTRONG: Could we look at Slide 37, please?

Jay, I\'m trying to understand the sample size here, and it just gives
me a repetitive number of the overall study rather than the patients who
were hospitalized according to these categories. Can I get the
appropriate sample size that lines up with those three categories?

DR. COHN: I\'m not sure what you\'re \-- this is the number of patients
that were randomized.

DR. ARMSTRONG: Right.

DR. COHN: And this is the mean days in hospital for those 2,511
patients, and just averaged out over the \--

DR. ARMSTRONG: But I just want to know how many patients were
hospitalized for heart failure.

DR. COHN: Oh, I guess I showed you that on another slide. What was the
\--

DR. ARMSTRONG: Okay. I couldn\'t find that.

DR. COHN: Yeah, what\'s the slide that shows the heart failure
hospitalizations?

DR. ARMSTRONG: Thirty-four, EC-34.

DR. COHN: Yeah, this is heart failure hospitalizations, and this is the
number of hospitalizations. So it\'s 1,000 in the valsartan group, and I
can\'t \-- you\'d have to extrapolate about 1,266.

ACTING CHAIRMAN BORER: You have the exact numbers in EC-34.

DR. COHN: There\'s 266 more here than here.

ACTING CHAIRMAN BORER: It\'s 923 against 1,189.

DR. COHN: Yeah, okay.

DR. ARMSTRONG: Nine, twenty-three against?

ACTING CHAIRMAN BORER: Eleven, eighty-nine.

DR. ARMSTRONG: And, Jeff, you may want to ‑-

ACTING CHAIRMAN BORER: No, that won\'t help any.

DR. ARMSTRONG: You may want to reserve the issue of adjudication in the
process. You may want to reserve that for later.

ACTING CHAIRMAN BORER: No, if you want to know how it was done, let\'s
ask now.

DR. NISSEN: I\'m a little confused about the process of adjudication. As
I understand from our briefing book, these were brought to the endpoint
committee if it was perceived that they were heart failure, but if they
\--

DR. COHN: No, all hospitalizations were brought to the \-- every
hospitalization was referred to the endpoint committee, and then they
determined whether the hospitalization was from heart failure. They
didn\'t even have available to them the investigator\'s assessment. They
just saw the data for every hospitalization.

So it was a monstrous undertaking as you can imagine, and then they
identified those that they felt were due to worsening heart failure.

DR. NISSEN: Our briefing book suggests that the sponsor screened all
hospitalization endpoints, and those that didn\'t meet endpoint criteria
were not submitted to adjudication so that there was some initial
adjudication.

And the second point was that I\'m confused about overnight stays in the
emergency room included in hospitalization. As I understand it, if a
patient was in the ER for 12 hours during the day, they would not be
categorized as hospitalization, but if they were in the ER for 12 hours
overnight, they would be. Can someone help me with that?

Because that\'s a troublesome issue we all face.

DR. COHN: Bob, do you want to address the process that you used in
Novartis?

DR. GLAZER: Robert Glazer, Novartis.

What happened with the documentation that went to the endpoint committee
was that clearly cardiovascular events per a serious adverse event
report that came to us, and those events that had any question of being
cardiovascular were sent to the endpoint committee, and what was sent to
the endpoint committee was the SAE narrative itself, case record forms
or case record form printouts, and any hospitalization data that was
collected after that, meaning hospital discharge summaries and, if
needed, depending on the case, histories and physicals, laboratory data,
ECGs, chest X-rays, and progress notes.

For those cases that were clearly non-cardiovascular, for example, an
orthopedic problem that the person was being admitted for, a listing was
provided with the patient\'s identifier and the diagnosis from the
serious adverse event report form, and that was provided to the endpoint
committee chairman.

If at that point in time he requested additional information, that
information was provided, and we collected the hospital records.

DR. LINDENFELD: Just to clarify, so a hospitalization recorded by the
investigator as hypotension due to over diuresis would not have been
reviewed?

DR. GLAZER: Oh, that definitely would have been reviewed because it
would have been considered cardiovascular. The key points are things
like orthopedic problems, for example. Those were put into listings, and
again, the endpoint committee chairman could ask for additional
information.

DR. ARMSTRONG: You\'ll excuse me for persisting, but again, in our
briefing book, there\'s an addendum to the endpoint manual that defines
an admission due to over diuresis or drug toxicity as a hospitalization
for reason other than heart failure.

DR. GLAZER: That\'s right.

DR. ARMSTRONG: So I\'m a little confused. Did you \--

DR. COHN: Well, they adjudicated those. Those \--

DR. ARMSTRONG: So if a patient came in with over diuresis and
hypotension or hyperkalemia or some complication we would ordinarily
associate with heart failure, it was classified as not heart failure
admission?

DR. COHN: Well, the definition for heart failure, maybe we can put up
that slide for the definition here while Bob is still there.

The issue was worsening heart failure. So over diuresis is not worsening
heart failure. It is a cardiovascular hospitalization.

This was the endpoint committee definition of hospitalization for heart
failure. It was obviously severe collapse, pulmonary edema, symptoms and
signs requiring intermittent or continuous IV therapy.

Hospitalization is defined as an overnight stay even if total duration
is less than 24 hours. Remember we also included as our primary endpoint
more than four hours in an emergency room. So we captured all of those
events, but by definition hospitalization is an overnight stay whether
it\'s in the emergency room or elsewhere, but the four hour criteria was
also captured.

DR. ARMSTRONG: If you got an inotrope, right?

DR. COHN: If you got an IV diuretic or an inotrope or a vasodilator and
you had to stay there for four hours. And there were very few of those,
as you saw on that previous. So almost everything was captured by
hospitalization.

But the committee made the judgment that if somebody came in because
they were over diuresed, that it is a hospitalization, but it\'s not a
hospitalization for worsening heart failure.

ACTING CHAIRMAN BORER: Steve.

DR. NISSEN: Yeah. You know, it\'s interesting because we all sort of
seem to flag the same points when we reviewed this, and I also was a
little bit uncomfortable. Let me make sure I understand this.

A patient that came in at 6:00 a.m. and went home at midnight, came in
with symptoms of some kind, was not adjudicated as heart \-- it could
not have been a heart failure admission.

DR. COHN: No, they were adjudicated, and they were counted as a more
than four hour stay out of the hospital.

DR. NISSEN: Well, I\'m just reading what the FDA reviewer said. It said,
\"Hospitalizations that were clearly less than 24 hours were not
submitted as events.\" That\'s what the book says. Now, is that right or
wrong?

DR. COHN: Bob, can you clarify what happened with those events which
were more than four hours in an intensive care unit?

DR. GLAZER: Again, if the information, any information concerned that it
was a cardiovascular event, it went to the endpoint committee. How the
endpoint committee classified it if they wanted to classify that as a
hospitalization for heart failure, they made a definition that it had to
be an overnight stay.

DR. NISSEN: So that would not have been a hospitalization for heart
failure no matter what?

DR. GLAZER: From my understanding.

DR. NISSEN: Okay. So the FDA reviewer was correct there. I\'m puzzled by
the rationale for that, that\'s neither here nor there.

DR. COHN: Well, you have to have a definition for hospitalization.

DR. NISSEN: Sure, but you know, what it means is that a 12 hour
hospitalization that occurred from 9:00 p.m. to 9:00 a.m. was a heart
failure admission, and one that occurred, you know, from 9:00 a.m. to
9:00 p.m. wasn\'t. I mean to us that seems bizarre.

DR. COHN: But they were captured by the four hour criteria. So they all
come out as a primary endpoint. There\'s no distinction made between
primary endpoint from hospitalization and for four hours or more in an
intensive care unit.

DR. NISSEN: No, no. But I\'m saying somebody didn\'t come into an
intensive care unit. They came into, you know, a hospital ward, you
know, for an 18 hour admission. If that admission did not have an
overnight stay, it would not have been a heart failure admission.

DR. GLAZER: And again, they would have had to receive from my
understanding four hours of an IV inotrope during that, and that met the
criteria. So I think, if I recollect, the problem was collecting the
times, and the dates were collected, but the times were not clear on
many of the cases.

So I think for adjudication purposes, change in date was a criteria for
calling it overnight stay.

DR. NISSEN: yeah, I understand where we\'re coming from, but so we all
understanding each other, you could come in at nine o\'clock in the
morning, and you could spend the day getting large boluses of
intravenous furosemide to get you out of heart failure and go home at
9:00 p.m. that night, and that would not have been a heart failure
admission.

DR. COHN: But it would have been captured as a four hour or more stay
with aggressive intravenous therapy.

ACTING CHAIRMAN BORER: I think you\'ve got to have an inotrope, Jay.

DR. COHN: No, no. It doesn\'t say inotrope. It says \--

ACTING CHAIRMAN BORER: It does in our briefing book. So we\'ve got some
confusion here \--

DR. COHN: Oh, really?

ACTING CHAIRMAN BORER: \-- that we need to sort out.

DR. NISSEN: That patient that I just defined \--

DR. COHN: Here. Let\'s just remind you of the morbidity endpoint.

DR. NISSEN: I mean, my reading of the book, and you\'re going to have to
tell me if I\'m wrong, is that a 9:00 a.m. to 9:00 p.m. admission
getting a bunch of intravenous furosemide for a patient with pulmonary
edema would not have been heart failure by this definition.

DR. GLAZER: That was my understanding.

DR. NISSEN: Okay. Now, you know, it may not have made any difference,
but it doesn\'t seem very logical to me, I must tell you.

ACTING CHAIRMAN BORER: Can you tell us, just so we can put this into
context, how many patients would have fallen into the category that
Steve is talking about?

I mean, you know, this was a big study. If the number of patients not
captured under this particular definition is relatively small, we can
probably just, you know, move on, but do we know what the numbers are?

DR. COHN: Well, if we didn\'t capture the data, the endpoint committee
would know. And I\'m sorry that Peter is not here yet, and hopefully he
will show up because he\'s the one who processed all of these, and all
of those data would have been provided to him.

If a patient was in the hospital from 9:00 a.m. to 9:00 p.m., they would
have gotten that data as an event, and then they would have decided what
to do with it.

Now, my understanding is that they used the overnight stay as a criteria
for hospitalization, and I guess we could ask him in how many instances
they reviewed a case that didn\'t meet the overnight stay, but actually
didn\'t get captured at all because they didn\'t by chance get
nitroglycerine or nitroprusside or something and got IV diuretic.

We\'ll try to ask him when he comes. My understanding is it was almost
zero that didn\'t meet the criteria.

DR. NISSEN: Can I just tell you? I mean, in our institution patients
come in all the time, and they come into the emergency department. They
will come in in the morning, and they\'re in pulmonary edema, and they
have known severe heart failure. They\'re put in a short stay unit. They
get IV diuretics to get them out of pulmonary edema, and they go home
the same day.

DR. COHN: Well, they probably get some IV nitroglycerine, too, which
would make them eligible for the four hours, and we can ask \--

DR. NISSEN: I actually wish they did, Jay, but they don\'t always.

DR. COHN: Yes.

DR. NISSEN: And so just to me it\'s a hole here that I think \-- I mean,
I just want to make sure I understand it well.

There\'s a reason why you\'re getting a lot of discomfort here, and I\'m
going to just speak for myself and tell you why we\'re \--

MR. MacNAB: I just want to make it clear that \--

ACTING CHAIRMAN BORER: Could you use the microphone?

DR. COHN: Use the microphone, Malcolm.

MR. MacNAB: I wish Peter was here, and we can get any additional
information you want, maybe not today, but we can get it for you.

I think the real problem \-- I remember discussing this with him \-- was
we wanted to be consistent, and we wanted to be accurate, and the worst
thing would have been to improperly classify people and without the
times, which were not consistent. The most consistent thing was the
date.

And, again, it was randomized. It was blinded, and I think the decision
of the endpoint committee was made to do it right and not make mistakes.

ACTING CHAIRMAN BORER: Can I just make one \--excuse me one second,
Steve.

Just to clarify this further, my understanding is that the data indicate
that valsartan was more effective than placebo on top of background for
all morbid events combined. That was driven predominantly by the
hospitalization, but it was true for all morbid events combined, which
would include the hospitalizations, and the non-hospitalizations.

DR. COHN: That\'s right.

ACTING CHAIRMAN BORER: Is that correct?

DR. COHN: That\'s correct. That\'s correct.

ACTING CHAIRMAN BORER: I mean, that may put this in a different context
perhaps.

DR. COHN: I\'ve given you the hospitalizations separately to show the
27.5 percent reduction, but the primary endpoint was all the events
combined, and I, frankly, believe \-- and I can\'t \-- Peter would have
to verify this \-- but I believe the number of events that were not
captured because of these rules is almost zero because the committee was
very attentive to every event, and they reviewed all of these events.

And if they had excluded a patient who was getting boluses of diuretic
every hour for 16 hours and came in at nine in the morning and went home
at midnight, and it didn\'t count as a hospitalization, they would have
been as disturbed as you are actually, Steve. So I think they \--

DR. LINDENFELD: Jeff.

ACTING CHAIRMAN BORER: JoAnn.

DR. LINDENFELD: I think JD-3 \-- I think the only things that were
included were if you got an inotrope or vasodilator for more than four
hours. I don\'t think the kind of admission that Steve was describing
would have been included in more than \--

DR. COHN: Well, that\'s right, but what I\'m saying, JoAnn, is I don\'t
think there were many, if any, of those events that would have
influenced the result, but we\'ll check with Peter.

DR. NISSEN: See, we don\'t know, and the reason we don\'t know is I\'m
going to read you what the FDA reviewer says. \"Hospitalizations that
were clearly less than 24 hours were not submitted as events.\"
Therefore, if they\'re not submitted \--

DR. COHN: No.

DR. NISSEN: \-- then they\'re not adjudicated.

DR. COHN: No, no, that\'s not true.

DR. GLAZER: That\'s not correct.

DR. COHN: They were submitted, and then ‑-

DR. GLAZER: And that\'s what we have in our briefing book.

DR. COHN: But the process that we used was outlined by Dr. Glazer.
Essentially most everything, unless it was very obviously, you know, a
patient was admitted for plastic surgery or something, and then that
would have been listed, and the endpoint chairman could have asked for
that if he wanted.

But all of those types of events you\'re talking about would have been
listed for him, and I believe, Dr. Cohn, the number of the types of
patients you\'re talking that came into the ER for what we would call a
little tune-up of IVs is not that great, but I can get you the \-- I
will get you those numbers for you.

DR. NISSEN: I guess the reason that a lot of us are uncomfortable is
that we would have preferred an independent adjudication process, and
when we read about a process where the company is submitting,
selectively submitting events to a committee, as opposed to a committee
that reviews everything, it makes me uncomfortable. I guess that\'s the
problem.

DR. GLAZER: Well, I can assure you it was done. Every event or every
possible hospitalization, the listing of what it was was available to
it, and most of the hospitalizations for this type of patient are
obviously cardiovascular. I believe it was pretty independent, and I
think when Peter is here, I think he will say the same thing.

DR. COHN: And it was all blinded, Steve. So I mean, there was no \--

DR. NISSEN: I understand. I understand.

MS. TARGUM: I just want to point out that the information the agency
received was relied upon in the manual.

ACTING CHAIRMAN BORER: Okay. So it sounds as if from the definitions
that we have here that if you got inotropes or vasodilating agents for
four hours, that would be included. But if you just got diuretics, that
wouldn\'t be included, and that may not be correct in practice.

So we\'ll have to wait for the endpoint committee to clarify that for
us.

JoAnn and then Alan.

DR. LINDENFELD: Just to come back to this adjudication process, I
understand that from trial end to trial completion there was a
difference of from 906 to 975 deaths. My question is: how many
additional heart failure hospitalizations were there in that same period
of time?

And our briefing book suggests that the deaths and the hospitalizations
between that period of time, trial end and trial completion, were not
adjudicated; is that correct?

I guess I wonder how many of them that is.

DR. COHN: No, everything was adjudicated up until the final \-- I\'m
sorry. I didn\'t quite hear your point, JoAnn.

DR. LINDENFELD: Well, our booklet says that between trial end and trial
completion there was a difference of from 906 to 975 deaths. It says
there was no adjudication on mortality/morbidity endpoints at trial end.

DR. COHN: No, that\'s not correct. The 906 was the number of reported
deaths. When the DSMB met, identified that it had passed the 906 point,
and recommended that the study be terminated. That recommendation goes
to the sponsor. The sponsor makes a judgment with the steering committee
to terminate the study, sets a date for termination, and then many more
deaths are still being reported during that period of time.

All the events that occurred until the end of the trial were adjudicated
by the committee, everyone.

ACTING CHAIRMAN BORER: Alan.

DR. GLAZER: Can I just clarify? Robert Glazer from Novartis.

The events that occurred from May 3rd to the end of the trial were not
adjudicated.

DR. COHN: But you don\'t mean by the end of the trial. You mean by \--
the trial ended on \--

DR. GLAZER: May the 3rd, and that\'s when the 906 deaths we were made
aware of, and that was the day that the trial was considered completed.
Subsequent to that, bringing in the last patient for last visit for
follow-up to conclude, officially conclude, the trial, there were events
that occurred. Those events were not sent to the endpoint committee
because \--

DR. COHN: And they aren\'t counted \--

DR. GLAZER: And they are not counted.

DR. COHN: \-- in our analysis either. The analysis is as of May the 3rd
when the trial ended.

DR. LINDENFELD: Is the analysis on 906 or 975 deaths?

DR. COHN: Nine, seventy-five.

DR. LINDENFELD: Well, but then that difference between 975 and 906,
those were the number that were then not adjudicated, and I assume
there\'s a similar percentage of \--

DR. COHN: No, no.

DR. LINDENFELD: \-- hospitalizations.

DR. GLAZER: I\'m sorry. There was a certain number of events that
occurred from when observed the 906 deaths. When we observed the 906
deaths, obviously when we collect documentation afterwards, there were
additional people who had an event, a morbid event or a mortal event
that hadn\'t been reported to us or was in the process of coming to us
through the process.

That\'s what accounts for the additional information. So those events,
yes, they were adjudicated and put into the analysis. that\'s why it
doesn\'t end at 906, because that was a date that we were made aware. We
found these extra events as we were doing the \--

DR. COHN: I mean, let\'s make it clear. Every event that occurred before
May 3rd, which was the termination of the trial, were adjudicated. Now,
other people had events, and then they have to be brought back, and they
have to be told about the results of the study. They have to be taken
off their study drug.

People don\'t go off study drug on May 3rd. They have to come back in
for a visit, and between May 3rd, which was the official termination of
the trial, and the time that they came back and were taken off of their
study drug, there were events that took place, but they weren\'t part of
the trial. That was post trial, and they\'re not counted in any of the
analysis that we\'ve shown here.

DR. FLEMING: May 3rd was the date of 906 deaths or 979?

DR. COHN: No, come on. You\'re waiting for reports to come, and when the
number of reports ‑-

DR. FLEMING: We fully understand that between a data monitoring
committee\'s review and when the database is finalized, additional
events come in.

DR. COHN: Yeah.

DR. FLEMING: The question is very simple, and I think your answers so
far seem to be confusing. The date at which the data monitoring
committee met, there were 906 deaths. The reports that we\'ve been
provided \--

DR. COHN: There were over. There were more than \--

DR. FLEMING: \-- give us 979 deaths. Presumably there were also emerging
during that time frame CHF hospitalizations as well.

Simple question: is the primary analysis that we\'ve been shown for
morbidity events, were all of the CHF hospitalizations in that analysis?
Were all of them adjudicated?

DR. COHN: Yes.

DR. FLEMING: Thank you.

MR. HAUPTMAN: Let me clarify \-- Lawrence Hauptman, Novartis \-- the 906
and the 975 number. The 906 was reported as hitting \-- that was
supposed to be the endpoint, that many deaths, and then it was decided
that that was May 3rd, but then in going back to the field and getting
all of the paper work in, those extra 70 people were discovered in terms
of deaths and also in terms of the morbidity endpoint.

But they all occurred before May 3rd. So anything that occurred before
May 3rd is what you see in the data that was submitted in the analyses.
Stuff that happened after May 3rd is after the trial ended and is not
part of any \-- you haven\'t seen any data on anything that happened
after May 3rd.

DR. FLEMING: So, Larry, then the final updated database indicated that
by May 3rd there were 979 deaths.

MR. HAUPTMAN: That\'s true.

DR. COHN: That is correct, and it\'s always true in trials. When the
reports come in, you wait for the reports of the target number and then
you terminate the trial. You don\'t terminate it the day the DSMB meets.
The DSMB has to meet and make a recommendation. There has to be a date
set when you\'re terminating the trial, and that is the date.

And at that point, by going back and reviewing every center, there were
900 and whatever it is, 70-some deaths.

ACTING CHAIRMAN BORER: Alan and Paul on this same issue, and then we\'ll
go to Tom for a new issue. Alan, did you have?

DR. HIRSCH: Well, my question is related. Obviously our goal is to make
sure that the medication if used by the public for heart failure breeds
a clear benefit. So we\'re sort of adjudicating right here.

What I\'m wondering is do we have in the room at this time data that we
can look at on emergency room use by the two groups in Val-HeFT.

Sort of opinions. We love to see data.

DR. COHN: You mean by the four hour criteria for emergency room or do
you mean just having to go to the ER?

DR. HIRSCH: I\'ll take either.

DR. COHN: Well, we showed you the \-- can we go back to the slide that
breaks \--

DR. HIRSCH: Not hospitalization.

DR. COHN: \-- breaks down the morbidity endpoint?

DR. HIRSCH: The breakdown of the morbidity endpoint.

Yes, ideally, in other words, Jay, without the use of inotropes or
vasodilaters. We\'re looking for raw data.

DR. COHN: No, I guess those data were not captured. These are the
primary endpoint data. This is the number of patients who got
intravenous therapy that were not hospitalized and met that criteria,
and you can see there\'s only five in each treatment arm.

We really did not capture such things as patients coming to the ER not
feeling well and being given an antibiotic or an extra dose of oral
lasix and then going home. We did not capture that because we wanted to
be rigid and maintain a very high standard for what represented true
worsening heart failure or events equivalent to a hospitalization.

ACTING CHAIRMAN BORER: Paul and then Steve.

DR. ARMSTRONG: Again, Jay, I\'m not trying to be difficult, but the
briefing book has said that the endpoints, the morbid endpoints at least
between May 3rd and the completion of last patient, last visit were
recorded by the investigators and not adjudicated.

DR. COHN: That\'s right.

DR. ARMSTRONG: You\'ve said that they are adjudicated.

DR. COHN: No, no. Anything after May 3rd was not adjudicated. I don\'t
know how more clearly to say that. Everything up to May 3rd \--

DR. ARMSTRONG: Thank you.

DR. COHN: \-- and all the data you\'ve seen is everything that happened
in the trial up until May 3rd. The duration in which patients stay on
drug after May 3rd varies, of course, depending on when they\'re able to
get back and visit with their health care provider and be taken off of
their therapy and a decision made what treatment they\'re going to go
on.

And there were events that took place there, and Novartis is obligated
since these patients are in a protocol and they\'re still on test drug;
they\'re obligated to monitor those events, but there was no purpose in
adjudicating them because they were not part of the primary analysis.

ACTING CHAIRMAN BORER: Steve and then Tom.

DR. NISSEN: Yeah. Jay, I agree with you that going to the emergency room
should not have been the primary endpoint in the trial. I think that the
right endpoints were used. The problem that we\'re having is that, you
know, patients with heart failure make frequent trips to emergency
departments. They use health care resources to do so, and collecting the
data and reporting it for purposes of further understanding the benefit
and risk of the drug would have been greatly helpful to us. This is kind
of a message to people who do such trials.

I mean, I can understand why you would not want to adjudicate those as a
heart failure event, but what if, you know, there were, you know, more
trips to the emergency department by patients taking the active drug
versus the control? That would suggest that there was a general safety
disadvantage to the therapy.

And so if that\'s not captured, we have no way of knowing about it, and
that\'s sort of what people are saying. That\'s more of an editorial
comment than a question because I know we don\'t have that data. We
don\'t know how many patients made a trip to the ED.

The other reason for the discomfort is that if you look \-- as I look
carefully at the data, the risk ratio for hospitalization was
substantially lower for the active treatment arm by the endpoint
committee than it was by the investigators.

So in the process of going through the adjudication process, there was a
\-- if you go on the briefing document on page 99 for the committee
members, what you see is there was a 27 and a half percent reduction
from the rest \--

DR. COHN: Yeah, here\'s the data actually, Steve.

DR. NISSEN: Yeah.

DR. COHN: These are what were defined as heart failure related
hospitalizations. You can see the endpoint committee eliminated a lot in
both the placebo and valsartan arm. So they were much more meticulous
about the criteria for heart failure hospitalization.

And here was the endpoint committee\'s adjudication showing a 27.5
percent reduction and a p value with four zeros.

The investigator \-- which was not the primary endpoint. Remember that
the protocol said this is the primary endpoint, but if we went by the
investigator assessment, there was still a significant reduction, but it
was 16 percent rather than 27 percent.

DR. NISSEN: Right, and so that\'s why we\'re focusing on closely on the
adjudication process, and I think you used the right endpoint. I\'m not
disagreeing with that at all, but I\'m trying to understand why there
was such a substantial difference.

I mean, the benefit of the agent was nearly twice as great if one looks
at the way the endpoint committee looked at it versus the way the
investigators looked at it.

DR. COHN: I don\'t know. I mean, I can\'t answer the question.

DR. NISSEN: Yeah, I know you can\'t, and that\'s why we\'re being so
nitpicky in understanding it.

DR. COHN: You know, unfortunately it was significant in both instances.

DR. NISSEN: Yeah, yeah.

DR. COHN: Obviously, and I\'ve been through this many years, Steve, as
you know. Investigator assessment of mechanism of death and of reason
for hospitalization is seriously variable from investigator to
investigator, and the reason we set up an endpoint committee is to have
some uniformity in the way we will adjudicate these things.

And when you do it uniformly, you\'re right. I have no understanding of
why there would have been a preferential effect, except that that\'s,
indeed, what we would have anticipated, that if you use much more
stringent, uniformed criteria, we\'ll find the benefit of valsartan.

If you just looked at all hospitalizations, which we did and I showed
you that slide, the difference is not statistically significant. So it
was very important to adjudicate and to identify what is identified as
worsening heart failure hospitalizations, and that\'s not what the
investigators did.

The investigators, and many of them put patients in the hospital for
reasons that weren\'t related to worsening heart failure, and they just
checked the box that said \"heart failure.\" And we did it much more
carefully. This is very casual.

DR. NISSEN: You don\'t have to convince me that adjudication is
important. But the trigger for many of us to look more closely at this
is this fairly substantial disparity between the investigator report and
the adjudicated endpoints.

DR. COHN: Well, I think the message is that it\'s very important to
adjudicate.

ACTING CHAIRMAN BORER: Tom, and then after that we\'ll move on to the
next topic because we\'re falling a little behind.

DR. FLEMING: Jay, just a quick question. You had mentioned that the
trial had been powered targeting a 20 percent reduction in the mortality
endpoint. Can you tell us what the targeted reduction was in the \--

DR. COHN: What the target what?

DR. FLEMING: Could you tell us what the targeted reduction was in the
morbidity endpoint?

DR. COHN: Well, we didn\'t power it for the morbidity endpoint. We knew
there would be many more events, and we knew that we would be well over
powered for morbidity. So there was no calculation made.

The monitoring was based upon mortality so that we powered the trial for
a number of deaths to identify that mortality reduction, and the DSMB
monitored mortality only, not morbidity.

MR. CHIANG: Tom Chiang, Novartis.

Yea, the sample size space on the calculation, but we did assess the
potential power for the morbid endpoint also as a primary, and the
powers are enlarged. You know, a certain percent reduction would have
more than 80 percent power.

DR. FLEMING: Well, I don\'t want \-- that\'s in retrospect. I was
interested in what your prospective targeted interest was, and it was 20
percent reduction and death, and in morbidity it was clearly \--

MR. CHIANG: We did not talk, as I say. As I say, sample size is based on
the mortality as the Dr. Cohn mentioned. We did not, say, target which
percentage reduction for morbidity, but we did calculate barriers
reduction to get a feeling to feel comfortable

DR. FLEMING: So there was no clinical sense. This was one of your two
primary endpoints. There was no preplanned clinical sense of what
magnitude of effect you wanted to get on morbidity, and it was one of
your two primary endpoints?

MR. CHIANG: Well, we did, as I say, we did try to calculate there as
possible. So we tried to insure \--

DR. FLEMING: Good. So what were those various possibilities? What were
they?

MR. CHIANG: As I say, beyond ten percent we assess all possible power,
and for 13 percent because that just give you an idea what is power
calculated for.

DR. FLEMING: So when the study was planned, you had planned a 13 percent
reduction in morbidity?

MR. CHIANG: It\'s not planned for that. We calculated various case to
feel comfortable. If this case happen, we do have sufficient power.

ACTING CHAIRMAN BORER: Okay. Maybe we can move on to the safety data,
and we\'ll get back to any other clarifications a little later.

DR. COHN: We\'ve got to go into the subgroup stuff first.

ACTING CHAIRMAN BORER: Oh, sorry.

DR. COHN: That comes next.

PARTICIPANT: Unless you don\'t care about the subgroup.

ACTING CHAIRMAN BORER: No.

DR. COHN: If you want to disregard it, we\'ll just disregard it, but \--

ACTING CHAIRMAN BORER: It\'s your presentation.

DR. COHN: Okay. Now, as you\'re all aware, when one does a large scale
trial like this, one often assesses subgroups to convince oneself that
there is homogeneity among various groups because we\'re dealing with a
very widely divergent population. So it\'s one of the dutiful things
that we all do to look at this kind of a plot of the primary endpoint
which was favorable, the morbidity endpoint, and we look at a number of
baseline demographics, for instance.

This was the point estimate and the confidence intervals for the overall
study favoring valsartan. This is in younger and older patients. This is
in males and females. This is in whites and blacks. And you\'ll notice
the only point estimate that goes to the right of that line is the black
population. It\'s only a modest size population and with very wide
confidence intervals, but we certainly were unable to convince ourselves
that we had demonstrated efficacy in the black patients in this study.

This was the other racial groups. This is the U.S. and the non-U.S. So
pretty close consistency for all of these groups.

Now, what about etiology of disease and severity of disease? Here was
ischemic heart disease and those without ischemic heart disease. Here
are diabetics and non-diabetics. Here\'s New York Heart Class II and III
and IV. Here\'s ejection fraction above and below the median of 27.
Here\'s ventricles smaller than and larger than the median ventricular
size, you know, and for the most part there seems to be no striking
difference among these groups.

Yes, maybe those with less severe heart failure, that is, a higher
ejection fraction and smaller hearts, don\'t exhibit quite as much
benefit as those who have more severe disease, but that\'s not terribly
surprising.

So no inconsistencies.

However, there was a clear baseline difference in therapy, and it\'s
really appropriate to look at background therapy using an angiotensin
receptor blocker on top of ACE inhibitors and beta blockers, mandated
that we look at that, as to whether that\'s influencing outcome, and of
course, that\'s not a continuum. That\'s a yes/or.

What drug were you on at baseline? And this is the analysis that we did.
Now, this was not preordained, and we did stratify for beta blocker use.
I didn\'t point that out in the methods, but we did stratify for beta
blocker use with an intent to make certain that we had an equal
distribution of beta blockers in the two treatment arms, not because we
expected necessarily any interaction.

We didn\'t stratify for ACE inhibitor, but 93 percent of the patients
were on an ACE inhibitor. But it is a yes/no answer. So what about the
patients who were not on an ACE inhibitor? There were 366, and you can
see that this favored valsartan. This is mortality now, not morbidity.
This is the mortality issue, and I show that for a really distinct
reason.

So in mortality there appeared to be a trend here for a benefit of an
ACE inhibitor. Those patients getting an ACE inhibitor, okay, close to
the line. Those patients not on a beta blocker here, those patients on a
beta blocker favored placebo, and that appeared to not overlap
neutrality here.

Now, this is mortality. We did not find a benefit on mortality. So
looking at these subgroups is perhaps not entirely appropriate, but we
felt it important to do it, and we seem to see a clear trend for a
benefit on mortality in the patients not on an ACE inhibitor, and a
worsening mortality in those receiving a beta blocker.

But then we realized, well we have to look at this in more detail
because there are four subgroups. There are those who are neither an ACE
inhibitor nor a beta blocker, and here was their point estimate.

There are those who are on an ACE inhibitor, but not on a beta blocker,
and that\'s their point estimate. There\'s those on a beta blocker but
not on an ACE inhibitor, and here is their point estimate favoring
valsartan, and there are those who are on both ACE inhibitor and beta
blocker, and this is the group that seems to exhibit a worsening
mortality with a risk hazard ratio of over 1.4.

Now, the interaction p value for overall interaction p value is .0091,
and Tom knows better than I, but it\'s kind of hard to get interaction p
values of that significance. So we thought this was something we really
couldn\'t disregard.

Now, let\'s look at the morbidity, which was the endpoint favorably
affected by valsartan. Now, here you\'ve got the patients not on an ACE
inhibitor, highly significant benefit. Even the ones on an ACE inhibitor
you can see their confidence interval just touches the neutrality line.

Here are patients not on a beta blocker. here are those on a beta
blocker, the trend in the wrong direction, and when we look at the four
subgroups now, those on neither neural hormonal inhibitor, risk ratios
down close to .5; those on an ACE inhibitor not on a beta blocker, still
a highly significant benefit of valsartan; those on a beta blocker but
not on an ACE inhibitor, there are only 140 of them, but clearly the
point estimate trending favorably toward valsartan; and those on both
background drugs, point estimate clearly on the placebo side.

And here the interaction p value was .0011. So can we disregard this?
Well, we can\'t for two reasons: one, that there is this highly
significant interaction, and it\'s not terribly surprising.

And, second of all, there\'s the safety issue because on mortality, the
group taking both an ACE inhibitor and a beta blocker exhibited a
statistically significant worsening of mortality.

So we felt we could not at all disregard this subgroup analysis.

Now, this is the actual data, which may help you a little bit in the
same four major groups, and then the four subgroups formed by the use of
the one or both drugs, and here you can see on morbidity here that the p
value favoring valsartan was highly significant in all three groups
except the one taking an ACE inhibitor and a beta blocker, where the
trend not only went in the other direction. The p value wasn\'t
significant, but it was trending adverse.

So these three groups all exhibit by themselves a significant reduction
of morbidity.

Now, these are all data based on administration of drugs at baseline.
How close does this correlate with the maintenance of these drugs during
the study? Because that\'s of importance. And this data attempts to show
you that.

Of the patients on an ACE inhibitor at baseline, 90 percent were still
taking the ACE inhibitor at the end of the study. On those with a beta
blocker, 92 percent were still on the beta blocker at the end of the
trial. That\'s true of all the drugs except spironolactone in which
there was a reduction of the use of the drug by the end of the study.

In those patients not on the drug at baseline, only 16, 13 percent went
on the drug during the trial. More patients who were not on a diuretic
started diuretic during the trial, which is expected because heart
failure worsens, and they then finally required diuretic.

I find it surprising that 12 or so percent or 15 percent of the patients
weren\'t on a diuretic even at baseline because heart failure almost
always requires a diuretic.

So pretty good congruence between what the therapy was at baseline and
what the therapy was at the end.

Well, one of the groups we said at the beginning we wanted to look at
because there are no data available in the literature prospectively is
the group not on an ACE inhibitor. Now, a third of these people were on
a beta blocker, but they were not on an ACE inhibitor, and one of the
important questions is: is valsartan a substitute for an ACE inhibitor
in patients who don\'t tolerate an ACE inhibitor.

So this was only seven percent of the population, 370 patients. This is
the mortality curves which separate pretty early and widen over time.
That\'s a 33 percent reduction in mortality. The p value is .017.

Here is the morbidity in that population. A 44 percent reduction of
morbidity, the curves really widen out over time. The p value here is
002. It\'s only 370 patients, but it\'s the first demonstration
prospectively that I know of that one can use an ARB, specifically
valsartan, and exert the kind of favorable effect we\'ve associated with
ACE inhibition.

Now, we also wanted to look at all of the secondary endpoints to see if
they were congruent with our clinical outcome data, suggesting that that
one subgroup didn\'t do very well. Now, these are independent
measurements. They have nothing to do with hospitalization or death.
These are completely independently measured secondary endpoints.

This is left ventricular ejection fraction, and here are the four
subgroups if you will, people taking neither an ACE nor a beta blocker,
those taking an ACE inhibitor, not a beta blocker, those taking a beta
blocker, not an ACE inhibitor, those taking both.

You will notice that although this group was very small, there was
certainly a trend for much greater benefit of valsartan and placebo on
ejection fraction. This group highly significant, this group highly
significant.

Ah-ha, here\'s our little culprit group. No benefit on ejection
fraction.

If we look at just the groups leaving that group out, the difference is
highly significant, and let me show you that.

Here then is the ejection fraction change in the patients who are \--
based upon their use of beta blocker, ACE inhibitors. Here was the
overall EF change, .00075, benefitting valsartan \-- favoring valsartan
over placebo.

Here are the group getting both a beta blocker and an ACE inhibitor, the
absence of a benefit. If we take that group out and look at the other
subgroups, the benefit goes up to .00002 on ejection fraction.

Here is left ventricular internal dimension by echo. You\'ll see a
benefit here, a benefit here, a benefit here, all three subgroups
exhibiting a significantly greater reduction of their ventricular size
with valsartan compared to placebo. Here is our culprit group. No
difference.

So here was the overall effect on left ventricle. Here is the lack of
effect in that subgroup, and actually a greater difference now in this
smaller group that excludes this one group over here.

Here is the living with heart failure score, independently measured by
the patient. Nobody has intervened to influence them.

Here\'s the benefit in this group, this group, and this group, all of
them exhibit rather striking greater worsening of heart failure in the
placebo than the valsartan group.

Here is our culprit group. No difference. If we take that group out, the
difference gets even greater, and I\'ll show that in the next slide.
Here it is.

Here was the overall benefit on living with heart failure score. Here is
the lack of benefit in that one subgroup, and here is the benefit on the
residual patients now with a Z of 0002 p value.

What about New York heart class? The same thing is true. There was
overall a benefit of valsartan compared to placebo, more improvement,
less worsening.

This group over here, no benefit. When you take them out, the p value
goes to 000003.

Now, what about the overall mortality? The mortality overall we said
neutral, heart risk ratio of 1.02. Here was the risk ratio in the group
taking both an ACE inhibitor and a beta blocker, 1.4, which was
statistically significant in that subgroup.

When we take that group out, now we\'re seeing a trend for a favorable
effect on mortality, a risk ratio of .92. So beginning to bring out what
might be a favorable effect of valsartan even on mortality, obviously
way under powered to pick anything up, but there it is.

And what about morbidity? Well, the morbidity \--

DR. FLEMING: Could you clarify, Jay, when you said \"way under
powered,\" go back to that slide?

DR. COHN: Well, I said we were under powered to pick up an eight percent
difference, which is what that slide showed.

DR. FLEMING: Oh, okay. It\'s not under powered to pick up a meaningful
difference. It\'s under powered to pick up a really small difference
because most \--

DR. COHN: Well, I don\'t know what \--

DR. FLEMING: \-- you\'ve got 30 \--

DR. COHN: I would object to your use of the word \"meaningful.\"

DR. FLEMING: \-- three hundred patients in that subgroup.

DR. COHN: I think an eight percent reduction in mortality is probably
meaningful.

DR. FLEMING: The study was targeting a 20 percent reduction.

DR. COHN: That\'s right. Yeah. I\'m saying we\'re under powered to pick
up the difference that we found.

DR. FLEMING: Well, let\'s come back to this. Keep going.

DR. COHN: Okay. And now, this is the morbidity endpoint. Remember that
the p value for our primary endpoint was .009. In that one subgroup, the
trend was in the other direction, the p of .104, and when we take that
group out and look at all the other subgroups now, the p becomes 00003,
and the risk ratio is reduced to .785.

So we now have to cope with this subgroup that we can\'t disregard, and
in fact, if we think this is an important subgroup that should not
receive the drug, and I do believe that that is the case today, until
more data are accumulated from other trials, and we say these patients
we\'re not going to treat, we\'re left with the rest of the patients in
whom the statistical significance of the data is far more dramatic.

So in summary, I believe that we can conclude that the benefit on
morbidity that we\'ve observed in the overall trial was seen
particularly in patients on neither ACE nor beta blocker or on ACE
inhibitors or beta blockers, but not in those patients receiving both
drugs.

Thanks.

ACTING CHAIRMAN BORER: Any clarification of fact questions for Jay on
this section?

No, sounds like you \-- oh, you do. I\'m sorry, Tom. Go ahead.

DR. GLAZER: We also do have Dr. Carson who has joined us if there\'s
further questions for him.

DR. FLEMING: Actually I\'ll wait until the end.

ACTING CHAIRMAN BORER: Do you want to hear from Dr. Carson about the
unresolved issue here?

DR. COHN: Perhaps we do. Steve?

DR. NISSEN: Shall we go back or do you want to go forward? Do you want
to talk about this part or do you want to go back to \--

ACTING CHAIRMAN BORER: Well, why don\'t we start with this? And then
we\'ll go back and test the other.

DR. NISSEN: Okay. Jay, there was another breakdown that I didn\'t see in
there that I\'m actually very interested in, and that was the U.S.
versus non-U.S. Clearly there was a much greater benefit almost across
the board in the non-U.S. population, and I wonder if you would help us
understand that.

DR. COHN: Yeah, let\'s look at that. This is the morbidity and mortality
in the U.S. and non-U.S. populations, and although the hazard ratio was,
indeed, a little \-- it was different here. Of course, this is
mortality, not morbidity. This was our primary endpoint in which the
non-U.S. had a slightly lower hazard ratio than the U.S., but the
confidence intervals are really almost entirely overlapping.

And for first heart failure hospitalizations, again, there is a
difference, but once again, the confidence intervals are really
overlapping. The interaction p value is very high.

So I think there is no real geographic \-- evidence for a geographic
difference here.

DR. LINDENFELD: The percentage of patients on beta blockers in the U.S.
and non-U.S., is there \--

DR. COHN: Very close to the same. It was a little higher in non-U.S.
than in U.S., but they were both within the 30 percent range.

DR. NISSEN: Yeah. Actually the data here are slightly different from the
data that we have from Dr. Targum\'s review, but in the FDA book, CHF
hospitalization, the risk ratio in the U.S. was .81 and the risk ratio
in the non-U.S. was .67. You know, from .81 to .67.

I asked the question, and I\'ll tell you why I asked it, Jay. This has
got to be the fourth or fifth trial I\'m aware of, major, mega trial
where the benefits were substantially greater in the non-U.S. population
than in the U.S. population, and it\'s something that has been troubling
many of us because obviously this agency regulates the use of drugs in
the United States.

And I have my own hypotheses here, which maybe later on we can talk
about, but any insight here would be useful because there really does
appear to be an across-the-board difference if you look carefully at the
data in the U.S. versus non-U.S.

Any insight?

DR. COHN: Well, I mean, we\'ve obviously thought a lot about this. I
think it was far more pertinent in a previous trial where this became a
major issue, as you know.

There is a somewhat higher incidence of hospitalizations in non-U.S.,
which probably reflects the health care system and much resistance in
the United States for hospitalization. So one possible explanation would
be you\'re more likely to be hospitalized when you\'re in Europe, and
therefore, a benefit of therapy might be more demonstrable in the
European population. It might be a more sensitive marker.

Many have raised the issue of African Americans because you can see from
this data, and you know that I\'ve had a great interest in possible
differences based upon African American and white patients, and one
possible explanation is the impact of African Americans, but in this
trial I think the number was too small to impact on that.

So we don\'t have any rational reason for seeing a difference.
Genetically there\'s not a major difference between Europe and the
United States. So I must say that I don\'t have a good answer to that.

ACTING CHAIRMAN BORER: Tom.

DR. FLEMING: Just a couple of questions. Jay, in your introduction, you
had given some of the motivation for the interest in valsartan in the
context of what you might already be able to expect to achieve with ACE
inhibitors, and in the briefing document, the sponsor has indicated that
the combination of angiotensin receptor blockers and ACE inhibitors may
be synergistic by providing the more complete inhibition of the renin
angiotensin system through the blockade of the AT-1 receptor, which was
exactly the presentation that you have given.

If we look then, in particular \--

DR. COHN: I don\'t think we used the term \"synergistic,\" but okay.

DR. FLEMING: This is exactly a quote. I\'m quoting exactly your briefing
document.

DR. COHN: Does it say \--

DR. FLEMING: \"May be synergistic.\"

DR. COHN: Well, I guess I\'d argue about that.

DR. FLEMING: Looking at your data specifically in the group of patients
that were on ACE inhibitors, which was 92 percent of the population,
when you look there, what you find is an eight percent reduction in the
morbidity, but a seven percent increase in mortality.

Essentially then do these data fairly strongly argue against a
synergistic effect in the presence of an ACE inhibitor?

DR. COHN: Well, in the absence of beta blocker, now, you can\'t talk
about ACE inhibitors any longer without bringing the beta blocker in
because it\'s another neural hormonal inhibitor. In the absence of a
beta blocker, the efficacy of ACE inhibitor, of valsartan on top of ACE
inhibitor was greater than that in terms of the morbidity endpoint, and
that represented the largest segment of the population, two thirds of
the patients.

So I don\'t think you any longer can look at a subgroup saying ACE or no
ACE when there\'s a beta blocker in a third of the patients who are on
the ACE inhibitor or not on the ACE inhibitor.

Do I think that there\'s a synergistic effect? I think that\'s a term
that \-- that\'s a pharmacologic term that I\'m very hesitant to apply
to this, and I guess I had missed the fact that that word was used in
the briefing document.

\"Additive\" would be the word that I would have used, and I believe we
have demonstrated an additive effect, and that would be the only word I
would have used in describing our proposal.

DR. FLEMING: But you only argue that by subdividing out those people who
were also on a beta blocker. That\'s your answer. Your answer is, yes,
you believe it\'s additive, but only by subdividing or eliminating those
people who also received a beta blocker.

DR. COHN: No. The question is: does adding valsartan to an ACE inhibitor
have an additive effect?

DR. FLEMING: That\'s correct.

DR. COHN: We know it has an additive hemodynamic effect. That\'s been
studied in 103 and 104. We know it has a neural hormonal effect. That
was studied in 104, and it\'s also been studied in 107.

The question is does it have additive benefit on morbidity and
mortality, and the answer \--

DR. FLEMING: On your primary endpoints, correct.

DR. COHN: On the primary endpoint.

Well, on mortality we haven\'t shown an additive benefit. So at the
moment we can\'t say that it has.

Does it have an additive benefit on morbidity? Yes, it clearly did,
especially when one takes out the beta blocker group.

DR. FLEMING: Not especially when. Only when.

DR. COHN: Well, the point estimate favored here, I mean, and let\'s look
at those patients on an ACE inhibit. The hazard ratio is .9, and the p
value is .096.

Now, you can argue whether that means we did or didn\'t have an effect,
but we have produced a ten percent reduction of morbidity when you add
an ACE inhibitor to valsartan regardless of beta blocker use.

Now, ten percent reduction of hospitalization rate is not infinitesimal.
It\'s not small. It\'s fairly substantial when you think of the number
of hospitalizations.

DR. FLEMING: I\'ll go back to what I said before, and I\'m quoting from
the FDA briefing document on pages 102 and 103. \"If you look at
morbidity, the relative risk given there is .92.\" You\'re giving it as
.90, but an eight to ten percent reduction in morbidity with use of ACE
inhibitors, but a seven percent increase in mortality.

So the same magnitude of mortality increases you see in reduction in
morbidity when you look at the biggest group of patients in the trial,
which are those on ACE inhibitors.

ACTING CHAIRMAN BORER: Jay, can I \--

DR. COHN: Well, I can \-- can we put up the mortality slide, the
comparable \--

ACTING CHAIRMAN BORER: Jay, before you answer the question \--

DR. COHN: Yeah.

ACTING CHAIRMAN BORER: \-- let me just introduce a concept that I\'d
like you to deal with as you respond to this.

You know, there were background ACE inhibitor therapy, but the doses
varied. There certainly is no suggestion that background therapy was
titrated to maximally tolerated dose of ACE inhibitor. Different ACE
inhibitors were used, one and on and on. You know, you can response, and
you should respond because the question was asked, but it seems to me
that we don\'t have a data set that allows us to discuss whether an
angiotensin receptor blocker is additive to ACE inhibitor because the
trials weren\'t set up to answer that question.

All we can say is that in this population getting these number of drugs
at these number of doses, when you added valsartan and there wasn\'t a
beta blocker on board, that we see this.

I mean, is that a reasonable \--

DR. COHN: Yeah, I showed you the mean dose of ACE inhibitors used,
which, you know, is like 18 milligrams a day of lisinopril. so that on
average it\'s close to target dose.

Is there a differential response based upon how much ACE inhibitor the
patient is getting? You know, you get into very \--

DR. FLEMING: Sure.

DR. COHN: \-- small subgroups, and this is ‑-

DR. FLEMING: You just can\'t answer that.

DR. COHN: \-- the analysis we plan on doing, but all we\'re saying is
this is the kind of ‑- this is good therapy. I think if you went to the
community at large, the dose of ACE inhibitor would not be that high.
This is the best doctors in the world treating patients the best way
they know how.

Now, if you add to that valsartan, do you further improve the outcome?
The answer is yes, with the provision that there seems to be one
subgroup that doesn\'t get benefit.

Now, the mortality, the issue that Tom raised \-- can we go back to that
mortality slide? Because you raised this, and I want to show you the
data.

This is those same subgroups based on mortality, and what Tom is saying
is that there was a 1.055 hazard ratio on mortality when you or on an
ACE inhibitor. However, when you go down here and you look at the group
that was on an ACE inhibitor and a beta blocker, it\'s 1.42. When you
look at the group that was on an ACE inhibitor without a beta blocker,
it\'s .959.

So the adverse trend here, which is clearly not significant \-- it\'s a
five percent increase \-- appeared to be entirely related to this group
that was also taking a beta blocker.

DR. FLEMING: Yeah, in the briefing document we have on page 103 ACE
inhibitor use, all cause mortality, relative risk of 1.07, numbers
similar to, but not exactly the same as what you have.

DR. COHN: Yeah. I\'m not sure. I guess the \--

DR. FLEMING: And so if you\'re looking at all of the ACE inhibitor
patients, there is a seven percent increase. As you point out, if you
further subdivide, and that is controversial, those on beta blockers
would have a 42 percent increase. Those off would have a four percent or
six percent decrease.

DR. COHN: Right, and the numbers to make that \-- I suppose we should
clarify. Perhaps somebody from the FDA should clarify why their numbers
in the briefing document differ from ours.

I believe it\'s they did not use the covariates in doing the analysis
which were prescribed in the protocol to adjust for covariates. Maybe we
could hear from the FDA.

MR. HUNG: Jim Hung, FDA statistician.

The number I have is unadjusted as a ratio because the primary test for
all these primary endpoints, morbid events, are low rank tests, and so,
therefore, I try to be \-- try to use the numbers to be consistent with
the test.

The sponsor\'s numbers are adjusted for the covariates. That\'s the
difference.

DR. COHN: This was in the protocol, prespecified adjustments of
covariates and the Cox regression \--

DR. FLEMING: And this is a peripheral issue. The main answers are the
same.

DR. COHN: Yeah, it comes out the same.

DR. FLEMING: The main answers are if you look at those on ACE
inhibitors, which is 92 percent of the cohort, you see a very small
increase in morbidity and a very small worsening of mortality, a net
comparable result.

ACTING CHAIRMAN BORER: Paul.

DR. ARMSTRONG: Jay, although the number of blacks was small, it was
comparable to the subgroup analysis in size of the patients not on ACE
inhibitors. So I wanted to ask you a series of questions related to that
population.

First, from the knowledge that you or the company has, what is the
information on the anti-hypertensive effect of valsartan in blacks?

Second, do we have information on the hormonal and the morbidity data in
blacks vis-a-vis whether this trend, if you will, which is not
significant on mortality, is supported by some of the other ancillary
measures that you so eloquently discussed in the other subgroups?

DR. COHN: Malcolm can probably answer the question.

MR. MacNAB: I can answer the first one. The package insert for valsartan
states that the efficacy in whites and blacks for hypertension is about
the same. That\'s what the database that we have shows.

ACTING CHAIRMAN BORER: What about the renin angiotensin profile?

DR. COHN: Yeah, let me answer your first question here. Can we get that?
Yeah.

This is the breakdown, if you wish, for plasma norepinephrine mean
change from baseline, and you\'ll notice that the blacks appear to
exhibit the same difference as do the whites in terms of lowering
norepinephrine.

So from a hormonal standpoint there doesn\'t appear to be a racial
difference in response.

DR. HIRSCH: And was there a mortality difference in the subgroup?

DR. COHN: In the blacks there was a trend in the other direction, right.
None of it was significant; very wide confidence intervals.

ACTING CHAIRMAN BORER: Do you have \-- I mean these are norepinephrine.
Do you have renin data on the \--

DR. COHN: No, we have norepinephrine and BNP only. That\'s the only two
hormones that we monitored, and the BNP also exhibited, I think, the
same.

Well, here. Okay. We can give you this, too. Boy, my team has more
slides there.

All right. Here\'s the BNP data just for completeness. Now, with BNP
there did appear to be a difference. That is, all these other groups
show a favorable effect of valsartan compared to placebo. In the black
patients that was not true. So now we find a differential between BNP
and norepinephrine.

I mean, you can chew on this all you want to. I don\'t know what to make
of these data.

And then we also have the mortality data that was addressed. I\'ll give
you that data directly. Can we have that mortality slide and the racial
breakdown?

Here we are. This is black patients. This is mortality. You can see the
trend was in the wrong direction. Obviously wide confidence intervals.
This is morbidity, a trend in the wrong direction, and these were in the
non-black patients in terms \-- and mortality obviously.

Now, turning down to .997 and morbidity being strikingly better. So we
obviously have no answers. This is retrospective analysis.

ACTING CHAIRMAN BORER: Okay. Why don\'t we hold any other issues for Jay
for his next presentation?

And we\'ll move on now to the safety \--

DR. NISSEN: Jeffrey we going to \--

ACTING CHAIRMAN BORER: Oh, I\'m sorry. Just one moment.

DR. NISSEN: Are we going to come back to the adjudication?

ACTING CHAIRMAN BORER: Yeah, right. What I was just remembering is that
we have the adjudication issue that we can deal with. Why don\'t we take
care of that now?

DR. NISSEN: Before you got here, we all had a number of questions, and I
want to see if I can focus on some of them. Since endpoints were not
independently adjudicated, we want to understand the process, and I
guess I\'d like to understand who identified the cases for adjudication,
what triggers did they use, what source documentation was provided. You
know, was it a narrative? Did you review the charts? Did you get
excerpts from the charts?x

And then the third question I had was I present a hypothetical case of
somebody that came in at nine o\'clock in the morning with an
exacerbation of heart failure, was admitted, got intravenous furosemide
for the day, and then that evening went home; whether that would have
been considered a heart failure hospitalization.

DR. CARSON: Let me just say, first, I\'m sorry to make you all go back
on this. I must say it is probably easier to fly to the NIH than to
drive across town to here.

You started off with a comment that they were not independently
adjudicated.

DR. NISSEN: Right.

DR. CARSON: I\'m not sure I understand.

DR. NISSEN: Well, the sponsor presented cases to the committee for
adjudication. The adjudication committee was not independent of the
sponsor.

Independent adjudication means that somebody who\'s independent of any
other interests in the trial, you know, gets source documentation, does
adjudication. That\'s not what happened here.

DR. CARSON: Okay. I don\'t know of any endpoint committee that has been
associated with any trial that has been independent in a way that we
weren\'t, and I actually have done more of this than probably anybody
else.

DR. COHN: Can you speak up, Peter? I don\'t think everybody can \--

DR. CARSON: Well, I\'m just saying I don\'t know of any way that this
committee was not independent than any other committee associated with
any large trial has not been independent. We were blinded. None of us
were investigators in the trial, and the materials were provided to us,
it is true, by the company, but this is as independent as any committee
I\'ve ever been associated with.

In terms of the materials that we received, we required that there be
primary source documentation. So we asked for some hospitalization
record, particularly a discharge summary. We often received a discharge
summary and an initial history and physical exam narrative. We sometimes
received hospitalization data.

Now, from the U.S., that data was stronger because that data is easier
to find, and in some countries, as I understand it, hospitalization
records really cannot be obtained, and in those cases then we relied on
a letter from the physician taking care of the patient to another
physician, referring physician, for example, or a physician involved in
enrolling the patient in the trial.

So there was always primary documentation obtained. There was also a
narrative from the physician that described the event, whether it was a
death or a hospitalization. So there was primary source documentation on
virtually every patient unless towards the end of the trial multiple
attempts had been made and there simply was no response.

In terms of the specific patient that you posed, we realized, I think,
after the first meeting we had put in a barrier that said in order to be
a heart failure hospitalization you had to have been in the hospital for
longer than 24 hours because we assumed that would make it then a
serious event.

And we found relatively early that it was very difficult to get exact
times, and so somebody went back and said, \"Well, how did you know it
was 24 hours?\"

Getting the exact times turned out to be quite difficult, and this has
been true in other trials also.

So we then put in the barrier of saying that we may not know the exact
times, but if the calendar date changed, we would take that as being a
surrogate for a 24-hour admission. So if the calendar date changed,
that\'s what we took.

Now, did it happen that there were patients that were admitted at six
o\'clock in the morning, got an IV dose of diuretic, and went home at
nine o\'clock at night?

It may have happened. We would not have taken them as being a
hospitalization. They not have had a calendar day change. I have to say
in my own experience in heart failure that is a very unusual
circumstance, and \--

DR. NISSEN: You would not have even seen those charts then? They
wouldn\'t have come to you?

DR. CARSON: I\'m not sure whether we would have seen them or not. I
think early on we may have seen them, and we may have then said it
didn\'t meet 24 hours. So we don\'t need to see this one.

Now, you did \--

ACTING CHAIRMAN BORER: Those require an assay.

DR. CARSON: I would also answer one part of your question, I guess,
which was what about the cases that did not come to us. We looked at the
work load of the committee, and we adjudicated something on the order of
was it 4,100 events? It was just a large number of events.

Initially we were seeing all hospitalizations. Initially we were seeing
second hospitalizations after the initial heart failure hospitalization.

But then it became apparent that the work load was extreme and that it
was just not going to be possible to gather members together, have
meetings, and actually adjudicate cases.

Now, you can sit and do five an 600 and 800 cases in a day, but you\'re
just turning pages, and so in order to actually have discussion of cases
and consideration, we felt the work load had to be altered a little bit,
and one alteration was that after a patient had a first heart failure
hospitalization, we did not adjudicate after that. They had met the
primary endpoint.

But secondly, patients who had a clear non-cardiovascular cause for
hospitalization, we did not see them. The first couple of meetings we
did see them, and we became confident as a committee that the sponsor
could recognize a non-cardiovascular hospitalization.

And then each meeting I would get a list of the non-cardiovascular
hospitalizations, and if there was anything that had a primary cause
from the investigator that looked like it might have involved heart
failure, then we said, \"Please send us that,\" and we got it.

ACTING CHAIRMAN BORER: Does that \-- Paul?

DR. ARMSTRONG: I just would like to follow up and also make a comment.
For some of us, this adjudication process has involved evaluating data
triggered by a data organization that is independent from the sponsor.
So I think the spirit of Dr. Nissen\'s question and my comment apropos
of your issue, I\'m not questioning the integrity of your committee. Our
question was what triggered your opportunity to evaluate things
independently, and there are different models, and you obviously used
one, but there are others.

As I understand it from the briefing booklet, at some point there was an
addendum to your committee perhaps driven by the work load in which over
diuresis or drug toxicity were perceived as hospitalization for reasons
other than heart failure.

And I just want to be 100 percent certain then that someone with syncope
due to dehydration, digitalis intoxication or hyperkalemia, all things
that we as clinicians care for in our heart failure patients, would be a
hospitalization that would be perceived not related to heart failure and
you would not have seen.

DR. CARSON: No, we would have. We would have seen that. In fact, we were
particularly sensitive to that particular area.

One of the two areas that we had a particular alarm bell about was the
group of patients who could be poor perfusion and poor perfusion with
hypotension, poor perfusion with renal insufficiency leading to
digitoxicity, et cetera, et cetera.

That was a group that we were very concerned about, and so anything that
even looked like that, if I saw that on the non-cardiovascular list, if
I saw renal failure, if I saw anything cardiovascular we\'ve got, but if
I saw renal failure, I said, \"I want to see that and make sure it\'s
not poor perfusion.\"

The other group that we were very concerned about was the abdominal
pains. Could that be passive congestion leading to abdominal pain? So
that was another group that we really targeted. We had to see all of
them.

DR. ARMSTRONG: Thank you.

ACTING CHAIRMAN BORER: Okay. Thank you very much.

If there are no other issues about the adjudication process, why don\'t
we try and move on to safety data now? We\'ll have a little discussion
afterwards.

DR. COHN: All right. Bob Glazer will present the safety data.

Bob.

DR. GLAZER: Safety of valsartan was evaluated in eight clinical trials
worldwide that included over 6,000 patients with chronic heart failure.
During this presentation I would like to summarize for you the valsartan
safety database, including patient exposure, demographics, adverse
events, including severe adverse events, and treatment discontinuations,
and laboratory evaluations.

Valsartan safety database for heart failure consisted of eight clinical
trials. There were four placebo controlled trials, one positive control
trial, and three open label trials.

The primary data set includes the four double blind control trials with
trial durations of up to four months and also the first four months of
safety data from Val-HeFT. There were over 6,000 patients in this
primary data set of which 3,289 patients received valsartan. The
long-term safety data set is comprised solely of data from Val-HeFT. The
focus of this presentation will be on these two data sets, namely, the
pooled, short-term, primary data set and the long-term data from
Val-HeFT.

There were only 94 total patients in the open label trials, and
therefore, these data were not pooled for any analysis.

The five completed double blind trials consisted of two small
hemodynamic studies, Studies 103 and 104; two trials whose primary
efficacy variable was exercise tolerance, Studies 106 and 110; and the
large morbidity and mortality trial, Val-HeFT.

All trials except Study 110 evaluated twice daily dosage regimens of
valsartan which ranged from 40 milligrams to 160 milligrams twice daily,
depending on the particular study. All trials were parallel design and
included patients in New York Heart Association Class II, III, and IV,
with the exception of Study 110 that excluded Class IV patients.

Most trials had an inclusion criteria for ejection fraction requiring
patients to have ejection fractions less than either 40 or 45 percent.

In the primary data set, which included trials with the maximum trial
duration of four months and also the first four months of Val-HeFT, over
3,200 patients were exposed to valsartan for at least one day. Over
2,700 patients were exposed to valsartan for at least three months.

Long-term exposure from Val-HeFT included over 2,100 patients for at
least six months, approximately 2,000 patients for at least one year,
and over 1,000 patients for two years or more. The majority of patients
in each time category were exposed to the highest dose of valsartan,
namely, 320 milligrams total daily dose, as these patients were force
titrated to that dose in Val-HeFT.

Overall there were no clinically important differences in baseline
characteristics between the valsartan and placebo treatment groups in
the primary data set. The mean age of the studied population was 63
years of age. The number of patients above and below the age of 65 was
similar in each treatment group. Approximately 15 percent of patients
were over the age of 75 years.

Ninety percent of patients were white, and approximately eight percent
were black. The majority of patients, 80 percent, were male.

The duration of heart failure in these patients was just over four
years. The ejection fraction was 27 percent. The mean blood pressure was
approximately 124 millimeters of mercury systolic and 75 millimeters of
mercury diastolic. The majority of patients, 61 percent, were Class II
and 37 percent were Class III.

Investigators were asked to select the one primary etiology of
patient\'s heart failure in all but one trial. Coronary heart disease
was chosen as the primary etiology in the majority of patients, 57
percent, followed by idiopathic cardiomyopathy, hypertension, and other
miscellaneous causes.

The majority of patients were receiving ACE inhibitors, approximately 90
percent; diuretics, 85 percent; and digoxin, approximately 68 percent.
Approximately one third of patients were receiving beta blockers and one
third were receiving nitrates.

Patient disposition was collected differently in Val-HeFT than in the
other trials. Therefore, these data were not pooled into the primary
data set. The patient disposition data from Val-HeFT has already been
presented by Dr. Cohn.

In the remaining trials shown here, patient disposition was similar to
that observed in Val-HeFT. The most common reason for premature trial
termination was an adverse experience occurring in nine percent of
valsartan patients and four percent of placebo patients.

In the primary data set the incidence of adverse events regardless of
trial drug relationship was 73 percent with valsartan and 69 percent
with placebo. Dizziness and hypotension were the two most frequently
reported events, and each occurred more frequently in valsartan treated
patients than those patients receiving placebo. Dizziness and
hypotension were reported in 17 percent and seven percent of valsartan
patients, respectively.

The incidence of cough in this population was similar in both treatment
groups.

Similar to this short-term primary data set, the two most frequent
adverse events in Val-HeFT shown here were dizziness and hypotension,
each occurring more frequently in the valsartan group compared to
placebo.

The incidence of aggravated heart failure was less in the valsartan
group compared to placebo. Diarrhea occurred slightly more frequently
with valsartan.

In the primary data set, there were no clinically important differences
among subgroups, including age, gender and race in the overall incidence
of adverse events compared to the overall population. Slightly more
female patients compared to male patients reported adverse events in
both treatment groups.

Dose response information can best be assessed using the data from Study
106, which was a fixed dose, placebo controlled, parallel design trial
that evaluated three different valsartan doses. In this trial, the
overall incidence of adverse events in each of the three valsartan
treatment groups was less than or approximately equal to that observed
with placebo.

There was no dose related effect for the more frequently reported
adverse experiences, including dizziness and hypotension. A suggestion
of a dose response was observed for hyperkalemia, which was reported in
one, three, and four percent of patients in the 80, 160, and 320
milligram dose groups, respectively.

Summarizing the data from the adverse event case report form in
Val-HeFT, permanent treatment discontinuation occurred in 9.9 percent
and 7.3 percent of patients receiving valsartan and placebo,
respectively. A significant difference in rates of discontinuation
occurred for dizziness, renal impairment, elevated serum creatinine,
diarrhea, and hyperkalemia.

The overall discontinuation rates for adverse experiences in the other
control trials were similar to that observed in Val-HeFT.

Both sitting and standing blood pressure were measured at all visits in
Val-HeFT. At all time points post baseline there was a consistent and
significant reduction in both sitting systolic blood pressure,
approximately four millimeters of mercury placebo subtracted, and
diastolic blood pressure approximately two millimeters of mercury
placebo subtracted in the valsartan treated patients.

The incidence of serious adverse events in Val-HeFT were similar with
valsartan, 51 percent, compared to placebo, 54 percent. The most
frequent serious event, aggravated heart failure, and also atrial
fibrillation were reported less frequently in patients treated with
valsartan. No other significant differences between treatment groups
were observed.

In the short-term primary data set, the occurrence of serious adverse
events was generally similar in each treatment group. Again, aggravated
heart failure occurred less frequently in patients receiving valsartan.
Incidence rates for individual adverse events were generally less than
one percent.

The most common cause of death in Val-HeFT as reported by the
investigators is shown here, was sudden death, which occurred in nine
percent of patients in each treatment group.

Pump failure was the next most frequently reported cause of death. In
the remaining four control trials, the number of deaths was small, 14 in
total.

Noteworthy laboratory changes observed in the valsartan heart failure
program relate specifically to the pharmacology of inhibitors of the
renin angiotensin system, and include changes in renal function.

Small increases in serum creatinine, potassium, BUN, and uric acid were
observed in those patients receiving valsartan compared to placebo as
shown here.

Specific criteria were prespecified to define clinically meaningful
changes from baseline in laboratory parameters. For creatinine, BUN, and
uric acid a 50 percent increase was defined; for potassium, a 20 percent
increase or decrease was defined.

In the short-term primary data set, four percent of valsartan patients
versus one percent of placebo patients had an increase in serum
creatinine. Ten percent versus five percent had an increase in serum
potassium, and 17 percent versus six percent had an increase in BUN.

In summary, valsartan in doses of 80 milligrams to 300 milligrams once
per day was well tolerated in patients with New York Heart Association
Class II, III, and IV heart failure. The adverse events observed were
not unexpected and included dizziness, hypotension, and postural
dizziness. Dizziness and hypotension were the most common reasons for
discontinuation of therapy.

Laboratory changes included increases in creatinine, BUN, potassium, and
uric acid.

Valsartan\'s safety profile in heart failure patients was consistent
with the pharmacology of an agent affecting the renin angiotensin system
and also the background therapies these patients were receiving.

ACTING CHAIRMAN BORER: Thank you very much.

Are there specific safety questions from the committee? JoAnn.

DR. LINDENFELD: I know there were a small number of patients on
spironolactone at the start of the study, but can you give us some idea
in that small number of patients what the incidence of adverse events
was? Patients on both ACE inhibitors and spironolactone, and then
between placebo and valsartan.

And I think this is an important point because aldactone is used
substantially more now than I think it was in the baseline group of
patients here. So it\'s something we have to consider.

DR. GLAZER: We have just started looking at this topic. We have some
laboratory data which showed that there was an increase in the number of
patients who made the pre-specified criteria for creatinine.

DR. LINDENFELD: I guess creatinine and potassium would be the two we\'d
be most interested in.

DR. GLAZER: Can I have Slide 020?

There was a small number of patients receiving background
spironolactone, and as you can see in this slide, the percentage of
patients who had an increase in potassium was essentially similar to
that that was seen in the overall population. The patients who had an
increase in serum creatinine are greater than 50 percent above baseline,
was greater in those patients receiving spironolactone.

Similar incidence rates were seen for BUN and uric acid, again, compared
to the overall population.

This patient population has to be \-- we\'re further investigating the
laboratory data and safety data.

DR. LINDENFELD: I guess it\'s important. We don\'t know how many of
these were on ACE inhibitors and were not?

DR. GLAZER: I don\'t have that information.

DR. LINDENFELD: Because we might expect that those on both would have
even a greater incidence. The incidence of withdrawal, I think, on Slide
No. 7 shows withdrawal was much greater in patients on spironolactone.

DR. GLAZER: Correct.

DR. LINDENFELD: And it just becomes an issue because we\'re using
spironolactone a lot more than the small number of patients that were in
this trial.

ACTING CHAIRMAN BORER: What kind of monitoring recommendations is the
company making with regard to electrolytes, BUN, creatinine? Any in your
proposed labeling?

DR. GLAZER: Yes. As with the care of any patient with congestive heart
failure, careful attention should be paid to monitoring BUN, creatinine
and potassium in these patients who were concurrently receiving
diuretics, beta blockers, as would be expected.

ACTING CHAIRMAN BORER: JoAnn?

DR. LINDENFELD: One other question. Can you tell us what the incidence
of the requirement for dialysis was in the two arms?

DR. GLAZER: The number of patients who had dialysis actually was similar
in \-- this slide shows the number of patients requiring dialysis in the
overall congestive heart failure patients, and you can see that there
was 12 patients total receiving valsartan and 12 patients receiving
placebo. All but one of those patients were in Val-HeFT. One patient was
in Protocol 106.

ACTING CHAIRMAN BORER: Paul.

DR. ARMSTRONG: As I understand it, 15 percent of the patients were over
the age of 75, and I wonder if you could develop for me any information
you have regarding the relationship between some of the serious adverse
events and obviously the hyperkalemia and the creatinine and any others
that are of interest in that important very elderly population.

Do you have data on that point?

DR. GLAZER: We don\'t have any laboratory data or adverse experience
data cut by the 75 year of age point.

DR. ARMSTRONG: And the second question coming back to the issue of
efficacy in blacks was, again, the serious adverse events in the
creatinine and the potassium issues in the blacks since we don\'t have
the renin angiotensin system measured, but indirectly we might be able
to get at it.

Do you have any information on that subgroup vis-a-vis serious adverse
events and some of the metabolic factors?

DR. GLAZER: Can I have Slide 023? Actually it\'s AEO-17.

This information is adverse events by racial subgroups looking at the
most frequent adverse events overall, and then looking at the
subcategories by race.

And essentially, with the exception of chest pain, congestive heart
failure and cough, which seem to be reported more frequently in African
Americans and black patients in this population, there didn\'t seem to
be any marked differences, though the number of black patients is small
and the percentages have to be interpreted cautiously.

DR. ARMSTRONG: Thank you.

ACTING CHAIRMAN BORER: Any other questions? Ray.

DR. LIPICKY: It doesn\'t appear as though there are dose limiting side
effects, that is, there aren\'t any side effects that come out as a
function of dose. Is that a correct interpretation?

DR. GLAZER: From Protocol 106, with the exception of hyperkalemia and
possibly if you pull out the MEDRA (phonetic) term postural dizziness,
there didn\'t appear to be any dose related effects in that parallel
designed trial, which included about a \--

DR. LIPICKY: From any other trial that you have that shows dose limiting
side effects?

DR. GLAZER: I believe in the hemodynamic trial, with the exception of
the laboratory events as you would expect with potassium, there were no
adverse events in that small, parallel design, hemodynamic trial that
showed any \--

DR. LIPICKY: Can you then tell me what the process, the thinking
process, was for deciding that the top dose you studied was the best
dose to study?

DR. GLAZER: The dose was chosen based on the hemodynamic trials, and we
wanted to \--

DR. LIPICKY: Yes. So just where does it fit on the dose response curve
do you think? The low end, the high end or where since you don\'t have
any reason to \-- from adverse effects to not have increased the dose?

MR. MacNAB: Dose selection was based on several factors. One, from the
hemodynamic trials that you saw where one clearly had much better dose
response than the other, but in the both clearly the 160 b.i.d. gave the
most effect.

DR. LIPICKY: But \--

MR. MacNAB: There was \--

DR. LIPICKY: \-- it was also consistent with the dose response still
going up.

MR. MacNAB: That\'s correct.

DR. LIPICKY: Right. So it didn\'t tell you you got to the top.

MR. MacNAB: I mean you get to a point of some practicability in that,
too. I mean, the 160 \--

DR. LIPICKY: No, no, no.

MR. MacNAB: \-- tablets are pretty big, but I just want to finish.

All right. We did see a sense of the dose response in a little bit of
potassium in 106. If you go back to some very, very early studies done
of the angiotensin infusion studies in normal volunteers where the
pharmacodynamics of hypertension appeared to be at 24 hours, you really
need to go up to higher doses to get full suppression of the system, and
it was generally believed, theoretically at least, to see optimal
effects that you needed the most suppression of the system that you
could get.

So I think based upon those angiotensin-1 infusion studies, while in
normal volunteers and not perfect, it was one the hemodynamics; a little
bit of potassium that we saw; the 160; and just the practical fact that,
you know, a 160 milligram capsule of valsartan is not \--

DR. LIPICKY: Big.

MR. MacNAB: \-- is pretty big as it is; that putting that all together,
that seemed the best to do. Now, obviously it would have been great if
we had done a, you know, 50, 60,000 patient trial with multiple arms and
multiple doses, but again, that\'s a practical matter.

So it was a sum total of all those reasons is why we picked that dose,
and it could have been too high, could have been too low. That\'s the
best we could do with what we had.

ACTING CHAIRMAN BORER: Alan.

DR. HIRSCH: Well, to come back to the same questions, I\'m not sure I
\-- I\'m not sure I\'m on.

(Laughter.)

DR. HIRSCH: I could raise my voice.

Could we come back to safety Slide 14?

I\'m not sure, as Ray said, that I\'m seeing a dose dependent adverse
effect profile that worries me, but nevertheless, let me just explore
the data if that\'s why we\'re here.

I just want to see if you can help me understand the sort of negative
dose response for dizziness and for hypotension. Does this mean that the
physicians stopped the dose titration at 80 because they perceived or
someone perceived dizziness and hypotension as being an end effect?

DR. GLAZER: This was a parallel design trial. They were on a fixed dose.

MR. MacNAB: One, oh, six was parallel. There was no \-- it was a fixed
dose.

DR. HIRSCH: One, oh, six. Sorry.

DR. GLAZER: That\'s why we couldn\'t use Val-HeFT, because of the forced
titration. This is the only trial you can get pure dose response
information.

MR. MacNAB: I mean, overall, if you looked at all of valsartan data and
from hypertensions, heart failure, there is not a real dose response for
side effects.

DR. HIRSCH: Right. I didn\'t see it either.

MR. MacNAB: I mean you have to really dig. Now, I imagine we could give
a 1,000 milligram capsule, but for what we\'ve seen up through 160 or
through these trials, no.

ACTING CHAIRMAN BORER: Okay. If there are no other questions about
safety at this point, although we can always revisit that a little bit
later, we\'ll take our break now and come back and have the risk-benefit
or the benefit-risk discussion after the lunch break and then go through
any additional discussion and the questions we\'ve been presented.

We\'ll take 45 minutes. So we should be back here ready to start at
12:30.

(Whereupon, at 11:40 a.m., the meeting was recessed for lunch, to
reconvene at 12:30 p.m., the same day.)

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

(12:31 p.m.)

ACTING CHAIRMAN BORER: It is now 12:31 and a half. So we\'re a minute
and a half behind, which we cannot be.

Jay, would you give the benefit-risk discussion, please? And then we\'ll
have whatever additional questions we have, and we\'ll go into the
structured review.

DR. COHN: All right. Thanks, Jeff.

I thought it would be appropriate to kind of place Val-HeFT in
perspective with what we have already learned about heart failure and
then how this should impact upon the management of the syndrome.

So if you\'ll bear with me for a few minutes, let me first review for
you where we have come in the management of heart failure.

The first trial I was involved in, and of course, it was the first trial
carried out in heart failure was V-HeFT 1, and at the time that we
introduced that trial, the one-year mortality in the placebo group in
V-HeFT 1 was about 20 percent, and we were really focused on reducing
mortality as a primary endpoint in this disease. Twenty percent of
people were dying each year with the disease.

By the time we got to V-HeFT 2, having been introduce to hydralazine and
nitrate and did the solve trial, the placebo arm had now exhibited a
lower mortality, right, a better cumulative survival. So we were clearly
down moderately in mortality.

In Val-HeFT, the one year mortality was nine percent. So we\'ve made
actually remarkable impact on this syndrome. Even in the absence of the
new therapies, this is the placebo arm in Val-HeFT, and we\'re getting
closer and closer to the epidemiologic data on the predicted survival in
nonselected 65 year old white males.

So the room for further benefit is still there, but the magnitude of
efficacy that we can hope to achieve with further trials in patients
with advanced heart disease is limited, and let\'s face it. All of these
trials enter patients with left ventricular remodeling. They\'ve had
their disease for years. They\'re sick. We\'re not going to restore
these people to a normal life expectancy, but we\'ve already made a
rather dramatic impact.

And I think when you look at the prevalence of this disease, five
million people in the United States estimated to have the disease,
500,000 new patients per year, we\'re dealing with a very monstrous
public health problem, and if, indeed, 91 percent of these people are
going to survive each year, which is what Val-HeFT told us, then is it
not time to stop focusing on the nine percent who die and begin focusing
on the 91 percent who are alive who suffer from impaired quality of
life, recurrent hospitalizations, frequent office visits, and incredible
cost of health care?

Now, remember in Val-HeFT there were four days per year per patient
hospitalized. If we have five million patients, that\'s 20 million days
in the hospital per year in the United States alone. So producing a
decrement in that hospitalization is of potentially important public
health, as well as a personal benefit.

So I\'m proposing, and this is not new. I\'ve been preaching this way
for a long time. So this is merely further evidence for my position,
that mortality should not, cannot long serve as the marker for efficacy
of treatment in a disease like heart failure.

Now, I would love to be able to monitor the biological process, and Ray
has heard me preach this many times, and we may not be there yet, but we
used all of the tools at our disposal in Val-HeFT to get a sense of the
biological process, and they all turned out favorable.

So I think we\'re getting closer and closer to being able to understand
this disease and deal with it before people die and intervene
effectively to alter the quality of life by altering the biological
process.

Now, let\'s look at where we\'ve come now. This is fascinating data
analysis from Val-HeFT which to me is the most impressive piece of data
in the whole study, even though it\'s not necessarily randomized
therapy. Let\'s look at the morbidity in Val-HeFT.

This is the morbidity in patients who were for reasons only their
physicians know, being treated with neither an ACE inhibitor or a beta
blocker. Almost 50 percent of them have had a morbid event during the
course of the study.

This is the group of patients who are on an ACE inhibitor, but not on a
beta blocker, and their morbidity was quite a bit lower. We\'ve made a
fairly important benefit by adding an ACE inhibitor.

And then, of course, we also have a group of patients who are on a beta
blocker, but not on an ACE inhibitor, and they also exhibited a benefit,
further evidence in a non-randomized way that giving an ACE inhibitor or
a beta blocker is good treatment for heart failure.

Now, let\'s look at the group that were on both drugs. Look at this.
Their morbidity has been reduced to about 20 percent. This is the best
therapy for heart failure, and we should continue to aggressively
attempt to get patients with heart failure on ACE inhibitors and beta
blockers, and to me this is kind of the nicest data we\'ve even got
because this isn\'t part of a randomized trial. This is real world use
of these drugs.

Now, let\'s see what Val-HeFT did to these four subgroups. In this group
a striking reduction in morbidity, p, .0033. In this group who were on
ACE inhibitors, but not on a beta blocker, a significant reduction, p of
.0019. In this group that were on beta blockers, but not on an ACE
inhibitor, a rather dramatic reduction, statistically significant even
though the group is a small sample.

And this is our favorite little whipping boy here, the patients who are
already on an ACE inhibitor and a beta blocker, and lo and behold, as
we\'ve already pointed out, it went in the other direction.

So these three groups exhibit a striking benefit on morbidity, mostly on
hospitalizations, and of course, mostly having a major impact on the 20
million hospital days, I guess, that occur in the United States each
year for worsening heart failure.

Well, what about getting everybody on ACE and beta blockers so we don\'t
need to use valsartan? That would be wonderful.

The estimated drug usage in the United States today of these drugs is
that ACE inhibitors are employed in somewhere between 50 and 75 percent
of the population. Remember in Val-HeFT it was 93 percent, but that\'s
not representative because those on an ARB were excluded, and that was
also highly specialized heart failure centers.

So in the real world it\'s 50 to 75 percent.

Beta blocker use, the estimate is somewhere between ten and 25 percent.
Well, what\'s the matter with the practicing community? Don\'t they know
that the ACE and the beta blocker is effective treatment?

Well, I guess they do and they don\'t. Why are more patients not being
treated with ACE inhibitors and beta blockers?

Well, number one, I guess, is physician education and the pharmaceutical
industry is out there promoting these drugs. So it isn\'t a hidden
message. Yet it hasn\'t had as big an impact as we might have thought.

Patient compliance. Patients are told to take drugs, and they don\'t
have a good idea why because their physicians don\'t really know why,
except that it was shown in a trial to reduce mortality. Patients want
to feel better, and these drugs like beta blockers often don\'t. In
fact, they have a period of time when they may feel worse.

There\'s wide perception about drug intolerance. Cough with ACE
inhibitors, renal dysfunction with ACE inhibitors, hypotension,
bradycardia, side effects that cause patients not to want to take pills;
co-morbidity which excludes drugs, chronic obstructive lung
disease,peripheral vascular disease, et cetera.

And with beta blockers particularly, the need for careful drug
titration, that many primary care physicians do not want to undertake.
So my view is that although we should continue to work on all of these
issues and reassure physicians and patients of the importance, we are
not likely to markedly improve the compliance with the currently
recommended drugs which appear in every consensus document and
recommendation. They are still not as widely used as they could be.

So is there room then for addition of a new drug like valsartan, which
is perceived to be very well tolerated and is well tolerated, does not
need careful \-- as careful drug titration, and can reduce some of the
burdens that the heart failure patient undergoes?

So here is my algorithm, if you will, for the current management of
heart failure. Patients with heart failure are generally treated with
diuretic, and they may be treated with digoxin. They also may be treated
with nitrates and hydralazine. I\'d like them to be, but Ray hasn\'t
seen fit to give us permission to market it for that purpose. Is that?

(Laughter.)

DR. COHN: They may be given spironolactone, as has been suggested,
although it\'s particularly advanced Class III and IV, but they will be
on all kinds of background therapy. We think that everybody with heart
failure, if tolerated, should be on an ACE inhibitor. If they tolerate
the ACE inhibitor, they should be given a beta blocker. And if they
tolerate the beta blocker, they should be monitored.

This is optimal therapy for heart failure. Are we going to get 100
percent of our population on these drugs? No way, no way. There\'s lots
of reasons why, as I\'ve already pointed out to you.

If they do not tolerate the beta blocker, I would suggest they be given
valsartan based upon the Val-HeFT data, and then they should be
monitored on ACE inhibitor and valsartan.

If they don\'t tolerate the ACE inhibitor, they should be given
valsartan based on the very good data in Val-HeFT on valsartan in the
absence of ACE inhibitor, and if they tolerate valsartan, they should be
given a beta blocker and monitored. Now they\'re on valsartan and a beta
blocker.

If they don\'t tolerate valsartan, what should you do? You should give
them a beta blocker anyway because the beta blocker is an effective form
of therapy, and this is not ideal therapy. Beta blocker alone is not as
good as beta blocker with either an ACE inhibitor or valsartan, but
it\'s better than neither.

So this is the algorithm then that I would propose based upon Val-HeFT,
superimposed on all the data that we\'ve accumulated over the last 15
years, and I believe we can make a major difference now in the outcome
in patients with heart failure with this approach to therapy.

And I\'ll stop there. Thank you.

ACTING CHAIRMAN BORER: Thank you very much, Jay.

Tom you had two overarching questions about efficacy?

DR. FLEMING: I think they can be incorporated in my comments at the end
in the general review.

ACTING CHAIRMAN BORER: Okay. Are there any other questions left for Jay
or for anybody else who\'s presented or hasn\'t presented?

DR. NISSEN: Just one, Jay. You know, one of the problems is, as you\'ve
said, we don\'t know why those patients that didn\'t get an ACE or a
beta blocker didn\'t, and so in your algorithm, what the algorithm
doesn\'t reflect is the fact that we don\'t know that the patients that
got valsartan in Val-HeFT got it because they couldn\'t tolerate ACE or
couldn\'t tolerate beta blockers. For some reason or another they
didn\'t get those drugs.

DR. COHN: I agree with you.

DR. NISSEN: And so it\'s a gap in our knowledge that doesn\'t reflect it
in your algorithm.

DR. COHN: Well, that\'s true. I can\'t address the beta blocker story at
all because only a third of people were on a beta blocker, and I think
that reflects physician judgment to a large extent.

I think the ACE inhibitor story is probably a little clearer. This is a
population of physicians who really do believe in ACE inhibitors, and
since 93 percent of the patients were on it, I suspect, but I agree with
you. I can\'t say that. We have no data to back that up, that the seven
percent who didn\'t were at least perceived to be ACE intolerant;
otherwise they would have been on it.

But I agree. We have a gap in our knowledge.

DR. NISSEN: But it obviously has major implications for how we think
about this because because do we think about valsartan as a drug that
can be used in ACE intolerant patients? You know, based upon those
patients that got it without ACE inhibitors, without really know what
their baseline characteristics were. It\'s a bottom \--

DR. COHN: You\'re going to have data in a couple of years from the CHARM
trial in which they specifically have a group that are ACE intolerant,
and however, I just forewarn you. Given the number of people in the
CHARM trial in the ACE intolerant group, it is clearly much higher than
any of us ever observe ACE intolerance.

So I don\'t even think you\'re going to be much off with that database.
These are not ACE treated patients, and they\'re not getting ACE
inhibitor for various reasons, and intolerance is the easiest one to
identify.

ACTING CHAIRMAN BORER: Paul.

DR. ARMSTRONG: Jay, two thirds of the Val-HeFT population was on
digoxin. Was the effect on hospitalization equally powerful with digoxin
versus non?

DR. COHN: Yeah, I think it was. That\'s a good question, and I don\'t
know that we have digoxin/no digoxin data, but we\'ve looked at that,
and there didn\'t appear to be any impact of dioxin on the efficacy of
valsartan. Good question though, Paul.

ACTING CHAIRMAN BORER: Ray.

DR. LIPICKY: As you carefully pointed out, I\'m old fashioned, and \--

DR. COHN: I didn\'t say that.

DR. LIPICKY: It used to be that one made people live longer or feel
better, and now there\'s this morbidity thing that is an index, that is
\-- or morbidity is counted as hospitalizations, and I don\'t know where
to put that in the spectrum.

For example, if one thinks about just feeling better, usually we\'re
fairly hard on the strength of evidence that\'s required to accept the
fact that people feel better , and morbidity is somewhere in between
there.

How should this be viewed?

DR. COHN: Could I have 027? Because I want you to expand your two
paradigm concept, Ray.

This is the goals for heart failure therapy: make people feel better.
That\'s short term. Prevent people from feeling worse because that\'s
quite different, and what we\'ve shown in Val-HeFT is that we kept
people from getting worse because we slowed the progression of the
disease.

Making people feel better, you can make them feel better with a
dobutamine infusion or a milrinone infusion short term because you
correct hemodynamics. That\'s short term benefit.

This is the progression of the disease, the second one, that we all
recognize is going on under our eyes and in every patient we see unless
we\'re optimally treating them, and that\'s more of an intermediate term
outcome.

And, yes, you\'ll begin to see a reduction of hospitalizations. We saw
it within about three months, but the long-term effects really do relate
to prevention of progression, and that\'s delaying death and reducing
morbidity, which is hospitalization.

DR. LIPICKY: Right. It \--

DR. COHN: So there\'s three phases.

DR. LIPICKY: If we could talk about that just for a minute longer, you
could make people feel better for a long time. It\'s not necessarily
short term, but \--

DR. COHN: But you have to prevent the progression \--

DR. LIPICKY: Right.

DR. COHN: \-- as well as improve the short term. It\'s two phases.

DR. LIPICKY: Now, if you take the prevention of hospitalizations as
evidence for altering the natural history of the disease, why don\'t you
affect mortality?

DR. COHN: Well, that\'s a good question. Why don\'t you affect
mortality? We are producing a modest slowing of progression of disease
with valsartan.

I think valsartan has two components to its efficacy. Now I\'m going way
beyond the data, but I\'m basing this on all my experience. It does have
a favorable hemodynamic effect. The BNP comes down. The wedge pressure
falls. There is a hemodynamic benefit.

That hemodynamic benefit may produce some modest improvement in the way
people feel, not enough to improve exercise tolerance with the small
sample that we\'ve done. So it\'s a modest benefit, but it\'s also
slowing progression of the disease even on top of existing therapy. The
change in ejection fraction is one percent.

You know, with ACE inhibitors it was about three percent. With beta
blockers, it was about seven or eight percent. So it\'s a modest effect,
probably not great enough to further reduce mortality on top of existing
therapy, ACE inhibitors and beta blockers, but enough to reduce
hospitalization, make people feel better, keep them out of the hospital.

Is it a gangbuster\'s drug? Should it replace an ACE inhibitor or a beta
blocker? I say no. I think ACE inhibitors and beta blockers do a little
more of both. They are hemodynamic, and they improve the left ventricle.

Now, if we gave valsartan in the absence of an ACE inhibitor and we
showed that, we get a benefit on mortality. So the reason we\'re not
seeing a greater benefit may well be that they\'re all on ACE
inhibitors, but we\'re getting further incremental benefit.

DR. LIPICKY: Right, but I guess I\'d just like to press it one more
minute. So if you\'re to take hospitalizations as an index of changing
natural history of disease, what you\'re saying is that the change in
natural history of the disease that you can demonstrate may not be
enough to affect mortality, but that it should be accepted as changing
the natural history of the disease and not as symptomatic care.

DR. COHN: Well, to say not affect mortality, I think, is perhaps an
overstatement. As you know, when we eliminated that one subgroup who we
really believed should not be treated with the drug now, there was a
nine percent reduction in mortality. Now, the point estimate was nine
percent reduction.

The confidence intervals don\'t tell us anything. Tom is shaking his
head appropriately. I mean, that\'s \-- but if we had powered the study
like some of reperfusion studies, TPA, et cetera, and found a one
percent reduction in mortality and went to the bank with it, you would
say, \"Gee, that\'s great,\" but we\'d have to put 40,000 people in the
trial.

So I\'m not sure you wouldn\'t see \-- we can\'t say that \-- but I\'m
not sure we wouldn\'t have seen a modest, but small reduction of
mortality had we powered this study like the TIMMI trials or the JC
trials.

ACTING CHAIRMAN BORER: Any other questions or comments? Is there any
other comment the company wants to make? Okay.

MR. MacNAB: Unless you have specific questions from the company\'s
perspective, I think what you\'ve heard this morning is essentially, you
know, our perspective of the trial and some of the ways we think the
drug should be used based upon the data of the trial.

But I\'m here for questions if you have them.

ACTING CHAIRMAN BORER: Tom, did you?

DR. FLEMING: Yeah, I just had one procedural question unrelated to what
we\'ve been discussing while we have the company at the microphone.

I was delighted to read in the FDA briefing document that the FDA had
requested the minutes of the Data Safety Monitoring Board\'s meetings,
which I think should be an absolute procedure in all settings, in all
trials that have data monitoring committees.

I was perplexed then to hear that the data monitoring committee didn\'t
produce minutes, which I don\'t think has ever been a situation in which
I was on a monitoring committee that we didn\'t produce minutes.

Can you clarify what happened in this setting?

MR. MacNAB: I\'d ask Dr. Glazer, the data safety monitoring at endpoint
committee. That was, again, an independent committee. So it was probably
more independent than you discuss.

Why they didn\'t keep minutes?

DR. GLAZER: The DSMB chairman is not here. I think it was their decision
not to do that. Again, it was an independent committee. I don\'t know
the precise reason as to why they didn\'t keep written minutes.

DR. FLEMING: Well, I guess in general terms these are very important
insights. I would urge that in the future all sponsors insure that their
monitoring committees are generating minutes where the open session
minutes presumably with recommendations would be disseminated to the
sponsor, and the closed session minutes would be archived precisely to
allow insights to regulatory authorities and review bodies such as this.

ACTING CHAIRMAN BORER: Okay. Well, we\'ll move on to the structure
portion of the session.

MR. MacNAB: Could I make just one procedural comment? You have in your
questions, I think it\'s Question 3, which is sort of a statistical
question. Once you get into that, will we be able to comment on the
comments or would you like us \--

ACTING CHAIRMAN BORER: Not unless somebody on the committee wants to
hear a comment.

MR. MacNAB: I mean, because perhaps Dr. Fisher would have some comments
on that.

ACTING CHAIRMAN BORER: If you have something you want to tell us about
now, Lloyd.

MR. MacNAB: Yeah, why don\'t you do that?

He can express these things much more elegantly than I will ever begin
to.

DR. FISHER: Well, I wanted to speak about several things, why I feel the
way I do, which you\'ll hear shortly, but I also wanted to discuss this
relative to the two study paradigm. I thought that would come up, and
the reason I was trying to flag Malcolm was because I think it will come
up in the questions, and I think you will hear a discussion of it.

And this is an unusual situation. In most trials where you have a
primary endpoint and a secondary endpoint, if you don\'t meet your
primary endpoint, then in a certain sense you\'re not allowed to look at
your secondary endpoints, although there have been some where they were
incredibly extreme.

Carvedilol comes to mind, which generates a lot of discussion, but you
think of it as an exception. Here we met the primary endpoint. There
were two of them, but according to the rules adjusting for multiple
comparisons, that endpoint was met, but it was met at a level that\'s
somewhat problematic.

The two trial paradigm, I\'ve heard some other arguments, but a p value
somewhere between .00125 and .0056 is kind of the range you would like
to get. But nevertheless we met the primary endpoint, which suggests you
can look at the secondary endpoint.

Well, the secondary endpoint of hospitalization predefined blows away
the p values usually required for the two study paradigm. So this bring
up the issue of, well, can you only look at it down to the p value of
the primary endpoint.

And as far as I know, there\'s no precedent for this. At least I\'ve
never heard it discussed in cardiorenal, but the point I wanted to make
is in any event this is not a clear violation, but it\'s something
that\'s a very subtle statistical point. It deserves consideration.

Another thing was Tom looked at all of the signs and symptoms data and
said, well, gee, this isn\'t surprising. They\'re all correlated.

I think I spent more time than Tom thinking about heart failure, and I
can assure you I have been in numerous meetings that said can\'t we find
one sign or symptom to show a benefit for the patient. And anybody here
that\'s been involved with heart failure, I\'d be amazed if you don\'t
think it\'s bizarre somehow that these things don\'t correlate like they
should.

So I found this amazing. In fact, I think of those signs and symptoms
there were five of them that also met the two study paradigm. They were
not prespecified secondary endpoints, but again, there was less than
.001, adding to the weight of evidence.

Some of you know, but I will say it in public. I will not come to a
meeting with a sponsor unless I think their compound should be approved,
and there have been meetings where I have been invited. I usually don\'t
tell them, \"Forget it. I don\'t think it will be approved.\" I say,
\"Well, gee, I\'d kind of like to, but I\'m busy,\" or you know, if
worse comes to worse, I tell them the truth.

And in this particular case, when I was asked, they gave me the data.
Nobody talked to me about anything, and I said, \"I\'m not sure I\'ll go
with you. I want to see how you\'re going to handle the subgroups.\"

And the subgroup that bothered me, the thing that bothered me
immediately was the high mortality in the people on both ACE inhibitors
and beta blockers, and certainly we have a number of experts sitting on
this panel who could run rings around me physiologically, but my gut
reaction was: I think this is true. Somehow it\'s too much of a good
thing.

That was just my gut reaction, and not only that. There are a lot of
interactions we worry about, but here was one that was significant at
the .009 level, and we hadn\'t looked at all this other stuff when that
came up. So we started looking at things.

Jay called an ejection fraction independent, and so on. It\'s not truly
independent. It\'s all part of the same picture, but there are very
different parameters, and as you saw, almost everything you looked at
went in the same direction.

So I would think it would be ridiculous for this committee if you voted
for approval to not say to the agency, \"Well, I can\'t stand here and
claim absolutely equivocally it\'s proven it\'s harmful,\" and I doubt
we\'ll ever know because I wouldn\'t picture somebody betting their
money on that being a mistaken idea.

But when I believe that, there\'s a flip side to that. That means the
other data look better. Now, we don\'t know how to interpret those p
values, and I\'m sure Tom is going to get up here, my guess is, and tell
us, \"Well, gee, in every study even if you have no effect, if you
search long enough you can find a subgroup that looks good, and then
there\'s a subgroup that looks bad by complementarity.\"

But this to me was a very different situation. To me, to the extent I
understand the biology, it went along with things. There was a question
about sample size. I was not involved, but I can almost bet how it went.

They went into cardiorenal. Cardiorenal said, gee, congestive heart
failure. All drugs either help or kill, and we don\'t which, and we\'re
not right enough to know which. Unless you get quite a bit of mortality
data, we\'re going to put a black box in there saying this drug may \--
Ray can correct me if this is wrong, but I\'ve heard him say it in
another context \-- there might be 20 percent excess mortality, and so a
relative risk of 1.2 or 20 percent often comes up.

The sponsor is hoping it works, but at least by powering things for
that, you would be attacking in a responsible way the mortality issue,
which I think the sponsor has done.

So when I put all of this together \-- oh, the other thing is the
Minnesota living with heart failure questionnaire. I told Jay to push
that, but since he helped design it and so on, he thought that was
inappropriate, but I really don\'t think it\'s the greatest
questionnaire in the world for heart failure because it almost never is
beneficial. No offense, Jay, but that\'s \--

(Laughter.)

DR. FISHER: But here\'s a case where it actually did turn out to be
statistically significantly positive, and so all of these things kind of
added up in my mind.

I don\'t think you\'re greatly violating the two study paradigm. If you
only focus on the one primary thing and say that\'s more than .00125, I
guess that would be a defensible thing. If Lemm Moyer were here, I would
expect to hear it pushed to the nth degree, but I think given that our
tradition is if you meet your primary endpoint, you can begin to open
things up, and then when you open it up, everything you look at goes in
the right direction, and it goes very strongly. It\'s not just that they
happen to hit two signs and symptoms at .02.

They had a whole bunch of them at less than .001, and almost all of them
were statistically significant. So when you get in your discussion, we
have a number of people here who have been involved with congestive
heart failure trials. I\'d be interested to hear in the discussion how
you inject yourselves into that, whether you think my comments make any
sense or if that\'s your experience.

For the audience, I\'m a know nothing biostatistician. So it\'s a little
bit frightening to get up here and start talking about the clinical
things where I hadn\'t heard a number of points today, and I don\'t
think there will be an opportunity for it to come out later, which is
why I\'m emoting so much.

Thank you.

ACTING CHAIRMAN BORER: Okay. Any other points before we move on?

It doesn\'t look like it.

We have the structured questions here. I think I\'d like to make just
one preliminary comment to put this in a framework that at least has
meaning for me.

I think this was a very interesting development program centered around
an extraordinary clinical trial, a landmark study really, and the fact
that that study is so powerful, I don\'t think there\'s much question
that it shows that there\'s a clinical benefit from the use of this drug
in someone, is a tribute to \-- the fact that it shows these things and
that it was so well done is a tribute to the sponsor, Novartis, and to
Jay in directing this and in presenting it as effectively as he did.

You know, I wouldn\'t have guessed that hitting the renin angiotensin
system from another angle would have been an easy thing to do and that
it would have been highly likely that one could find something even if
there was something there to find.

And this study did find something, and I think, again, that that\'s a
tribute to the precision and perseverance of the people who were
involved in organizing and directing this.

So, you know, I don\'t think there\'s much question here that there is a
clinical benefit. I think the question that we\'re going to have to
focus on is exactly what is the benefit and who benefits, and those are
laboring issues, and that\'s what I think we have to grapple with here
today.

We\'ve heard several comments about how we might grapple with those
things, but I think that the way these questions are written, and
they\'re superbly logical in their sequence of reasoning through an NDA
to a final conclusion will lead us to have to conclude how we want to
advise the FDA to write a label and if we, indeed, believe Lloyd\'s
argument and everything else and conclude that the drug actually does
something.

So with that having been said, we\'ll begin the comments, and our
committee reviewer, Tom Fleming, will lead the discussion for most of
these questions, I think. He hasn\'t given a formal review, but he\'ll
give what would have been his formal review in the context of the
questions.

I will not read the preamble here. I think that everybody has a copy,
and you can read it and the sponsor knows what the study was, studies
were. They\'re described here, and the committee knows.

So we\'ll start with the question. Consider the exercise tolerance
Studies 110 and 106. In Study 110, all subjects were on ACE inhibitor
for at least three months prior to enrollment. Therefore, the subjects
randomized to valsartan were withdrawn from ACE inhibitor.

What is known about the time course for the loss of effects of an ACE
inhibitor on exercise tolerance?

Would anybody disagree with me if I said nothing?

No. Okay. So the answer, Ray, is nothing, is no.

One, point, two. In Study 110, subjects walked 420 meters in six minutes
at baseline. What degree of impairment does this represent?

Does anybody want to speak specifically about that? I think the answer
will be not much, but does anybody want to elaborate on that?

No. Okay. That\'s not an overwhelming impairment it sounds as if.

What is known about effects of valsartan and enalapril on exercise can
be summarized as follows. We have the table here, enalapril versus
placebo. Treadmill is improved. Six minute walk data not available.
Valsartan versus placebo, treadmill unaffected from the data we have.
Six minute walk, it says no data. There are some, but really versus
placebo there\'s not much.

Valsartan versus enalapril. So it doesn\'t look as if this \-- from the
way that the question is framed that there\'s a great deal that we can
say about exercise tolerance from the available data, and therefore,
what is the effect of valsartan on exercise tolerance?

Tom.

DR. FLEMING: Well, let me comment, and let me preface my comments by
mentioning that as primary reviewer, I think these questions have really
laid out to my way of thinking what the critical issues are that need to
guide this committee discussion.

So in the efficiency of time, I would like to imbed or incorporate my
primary review just into these as we go question by question, and before
I do, let me just generally reaffirm some of what Jeff has said.

I also believe that this study is an extremely informative study. I\'m
very appreciative of the efforts of the team of investigators and the
sponsor in conducting the study, and I would argue the challenge that\'s
facing us is very much getting a sense of not as much about the
reliability of results, but the interpretation of results and
understanding the populations that may benefit.

Relative to this Question 1.3, what is the effect of valsartan on
exercise tolerance, and the question is in the context of Studies 110
and 106, 106 involving 770 patients looked at exercise tolerance time.
Page 78 in the briefing document, FDA, and there\'s no difference. One,
oh, seven, also no difference. Six minute walk test on page 112.

Study 110 looked at an active comparison against enalapril with a six
minute walk test on page 121 and essentially showed roughly comparable
results, although the two placebo controlled comparisons say that there
is no difference.

So essentially \-- and I think the sponsor\'s presentation acknowledged
this \-- the data from these trials suggests that there was no
detectable effect on exercise tolerance.

ACTING CHAIRMAN BORER: Okay. Does anybody have any additional comment to
make about these?

Okay. If not, let\'s go on to Question 2. Ignoring Val-HeFT, what role
did the two studies of hemodynamics and two studies of exercise ability
and quality of life have in the case for approval? Do they contribute to
demonstration of clinical benefit?

Does anybody want to take that? Tom.

DR. FLEMING: Yeah, I might comment. Essentially the two studies on
hemodynamics, assuming it\'s 103 and 104, the two studies on exercise
tolerance, 106 and 110; the two studies on hemodynamics certainly do
provide evidence of effects, for example, on pulmonary capillary wedge
pressure. It\'s my sense that such evidence is relevant to the extent of
establishing a biological effect and potentially plausibility of
clinical effects, but I think it\'s very noncontroversial to say that it
can\'t be; it\'s not a validated surrogate.

The two studies on exercise tolerance and quality of life, and we\'ve
already alluded to this in the answer to Question 1, the results in 106
and 110 on exercise tolerance time were essentially negative, as they
were in 110, and the Minnesota living with heart failure questionnaire
results in 106 were also negative.

So kind of incorporating these issues into the answers to 2.1 and to
2.2, information on quality of life and biologic activity measures are
certainly potentially helpful in getting a sense about mechanisms of
action and biological plausibility of efficacy.

But in the context of having such an extremely informative study in 107,
in a relative sense they don\'t provide very much more insight. Hence I
would say probably are largely irrelevant in the context of what we have
already learned from 107.

Would you be better off without them? No. The sponsor certainly might
have had a stronger case in the sense that 106 and 110 are negative.
One, oh, seven is also negative on six minute walk, but is positive on
quality of life and signs and symptoms.

So, in essence, overall I would argue that measures of biologic activity
are of relevance in establishing plausibility, but don\'t provide direct
evidence about that, and the information on quality of life was actually
in those two studies, was fairly negative.

ACTING CHAIRMAN BORER: Steve.

DR. NISSEN: A very brief comment. I agree with everything Tom said,
except the hemodynamic data. I think it\'s important for us to all
remember the paradox of inotropic agents, which make hemodynamics better
and probably adversely affect survival, I think.

So I don\'t see a lot of signal. If one shows me an acute hemodynamic
effect of a drug, it doesn\'t really tell me very much about whether
it\'s going to be useful in chronic heart failure, and so they\'re
noncontributory from my perspective.

I think they help the sponsor more to understand whether there is some
additive hemodynamic effect over ACE inhibitors, but other than that, I
think they\'re not really very helpful for this application.

ACTING CHAIRMAN BORER: Alan, did you have a comment? No.

Ray.

DR. LIPICKY: Well, in part it might be worth hearing a comment or two if
you think it\'s worth commenting on. Those other studies did have some
aspect of do patients feel better, and it wasn\'t striking that there
was or that they found anybody feeling better, and that is sort of not
the case in Val-HeFT.

It rather strikingly seemed to make people feel better even when they
were on ACE inhibitors. So part of the question is does that detract
from how you\'re going to interpret what you see in Val-HeFT.

DR. COHN: May I remind Ray?

ACTING CHAIRMAN BORER: Yes.

DR. COHN: What we found is there\'s benefit for the long term. These
other studies were before any of the curves began to separate. So we
kept people from feeling worse. We didn\'t make them feel better.
That\'s why I made the \--

DR. LIPICKY: Okay, okay.

ACTING CHAIRMAN BORER: Actually I was going to remind you of that, too.

DR. LIPICKY: You were?

(Laughter.)

ACTING CHAIRMAN BORER: You know, for me, and anybody on the committee,
chime in and disagree, I don\'t find the lack of clinical efficacy in
these trials to be particularly compelling against because there were
small trials over a short period of time, and we have this overwhelming
data set that is compelling in the other direction.

So it\'s not a big negative for me. Is it for anyone else? No.

Okay. Number 3, consider the components of the morbidity and mortality
endpoint of Val-HeFT, and we have this nice table here. What role do
each of the components have in the case for approval? And how do we
reconcile the large effects on hospitalization with post hoc analyses
that show little or no effect on certain of the subcomponents?

Tom, why don\'t you just go ahead?

DR. FLEMING: All right. This is certainly an important question. The
first part, what role do the components play, my sense in interpreting
the components is what is apparent as you look at these four components.
The results are really driven by all cause mortality and CHF
hospitalizations.

And the sponsor\'s presentation today certainly did focus appropriately
on those two elements. They\'re complementary. All cause mortality,
obviously a critically important endpoint to these patients, but
certainly CHF hospitalization is a separate measure that is going beyond
mortality looking at issues of quality of life, although as was
discussed by the committee, one would have possibly hoped that through
affecting CHF hospitalization that might have led you to anticipate a
mortality benefit as well.

But I look at them as largely complementary and very important elements,
and those two elements are the two for which we have very substantial
information about benefit.

Ray, if you could put up the first of those two slides.

The 3.2 though gets into the question about reconciling evidence for a
favorable result on CHF hospitalization, but with little or no effect on
all cause hospitalization.

And a slide that I had generated, and actually I think these results are
similar to what the sponsor had presented in their attempt to clarify
this issue as well; what we\'re looking at here on the top of this slide
are events, are basically numbers of people in the trial with events
where, of course, we have about 2,500 per arm, and we know that the
death rate is about 20 percent of the patients in each of these two arms
had died, where there is a two percent increase in deaths.

But we see if we \-- and it is problematic to censor the deaths, but
that\'s the approach here in analyzing what I underlined in green, which
was the result that was referred to as the favorable result in this
Question 3.2, and it certainly is.

I think we would all accept the most important positive signal in this
study is this 27 percent estimated reduction in CHF hospitalizations,
and these together provide the second primary endpoint, the 13 percent
reduction in morbid events.

Now, the FDA asks the sponsor to provide in addition to that the numbers
of patients that had hospitalization for any cause or death, and when
you look at this data, you see no reduction, and so the paradox or the
uncertainty here is why is it that we have no reduction in the numbers
of patients that have death or all cause hospitalization when we\'re
seeing a reduction from about 18 and a half percent to 14 percent of
patients that would have CHF hospitalization? Does that mean that, in
essence, there is an increase in non-CHF hospitalization?

Well, I think to really more appropriately address that and to get
better insight into that, we have to look in addition to this analysis,
which is the numbers of people with an event; we need to look at the
numbers of events overall.

And I think a very important \-- and these data, by the way, are in the
FDA briefing document on pages 96 to 99. What\'s relevant here is that
whereas there are about 1,365, 1,398 patients with at least a
hospitalization or death event, the actual number of hospitalizations is
two to two and a half fold as high. This is the total number of
hospitalizations that are recorded over the duration of follow-up, 2,056
versus 3,106, which separate into those hospitalizations that are CHF
and those that are non-CHF.

And in essence, what you see here is not that there\'s an increase in
non-CHF hospitalizations, but there is this overall decrease of about
260 CHF hospitalizations that shows up in all cause.

Ray, I would say, in essence, what\'s happening here is what\'s giving
the impression that there isn\'t a reduction in all cause events, is
that basically we\'ve having multiple repeated hospitalizations of both
types occurring to many of these patients over the two year period of
follow-up.

So I found these analyses that looked at the total numbers of events to
be very informative, and in fact, the all cause hospitalization days
data, I think, is very consistent with all cause hospitalization.

You\'ve got roughly an eight percent reduction in the number of all
cause hospitalizations. You\'ve got about a ten percent reduction in the
number of days.

What I\'d like to do is in a minute go to another slide that basically
then puts into context what the survival data, all cause hospitalization
and all cause hospitalization days would look like for a hypothetical
100 people.

Ray, could you put that other slide up to basically finish this response
to this part of Question 3.2?

If we looked at a typical 100 people then based on data from the
Val-HeFT trial, where that data provided approximately two years\'
follow-up, what we would have in 100 typical people with about two
years\' follow-up is about 20 deaths on placebo and about 20 deaths on
valsartan. There would be no change in the numbers of deaths.

Essentially, you would reduce for these 100 people the total number of
all cause hospitalizations from 124 to 114. That\'s entirely \-- those
ten are entirely CHF hospitalizations.

So the total number of non-CHF hospitalizations would be 77 in both
groups, but would be reduced by ten the number of CHF hospitalizations.

And correspondingly, with this ten percent reduction we would reduce by
ten percent the number of hospitalization days.

So, in essence, what we\'re looking at here in a typical 100 people that
would receive valsartan versus 100 that would receive placebo, no
difference in deaths; no difference in non-CHF hospitalizations; but ten
less CHF hospitalizations per 100 people, translating into about a day
less.

So the number of days alive and non-hospitalized is about 690 per arm,
and the number of hospital days is reduced by about a day. So one way of
looking at all of these data is that these results suggest no effect on
mortality, an average of one-tenth of a CHF hospitalization per patient
eliminated. That translates into one less hospitalization day.

And in essence, then that really addresses all three of these elements
of 3.2.

ACTING CHAIRMAN BORER: Does anybody have any additional comment about
this question? Steve.

DR. NISSEN: Yeah. Well, I also want to complement the sponsor on it. I
think this is an incredibly important and informative study, and if I
seemed nitpicky about it, it\'s because that\'s our job, and I\'m going
to nitpick a little bit more here before we finish and say that, you
know, while these differences are impressive, I would have been happier
if the adjudication had been done differently. I think it would have
made my confidence of the data a lot higher in terms of looking at these
differences because the differences, as pointed out by Tom\'s analysis,
are relatively modest.

I can\'t accept that coming into the hospital at nine o\'clock in the
morning, getting furosemide intravenously all day long and going home
that evening is not an event, but doing the same thing at nine o\'clock
at night and being overnight is.

Now, the excuse that the data on the time were not available is a flaw
in the design. It\'s easy to put in the case report form. What time did
the patient get admitted to the hospital and what time do they get
discharged? You can collect that data.

And, you know, suggesting that the time of day at which you get admitted
would make such a big difference on whether it was counted as an event
or not is a weakness that could have been overcome by more meticulous,
you know, design.

And, secondly, I think that to me independent adjudication means the
sponsor is not present. In many, almost all of the trials I\'ve been
involved with that\'s how it\'s been done, and I think there\'s good
reasons for that because if the committee is doing the selection of the
cases along with an independent, you know, group or CRO, whatever, it
protects the study.

I think that it didn\'t make a difference here. Please don\'t get me
wrong, but I think it helps our confidence more if it looks like the
adjudication for a trial where these events were the endpoint of the
trial, where you\'re spending I don\'t know how many tens of millions of
dollars on a trial. It\'s worth a little extra effort to adjudicate
these endpoints in the cleanest fashion possible and trying to
understand that right up front.

Having said all of that, you know, I do think that Tom\'s comments are
really along my line. I agree with his comments.

ACTING CHAIRMAN BORER: Ray.

DR. LIPICKY: I guess I\'m setting myself up to be hollered at, but the
way you presented the results, Tom, I was more impressed before. A tenth
of a hospitalization? Is that worth adding the seventh drug to a
treatment regime, et cetera, et cetera?

You made it seem like the effect is very small.

ACTING CHAIRMAN BORER: That\'s in 100 patients.

DR. LIPICKY: A tenth of a hospitalization per patient.

DR. FLEMING: Yeah, basically if we put it in the context of per 100
patients followed over two years, then in those 100 patients treated,
you would prevent ten hospitalizations. That is a tenth of a
hospitalization per patient, but that\'s basically ten hospitalizations
prevented for 100 patients treated and followed for two years.

DR. LIPICKY: So you do think it\'s small?

DR. FLEMING: Well, that\'s what we \--

DR. LIPICKY: You\'re not going to holler at me?

DR. FLEMING: I mean, my assumption is we\'ll be coming back to that
specific discussion later on.

DR. LIPICKY: Okay, fine.

DR. FLEMING: One of the issues that you were raising here was
specifically how is it that we could see reductions in the number of
patients experiencing CHF hospitalizations and not see any reduction in
the total number of all cause hospitalizations.

And the answer for that really reflects the fact that these patients are
being followed over a time frame where there are really repeated events
occurring, and as those repeated events occur, the numbers of people
then that have at least one event becomes less informative about what
the overall effect is.

But you think step back and look at the analyses that I was showing at
the bottom of that first transparency. Then you do see that, in fact,
even though the numbers of patients that have at least one
hospitalization are the same, there really is not a negative effect in
these data on non-CHF hospitalizations.

But the overall level of effect is, therefore, none. In the typical
hundred patients, I don\'t have that slide in front of me now, but I
think it was 77 versus 77 non-CHF hospitalizations and CHF
hospitalizations were what was it? Thirty-seven against 47?

DR. LIPICKY: That\'s okay. So you will make a value judgment later. So
we \--

DR. FLEMING: I definitely think we should.

DR. NISSEN: Just let me holler at you a little bit and say that I would
agree with you if it weren\'t for the fact that the study design was
adding this agent to what was already very good state-of-the-art
therapy, and so this degree of reduction on top of people who were very
well treated is much more impressive than it would have been in another
setting.

DR. LIPICKY: Agreed, but in a public health sense it\'s not too
important.

DR. NISSEN: Again, I wouldn\'t necessarily agree with you about that. I
mean, if you have to treat ten patients to prevent one hospitalization,
hospitalizations use a lot of resources. They cost a lot, and they\'re
not very pleasant for patients that undergo them.

I don\'t have a problem with giving a drug to ten people to prevent one
of them from being hospitalized. That to me is a fairly impressive
effect in this setting.

DR. LINDENFELD: Don\'t you think, Steve, though that we should consider
it 20? Because we usually think about number needed to treat per year.
So this is ten for two years. So that would mean you\'d have to treat 20
to save one hospitalization.

DR. NISSEN: JoAnn, and this is in the same range \--

DR. LINDENFELD: So that makes it a more modest \--

ACTING CHAIRMAN BORER: Can I just add? Let\'s hold this discussion, if I
may, because \--

DR. LIPICKY: I withdraw the question.

ACTING CHAIRMAN BORER: \-- you know, the question we\'re actually going
to have to answer at the end of the day will incorporate elements of all
of this, and we can get to it.

And with that in mind, can I ask, Ray, that we be allowed to skip number
four? Because unless, Tom, you have something very much to say about it,
because it\'s sort of a speculative value judgment that we\'re going to
get to.

DR. LIPICKY: No, no, no.

ACTING CHAIRMAN BORER: I mean, we\'re going to get to it.

DR. LIPICKY: No. It gets at the very thing that Lloyd got up to talk
about.

ACTING CHAIRMAN BORER: Well \--

DR. LIPICKY: What is the level of significance you want from a single
trial before you start paying attention to it if you only deal with the
primary endpoint?

DR. FLEMING: I think I can comment.

DR. LIPICKY: Or endpoints.

ACTING CHAIRMAN BORER: Okay.

DR. LIPICKY: And then are you going to modify that based on what you see
later?

ACTING CHAIRMAN BORER: I withdraw my request, Ray, and I\'m going to ask
Tom to give the definitive answer.

DR. FLEMING: Well, I think I can give at least a brief comment in
response to this, and I think it does \-- it really is. The way this is
phrased is very specifically asking us to just look at the co-primary
endpoints, not look at secondary. The questions will lead us then
through five and six to bring in secondary endpoints and how does that
influence our perspective?

As I see it, there are in Question 4, there are two aspects to address
in this question. Basically what it\'s asking us is if we focus on the
primary endpoints, how strong do these results have to be for us to view
that these are significant or established benefits of clinical
importance, and I think there are two elements.

There\'s statistical significance below the line, and there\'s clinical
significance above the line, and this trial is a very important, very
good trial. It\'s 5,000 patients. It\'s a trial that was adequately
powered for mortality, which was a negative outcome, and by all
likelihood, I was probing this and didn\'t really get a clear answer,
but in all likelihood, it\'s more than adequately powered to address the
other primary endpoint of morbidity, given that there are a lot more
morbidity events than mortality events.

That being the case, it\'s a study with considerable potential to
achieve a very strong level of significance if there\'s a strong signal
of effect.

Traditionally, it\'s only a guideline, but statistical procedures are
useful as a guide. We have considered results at the 025 level, two
sided, as the strength of evidence that we would need to see to call a
study positive. In this trial with co-primary endpoints and adjusting
for multiple testing, that two-sided p value guide is not 05, but 02.

If we looked at this as a trial that provided, in essence, comparable
strength of evidence to two independent studies, each of which would be
positive, that\'s where the 00125 has arisen, and technically here I\'d
remind you it\'s not .00125 in this trial,b ut that divided by two or
even more than two, because there was interim monitoring and there were
two primary endpoints. So \--

DR. FISHER: Could I say \--

DR. FLEMING: \-- essentially \-- essentially what we are dealing with
here is a trial from a strength of evidence if we focus only on, as
we\'re asked to in this question, the primary endpoints that would meet
the standard for what would be called a positive study on the morbidity
endpoint.

But it doesn\'t really come close to what we would consider as
essentially comparable to the evidence of two independent positive
studies each of which just exactly hit strength of evidence that we
would need individually.

The issue above the line of clinical relevance is certainly also very
important. What we\'re estimating is a 13 percent reduction in the
morbidity events, and I think that is an issue that we need to revisit,
as we\'ve said, toward the end in talking about what level of effect is,
in fact, clinically important.

ACTING CHAIRMAN BORER: Tom, Lloyd raised a question. In general we
wouldn\'t have more comments here, but specifically about your
calculations.

DR. FISHER: I wanted to give a point of information. This is just a
numerical fact according to \-- the number .1 is wrong. There\'s
actually a gain of one day per patient per year.

So in other words, if you\'re treating somebody for a year on the
average, that person is prevented for a day of hospitalization.

Of course, as it turns out, it\'s very skewed. Some people are not
hospitalized at all, and some more. So but on the average, each person
each year gains a day.

DR. LIPICKY: That makes it a big number, does it? No, you don\'t have to
answer that.

DR. FISHER: Well, no.

(Laughter.)

DR. FISHER: I\'ve been in the hospital, and all I can tell you it
wasn\'t a particularly wonderful experience given the \-- for me it
would be worth avoiding it, but \--

DR. LIPICKY: Yeah, I agree. I don\'t disagree.

ACTING CHAIRMAN BORER: Okay. Ray, can we postpone further elaboration on
four until we move a little further?

DR. LIPICKY: Yes.

ACTING CHAIRMAN BORER: Thank you.

Okay.

DR. LIPICKY: You\'re welcome.

ACTING CHAIRMAN BORER: Let\'s move on to number five. Consider other
endpoints that were not individual components of the morbidity/mortality
primary endpoint of Val-HeFT, and we have a table here with a number of
them with some p values.

What role do each of these secondary endpoints have in the case for
approval?

Tom, why don\'t you go through that?

DR. FLEMING: Okay. In any trial, it certainly is very important to
focus, first and foremost, on the primary endpoints, in this case two
primary endpoints, mortality and the morbidity hospitalization/mortality
endpoints.

But in any trial it\'s also clearly important to focus beyond or to take
into account, as well, the supported information. It becomes more
subjective in interpreting those data. I tend to put less emphasis on p
values and more emphasis on what is the magnitude of effect and how
reliably is it estimated.

It is certainly relevant though to be considering how the safety
profile, as well as secondary quality of life measures influence one\'s
overall perspective of benefit to risk. What we have here, as has been
summarized in the question, there are an array of secondary measures.

In particular, I might focus on the overall signs and symptoms results,
as well as the Minnesota living with heart failure questionnaire
results, and these data are presented in the sponsor\'s presentation on
pages 51 and 52.

There is important positive reinforcement with these results. It is
important though or relevant, at least as I look at these results, to
note in the signs and symptoms that what we\'re seeing is on the order
of a one to three percent increase in the number of patients that have
improvement in fatigue, dyspnea, rales, et cetera, and one to three
percent decrease in the numbers who have worsening, which in these
sample sizes are showing up as statistically significant.

In the Minnesota living with heart failure where the overall scale at
baseline was about 32, we\'re seeing a relative benefit of about one and
a half points, preventing a one and a half point decline, in essence,
and in these sample sizes, these results are showing up as significant
at the 01 to 001 level in many cases.

The key question that I would throw out though is more than from a
statistical perspective, from a clinical perspective. How important is
this? It is a benefit. It is of some importance, but how important, for
example is a change of one and a half points in the Minnesota living
with heart failure questionnaire?

ACTING CHAIRMAN BORER: Steve?

DR. NISSEN: Yeah, this actually has a fair amount of weight with me, and
again, when you only have one really good trial, big trial to look at is
what we really have here, and I would point out something to you, Tom.
You know, for those of us who see patients with heart failure, it\'s not
a pleasant disease. Waking up in the middle of the night with paroxysmal
nocturnal dyspnea is not any fun, and the fact that, you know, highly
significant differences, particularly the symptoms here of heart
failure, because ultimately making these folks feel better is important.

I know, Ray, you\'ve emphasized in a lot of development programs, you
know, the feel good aspects of this, and this is very supportive, and I
would have been much less enthusiastic here if these things had all been
neutral.

But you know, dyspnea on effort, you know, walking out to get your mail
and doing the things you do, if you have less shortness of breath doing
that, that\'s a good thing.

And so to me it\'s very supportive and it\'s very important, and I\'m
glad it was here and I\'m glad it was done in this trial.

DR. FLEMING: Steve, let me be specific though. The issue that I\'m
trying to raise is not whether these symptoms are important. The
question is: how much of an influence on these symptoms does one have to
generate in order to say this is an important effect?

So if we look at paroxysmal nocturnal dyspnea, whereas in the
intervention arm 6.6 percent improve, in the placebo 5.7 percent
improve. Are you impressed that there is that extra .9 percent
improvement?

DR. NISSEN: I am impressed.

DR. LIPICKY: Well, let me stake out a position so that you can argue
with it.

DR. NISSEN: Okay.

DR. LIPICKY: Okay? With respect to the primary endpoint, this is one
trial that has some other data that doesn\'t really tell you much. I
would say the primary endpoint had to have a p value of .00125, and if
it didn\'t, I wouldn\'t want to look much farther.

But in fact, you say, \"Well, I\'m going to look a little farther.\"
Okay, and so you look at the secondary endpoints, and as Tom said, you
don\'t know how to evaluate the strength of evidence there, but you have
sort of magnitude effects to look at.

This is a five percent change, ten percent change, 20 percent change. So
does that mean then that you\'re going to say based on the secondary
endpoints I modify my stand? I\'m satisfied with the p of .01 for the
primaries because this really means something to me. Then we\'d have to
start talking about, well, when do you modify the way in which you make
your first decision on the basis of looking at other data when you
don\'t have much ability to be able to decide whether the other data is
real or not in any usual sense.

That\'s what\'s going on here, and I contracted it and took extreme
positions so that you\'d recognize that.

ACTING CHAIRMAN BORER: Paul, do you have any thoughts about this that
you want to share, the importance of these secondary endpoints?

DR. ARMSTRONG: I am impressed by their consistency, and so I think in
that \-- but also the fact that I don\'t put as much weight on them
individually as a package in terms of clinical signs and symptoms and
their concordance with the other measures. So to the extent that they
support that, I think they\'re useful.

DR. FLEMING: Paul, this is a really tough question to ask because
there\'s obviously a lot of subjectivity to this, but is there any
literature? We talk about the Minnesota living with heart failure
questionnaire as being validated. Is there a literature that says
essentially something about magnitude of change on that scale for it
really to be clinically relevant?

I guess what I\'m really getting at is a one and a half point change,
something that clinically is profound, moderate, but important, really
modest, or close to irrelevant.

DR. ARMSTRONG: Well, as you would know, a one and a half point change on
a denominator of six in a common problem might be quite relevant. And
rales and third heart sounds track morbidity and mortality pretty well
in heart failure.

DR. FLEMING: Well, I\'m looking specifically at the Minnesota where the
baseline average was 32, and based on having that baseline average of
32, which we were informed basically was moderate in the range, what do
we know about a one and a half point change and how important that is
clinically?

DR. COHN: Well, I guess I should probably address that because this is
our form.

DR. FLEMING: Although to be candid here, this is \-- I\'d really like to
get an opinion independent of the sponsor here, Jay, if you would. I
would prefer not to get \-- I think \--

DR. COHN: I mean, I can give you the background on the form.

DR. FLEMING: But at this point \--

DR. COHN: Okay.

DR. FLEMING: \-- I would prefer to get my colleagues\' opinions on the
committee.

DR. HIRSCH: Maybe we should all speak up. I don\'t think we know the
answer to that question. I think it\'s just another signal like the
change in signs and symptoms that can\'t be quantified or qualified even
though there isn\'t development information.

Going down the panel, as we always do?

DR. LINDENFELD: I agree it\'s a signal, but I think it\'s very much like
the signs and symptoms. It\'s a modest \--

DR. HIRSCH: Okay.

ACTING CHAIRMAN BORER: I must say for myself I\'m very impressed with
the consistency of the data over so many areas that might be evaluated,
and personally I think that tools for measuring symptomatic benefit are
inherently \-- inherently provide information that\'s extraordinarily
imprecise, and the result is that you should see anything at all is
impressive to me, whether it\'s one point, two points, or three points
on a scale that\'s fundamentally subjectively defined.

We see many trials with many tools, not only the Minnesota scale, but
New York Heart Association functional class for a lot of cardiovascular
problems. You know, it\'s often very difficult to see anything despite
overall benefit as measured by certain objective endpoints.

So I\'m impressed that there\'s anything positive here with the
Minnesota scale.

DR. FLEMING: But, Jeff, you\'re saying what impresses you is that in
many other trials of other agents there are no effects. So the fact that
there is anything here is impressive, and yet I\'d say, \"Well, look at
other endpoints. Look at mortality.\"

ACTING CHAIRMAN BORER: Right.

DR. FLEMING: We have other agents that show big effect on mortality.
There\'s no effect here.

ACTING CHAIRMAN BORER: That\'s true, but what I see \-- I don\'t want to
jump ahead here, but what I see here is a drug that in this population
overall didn\'t have much of an effect on duration of life, but it did
seem to have an effect on how patients felt in terms of the various
measures that were made.

If you look at the co-primary endpoint, which is one of the feel good
endpoints, and you look at collateral data that might support that that
endpoint should be accepted as meaningful, when I see the Minnesota
quality of life scale coming out positive, that makes me more confident
that the co-primary endpoint is a meaningful one, that the result on the
co-primary endpoint is a meaningful one.

If it were the only positive and all of these others sort of fell out
50-50, I\'d be much less impressed, but that as a component of 12 or 15
other parameters that were looked at here, all coming out the same way,
impresses me a great deal.

DR. LIPICKY: Well, but \--

ACTING CHAIRMAN BORER: Knowing as I do how difficult it is to get a
history of symptoms that is positive when you apply a drug. So, you
know, I think the package has to be taken together. This is one
component of the package, and it wouldn\'t matter to me if it was one
point or five points. If it were the only component of the package that
went the right way, I\'d be concerned.

But if it goes the right way and it\'s one point and the other data are
consistent with it, I find it impressive.

DR. LIPICKY: Jeff, are you talking about the point estimates going in
the right way or how do you know that that\'s real?

ACTING CHAIRMAN BORER: Well, I don\'t know that it\'s real, but I know
\--

DR. LIPICKY: Are you looking at the p values here?

ACTING CHAIRMAN BORER: I know that it\'s consistent \--

DR. LIPICKY: How can you interpret those p values? Tell me how you
interpret. What does a p of .004 mean?

ACTING CHAIRMAN BORER: I interpret it as indicating to me that the
result was consistent across the population. That\'s the only
interpretation I make of it.

DR. LIPICKY: But on the basis of the point estimate or the p value?

ACTING CHAIRMAN BORER: No, on the base of the p value I say that the
result for that point estimate, the result that that is the point
estimate of is consistent through the population.

DR. LIPICKY: I see.

ACTING CHAIRMAN BORER: And now we have consistent through the population
for many, many point estimates. The magnitude of the point estimate
really doesn\'t mean all that much to me.

DR. FLEMING: But here, Jeff, the challenge is really getting a sense of
whether the changes that we see in dyspnea at rest and dyspnea on effort
and fatigues, all of these measures showing two or three percent more
people showing improvement. Are these \-- I mean, we keep making it a
point. It\'s really impressive that there are all of these endpoints. Is
it maybe very much the same two or three people that are showing this
level of improvement on measures that are highly correlated with each
other?

ACTING CHAIRMAN BORER: Yeah, it may well be. It may well be.

DR. FLEMING: So it doesn\'t take away from the argument. It\'s favorable
that we showed an effect rather than not showing an effect. I\'m not
debating that, although I\'m worried about how much of an effect it is,
but I guess I\'m concerned about the conclusion that there\'s a page
here of results, and most of them are significant, and this is
overwhelmingly strengthening the conclusions because they\'re all
significant when many of them must be heavily correlated.

ACTING CHAIRMAN BORER: Yeah, I think they are, but even with relatively
heavily correlated parameters, when you look at a heart failure
database, they generally don\'t all go the same way, you know.

DR. ARMSTRONG: Tom, maybe it would be helpful to say that the New York
heart class, which is common to many other studies, does show a 2.4
percent improvement, and I think we could collect all of the symptoms in
the New York heart class and say there\'s not a bad bed rock based on
the other studies that looks like it moves, and then we could look at
the signs, edema, the third sound, and others, and say there\'s probably
two clusters in this data. One is symptoms and the other is signs, and
they go in the right direction.

And for me as a clinician, that seems to make sense.

DR. LIPICKY: Well, a third heart sound doesn\'t do that.

ACTING CHAIRMAN BORER: Yeah, the other point \--

DR. LIPICKY: Orthotomy (phonetic), it doesn\'t do that.

DR. ARMSTRONG: That\'s the toughest one to get.

ACTING CHAIRMAN BORER: Before you speak, Steve, the other point here is
a point that you made, that you, Steve, made just a few minutes ago. You
know, when I try to interpret these data, one of the confounders in the
interpretation is the fact that there were so many other drugs on board
at the outset of therapy. So that my expectations may be less than they
would be if those confounding drugs weren\'t present.

Now, it\'s difficult to make the interpretation of what the confounding
drug \-- of how the confounding drugs affect the data. We don\'t know.
The studies weren\'t designed to tell us.

If you go back then and look at the sub-analyses that were done, which I
don\'t want to make too much of yet because we\'re going to get back to
them later in a way that perhaps is a little different from the way that
the sponsor presented them, but if you do look at them, if you look at
the sub-analyses and you look at the people who weren\'t on ACE
inhibitors and weren\'t on beta blockers, why, my goodness, the results
come out the way I would have expected them to if this is a drug that
works and does good things.

So, you know, when you take the totality of the little pieces, they seem
all to point in a reasonable direction if one were to conclude that this
drug has a clinical benefit in terms of relief of symptoms and
prevention of hospitalizations.

Steve?

DR. NISSEN: Yeah, two quick points that may make Tom a little more
comfortable. To really look at this fairly, you have to kind of look at
this to some extent as a risk ration. Not everybody has PND, and so you
know, if you reduce from seven percent incidence of PND to five percent
incidence, then as a risk ratio, you know, that\'s a pretty big decline.

Looking at it in terms of absolute numbers for any individual marker is
really not fair to that marker.

And the second point I wanted to make is that what to me really struck
my eye when I reviewed this is that these are not all markers that are
markers of symptoms. Some are markers of symptoms. Some, like left
ventricular diastolic diameter, are markers of anatomy suggesting that
there\'s effect on the anatomic progression of ventricular dilation or
ejection fraction is another measure like that.

And some were measures of physiology, like jugular venous distension and
rales. And so to me it is comforting when I see symptomatic changes,
physiological changes and anatomic changes all going in the same
direction, and so it\'s just reinforcing.

And so, Ray, it does have an impact. Now, how big an impact should \--

DR. LIPICKY: Well, that\'s what we wanted to find out, and since we
failed in answering Question 4 \--

ACTING CHAIRMAN BORER: Yeah.

DR. LIPICKY: \-- because you were supposed to name a number, will you go
back to four and name a number? That is, you would expect that you would
act favorably just based on the primary endpoint on \--

ACTING CHAIRMAN BORER: And you want a p value or a \--

DR. LIPICKY: \-- something that was in the first \--

ACTING CHAIRMAN BORER: \-- hazard ratio or both?

DR. LIPICKY: \-- block, second block, third block or fourth block of
that line. It\'s a visual analogue scale. You can draw the line anywhere
you want to.

DR. HIRSCH: The fact that we\'re spending so much time trying to avoid
the question, I think, gives you part of the answer. Bill you want to
try?

DR. LIPICKY: Well, just, you know, there\'s one, two, three, four \--
four possible places you can put the line. Pick one.

ACTING CHAIRMAN BORER: Alan, would you or, Tom, go ahead.

DR. LIPICKY: Let\'s number them from left to right, one, two, three,
four.

DR. FISHER: Can I make one quick technical comment?

Jeff is right. Hazard ratio is a biological parameter of the population.
P value depends upon the study design and sample size, and so you might
want to separate your lines. I mean this is the values that may go
together with this study, but there\'s no necessary linkage between the
two.

DR. LIPICKY: Yeah, that\'s true, but it makes it \-- we can\'t answer
even this question, let alone making it a little more complicated.

DR. HIRSCH: And these values pertain to populations, and we\'re talking
about individuals here at risk with quality of life questionnaires. So
it really is a mental segue I\'m not really comfortable going down.

DR. LIPICKY: So all we want to know, and it\'s just name and number,
one, two, three, four starting from the left \--

DR. FLEMING: Well, there are two issues here, and in fact, \--

DR. LIPICKY: Well, lower \--

DR. FLEMING: \-- Ray, I tried to \-- I tried to answer this \--

DR. LIPICKY: Yeah, you\'re right.

DR. FLEMING: \-- the first time through when we got to Question 4, and I
stated there are two issues. There\'s the below the line issue, which is
strength of evidence, and there is the above the line issue, which is
magnitude of effect.

DR. LIPICKY: Fine, fine.

DR. FLEMING: An they are both \--

DR. LIPICKY: Fine.

DR. FLEMING: \-- important issues.

DR. LIPICKY: So then if \--

DR. FLEMING: The below the line issue, if we would follow what would be
a traditional guide, and the critical word there is \"guide\" \--

DR. LIPICKY: Right.

DR. FLEMING: \-- any interpretation of a study requires careful
consideration of totality of results, but should be heavily influenced
by strength of evidence on the primary endpoint.

A tradition here would be to call this a single positive study or the
evidence consistent with that, we would typically be using two sided
.05, but remembering here that it\'s really two sided about .02,
reflecting multiple testing and multiple primary endpoints.

Correspondingly, if you said, \"Gee, what would it take in statistical
strength of evidence to equate with two trials, each of which would hit
the standard that I just mentioned?\" it wouldn\'t in this case be
.00125. It would be about less than half that because .00125 doesn\'t
adjust for the two primary endpoints.

DR. LIPICKY: So it would be \--

DR. FLEMING: It would be about .0005, and so \--

DR. LIPICKY: So it would be line segment one.

DR. FLEMING: so the simple answer to your question purely from a
statistical perspective is that this study does hit the strength of
evidence just by a bit, not by a lot, standard for being called a
positive trial, but really doesn\'t come close to what we would have
typically stated was equivalent to the strength of evidence that you
would have from two such positive trials.

DR. LIPICKY: Right. Okay.

DR. FLEMING: Now, the clinical issues are ‑-

DR. LIPICKY: So that\'s a very clear answer, right? So \--

DR. FLEMING: So now you want a number.

DR. LIPICKY: Well, if anyone disagrees with that answer, that would be
important to say, and if no one wants to disagree, that\'s fine, and
then we can go on because that same question comes up. Now you\'ve got
secondary endpoints. What are you going to do?

DR. FLEMING: Okay.

DR. LIPICKY: Okay?

DR. FLEMING: Okay.

DR. LIPICKY: But that will give us a feeling for what you think.
Otherwise you\'re leaving us up in the air.

ACTING CHAIRMAN BORER: Can I just add one comment to Tom\'s comment?
You\'ve asked about the hazard ratio, as well.

DR. LIPICKY: Yes.

ACTING CHAIRMAN BORER: Which is the above-the-line part that Tom spoke
about before, and I would say that it would be very difficult for me to
provide a hazard ratio which is appropriate except in the context of
this trial not to be extrapolated elsewhere because of the issues that
we\'ve already discussed \--

DR. LIPICKY: Fine.

ACTING CHAIRMAN BORER: \-- confounders that we can\'t interpret.

If you want a number, I\'ll tell you a number.

DR. LIPICKY: No, that\'s fine. I think you can ignore, I\'m sorry to
say, what Lloyd said because these hazard ratios were calculated on the
basis of this data. So you could look at this sort of as the same scale,
and what that was really asking for was do you have to have a 20 percent
effect, a ten percent effect, and it gave a feeling for or gave you the
opportunity to say, \"Well, geez, if I saw a ten percent effect and a p
of .05, that would be good enough for me,\" or that you\'d want to vary
that around.

So you can, indeed \-- it\'s too complicated.

ACTING CHAIRMAN BORER: Okay. Then since it\'s too complicated \--

DR. LIPICKY: Yes.

ACTING CHAIRMAN BORER: \-- we\'ll avoid it. Let\'s go on to \--

DR. LIPICKY: Let\'s leave the hazard ratios alone.

ACTING CHAIRMAN BORER: Okay. We\'ll go on to number six, which is the
same question, but about the secondary endpoints.

DR. LIPICKY: But now you have more information. So are you going to
change your mind with respect to how much evidence you need for the
primary strength of evidence? Because that\'s the only guy you can
really calculate a p value for.

ACTING CHAIRMAN BORER: Okay. Tom, do you have any additional comments
about that?

DR. FLEMING: Well, this one is one that I think is intrinsically
difficult to judge. If you have a level of evidence in your primary
endpoint, how much influence do you allow secondary endpoints to play or
to have?

It matters a lot on the relative importance. Lloyd was giving an example
earlier of a secondary endpoint that was mortality and had very strong
mortality trends or mortality influence.

Certainly mortality is a secondary endpoint; if it showed a very
striking result, would appropriately influence your assessment of
strength of evidence overall, and it, of course, could go in either
direction.

A study that looked good on the primary endpoint with every unfavorable
secondary measures could readily move you away from a judgment of
positivity, and the reverse could happen where you didn\'t hit the
primary and you had reinforcement on a secondary measure that was
profoundly established and highly clinically relevant, and that should,
in fact, substantially influence your judgment in a positive direction.

Here I guess what I would throw out is that certainly whereas I had
argued that from a traditional perspective we would call it a positive
study if we had a significance level less than .02 and it was .01, but
not judged as the same strength of evidence as two such studies because
that should have been more on the order of .0005 in this setting, and it
wasn\'t close to that.

The question is: should the results be influenced or should your
interpretation be influenced by secondary measures?

I think we\'re hearing pretty consistently that all of us would say yes.
Where it might be more difficult though is to say how much should it
influence, and where I\'ve been probing with my colleagues is to try to
get a clear sense from them as to whether these secondary measures on
symptomatic effects represent profoundly important changes, moderately
important changes, modest changes, or relatively trivial changes because
that would influence.

I wouldn\'t move greatly on this pathway. I would still be heavily, most
heavily influenced, Ray, by the primary endpoint

DR. LIPICKY: Right.

DR. FLEMING: But I would be somewhat influenced, and how much would
depend on these clinical judgments.

DR. LIPICKY: Well, in part, to take it out of p values \-- let\'s say
that you\'re selling a watch, and you\'re asking \$1 million for the
watch, and someone comes up and says, \"Well, I\'ve got 100,000. Isn\'t
that good enough?\"

Roughly how the primary endpoints line up here, and you look at them and
say, \"Well, you\'re very nice looking and you talk to me nice and
you\'re sweet and so on and so forth, and you have all these other
things that I like about you. Yeah, that\'s enough.\"

And that\'s what we\'re trying to get a feeling for. How are you putting
this together?

ACTING CHAIRMAN BORER: I don\'t think that analogy is apt here, Ray.

DR. LIPICKY: You don\'t think that\'s \--

(Laughter.)

ACTING CHAIRMAN BORER: Because \-- because I think what the \$100,000
person may be offering is a little bit more, and I would infer those \--
or in some cases a little bit less \-- and I would infer that from the
sub-analyses that were done, which we haven\'t gotten up to here yet.

So, you know, I think that once again you have to accept this as
package. It\'s a very complicated package. We\'ve not been asked to
review an application like this before, to the best of my knowledge.

DR. LIPICKY: I understand.

ACTING CHAIRMAN BORER: So we\'re sort of breaking new ground. I think
you have to see it as a package.

Can I ask? We haven\'t heard from \--

DR. LIPICKY: We want \-- we want you to clearly outline how you\'re
thinking.

ACTING CHAIRMAN BORER: And we will.

DR. LIPICKY: Okay.

ACTING CHAIRMAN BORER: We haven\'t heard from Mike Artman down there.
Mike, did you have any thinking about this?

DR. ARTMAN: Just going on with this package issue, I was very impressed,
and Steve alluded to the anatomical and function and physiological
changes, but there were also, I think, some very important biochemical
changes that we really didn\'t focus on that provide me with a great
deal of additional comfort.

The fact that norepinephrine and BNP levels went down in the treatment
group and went the other way in the placebo group I think is a very
positive finding that if I were the sponsor I would have stressed a lot
more.

So, again, taking all of this together, drawing on Ray\'s analogy, yeah,
it\'s 100,000 for the watch, but I\'ll give you another 250,000 for the
band, and you give me a case. There\'s another 300,000, and now we\'re
getting pretty close to the million that you were asking.

ACTING CHAIRMAN BORER: Well, at least we\'ll give it to you in kind.

Dr. Anderson, do you have any thinking about Ray\'s analogy or anything
else with regard to this particular issue?

DR. ANDERSON: Mr. Chair, I read these documents, and I had a great deal
of difficulty trying to sort things out, and particularly in terms of
magnitude, you know, how big should it be and all of that.

In reading the FDA reviews, I notice that there were several statements
which said we could not draw a conclusion or something to that effect,
and I had hoped that in hearing my distinguished colleagues during the
discussion I would be able to sort these out and decide which direction
to move in.

But it seems to me that we\'re all somewhat perplexed by whatever it is
that I can\'t figure out. So right now I don\'t really have anything to
add to the discussion.

ACTING CHAIRMAN BORER: Okay. JoAnn, do you want to weigh in on this
before Steve does?

DR. LINDENFELD: You know, I don\'t think ‑- first of all, I\'m not
buying any watches from Ray.

(Laughter.)

DR. LINDENFELD: I think that the strength, as Michael said, too, I think
that all of the things are going in the right directions. The neural
hormones provides several different areas, neural hormonal findings,
echo findings. Those just give me additional; they add to the strength
of that. Each one I think separately does when you have those three
separate areas.

So I think it adds at least a modest amount to the value of the primary
endpoint.

ACTING CHAIRMAN BORER: Steve.

DR. NISSEN: Ray, I think different secondary endpoints should have
different value, and you know, for me endpoints that relate to how
patients feel are much more important, much more weighty in my view
than, you know, some very contrived line.

And so I think somebody, you know, that can, in fact, walk without
dyspnea, that that ought to have a lot more weight than some finding of
JVD on physical examination.

And so for me, while I was very impressed by the fact that this
constellation was all going in the right direction, the ones that I
think as we consider other applications that ought to have, you know,
some weight in our minds are ones that relate to how patients feel
because that\'s part of our goal as physicians: make people feel better.

And so that did have a lot of weight. Now, how much I think is very hard
for me to tell you out of the context.

And I\'m going to say something equally heretical and say that I view an
active control trial a little bit differently than a placebo controlled
trial in terms of what I\'m looking for in the weight of evidence. You
know \--

DR. LIPICKY: Well, this is placebo controlled. We\'re talking about
Val-HeFT here.

DR. NISSEN: Yeah.

DR. LIPICKY: And we\'re talking about whether you are willing to accept
the primary endpoint ‑-

DR. NISSEN: Sure.

DR. LIPICKY: \-- and make decisions or whether you\'re going to let the
secondary endpoints be the major things that influence your decision.
That\'s what we\'re talking about.

DR. NISSEN: Yeah, I understand, Ray. I guess what I\'m also trying to
say is I think that the judgment that I might offer for a trial like
this, I might not have the same opinion, you know, in another setting.

DR. LIPICKY: Of course.

DR. NISSEN: I can\'t take this out of context very easily.

DR. LIPICKY: Of course.

DR. NISSEN: Yeah.

DR. LIPICKY: But I just want you to say whether you\'re throwing the
primary endpoint away, although it was okay. This was a single trial and
you\'re basing your opinion primarily on the second endpoints which you
admit you don\'t quite know how to evaluate except they look impressive.

DR. NISSEN: Well, I didn\'t say that I don\'t know how to evaluate them.
I mean, I think some people said that, but I guess what I would say is
that we fell somewhere between, you know, what we\'d like to see for two
single trials and we\'d like to see for a single trial, and so \--

DR. LIPICKY: Well, how far between?

DR. NISSEN: Well, we were closer to \-- we were pretty close to what
you\'d want to see for, you know, a single trial.

DR. LIPICKY: No, no, no. No, no, no, no, no.

ACTING CHAIRMAN BORER: That wouldn\'t make it on a single trial.

DR. LIPICKY: Don\'t misunderstand what went on. It was .09. That\'s
nominal p value, but you could double that. So that\'s .02. So what you
want for two trials \--

DR. NISSEN: Okay.

DR. LIPICKY: \-- is half of .00125. So it\'s .0006. So the .09 is one
and a half line segments off.

DR. NISSEN: Okay.

DR. LIPICKY: Okay? So it\'s not close in any sense of the word to
\"close.\"

Yeah, .009.

DR. NISSEN: Oh, oh, nine, yeah.

DR. LIPICKY: It\'s still orders of magnitude off, not a little bit, a
lot.

DR. NISSEN: It\'s off some.

DR. LIPICKY: Some? Ten times.

DR. NISSEN: You know, we could quibble about what word to apply to it. I
guess \--

DR. LIPICKY: It\'s \$100,000 to a million. That\'s how far off it is.

DR. NISSEN: Well, all I can tell you is I guess that I do think that the
secondary endpoints have a considerable weight in my view, and in this
case I think they help me a great deal.

DR. LIPICKY: Okay.

ACTING CHAIRMAN BORER: Okay. Did you have another comment, Ray?

DR. LIPICKY: No, but where are you in the questions?

ACTING CHAIRMAN BORER: We\'re about to hit the meat of the issue here,
but, Paul, did you want to make another point here first?

DR. ARMSTRONG: I just wanted \-- I mean, in my view, the overall effects
on secondary endpoints are modest, and I\'m guided by sort of five
questions. One is the severity of the disease and its impact,
mortality/morbidity in terms of what we\'re dealing with, and there\'s
no argument about this disease.

The second is what other proven therapy exists for that disease, and
there\'s no argument about that, and there\'s also no argument that the
patients that we are looking at were treated arguably suboptimally, and
we can argue why.

The third is the commonality of the problem and its public health
implications, and there\'s no argument about that.

The fourth is the consistency of the effect across subgroups, and there
are some concerns that we\'ve heard here in terms of coming to a
judgment.

And the final issue is the hazard of the therapy and its implications
across subgroups, and we\'ve had some discussion about that.

And so coming back to Ray\'s question, I put my line between ten and 15
percent relative in this disorder, and how close I am to ten as opposed
to how close I am to 15 is modulated by trying to work within that \--

DR. LIPICKY: Is that line segment one, two, three, or four?

DR. ARMSTRONG: It\'s between about .87 and .9.

DR. LIPICKY: I see. Okay.

ACTING CHAIRMAN BORER: We\'re about to enter the phase of dealing with
those issues that Paul just talked about, and I think maybe we can come
back to or maybe even obviate how important or how significant we think
these data are, how important and significant once we go through the
next couple of questions.

Because here\'s where the confounders really hit us. Consider the
effects on mortality and morbidity endpoints by non-randomized use of
ACE inhibitors and beta blockers, and here we have a table. We\'ve seen
these data.

Which of the following hypotheses are these \-- with which of these are
the following hypotheses are these data most consistent?

Number one, valsartan is an effective treatment added to ACE inhibitor
and beta blocker. Can we say that? Can we not?

Valsartan is an effective treatment as an alternative to ACE inhibitor
or beta blocker. Can we say that?

Tom, why don\'t you lead off and then this is something I think we need
some discussion around the table with.

DR. FLEMING: All right. Could I ask the sponsor to put up two slides
that I\'d like to run back and forth very frequently? They\'re the ES-5
and ES-4 slides that I thought provided kind of a good overview of these
issues.

And actually I might fold in a little bit of the Question 8 at the same
time as addressing this.

The first part of seven relates to trying to interpret whether valsartan
is an effective treatment when added to an ACE inhibitor and a beta
blocker, and the second bullet point on eight says \-- refers to the
very small apparent effect in patients taking ACE inhibitors.

This is looking at morbidity. I\'m going to jump back to ES-4 in a
moment, but if we look in those taking ACE inhibitors, which is 92
percent, I think, of the study, we have approximately an eight to ten
percent reduction in morbidity.

Go to E-4, please.

And approximately a seven percent increase in mortality. I look at that
in a very subjective way as in Question 8 where it said the very small
apparent effect. I look at it as actually data suggesting little to any
effect, slight decrease in morbidity, slight increase in mortality.

Go back then to E-5, and now we\'re asking the question about addition
to beta blocker, addition to beta blockers. Here\'s the cohort here of
1,750 people where there is for morbidity actually an estimated increase
in morbidity not significant.

Go to mortality, and it is significant here in mortality, although I use
the word \"significant\" with great caution in subgroups like this, but
at least I wouldn\'t use the words at the end of Question 8 \"the
apparent treatment effect in patients.\" I would actually suggest if
anything there is a slight suggestion of an adverse effect.

Let me go back though to E-5 because I\'d like to really step back and
look at these results more globally. It is relevant. Certainly I think
the sponsor has made a very good case that it\'s relevant for us to give
careful consideration about what is the evidence about effects in the
presence or absence of ACE inhibitors and the presence and absence of
beta blockers.

But it\'s really important to look at these numbers and realize that we
already have to be very cautious about interpreting subgroups. I think
there is \-- we have three to five percent of the patients in the no/no
category and in the no/yes category. In other words, what we\'re saying
is 92 percent of these people are on ACE inhibitors. This study tells us
in my view when we start subdividing into subgroups and then focusing on
subgroups with three to five percent of the study, to my way of thinking
that is really stretching past what these data are able to tell us.

So when I look at these data, I would focus as we have here first on the
global results, but if we\'re going to then start looking at subgroups,
it seems as though the subgroups that we can say something about are
those on ACE inhibitors. It\'s 92 percent, and then subdividing that 92
percent into those that are on ACE inhibitors without beta blockers and
ACE inhibitors \-- excuse me \-- those that are not on beta blockers or
on beta blockers, and these two are essentially the same results. These
are incredibly unreliable.

Essentially as I look at it what we have is the results indicate for
morbidity that we have a slightly positive result, although use of beta
blockers is a qualitative interaction where we would say, yes, there is
a modest benefit when you\'re on ACE inhibitors adding valsartan, but if
you\'re not also on a beta blocker, it\'s a bit more of a benefit, but
it\'s a harmful effect if you are on beta blockers.

Now go to mortality, and the same basic issue. Here you have a slightly
adverse trend when you\'re on ACE inhibitors, and again you have this
qualitative interaction where there\'s a slightly favorable \-- I think
it\'s about a four or five percent decrease and a 40 percent increase,
and then, of course, you see the exact same pattern here when you look
at those that are exclusively on \-- basically those all of whom are on
ACE inhibitors.

These comparisons are 92 percent ACE inhibitors. These are 100 percent
ACE inhibitors, but basically this is the story here. It\'s what is the
effect for people on ACE inhibitors, and when you subdivide those on ACE
inhibitors into non-beta blockers and beta blockers, you see a
qualitative interaction.

My interpretation of these data are if you\'re on ACE inhibitors, which
is 92 percent of the population that are on ACE inhibitors, you have a
modest positive effect on morbidity, a modest adverse effect on
mortality that seems to be a wash.

The data seem to suggest there\'s an effect modifier here. If you\'re on
ACE inhibitors and you\'re not on a beta blocker, then it\'s a slightly
or it\'s a modestly positive effect, but if you\'re on ACE inhibitors
and on beta blockers, it\'s an adverse effect.

If we want to interpret this modestly positive effect, then we have to
accept the negative effect here. I\'d like to understand. Do we have a
theory? Do we have a theory for why it\'s actually harmful to add
valsartan when you\'re on ACE inhibitors with a beta blocker, but it
might be beneficial when you add valsartan when you\'re on ACE
inhibitors, but not a beta blocker?

It may be true, but in my experience in interpreting subgroup analyses,
there has always been an historical caution used in interpreting
subgroups and even greater caution with it\'s a qualitative interaction
rather than a simple quantitative interaction, quantitative interaction
meaning, yes, there\'s effect modification, but it\'s just a matter of
how much benefit by subgroup.

Qualitative means harmful in one group, helpful in the other.

So in summary, there is certainly very interesting and important
evidence here. I find it though very difficult to interpret why it is
that valsartan could be beneficial when you\'re on ACE inhibitors
without beta blockers, but harmful when you\'re on ACE inhibitors with
beta blockers.

ACTING CHAIRMAN BORER: Okay. Why don\'t we go down the table and get
additional comments about this issue? I think this really is going to be
the crux of our final conclusion.

Alan, what would you say about \-- take seven and eight together just as
Tom did if you want to.

DR. HIRSCH: Well, I think Tom said it very well. I also pulled out the
same two slides, and I think I\'ll just take off where he left.

Didn\'t we fail today to really examine this potential paradox? Because
that is most worrisome to me. I\'m concerned with how the implications
of these data would be applied in real life practice where we ten to
maximize treatment, add drug onto drug.

As Ray said earlier, this will be the seventh or eighth drug people are
taking. So I don\'t know. I think to summarize, yes, in addition to an
ACE inhibitor alone there is both a potential slight benefit, but
potential wash. With both an ACE inhibitor and beta blocker together we
have potential net detriment.

ACTING CHAIRMAN BORER: Let me just ask you to comment just a little bit
further.

DR. HIRSCH: Okay.

ACTING CHAIRMAN BORER: Question 7, actually the second is not is it any
good when you add it onto ACE inhibitor. The question is: is it an
effective treatment as an alternative to ACE inhibitor or beta blocker,
a slightly different issue for which we\'ve have to make inferences
based on those small groups as well as the larger subgroups that Tom
pointed out?

And in answering this, you know, I want to mention again that there were
eight different ACE inhibitors used, and the doses varied across the
board, and the targets that Jay talked about that were sort of \-- the
on average were approached are targets of consensus, not of scientific
database because we really don\'t know the dose response of ACE
inhibitors for the hard endpoints for heart failure.

There are some data that suggest the more you give the better off you
are, but we really don\'t have any dose response data. So we don\'t even
know what doses people are supposed to be on. They certainly were on a
whole load of varying doses.

The reason I\'m emphasizing this, again, is that if you want to say that
we can say something from these data about the additivity of ARBs or of
this ARB to an ACE inhibitor, I\'d like to know how we can make that
statement. My opinion is that we can\'t say very much.

The issue, however, of alternatives to ACE inhibitors is something we
need some comment about because that\'s not the way the trial was done,
but we have some data. So \--

DR. HIRSCH: Why don\'t we start the discussion and go around the table?

DR. FLEMING: Okay. I should have \-- what you\'re pointing out, Jeff, is
really there\'s another step I should have taken in my comments.
Basically in my comments what I was referring to were the data that I
believe at least answers the first part. Is an effective treatment when
added to ACE inhibitor?

In my view it\'s very modestly effective in morbidity, but you have a
comparable opposite effect on mortality. So I would say it\'s very
minimal, and when added to a beta blocker, well, certainly it\'s
unfavorable is the estimate.

ACTING CHAIRMAN BORER: With ACE inhibitor and beta blocker, yeah.

DR. FLEMING: Well, unfortunately, the only data I believe we really have
to answer the added to beta blocker with any reliability is in the
context of ACE inhibitors because there are very few people without ACE
inhibitors and without beta blockers.

Is it effective as an alternative to ACE inhibitor? Well, it\'s a great
study. We\'ve got 5,000 people. Now we\'re trying to answer a question
based on 366 if you\'re trying to answer that question. I believe you
need a \-- if you want an answer to that question, you need another
study.

DR. HIRSCH: I would agree with that. The sample size is too small
despite the point estimated and p value to be really confident.

DR. FLEMING: And then is it an effective treatment as an alternative to
a beta blocker? There we have some data. We have the ACE inhibitor, yes,
beta blocker, no, cohort, but here is where I want to probe because if
we accept the favorable pattern there, ACE inhibitor, no beta blocker,
then we should be equally accepting the reliability of the very negative
result in the ACE inhibitor with beta blocker, and I and to know. My
belief is if you\'re going to put stock in subgroups, there needs to be
more than statistical evidence. There needs to be a strong biological
plausibility that backs up the argument for why if you\'re on an ACE
inhibitor and you\'re going to add valsartan when you\'re not on a beta
blocker, that\'s a good thing to do.

But if you\'re going to add valsartan when you\'re on a beta blocker and
an ACE inhibitor, that\'s a bad thing. I don\'t understand the
biological rationale for that qualitative interaction.

ACTING CHAIRMAN BORER: Okay. Why don\'t we come back to try to answer
that? I mean that gets us into the realm of speculation, and there are a
whole load of mechanisms that one might propose to account for a
negative interaction when you have multiple neurohumoral systems blocked
at the same time and then you add a drug that might cause hypotension.

But rather than get into that, let\'s just move on for a moment to the
specific questions here.

Tom gave a very comprehensive answer to what we have. Alan, you were
about to discuss this again.

DR. HIRSCH: If I simply say it at face value to make it simple, let\'s
take both subgroups and take the data as they are without speculating
and assume that there is a potential value in the one and a potential
adverse effect in the other.

ACTING CHAIRMAN BORER: Okay.

DR. HIRSCH: Let\'s go down and see if we see that clinical data the same
along the group, and then I think we should bore into mechanisms a
little bit.

ACTING CHAIRMAN BORER: Okay. Paul.

DR. ARMSTRONG: What\'s the question? Am I answering the first question
here?

ACTING CHAIRMAN BORER: Number seven.

DR. ARMSTRONG: Right.

ACTING CHAIRMAN BORER: Valsartan is an effective treatment added to ACE
inhibitor and beta blocker. Valsartan \--

DR. ARMSTRONG: I don\'t know about the alternative, and maybe it looks
like a wash to me relative to the first, and I would be interested in
knowing if we\'re into subgroups whether the characteristics of these
subgroups are similar or different.

ACTING CHAIRMAN BORER: Okay.

DR. LINDENFELD: I can\'t add to that, except I agree with Paul. We
haven\'t seen a lot of that data to evaluate these subgroups, but I
would be hesitant to do it.

The only data \-- we have previous data that suggest that an angiotensin
receptor blocker would be valuable in the absence of other drugs, and
that strengthens a little bit this subgroup, but the subgroup not on any
other therapy is so small that it makes me very hesitant to interpret
it.

ACTING CHAIRMAN BORER: Can I just point out, I mean, just so that we
keep it in mind because Paul has made a very important point here, and
we\'re going to get to it again in Question 8?

The composition of the subgroups is something that is very important to
know. However, the trial is a trial. These are the people we got. This
is it. So we\'re going to have to extrapolate as best we can from, you
know, an overwhelming group of Caucasians with a very small group of
blacks, and on and on.

We can try and get down to smaller and smaller subgroups, but then
we\'re getting into potential hot water here. I think we\'re going to
have to take the population here as it was given to us and extrapolate
as best we can.

Steve.

DR. NISSEN: I think we\'re trying to tease too much out of the subgroups
here, and I\'m really concerned that we can easily make a mistake by
doing so, and you know, it\'s hard to interpret data, you know, with a
few hundred patients in a subgroup either way, either affirmatively or
negatively.

And so my view is that we have to take the trial as its entirety and say
that in a group of patients and maybe even describe this; and in a group
of patients, 93 percent of whom or 92 percent of whom were receiving ACE
inhibitors, valsartan produced the following benefits.

And I am unwilling to make any strong commitment based upon subgroups in
a single trial, and I think that this is hypothesis generating for those
subgroups, but it is not proof, and therefore, we ought to be very
careful here and fairly narrow in our interpretation.

ACTING CHAIRMAN BORER: Mike, what about the safety issue here? I mean,
you know, a negative result we generally don\'t require the same degree
of statistical certainty when we see detriment, and we\'ve seen
detriment here. Mike, can you talk about that a little bit?

(Pause.)

ACTING CHAIRMAN BORER: Or talk about whatever you\'d like to talk about.

(Laughter.)

DR. ARTMAN: No, let\'s get back to Ray\'s watch.

No. Yeah, I am concerned about the addition of valsartan to a patient
who\'s on a beta blocker, right. And I agree with everything that\'s
been said coming down the table, and I\'m not sure I can shine any more
light on this, and to try and make some judgments on these subgroup
analyses I agree is very difficult, and we\'re in treacherous waters.

But I think that the data are sufficient to make me very concerned about
using this in a patient who is on a beta blocker, and so I think that
that part of the question I feel we can\'t answer.

ACTING CHAIRMAN BORER: JoAnn?

DR. LINDENFELD: I just think, again, this is a difficult subject, but
whether or not we like the subgroups or want to evaluate them, if we
choose to recommend this drug for the treatment of heart failure, we\'re
recommending a group of people of whom probably 25 percent it was
estimated, maybe a little less than that, but at least 25 percent are
taking beta blockers.

And I think we\'re going to have to give people, practicing physicians,
some idea of what we think after reviewing this data. I don\'t think we
can just let that drop and say we can\'t evaluate it. That has to be
part of this process.

ACTING CHAIRMAN BORER: What would your opinion be?

DR. LINDENFELD: Well, I\'m very hesitant when I don\'t know. I\'m
concerned. There\'s a very modest benefit to this drug, and there\'s
some potential for harm in at least a quarter of the patients that
we\'re recommending it for, and that makes me feel less positive about
the very small benefit that we\'ve seen.

ACTING CHAIRMAN BORER: Steve?

DR. NISSEN: There is an alternative. There\'s a way around this, and the
way around this is in the context of giving an indication for the drug
doesn\'t preclude adding to the label a warning that in a single trial
the combination of the three drugs together in a small number of
patients showed an adverse outcome, and so you\'ve told the prescribing
physician that the drug has an effect in a broad population, but there
was a subgroup.

And that protects, I think, the patient, the physician, the agency. It
protects everybody until further data can be collected to confirm or
refute this notion that triple drug therapy is bad, and that would be to
me a logical approach.

ACTING CHAIRMAN BORER: Alan?

DR. HIRSCH: Can I take this? Because I was going to take you to task, my
distinguished friend on the left, Dr. Nissen, because your usual
carrying forth that we should not look at subgroups I usually respect,
but I\'ve been so quiet all day, troubled by the subgroup data.

And this is not a small subgroup. I mean, 1,600 \--

DR. FLEMING: Right.

DR. HIRSCH: \-- more or less patients \--

DR. FLEMING: Steve, I wanted to follow up on that, too.

DR. HIRSCH: \-- is significant.

DR. FLEMING: Yeah, it\'s 1,750 in what you\'ve called a small subgroup.
Can we flash up \--

DR. HIRSCH: I\'m not quite done.

DR. FLEMING: Go ahead. Go ahead.

DR. HIRSCH: The subgroup actually following Dr. Cohn\'s line of
reasoning is actually hopefully going to be growing as we become more
aggressive in the treatment of heart failure, and we don\'t quite know
yet what\'s down the pike in terms of the ability to both educate
physicians. Other trial data will have us adding more and more agents.

So when I look at this historically, actually the first signal where we
finally hit sort of a wall potentially, we should as an agency or as an
advisory panel be concerned.

So just that point has been made. I feel very strongly we have to
respect that subgroup at least as a \-- more than a hypothesis
generation, but as a time right now to educate physicians to be careful.

ACTING CHAIRMAN BORER: Tom, did you have an additional point you wanted
to make?

DR. FLEMING: That was well spoken. I was just going to, if we could put
up for a moment again E-4 and E-5.

Just to reiterate, here is where the data are. The data are in these
three groups, and, Steve, this is a \-- I mean, your comments are wise,
causing us \-- encouraging us to be cautious about subgroups. I consider
that a very wise comment.

At the same time, there is something here that\'s not easy to walk away
from, and I would concur with the sponsor raising this issue as one that
has to be seriously considered. I look at the analysis in these three
groups as where the essence of the information is, and if we look at
this as essentially what we know the most about, basically, Steve, it\'s
the global analysis. It\'s a very slight harmful effect in ACE
inhibitors.

If we went to E-5, it would be a very slight positive effect on
morbidity as you see right there.

When we subdivide into these subgroups, there is a fair amount of data
in these subgroups. It leads me to state or leads me to conclude that
adding it to an ACE inhibitor is a very, very negligible situation.
Adding it to a beta blocker, if I believe the qualitative interaction,
is a bad thing to do.

And I keep putting forward to my colleagues here if we look at this and
go to E-4 as well, go to E-4 as well, we see this exact same qualitative
interaction.

And if we\'re going to believe that we should market it in these people
who are on ACE inhibitors without beta blockers, I want to understand
why we think that when you\'re adding it to the ACE inhibitor and the
beta blocker it\'s bad. I need to understand the biology for why you\'ve
got an ACE inhibitor on board.

If you\'re going to add valsartan without a beta blocker, that\'s a good
thing. With a beta blocker, it\'s a bad thing. And if I can\'t believe
that, then I really want to go back to Steve\'s wisdom and say really
the only thing I can take home from this is the overall ACE inhibitor
result, and I will say I want two studies then. If you really want to
understand the answer to the question do you provide it when you\'re not
giving an ACE inhibitor, we need a trial, and those aren\'t ACE
inhibitors.

We only have 300 patients of that type, and if you really want to know
if it\'s an agent that could be given as Jay Cohn suggested, I believe,
in his presentation, for people who are not able to take a beta blocker,
I don\'t know that that\'s this group. I just know that this is a group
that wasn\'t on beta blockers. I don\'t know that this was a group that
couldn\'t take beta blockers.

DR. HIRSCH: So to accentuate that further, the bet blocker treatment
almost looks like a light switch in terms of benefit on and off.

DR. FLEMING: Yes.

DR. HIRSCH: And it is hypothesis generating. So I hate to do this. We
usually just talk amongst ourselves, but I have to ask that question,
which is: did we see or did I miss in the application something that
I\'m suspicious as a clinician, which is as I add agents, I often face
the wall of hypotension. Did we see blood pressure trends across these
beta blocker, ACE inhibitor plus/minus treatment groups to say whether
we had finally unloaded the patient so much?

Did we see it or did we ask for it?

DR. COHN: We can show you that.

DR. HIRSCH: Please. Well, then if it doesn\'t, it will get them to stop
worrying, and we can move on to the next subject.

DR. COHN: WE have the blood pressure.

DR. HIRSCH: You must have looked at it.

DR. COHN: Oh, yeah.

DR. HIRSCH: Or some other hormonal paradox.

DR. COHN: All the explanations. Obviously Tom is raising the biological
issue, and I think it\'s wonderful to have a biostatistician want to
raise a biological issue.

These are the blood pressure changes in the four main subgroups and then
the four combined subgroups, and you\'ll notice that there\'s no trend
for more blood pressure reduction.

DR. HIRSCH: Thank you.

DR. COHN: Now, these are the last blood pressures recorded in those
patients before they died or the trial ended. They aren\'t measured
contiguous with their death, of course, but there didn\'t seem to be any
striking trend that there was a greater blood pressure reduction in the
combined therapy.

ACTING CHAIRMAN BORER: There is something there though, Jay. I mean,
it\'s flashing at me too quickly to do a calculation, but the bottom
group actually has the lowest blood pressure to start with, and then
you\'re lowering it about the same as everybody else is being lowered.

So, in fact, it may be that the final absolute blood pressure you reach
is a little bit lower with yes/yes than with the other groups.

DR. COHN: It could be. It\'s a pretty small difference I would agree
with you in the valsartan group. In the placebo group, the baseline was
not lower. In the valsartan group it was on the combined drug.

So, you know, you get into small numbers here. If I could just make a
couple of comments on what\'s going on, and I don\'t really want to
intervene myself, I share with all of you the concern about what to do.
The reason we\'ve brought this forward is because of the safety issue on
the mortality in the combined treatment group. ACE is yet, beta yeses.

I don\'t quite share Tom\'s view that there\'s a mortality adverse
effect. The confidence intervals overlap one, and it could just as well
be that there\'s a mortality benefit.

I also think despite the small numbers that there is some virtue in
looking at the combined drugs, not just the first stage because it
clearly, both by secondary analyses that Al showed you and by everything
else that we have, the quality of life and the ejection fraction, et
cetera, there seems to be a difference whether you\'re on a beta blocker
alone or on a beta blocker with an ACE inhibitor.

And I think that that makes a big difference, and from a mechanistic
standpoint, I do believe it is multiple drugs, and we now have evidence
from other trials that have recently been completed. We\'ve demonstrated
that if you lower plasma norepinephrine, pharmacologically with a
central inhibitor of the sympathetic nervous system, you get an adverse
effect on mortality.

We\'ve demonstrated that when you block endothelia these days in a most
recent study or you block cytokines in patients treated with all of
these other drugs, you seem to see no benefit and, in fact, a trend for
an adverse effect.

So we may be getting to the point where there\'s too many systems being
blocked. So biologically I don\'t have a lot of trouble with this, Tom,
although I can\'t cite you a mechanism, but it just is intuitive, and I
think Alan has sort of said the same thing, and so has Steve.

You know, it\'s just a little too much blockade, and I don\'t know
whether it\'s working through blood pressure or through conduction or
through something else, but it puts the patient at some risk
potentially.

Now, we\'re going to have more data from the CHARM trial. We\'re also
going to have a lot of data from the VALIANT study post MI on the
combination of all three drugs: beta blockers, ACE inhibitors, and ARB.

Those trials are ongoing. The data safety and monitoring boards for
those trials are very aware of our data. We keep shipping them updated
data. So they\'re watching it, and they have chosen not to stop the arm
with ARBs added to ACEs and beta blockers.

So we\'re going to have a lot more data, and I think we\'re at the
cutting edge of this now. How do we respond to this subgroup in terms of
labeling of the drug?

Now, let me just say one more thing about the p value for the whole
study, and this is to help Ray a little bit. One of the reasons \-- I
always like to help Ray.

DR. LIPICKY: Thank you.

DR. COHN: One of the reasons that the company did the exercise study was
that they were told that would be a second trial, and if that had been
positive, .05, that would have been the second trial, and all this trial
had to do was achieve .025 or .020, whatever it was.

That trial was not positive, as you\'ve seen. It was a wash. Now, I
appreciate the committee telling Ray whether a positive exercise test on
a 12 week study of .05 would have been more valuable to you than all of
the secondary endpoint significance that we\'ve shown on ECHO and LV
dimension and quality of life and signs and symptoms and neural
hormones. Is that more valuable or less valuable than a 12 week exercise
test would have done for the statistics?

Because from the regulatory standpoint, that study, .05 on exercise,
would have meant it brought us home free.

ACTING CHAIRMAN BORER: Before we try to answer that question, let\'s try
to answer the questions we have. I\'d like to give my opinion before we
move on to number eight here about number seven.

It\'s just a little bit different from what you\'ve heard, and I throw
it out to everybody for whatever it\'s worth. As I suggested earlier, I
think safety issues have to be dealt with a little bit differently than
efficacy issues. We may demand a great deal of strength of evidence, a
great strength of evidence to conclude that efficacy exists, but I
don\'t think we need quite so much evidence to suggest or to conclude
that there\'s real potential for a safety problem if you see some data
that suggests that.

And I think we see it, but I don\'t see it, though the statistical gods
may kill me. I don\'t see it as a beta blocker issue. Virtually
everybody who is on beta blocker was on ACE inhibitor. That\'s more than
1,600 people, and it\'s the combination of the beta blocker and the ACE
inhibitor that was associated with the bad outcomes on all the things we
looked at, and that was consistent across the board, even with the
secondary endpoints and the tertiary endpoints, you know, everything
that was looked at here.

So my concern, and I don\'t have any problem. You know, I would echo
what Jay says. I don\'t know what the mechanism is, but I could argue in
favor of one.

I think that we have to make a comment about the inappropriateness at
this moment of adding valsartan to a combination of beta blocker and ACE
inhibitor. When I look at the very small subgroups that you couldn\'t
draw any conclusions from on their own and see that the people who were
on beta blocker alone, small subgroup though it may be, look different,
and that is intuitively not unreasonable to me.

Then I\'m less concerned about adding an ARB, valsartan specifically, to
people who are on beta blocker alone. When I look at the ACE inhibitor
data, however, I fall right on the line with Tom. It looks to me no
matter how you slice it that there\'s a little bit of a benefit when
you\'re on ACE inhibitors alone, but I don\'t know what dose, and I
don\'t know what drug.

In terms of morbidity and equally a little bit of a detriment in terms
of mortality, and I would caution people about the addition of valsartan
to an ACE inhibitor. I can\'t say it\'s bad. Overall it looks like it\'s
a little bit good, maybe, but I don\'t know the dose. I don\'t know the
drug. I don\'t know the combination that\'s appropriate.

Since I don\'t know, I\'d like to say I don\'t know, but I wouldn\'t
want to proscribe doing it because I just don\'t know enough, and when
you look at the totality of the data, in fact, it looks like there\'s a
benefit.

And I want to make another editorial comment here. I\'ve been looking at
NDAs now off and on for 24 years, and I made this comment at a
conference once when I was sitting in the audience, and the response I
got I\'ll tell you in a moment.

But I don\'t know how any drug works. I know the pharmacological effects
that are associated with a lot of drugs. I have no idea what the
mechanism of action is, that is, how the drug produces its clinical
benefit.

And I can cite chapter and verse of disapprovals based on the lack of a
putative mechanism of action for drugs that we now know have exactly the
same effects as other drugs that subsequently have been approved and now
we think we know the mechanism of action.

So I look at the data first. When I said that at a meeting in which I
was sitting at the back of the room, I said that to somebody else
sitting back there, and he said, \"Well, gee, you\'d better start
reading the journals and reading the textbooks.\"

And I said, \"You\'d better hope that\'s not true because,\" I said,
\"I\'m the committee that approved these.\"

But I think we have to look at the data first, and to me the data show
for the population that was studied a clinical benefit, not all of the
clinical benefits that we would have liked to have seen. When I look at
the subgroups, I see what intuitively I would have expected, that in the
subgroups that didn\'t get the other drugs, the benefits that I would
have expected with at least one of the other drugs is there.

So I don\'t have a problem with concluding that this drug does something
good, but I\'d sure as heck not want to give it to people for whom I
have a strong signal that I\'m going to hurt them when I do it, and that
to me is the group that\'s taking this combination of beta blockers and
ACE inhibitors.

So that\'s my opinion. Put it in the hopper, and we\'ll move on to
Question No. 8.

Evaluate the following findings with respect to whether they are
considerations related to approval or to labeling, the lack of apparent
treatment effect in blacks, the very small apparent treatment effect in
patients taking ACE inhibitors, which we just now talked about, lack of
apparent treatment effect in patients taking beta blockers we talked
about.

I think the one issue to deal with here is the lack of apparent
treatment effect in blacks. We\'ve mentioned it. Is there anything more
that we want to say about it? Is it something that should be highlighted
in some way if we were to give labeling advice to the FDA?

Paul.

DR. ARMSTRONG: Jeff, having picked up on that this morning, I would just
say that looking at the data that has been presented that wasn\'t in our
briefing book, there are really four factors, I think. One is the
mortality. The other is the morbidity, both of which go the wrong way.

Then there\'s the BNP, which goes the wrong way, and then we\'ve learned
that there\'s also the safety that goes the wrong way.

So there\'s a quartet of factors that for me are concerning,
notwithstanding the fact that we\'re talking about seven percent of the
population or about 360 patients.

ACTING CHAIRMAN BORER: Do we know at all \-- and, again, I don\'t want
to get into sub-sub-sub-sub-analyses, but do we have a gestalt of how
what other drugs the black people were taking? I mean, were a lot of
them taking the combination of beta blocker and ACE inhibitor, for
example? Do we know at all?

MR. MacNAB: No, they were very similar. There were, I think, to some
degree fewer beta blockers. The black patients were a little younger,
but, again, in a small group with a wide confidence interval it\'s hard
to make definitive conclusions.

ACTING CHAIRMAN BORER: Okay. So Paul has verbalized a concern, a real
concern, that maybe has to be highlighted as we move forward. Would
anybody disagree with that?

No. Okay. Let\'s go on to number nine then. Has adequately information
\--

DR. LIPICKY: Hold it \--

ACTING CHAIRMAN BORER: \-- to describe instructions \--

DR. LIPICKY: Wait, Jeff. You\'re skipping a couple of things. That said
is that \-- is you statement that conclusion with respect to provability
or labeling? Because \--

ACTING CHAIRMAN BORER: Oh.

DR. LIPICKY: \-- you made it sound like, of course, it\'s just a
labeling issue, and it doesn\'t influence my approvability conclusion

ACTING CHAIRMAN BORER: Well, we haven\'t talked about approval yet.

DR. LIPICKY: Well, it says how do you evaluate this.

ACTING CHAIRMAN BORER: Oh, I\'m sorry.

DR. LIPICKY: Approvability or labeling?

ACTING CHAIRMAN BORER: No, you\'re quite right. You\'re quite right.
Okay.

DR. LIPICKY: Because up until now it\'s been approvability, and this
starts to get mixed now.

ACTING CHAIRMAN BORER: To me it\'s a labeling issue. I\'d like to hear
what everybody on the committee has to say.

Go ahead.

DR. HIRSCH: Labeling.

ACTING CHAIRMAN BORER: Paul?

DR. ARMSTRONG: I\'m okay with that.

DR. LINDENFELD: I think it\'s an isolated thing. It\'s a labeling issue,
but with a number of other subgroups we have questions about, it might
be an approval issue.

DR. NISSEN: Label.

DR. ARTMAN: It\'s a labeling issue.

ACTING CHAIRMAN BORER: Glorea?

DR. ANDERSON: I think it\'s a labeling issue, but I also have some
concerns because, one, the population was small and, two, I couldn\'t
find enough information to answer some questions that I had.

And incidentally, I had the same question about the size of the
population of women who were included in the study, 20 percent, I think
it is, about 20 percent.

ACTING CHAIRMAN BORER: Tom?

DR. FLEMING: I have nothing to add.

ACTING CHAIRMAN BORER: Okay. Now, let\'s go on to number nine. Has
adequate information been obtained to describe instructions for the use
of valsartan in heart failure?

Would anybody like to give an answer and then we\'ll see if there\'s a
lot of dissent?

DR. FLEMING: Can I have a clarification? Does this include if in
Question 10 it\'s the perspective of some committee members that one
needs to take into consideration whether one is on ACE inhibitors or
beta blockers or whether they\'re contraindicated, is that part of \--
for example, what we don\'t know, I would argue, is what\'s the level of
effect of valsartan in someone on ACE inhibitors where beta blockers are
medically contraindicated.

ACTING CHAIRMAN BORER: Yeah. You know, I don\'t want to answer for the
FDA, and I think we\'ll have an answer from the FDA in a second if I say
something incorrect, but at the end of a development program, there are
many questions that are left unanswered, and if we have enough
information to provide instructions for use, which also can provide
instructions about what we don\'t know so that you ought to be very
cautious and maybe not even do it until more information is available.
We can do that.

We can provide a very directive or the FDA can provide a very directive
label. It can say you should only do this in this situation.

We don\'t know anything about this. This is a potential show stopper.
Don\'t do it till we have more information.

So I think that the question has adequate information been obtained is a
question about how well we believe we could describe to a physician how
the drug could be used effectively and acceptably safely today.

That may exclude a lot of groups. It may exclude a lot of drugs. It may
do this. It may not.

DR. FLEMING: Then my sense \--

DR. LIPICKY: And it includes those.

DR. FLEMING: \-- is there are some additional sources of information,
but I\'d like to clarify my answer to that after I answer Question 10.

ACTING CHAIRMAN BORER: Ray, do you want to add to what I or to \--

DR. LIPICKY: No.

ACTING CHAIRMAN BORER: \-- refute what I said?

DR. LIPICKY: No, but it includes dose. And I\'ll take just a minute, and
I know you\'re in a hurry and want to get done in ten minutes.

ACTING CHAIRMAN BORER: No, no. We\'ll give you a few more minutes.

DR. LIPICKY: But, you know, the sense of these questions as they have
evolved up until now was we want to know whether you think a single
trial gets approval and whether it gets approval on the basis of its
primary endpoints or its secondary endpoints or a combination of the
two, and whether you think the subgroups that are here are adequately
enough portrayed that they cause concern.

So it\'s possible that the overall trial result might be weakly
positive. Let me put it that way. Okay? And that the subgroup business
makes you worried about not knowing who to give it to, in which case you
wouldn\'t care about Question 8 because you don\'t have enough \-- you
know, the dose and stuff like that doesn\'t matter or, conversely, that
the principal \-- the primary endpoint is so convincing on its own that
it absolutely has to be approved for that, and that the rest of this is
all just window dressing then.

Okay? So we\'re \-- it\'s sort of been graded through this whole
business of what is most important and what is next most important and
trying to get a sense of what you think. I\'m not sure I did, but I
don\'t know why I said this.

Forgive me.

ACTING CHAIRMAN BORER: Okay. Let\'s move on then to Question 10, which
we\'ll have to take in parts. And for this we\'ll need a vote from
everybody, I think.

Should valsartan be approved for use in treatment of patients with
chronic congestive heart failure, and if so, what should labeling say
about these various things?

Let\'s start with the global issue because if the answer were no, then
we have nothing else to add to talk about.

Should valsartan be ap-proved for use in the treatment of patients with
chronic congestive heart failure? Let\'s start at the far end of the
table. Glorea, why don\'t you go ahead?

DR. ANDERSON: I would disagree at this point based on the fact that I
don\'t think we have enough information. At least I don\'t.

ACTING CHAIRMAN BORER: That\'s a no.

DR. ANDERSON: No.

ACTING CHAIRMAN BORER: Mike.

DR. ARTMAN: I would say yes.

ACTING CHAIRMAN BORER: Steve.

DR. NISSEN: Yes.

ACTING CHAIRMAN BORER: JoAnn?

DR. LINDENFELD: I would say no. I think that the endpoint here doesn\'t
meet the level of statistical significance that we want, and it\'s a
modest improvement, and then we have major questions about subgroups and
who to treat.

ACTING CHAIRMAN BORER: Paul?

DR. ARMSTRONG: Overall, no, but I think there\'s a niche.

ACTING CHAIRMAN BORER: I\'m sorry?

DR. ARMSTRONG: Overall the answer is no, but I want to come back to
potential subgroup.

ACTING CHAIRMAN BORER: Well, okay, but we can\'t. If the vote is no,
then it\'s \--

DR. ARMSTRONG: All right.

ACTING CHAIRMAN BORER: Okay. Alan?

DR. HIRSCH: I was going to say yes.

ACTING CHAIRMAN BORER: tom?

DR. FLEMING: I actually had a similar response to Paul. It\'s a no, but
it\'s a qualified, and I will make very clear what that qualification is
before we finish answering these questions.

ACTING CHAIRMAN BORER: Okay. I\'d vote yes.

How does that come out?

Okay. Now we have to get some qualifications because it\'s four to four.
Tom, why don\'t you start with your qualifications?

DR. FLEMING: Well, let me \--

DR. LIPICKY: You\'ve helped us a lot.

DR. FLEMING: \-- comment on a couple of things that \--

(Laughter.)

ACTING CHAIRMAN BORER: Be could stay until tomorrow.

DR. FLEMING: Let me comment on a couple of things, and actually the
qualifications relate to the specifics in 10-1 and 10-2, but there\'s
one or two comments I haven\'t given yet, and one of them relates to
just interpreting these data first on the primary endpoints. The first
is mortality.

I believe the study is, in fact, more reliable on its primary endpoint
than might have been apparent in the sponsor\'s presentation of the
study. Looking at mortality first, it was pointed out that the
anticipated death rate was 12 percent. It was only observed to be nine
percent, and that may have left the study under powered for mortality.

And in the presentation it was mentioned there was no demonstrable
effect on mortality, which suggests that maybe there is an effect, but
we just didn\'t demonstrate it.

The study was targeted for a 20 percent reduction in mortality. It seems
to me in the context of other agents that are out there, such as beta
blockers and ACE inhibitors that provide more than that level of effect,
I think it was reasonable to have targeted that level as what was
clinically relevant.

The study did achieve essentially 1,000 deaths, which by my calculation
is a high power for detecting that 20 percent reduction, even with the
adjustment for the two primary endpoints. The estimate is a two percent
increase in mortality where the lower limit of the confidence limit is
.9, meaning it rules out half the level. These data are inconsistent
with even as much as half the level of mortality effect that the study
was powered to detect, half the level, less than half the level of
effect that we would know we can achieve with other agents.

So my sense is this was an excellent study in many ways, and certainly
one of those ways was in providing us a very good sense about the effect
of mortality. I believe these data are not only not significant. I
believe these data are suggestive of no effect and ruling out anything
more than a modest effect on mortality.

We\'ve already discussed at greater length the morbidity endpoint. As I
see it for 100 percent, what we\'re doing is we\'re presenting ten
hospitalizations over a two year period per 100 people.

We\'re also, as was corrected, we\'re preventing one day or reducing one
day hospitalization per year. That is a modest \-- that is a moderate,
modest, whatever adjective, benefit you want to put on, you want to
acknowledge, and the effects on symptoms and the Minnesota living with
heart failure are reinforcing, although I\'m still struggling with how
strongly because I\'m still struggling with getting a sense of how
strong those data are.

Having said all of that then, if we look at the data where we have
information, where do we have information? We have a lot of data in the
global analysis, and like Steve says, I look at that first and foremost,
and when I look at that, I see one study that does meet standards for
strength of evidence for a positive trial. I\'m really reluctant to call
it though the level of evidence that would be similar to what we would
have from two independent studies, each of which would meet that
standard for positivity.

It was possible that we could have met that standard if we had had,
rather than modest, if we had had moderate effects on morbidity. This
was a very large trial that would have been powered to achieve that
level of effect.

Now, what adds a lot of complication here is the sponsor\'s
acknowledgement that there is evidence here about potential effect
modification. As I look at it, the data on effect modification are
specific to refining the question about what it means to add valsartan
to ACE inhibitors because we don\'t have data of any substance in people
that aren\'t on ACE inhibitors.

So in that context, when you\'re adding to an ACE inhibitor, what we
have is, as has been mentioned many times, a modest positive effect on
morbidity, but a comparable modest negative effect on mortality. The
mortality confidence interval, as Jay points out, includes equality, but
so does the morbidity confidence interval include equality.

What we\'re left with then is this complex issue about whether there is
an effect modifier such that it\'s a good thing to be on an ACE
inhibitor; it\'s a good thing to be on an ACE inhibitor and beta
blocker, but in the former case, it\'s good to add valsartan to the ACE
inhibitor. In the latter case it\'s bad to add valsartan to the ACE
inhibitor and beta blocker.

I don\'t understand that. I don\'t understand that. If the FDA
understands that, then I would argue approval in the context of patients
who are on ACE inhibitors and not beta blockers, if the FDA understands
the mechanism for that interaction.

I would argue that, in essence, coming back to where I left unanswered
in Question 8 and Question 9, what would I like to know that I don\'t
know. What I really would like to know is what is the role of this agent
in the setting in which ACE inhibitors are contraindicated. There\'s too
little data to answer that here. It\'s a subset analysis, much worse.

Secondly, what I don\'t really know \-- I\'ve got clues, but I don\'t
really know \-- is what is the effect of adding valsartan to an ACE
inhibitor when a beta blocker is medically contraindicated. I don\'t
know. That is also unknown, and that could be addressed in a second
supportive trial if, in fact, the FDA remains as uncertain as I am as to
what\'s causing this critical effect modification.

So in summary, my sense is clearly in the answers, in my view, the
answer is this an alternative to an ACE inhibitor, is this an
alternative to a beta blocker, I think the sponsor answered that
question. It\'s not an alternative we would wish to give beta blockers
and we would wish to give ACE inhibitors in settings in which they\'re
not medically contradicted. So the question is: are these agents that
would be given in second line?

And my belief is there is additional data that\'s necessary, but bottom
line is if there is a clear understanding or a reasonable understanding
of this critical effect modification issue, then I would be more
positively persuaded toward an approval for the setting in which
somebody is on an ACE inhibitor, but beta blockers are medically
contraindicated.

ACTING CHAIRMAN BORER: Okay. Does anybody have any others? Paul.

DR. ARMSTRONG: Well, I guess I should justify my vote, and it\'s the
concern about safety and the uncertainty about questions, questions in
15 percent of the population over 75 in which we have no information,
concerns about the spironolactone story, uncertainty about the effect in
patients on digoxin, clear concerns about the beta blocker issue.

And so notwithstanding the fact that I believe this drug has an effect,
as a clinician trying to inform others as to how to use it with the
evidence available, I wouldn\'t know what to say, Mr. Chairman.

ACTING CHAIRMAN BORER: Steve?

DR. NISSEN: Yeah. I hear everything that, you know, the folks saying no
are saying, and I understand your convictions, and I appreciate them
very much. Let me just think out loud with you a little bit about this
because we\'re obviously on the fence here.

We all look at a trial, I think. The most compelling data is obviously
the data that relates to the primary prespecified endpoint of the trial,
and I want to point out to the committee that this sponsor and these
trial investigators set an extremely high bar for themselves. They took
a bunch of patients that were very well treated with dig., diuretics,
90-plus percent getting ACE inhibitors, a lot getting beta blockers.

These are much better treated patients than the average heart failure
patient in America or anywhere else is treated, and they said, \"Would
adding valsartan to a group of very well treated patients do anything?\"

What did it do? Well, for one of the two primary prespecified endpoints
at a p value of .009, not .00125 \--

DR. LIPICKY: Oh, oh, two.

DR. NISSEN: Yeah, okay. Okay, all right. Again, we can \--

DR. LIPICKY: \-- oh, two.

DR. NISSEN: Okay, but at a level of significance we can argue about and
supported by a whole constellation of symptomatic, functional,
structural, and biochemical endpoints.

And so, you know, looking at this on balance and trying to decide, you
know, whether there\'s more harm or good here, you know, I think you
have this trial as to live or die by that primary endpoint, and I am
influenced by the fact that this endpoint was obtained in a setting of
extremely well treated patients.

Now, the big problem is we\'ve got this subgroup. I don\'t even know if
it was a prespecified subgroup. Maybe it was; maybe it wasn\'t, where
something fell out that we didn\'t like, and I do think we have an
ethical duty to make sure people are informed about that.

And I, therefore, think that there is a compromise position here, which
is to come up with some labeling that suggests that this agent may be
useful because I happen to think it\'s a good thing to prevent PND,
dyspnea on exertion, and hospitalization.

But to provide very clear warnings that triple drug therapy was
associated with increased mortality and morbidity, and let the
prescribing physician then make a judgment about that. I personally
don\'t want to give this drug in triple drug therapy, but I think I
might well add it to patients, particularly those who are still quite
symptomatic, who have heart failure and are on ACE inhibitors.

And one more point I want to make is that if you look carefully, the
worst the heart failure was, the more the efficacy signal was in this
trial, and that to me suggests to me that if I have a patient that has
fairly severe heart failure symptoms and is not adequately managed, you
know, with current therapy, that I could add valsartan and get
additional benefits, and I think that\'s the take-home message of the
trial.

We need the warning in there, but I think the efficacy convinced me.

ACTING CHAIRMAN BORER: Tom and Alan, both have comments.

DR. FLEMING: I just wanted to query Steve about his thoughts. You had
mentioned at the beginning of your comments, Steve, that the sponsor
basically sat a very high bar, a high standard, and you explained that
in the context of having tried to show that there was additional benefit
to adding valsartan in the context of patients who are already well
treated with ACE inhibitors, beta blockers, et cetera.

I would argue the challenge for any of us as sponsors and investigators
is to address the efficacy and safety of our intervention in the real
world context of how they would be delivered.

Are you arguing that there was a lot more ACE inhibitor and beta blocker
use in this trial than there should be in the real world, and as a
result we were assessing this in a setting in which there was too high a
goal to hit?

DR. NISSEN: No, I guess, Tom, what I\'m suggesting is that it\'s very
challenging to show efficacy on top of good therapy, and therefore, I
give some significant sort of weight to the significance of those p
values when I understand the context in which the therapy \--

DR. FLEMING: But I would agree with you that in many instances in
clinical practice it is harder to incrementally improve upon clinical
practice when that clinical practice has already reached an effective
level of benefit.

But nevertheless, the reality is fortunately we are in a setting now
where we have these effective agents, and so the real question is: can
we improve on what we already are able to accomplish with those agents?

I thought this was a very good study that was actually answering the
question that was in need of being answered.

ACTING CHAIRMAN BORER: Before we go on to Alan and to Ray, let me \--
you wanted a biological discussion, Tom, and let me suggest my thinking
about this trial, this development program, and this drug.

I must tell you I think it took extraordinary courage for a drug
manufacturer to take an angiotensin receptor blocker and study it for
this indication with these kinds of a priori projections of effect in
people who are being treated with a drug that affects exactly the same
neural hormonal system. I never would have expected that an angiotensin
receptor blocker would have any particular effect on top of an ACE
inhibitor.

It might. You know, you saw Jay\'s slide with the putative mechanism by
which maybe you could get some effect, but I wouldn\'t have expected
much.

You know, I think because of analogy with results with angiotensin
receptor blockers and ACE inhibitors in other settings that one might be
a replacement for the other. I don\'t know that for sure in this setting
because, of course, that\'s not the way the trial was set up, although
if you look at the subanalyses, they\'re consistent with the hypothesis
I\'m suggesting now, that one can be a substitute for the other.

It would have been extraordinary to me that adding a drug of this
particular class on top of the drugs that were being used that you would
see a tremendous additional beneficial effect.

Nonetheless, I\'m impressed that we saw something. We actually saw a
reduction in morbidity, and again, I don\'t want to make too much of
small group analyses and all of this kind of stuff, but starting with
the hypothesis with which I began, that is, how this drug works
pharmacologically, what system it\'s affecting, I would have expected
there wouldn\'t have been much of an effect in the group as a whole, but
if you looked at the subgroup that wasn\'t getting the other drugs, you
would have seen an effect, and, lo and behalf, we did.

So let me just finish. You know, what I saw is what I would have
expected to see. It obviously isn\'t what the sponsor expected to see,
but it\'s what I would have expected.

I think that when you ask how could it be that it\'s beneficial when
added to an ACE inhibitor and not to an ACE inhibitor with a beta
blocker or whatever, I would suggest that a lot of people who are on ACE
inhibitors were on relatively low doses of ACE inhibitors where addition
of the ACE inhibitor could have given you additional benefit just as
addition of the ARB might have given you additional benefit.

So I have no trouble with seeing how there could have been some people
in the group that drove the group as a whole to show an additional
benefit when valsartan was added to ACE inhibitor.

I still don\'t think, given to maximally tolerable doses, whatever those
may be, that you would see such an effect. That\'s my bias. I don\'t
know if it\'s true or not. Maybe the data could be plumbed to see if
there\'s a cut point in the doses of drugs that were used to see whether
the addition of the ARB was better with the higher dose or the lower
dose or if there was any difference at all.

But that would be my bias. When you add the two drugs together, the ACE
inhibitor and the beta blocker and, thus, block a great deal of the
neural humoral activity, I could then see how adding another drug could
cause a problem.

So this doesn\'t seem intuitively unreasonable to me.

To talk about giving the drug to people who medically can\'t take a beta
blocker for whatever reason, the largest subgroups of those patients are
people with pulmonary disease and a smaller subgroup with diabetes who
can\'t be easily controlled on a beta blocker. It\'s not for cardiac
problems.

So you know, that issue of people who medically cannot \-- for whom beta
blockers are medically contraindicated seems to me to be a side issue.
If it were people for whom ACE inhibitors were medically
contraindicated, that might be another issue, but even there Steve said
it before. The primary reason why people don\'t get ACE inhibitors when
we think that, in general, with their disease pattern they should is
that they cough, and it\'s annoying to them. So they don\'t get it, and
then they\'re left with nothing except a beta blocker alone if we use
the current algorithm for treatment.

I\'m looking at these data and saying that valsartan represents a
reasonable drug to give to those people. Now, was a study done to test
that hypothesis? No, but the data from the study that was done are
completely consistent with what I\'m suggesting.

Now, that may not be a sufficient basis for approving a drug, but
that\'s the way I would look at these data.

DR. FLEMING: Jeff, I think there\'s a critical distinction to be made in
what you\'re saying. I\'m quite sure I heard you say that you
interpreted these data to be suggestive that you could give this agent
instead of another neural hormonal inhibitor.

ACTING CHAIRMAN BORER: Well \--

DR. FLEMING: And the data are suggestive that you would achieve a
comparable effect. I think these data tell us essentially nothing about
that question.

What these data are telling us is in the absence of these other neural
hormonal inhibitors, there\'s evidence of some benefit, but there\'s
nothing to say that that level of benefit matches what you would have
gotten if you had randomized those patients against the beta blocker or
the ACE inhibitor.

In fact, I think there\'s strong evidence to suggest that if you did
randomize these patients to an ACE inhibitor or beta blocker, the ACE
inhibitor and beta blockers would substantially improve survival, and
valsartan wouldn\'t affect survival because that\'s what these data are
showing.

These data are though showing that you\'re affecting \--

ACTING CHAIRMAN BORER: I don\'t think so.

DR. FLEMING: Let me finish. Let me finish. These \--

DR. COHN: These drugs were given on top of those drugs. The only group
where they weren\'t on the top showed a benefit on mortality. So you
can\'t say that.

DR. FLEMING: Well, that\'s correct. These data were given for the most
part on top of ACE inhibitors, but specifically what we\'re seeing here
is evidence that is suggesting that the use of this agent on top of an
ACE inhibitor is essentially not impacting overall survival.

Now, there\'s nothing in these data that would argue that if you added
the beta blocker on top of this ACE inhibitor that it also wouldn\'t
impact overall survival, and the bottom line point that I\'m making is
that the evidence that\'s more favorable here for the effects of
valsartan are in individuals who aren\'t as heavily exposed to the beta
blocker or the ACE inhibitor.

But that doesn\'t tell us anything about whether if we did a randomized
head-to-head trial of valsartan against those other agents that we would
expect comparable results.

ACTING CHAIRMAN BORER: Well, okay. Ray?

DR. LIPICKY: Were you going to? Go ahead. Finish your thought.

ACTING CHAIRMAN BORER: No, I was going to suggest I know that Ray and
Alan both have comments here, but I think we\'ve just discussed a number
of these secondary points that you\'ve made. I was going to begin to ask
if there were any other \-- any change in position or change in vote
because if there isn\'t, I think we\'ve answered the questions, but
there are other comments here.

I mean, Alan, did you have something you wanted to \--

DR. HIRSCH: I had a long comment, but I think ultimately it comes down
to one sentence. There was biologic efficacy that can benefit patients,
but labeling is critical.

ACTING CHAIRMAN BORER: Okay. Ray.

DR. LIPICKY: But, see, we\'re fine. I think you have answered all of the
questions in that we know where things are, and we\'re about as equally
divided in the division as you guys were in your conclusions. So that\'s
fine. We understand that.

But there are two things I wanted to say before we quit. One is that if
the task is hard, that doesn\'t mean the standards for whether or not
you found something have to be relaxed. I think those two things have to
be disconnected and also part and parcel of the same thing, and it\'s
not clear they were; and also part and parcel of the same thing is that
a study may, indeed, find a treatment effect. That doesn\'t mean it has
to be approved. Okay?

The level of evidence, how well you believe that the trial results as a
whole are applicable to a general population are, indeed, something that
is critical, and so something may well say it very well looks like you
have a treatment effect, and as a single trial, I\'ll buy you do.

That\'s just not good enough, and I don\'t think you thought that
through well enough, but that\'s okay. All right?

ACTING CHAIRMAN BORER: Can I \--

DR. LIPICKY: And then the last thing along those same lines was Jay\'s
comment, and you know, I can\'t remember the valsartan congestive heart
failure discussions, and I never bothered looking up the minutes, but
you\'re probably right that the mistake that was made that you cited was
made was stupid, wasn\'t it?

You know, I think that that was a bad bargain. I think if that\'s the
advice you got, we gave bad advice because to equate the business of
exercise tolerance and morbid mortal, and to consider them to be equal
with respect to coming up with two positive trials is a stupid bargain
retrospectively and over the course of years.

But I know we have done that. Okay? I\'m not denying that. I just say
that\'s very bad advice. It got you into the pickle you\'re in, and I\'m
sorry.

Pardon? Well, I understand, but we \-- you know, I\'ll just acknowledge
if that advice was what we gave and the program was developed on that
basis, that\'s partly our fault.

DR. FLEMING: But, Ray, it\'s not entirely clear to me why you\'re as
apologetic as you are. Let me see if I understand.

Basically what you\'re acknowledging is that you have a study here with
a very large sample size and duration of follow-up to tell us something
extremely important about morbidity and mortality primary endpoints and
about secondary measures.

DR. LIPICKY: Right.

DR. FLEMING: And you were looking for, in a sense, some independent,
confirmatory evidence, and you chose exercise tolerance.

DR. LIPICKY: Well, that \--

DR. FLEMING: Let me go on.

And you\'re apologizing for having identified exercise tolerance as in
any way a relevant supportive measure that should be weighed in this
decision.

And yet the question that I would be uncertain about is at the same time
what we\'re saying today is but some secondary measure, such as the
Minnesota living with heart failure measure, dyspnea, and fatigue were,
in essence, being asked do those things, in fact, elevate this to the
same strength of evidence as two positive trials, and I just wonder a
little bit in retrospect.

Those were positive and exercise tolerance was negative. Would we be
having this discussion if ‑-

DR. LIPICKY: Well \--

DR. FLEMING: \-- exercise tolerance was positive and those were
negative.

DR. LIPICKY: I hear you, except, you know, if it had been a positive
trial, there would probably have been \-- if it had an effect on
exercise tolerance, it would probably have been a somewhat difference
discussion, but I guess what I was really saying in shorthand was if a
morbid mortal trial were being done, we should have argued as opposed to
doing hemodynamics and neural humors and all that and an exercise
tolerance trial, which delays things before you get the other trial
started because those precede, cost money to do them; we should have
argued do a real morbid-mortal trial. It will have another dose in there
at least or double the power, and don\'t give me this p of .05 for a
morbid-mortal trial because you get into trouble every time.

MR. MacNAB: I think the discussion we had ‑-

ACTING CHAIRMAN BORER: Well, wait, wait. Let us finish here first.

MR. MacNAB: I\'m sorry. I just want to ‑-

ACTING CHAIRMAN BORER: No, it\'s \--

DR. LIPICKY: It\'s all right. He can argue.

ACTING CHAIRMAN BORER: Well, let\'s \--

MR. MacNAB: I don\'t want to argue.

ACTING CHAIRMAN BORER: No, he wants to support you.

Can I ask, Ray? I mean, we\'ve come down four to four, and we\'ve
answered all of the subissues as best we can, and there\'s obviously
some \--

DR. LIPICKY: You\'re fine.

ACTING CHAIRMAN BORER: \-- concern because we lack knowledge here and we
lack information, but it seems to me we have some responsibility to
provide a statement about what additional information we would expect.

DR. LIPICKY: No.

ACTING CHAIRMAN BORER: Do you want us to say anything about that?

DR. LIPICKY: No, I don\'t think you have that responsibility.

ACTING CHAIRMAN BORER: Okay.

DR. LIPICKY: I think the way I take what the discussion has said is that
as a whole there\'s a divided bottom line, that on a whole there is a
divided way of how you look at this and what you regard as being good
stuff and what you regard as being bad stuff, and that that basically
will give us in the division a reasonable amount of latitude with
respect to what people will be able to say and what they send to Dr.
Temple, and will give Dr. Temple all of the ability to exercise his
judgment.

ACTING CHAIRMAN BORER: Okay. Okay.

DR. LIPICKY: So it\'s just fine. I mean, I think you did the right
thing, and it\'s how it came out. It\'s a tough problem.

ACTING CHAIRMAN BORER: Any other comments?

MR. MacNAB: Really, just to this whole issue about what was agreed to
because I want the record to be straight, if you really go back to some
time in 1996 the discussion about what had to be done was about as
complex as the discussion that we\'ve had today because it talked about
many things, not just two trials. Totality of data, mortality, other
endpoints; so I think in fairness to everyone we shouldn\'t have an
impression that there was some disagreement or a mistake or you gave us
the wrong advice.

DR. LIPICKY: Somebody screwed up.

MR. MacNAB: I think if you go back and look at that, we talked a great
deal about totality of data.

DR. NISSEN: I just wanted to say one more thing, Ray. The reason I made
the comment about the high bar is that if they had treated these
patients the way contemporary \--

DR. LIPICKY: They should have changed the standard. That\'s the only
thing.

DR. NISSEN: No, no, no. Just hear me out for a second. If you take a
group of patients \-- if they had taken a group of patients 50 percent
of whom were on ACE inhibitors \--

DR. LIPICKY: I understand, but that shouldn\'t allow you to accept
\$100,000 for a million dollar watch.

DR. NISSEN: All right.

DR. LIPICKY: Okay?

ACTING CHAIRMAN BORER: Okay, but we don\'t all agree it\'s only
\$100,000, but it doesn\'t matter.

We\'ve given you the best that we can do, which is a resounding 50 to
50.

Are there any other comments from the committee? If not, we\'ll conclude
the meeting.

(Whereupon, at 3:22 p.m., the Advisory Committee meeting was concluded.)