1
DEPARTMENT OF HEALTH AND HUMAN
SERVICES
FOOD AND DRUG
ADMINISTRATION
CENTER FOR DRUG EVALUATION AND
RESEARCH
ADVISORY COMMITTEE FOR PHARMACEUTICAL
SCIENCE
CDER Advisory Committee
Conference Room
2
PARTICIPANTS
Arthur H. Kibbe, Ph.D., Chair
Hilda F. Scharen, M.S., Executive Secretary
MEMBERS
Patrick P. DeLuca, Ph.D.
Paul H. Fackler, Ph.D.
Meryl H. Karol, Ph.D.
Melvin V. Koch, Ph.D.
Michael S. Korczynski, Ph.D.
Marvin C. Meyer, Ph.D.
Gerald P. Migliaccio,
Ph.D. (Industry
Representative)
Kenneth
R. Morris, Ph.D.
Cynthia
R.D. Selassie, Ph.D.
Nozer
Singpurwalla, Ph.D.
Marc Swadener, Ed.D.
(Consumer Representative)
Jurgen Venitz, M.D., Ph.D.
SPECIAL GOVERNMENT EMPLOYEES SPEAKERS
Judy Boehlert, Ph.D.
Gordon Amidon, Ph.D., M.A.
FDA Staff
Gary Buehler, R.Ph.
Lucinda Buhse, Ph.D.
Jon Clark, M.S.
Jerry Collins, Ph.D.
Joseph Contrera, Ph.D.
Ajaz Hussain, Ph.D.
Monsoor Khan, R.Ph., Ph.D.
Steven Kozlowski, M.D.
Vincent Lee, Ph.D.
Qian Li, Ph.D.
Robert Lionberger, Ph.D.
Robert O'Neill, Ph.D.
Amy Rosenberg, M.D.
John Simmons, Ph.D.
Keith Webber, Ph.D.
Helen Winkle
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C O N T E N T S
PAGE
Call
to Order
Arthur Kibbe, Ph.D. 5
Conflict of Interest Statement
Hilda Scharen 5
Introduction to Meeting
Helen Winkle 8
Subcommittee Reports - Manufacturing Subcommittee
Judy Boehlert, Ph.D. 26
Parametric Tolerance Interval Test for
Dose
Content Uniformity 53
Critical Path Initiative
Topic Introduction and OPS Perspective
Ajaz Hussain, Ph.D., 64
Research Opportunities and Strategic
Direction
Keith Webber, Ph.D. 105
Informatics and Computational Safety
Analysis Staff
Joseph Contrera, Ph.D. 117
Office of New Drug Chemistry
John Simmons, Ph.D. 165
Open Public Hearing
Saul Shiffman, Ph.D. 192
Critical Path Initiative--Continued
Office of Generic Drugs
Office of Biotechnology
Products--Current
Research and Future Plans
Amy Rosenberg, M.D. 248
Steven Kozlowski, M.D. 282
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C O N T E N T S (Continued)
PAGE
Office of Testing and Research--Current
Research and Future Plans
Jerry Collins, Ph.D. 316
Lucinda Buhse, Ph.D. 338
Mansoor Khan, R.Ph., Ph.D. 362
Wrap-up and Integration
Jerry Collins, Ph.D. 410
Challenges and Implications
Vincent Lee, Ph.D. 419
Committee Discussion and
Recommendations 428
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P R O C E E D I N G S
Call to Order
CHAIRMAN
KIBBE: Ladies and
gentlemen--welcome. I want to take a little page
from the coach at the New York Times, who
says that
a meeting that starts that
starts at five minutes before. And to get us
rolling in about 30 seconds, ahead of
time.
Do we know--
[Comment off mike.]
--he'll be here tomorrow. All right.
So--Dr. Amidon, my co-pilot here, will be
here
tomorrow.
I'd like to call you all to
order for my
last go-round as Chairman of this August
body. And
the first order of business, of course,
is to read
about all of our conflicts.
Conflict of Interest
Statement
MS. SCHAREN: Good morning.
The following announcement
addresses the
issue of conflict of interest with
respect to this
meeting, and is made a part of the record
to
6
preclude even the appearance of such.
Based on the agenda, it has
been
determined that the topics of today's
meeting are
issues of broad applicability, and there
are no
products being approved. Unlike issues before a
committee in which a particular product
is
discussed, issues of broader
applicability involve
many industrial sponsors and academic
institutions.
All special government employees have
been screened
for their financial interests as they may
apply to
the general topics at hand.
To determine if any conflict of
interest
existed, the Agency has reviewed the
agenda and all
relevant financial interests reported by
the
meeting participants. The Food and Drug
Administration has granted general
matters waivers
to the special government employees
participating
in the meeting who require waiver under
Title 18,
A copy of the waiver statements
may be
obtained by submitting a written request
to the
Agency's Freedom of Information Office,
Room 12A30
7
of the
Because general topics impact
so many
entities, it is not practical to recite
all
potential conflicts of interest as they
may apply
to each member, consultant and guest
speaker. FDA
acknowledges that there may be potential
conflicts
of interest, but because of the general
nature of
the discussions before the committee,
these
potential conflicts are mitigated.
With respect to FDA's invited
industry
representative, we would like to
disclosed that
Paul Fackler and Mr. Gerald Migliaccio
are
participating in this meeting as a
non-voting
industry representative, acting on behalf
of
regulated industry.
Dr. Fackler's and Mr.
Migliaccio's role on
this committee is to represent industry
interest in
general, and not any one particular
company. Dr.
Fackler is employed by Teva
Pharmaceuticals,
Incorporated.
In the event that the
discussions involve
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any other products or firms not already
on the
agenda for which FDA participants have a
financial
interest, the participants' involvement
and their
exclusion will be noted for the record.
With respect to all other
participants we
ask, in the interest of fairness, that
they address
any current or previous financial
involvement with
any firm whose products they may wish to
comment
upon.
Thank you.
CHAIRMAN KIBBE: Thank you.
And now we'll hear from the
Director of
the Office of Pharmaceutical Sciences,
Ms. Helen
Winkler.
Introduction to Meeting
MS. WINKLE: Good morning, everyone.
All right, I want to welcome
everybody
this morning to the Advisory Committee
for
Pharmaceutical Science. This is, I think, a very
important meeting, and I"m really
looking forward
to the discussion. But before we get there, I want
to welcome all of the members. We have one new
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prospective member, Carol Gloff--Dr.
Gloff--has
joined us. And we have two other prospective
members who we're having a little
complication with
in
getting on board. So we're working on
that.
We also will have a number of
SGE's here
today; Dr. Boehlert, Dr. Amidon and
several others
who are going to participate with us in a
number of
things.
So I want to welcome everybody.
I also want to thank Dr.
Kibbe. This is
his last time as Chair. It will break all of our
hearts to see Dr. Kibbe go out of this
position.
He has been very, very enthusiastic as
the Chair of
this committee, and I think all of us
have enjoyed
working with him. But he's not to go very far.
We've already told him that we anticipate
him
coming back to a number of meetings and
helping us
with some of the discussion in the future. So we
really want to, again, thank him for all
he's done.
Dr. Cooney--Charles Cooney--has
agreed to
be the chair of the committee for the
next two
years.
Unfortunately, Dr. Cooney couldn't be
here--after he accepted, he couldn't be
here today.
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But he will be here at the next
meeting. So--he's
been very gracious to accept this
position. He and
I have talked at length about some of the
issues we
want to cover on the Advisory Committee,
and he's
very enthusiastic about moving ahead for
the future
of the committee.
The agenda for the meeting
today: there's
a number of things we want to take
up. I'm going
to talk a little bit about next
year--2005 being, I
guess, this fiscal year--and some of the
things
that we plan to take up with the Advisory
Committee, where we're going in OPS, just
to give
the committee a little feel about some of
the
things that we're looking at.
I also want to give a
quick--and I mean a
quick--update of the cGMP Initiative for
the 21
st
Century.
We're also going to have an update on a
number of the subcommittee and working
groups. Dr.
Boehlert is going to talk about the
Manufacturing
Subcommittee meeting that we had several
months
back.
It was a very, very--we accomplished a lot,
I think.
It was a very good meeting. And
Judy can
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fill us in on some of the highlights of
that
meeting.
Also Bob O'Neill is going to talk about
the Working Group with IPAC RS, and some
of the
accomplishments--or the focus that we've
had in
that Working Group.
We're also going to talk about
the
Critical Path Initiative. And I think this is a
really important discussion that we can
have with
the committee today. Critical Path is, of course,
one of the main initiatives in the agency
now, and
what we would like to talk about with the
committee
is give you some idea of our thoughts, as
far as
Critical Path; some of the things that
we're doing
in the Critical Path Initiative, in the
office of
Pharmaceutical Science in the various product
areas, and get some input from you as to
what
direction we need to go; if there's other
things we
need to be thinking about; and if there's
other
types of topics that we need to be taking
up, we'd
like to do that.
Dr. Woodcock talked about the Critical
Path Initiative when she introduced it,
saying that
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FDA was really in the best position to
identify
those areas, or those gaps, in drug
development,
and to work with others--collaborate--on
how we
could get the data necessary to fill
those gaps.
So this is really what we're
looking for
doing under the Critical Path Initiative.
And we
need to be certain that we are
identifying the gaps
correctly, and that we are able to do the
types of
research that needs to be done to fill
those gaps.
Of course we can't do everything, so I
think some
of what we want to talk about and think
about, too,
is how we can prioritize some of that
research.
Tomorrow, we're going to talk
about
manufacturing, and moving toward the
desired state.
As I said, we had a very productive meeting
of the
Manufacturing Subcommittee. A number of things
were identified at that meeting that we
need to
discuss further; that we needed to look
at and
determine how we're going to do it. A number of
questions that we need to answer--and
we're looking
at possibly having a subgroup to do some
of that--a
fact-finding group. So Judy will talk to that.
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But there are a number of
things, too,
that we want to talk about with the
committee
today; a number of--the gaps that we
recognize that
we have in OPS and the agency, in moving
toward
that desired state.
So several of us are going to
talk about
those gaps. We're going to talk about the
organizational gaps, the science gaps,
and the
policy gaps--all of which are important
if we in
the agency are going to be prepared as
the
manufacturers and others move toward that
desired
state.
So I think that will be a
really
interesting issue, and I think there are
a number
of things that the committee can help us
with in
identifying how best to address these
answers and
to address the gaps.
We also have a number of
bio-equivalence
issues that we want to discuss. We want to
continue the conversation from the last
Advisory
Committee we had on bio-equivalence. And Dr. Yu
and
some of his staff are going to talk about some
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recommendations from that. And we're also going to
bring up a new topic on gastroenterology
drugs.
So--moving on to OPS in
2005. I think
2004, we had an extremely busy year,
mainly focused
on the GMP Initiative, and all of the
aspects of
that initiative--especially the areas
concerning
manufacturing science and how wee were going
to
really address those issues and concerns,
and how
we were going to incorporate those into
the
regulatory framework.
As we move into 2005, I think
we still
have a lot of issues that we have to
handle under
Pharmaceutical Quality Initiative. We've already
said that that's going to be some of what
we take
up with the Advisory Committee
today. But we
really need to pursue those next
steps. In doing
that, though, we also need to be looking
at
continuing to streamline the review
processes. We
continue to get more and more products in
for
review, and there's got to be some way to
offset
that increasing workload. And streamlining the
review processes seems to be--we're
moving in that
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direction, and it seems to be the answer
to
handling some of the enormous workloads
that we
have.
Also, we need to incorporate
best
practices. We've added the Office of Biotech
Products in the last year. They joined us in
October of 2003, and they have a lot of
practices
in their review that I think can be very
helpful as
we
move forward in looking at ways to improve--both
in out office of New Drug Chemistry, and
our Office
of Generic Drugs.
So we're going to be looking at
incorporating best practices across the
entire
organization.
Supporting the Critical Path
Initiative--I've already brought this
up. It's a
very important part of where we're
going. I think
much of our research is going to be done
there, and
I think we're talking about much more
than
laboratory research. I think there's a
number of
activities that we hope to take on in
2005 where
we're looking at improving on how we do
the
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regulation, and in actually working through the
Critical Path Initiative to get some of
this done.
So we'll talk more about that as we get
into
Critical Path and some of those projects
that we're
looking at doing.
We're looking at further integrating
the
whole Office of Biotech products. There are still
some things that need to be accomplished
there. I
think there are still a number of
questions that
the Advisory Committee can be very
helpful to in
answering. So you will hear more about this in the
next fiscal year.
And, last of all, I think there
still
continues to be a number of regulatory on
follow-on
proteins, as well as a number of general
scientific
issues that we'll want to discuss with
the
committee.
So I think we have a lot on our
plate
during the year, and I look forward to
working
closely with the Advisory Committee in
the next
fiscal year to help us identify some of the--other
things that we need to look at, as well
as help us
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with the issues that we already have
identified
ourselves.
Okay. As I said, I'm going to talk real
quickly about the CGMP Initiative for the
21st
Century.
I think most of you all have probably
read the background material, which
included the
report.
We've actually come to the end of the
first two years of the initiative. And I"d like to
emphasize: I don't think that's the end of the
initiative. I think it's just the beginning. I
think that the initiative helped us
identify a
number of things that we need to be
looking at in
review, that we need to be looking at in
inspection. We still have a lot of changes to
make.
I think we've made a lot of progress--and
I'll talk a little bit about some of that
progress.
But I think we've got a lot more that we
have to
focus on.
So that was only, in my mind,
the first
step.
But I thought it would be
helpful just to
step back real quickly and look at what
the goals
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of the initiative were. Because I think you can't
really appreciate the accomplishments
without
really understanding what the goals were.
So there were basically six major goals.
The first one was to incorporate the most
up-to-date concepts of risk management
and quality
systems approaches; secondly, was to
encourage the
latest scientific advances in pharmaceutical
manufacturing and technology, ensure
submission
review program and the inspection program
operating
in a coordinated in synergistic manner;
apply
regulation and manufacturing standards
consistently; encourage innovation in the
pharmaceutical manufacturing sector; and
use FDA
resources most effectively and
efficiently to
address the most significant health
risks.
And you can see, when you look
back at
these initiatives, the role OPS has had
to play in
all of these goals. I think they're very
important, not only to the agency, but
important to
us at OPS, and important to the industry
and others
involved in the manufacturing of
pharmaceutical
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goods.
So, quickly, through the
accomplishments--again, you can read the
report.
You'll get a lot more out of the
report. But I
just want to emphasize that there was an
awful lot
done in the last two years; a lot that
will affect
how we move forward in the future, in the
21st
century.
So I wanted to highlight those.
The first thing was Part
11. We did a
last in the last two years to clarify the
scope and
application of Part 11. There were quite a few
questions; quite a bit of complication in
implementing Part 11. And I think we've moved
forward in trying to eliminate some of that
complexity and complication. We issued two
guidances during the two-year period that
have
helped in that clarification.
Technical Dispute Resolution
Process--this
was also a very important part of the
initiative.
And it really has had a very positive
effect, I
think, on the industry, and a positive
effect on
how the field has dealt with inspections
and has
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increased the time and effort that the
inspectors
are putting into the inspections, and the
time and
effort that they're spending with
industry when
they go in and do these inspections. And it has
really been the basis of much discussion
in the
inspection process. And the outcome--we have not
had any technical disputes. We have a very good
process--as I said, the process has sort
of set the
framework for opening up the
discussion. And so I
think that it has had a really positive
effect.
I'm actually a co-chair of that group. I
kept
waiting for disputes. I thought we were just going
to have tons of them. We have a pilot program, and
I thought in the 12 months of the pilot
we'd be
able to figure out how best to run the
program.
But not having any disputes, we haven't
learned a
whole lot of lessons.
But, again, it's had its very
positive
effects.
So I think that it has really been useful
under the initiative.
The GMP warning letters--this
was an issue
that was handled very early on. And we
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accomplished the goals that we wanted
under this
particular working group of the
initiative; and
that's that warning letters now are
reviewed by the
Center to ensure--in the Center before
they go out
to the companies--to ensure that they
have adequate
scientific input. Many of the warning letters that
went out in the past were not reviewed to
make sure
that the issues were scientifically
sound. So that
has changed now. And I think that's had a very
positive effect.
International collaboration--I won't
go
into that, but we have spent a lot of
effort in
ICH, and Q8, Q9, and hope to do a lot in
Q10. And
also one of the things we are planning on
doing is
getting more involved with PICS, which is
looking
at inspections on a worldwide basis.
Facilitating
innovation--including doing
standards and policies--we were very
fortunate to
put out a number of different guidances
under this
part of the initiative; the aseptic
processing
guidance--which industry is very familiar
with.
They've been waiting for this guidance
for a long
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time.
And I think it addresses many of the
questions that have been out there in
industry's
mind.
So I think it's a very, very positive part
of the initiative that we were able to
accomplish.
The next guidance that was put
out--I
think many of the people--in fact,
everyone on the
Advisory Committee is very familiar with
this
guidance, because we did have a
subcommittee on the
PAT--the Process Analytical
Technologies--under the
subcommittee, and we were able to put,
under Dr.
Hussain and others in the group, we were
able to
put out a guidance to industry which has
had an
extreme effect, I think, on how industry
and others
are looking at manufacturing in the
future. I
think it's been probably one of the best
parts of
the whole initiative. It really has promoted the
two--the team approach to doing work;
working on
standards. We've worked with ASTM under E55. And
I think, all in all, this has been an extremely
successful initiative under the GMP
initiative.
The last guidance that we've
had, that was
comparability protocol. That guidance is still in
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limbo.
We're trying to make sure that before we
issue the guidance that we're not
increasing the
regulatory burden--which I think many of
us felt
when we read the original draft
guidance. So we're
busily working on that to make sure that
what we
come out of is very beneficial to
industry and to
FDA, and that we don't put any additional
resource
requirements on either part of the
regulatory
system.
Manufacturing science--the
desired state
under !8 of ICH has become a very
important aspect
of where we're driving to. And, of course, we're
going to talk to that tomorrow morning;
continuous
improvement and reduction of variability
have been
an important part of manufacturing
science, and
areas that we need to explore more in the
future,
and assure that we can accomplish that,
especially
being able to open up in the agency and
allow more
continuous improvement for manufacturers.
Product specialists--this
includes
enhancing the interactions between the
field and
the review. We're looking at a team approach, in
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having our reviewers all out on
inspections. And
we're looking at best practices from both
the PAT
team and Team Biologics. I think there's a lot of
best practices there that we can
incorporate in out
thinking in the future on how we handle
review and
inspection.
Integration of approval and
inspection--this is more of that. We have
developed the pharmaceutical
inspectorate, and
we're looking also at changes in
pre-market
approval program.
Quality management
systems--there's a
number of things that we've worked on
here. They
take a number of directions. We've developed a
standard quality systems framework; a
quality
systems guidance. We've worked on GMP
harmonization, analysis process
validation, and
good guidance practices--none of which
are going to
go into in detail, but I think all very
beneficial
to helping us in the future in the
21
st century.
Risk management--risk
management, I had
thought--we did introduce a
site-selection model
25
for inspection under this part of the
initiative.
I believe there's a number of other
things that we,
especially in Review, need to focus on as
far as
risk management, and have a much better
idea of
what the risk of products are, and how
we're going
to mitigate those risks. And I think this is
something that we will bring up in the
future at
the committee.
Team Biologics was to look at a
number of
initiatives that were already underway,
and adopt a
quality systems approach.
And last of all was the
evaluation of the
initiative, which hasn't been completed
yet, but
it's a very important part of what we've
done.
So that, in a nutshell--I mean,
that's a
lot of effort, obviously, that we've
done. And if
you, again, will read the report I think
you'll get
a much better feel. But I felt like, since we've
talked about it so much during the last
few years,
that it was very important to sort of
wrap up what
has happened in the last two years with
this
committee.
26
So that's all I have to talk
today. I'm
going to give it back to Art, and I look
forward to
very lively discussion on a number of
these issues,
and look forward to working with you for
the next
two days.
Thank you.
CHAIRMAN KIBBE: Thank you, Helen.
We now have a report from the
chair of one
of the subcommittees--the Manufacturing
Subcommittee.
Judy?
Subcommittee Reports
Manufacturing
Subcommittee
DR. BOEHLERT: Good morning, ladies and
gentlemen. Before I just get started here--I tried
pressing down, and--aha. I need an SOP for how to
operate the slides.
[Slide.]
It's a pleasure for me to be here this
morning to update you on the
Manufacturing
Subcommittee. We met in July. And I think you'll
find that a lot of the topics we
discussed tie in
27
very well with what Helen was talking
about this
morning, and also with some of the topics
that are
going to be on your agenda.
[Slide.]
We met for two days in
July. Just a brief
overview of the topics that we
discussed: quality
by design--we've heard that this morning;
introduction to Bayesian approaches--and
we'll talk
just a little bit about that; research
and training
needs--the industrialization dimension of
the
Critical Path Initiative--another topic
we heard
about this morning; manufacturing science
and
quality by design as a basis of
risk-based CMC
review; and risk-based CMC review
paradigm.
[Slide.]
On the 21
st:
introduction to
pharmaceutical industry practices
research study; a
pilot model for prioritizing selection of
manufacturing sites for GMP inspection;
cGMPs for
the production of Phase I INDs; and
applying
manufacturing science and knowledge,
regulatory
horizons.
28
What I'm going to do is just go
over,
briefly, some of the topics that were
discussed,
and also the comments that were made by
committee
members.
[Slide.]
Quality by design: topic updates. This
addressed three guidances that should be
coming out
of ICH.
The first of ICH Q8, which is a guidance
on pharmaceutical development section of
the Common
Technical Document. It's going to describe
baseline expectations and optional
information;
requires FDA and industry to think
differently.
Industry needs to be more forthcoming
with
information in their submissions, and FDA
needs to
look at the review process; focuses on
process
understanding and predictive
ability. And if you
really understand your process, you'll
gain
regulatory flexibility. It's a framework for
continuous improvement. And Step 2 is expected in
November this year. That means it will be out for
public review and comment.
[Slide.]
29
ICH Q9 is quality risk
management. It
looks at risk identification--should link
back to
the potential risk to the patient,
because, after
all, that's what's important; risk
assessment--what
can go wrong? What is the likelihood? What are
the consequences?
Risk control--options for
mitigating,
reducing and controlling risks; risk
communication--between decision makers
and other
shareholders. And this may also reach step two in
November of this year, although that was
a bit
questionable.
[Slide.]
And then we're going to talk
about quality
systems needed to recognize the potential
of !8 and
Q9.
And this is ICH Q10: monitor and
evaluate
processes with feedback groups in a
manner to
identify trends and demonstrate control
or the need
for action; manage and rectify
undesirable
occurrences; handle improvements;
management,
implement and monitor change.
This is currently on hold, not
because
30
it's not a good topic, but primarily
because all
the resources that would address Q10 are
tied up
with Q8 and Q9.
[Slide.]
We also talked about the ASTM
E55
Committee. And Helen mentioned that this morning.
Their involved in the development of
standards for
PAT.
And the important things here are consensus
standards, with input from industry,
academia and
regulators. There's an established process, with
an umbrella set of rules. And ASTM is recognized
worldwide.
They have three functional
subcommittees
on management, implementation and
practices and
terminology. But one of the concerns expressed by
the committees is are they going to
duplicate other
initiatives. There area lot of people right now
working on PAT initiatives, and are they
going to
duplicate some of that. So we need to make sure
that everybody gets on the same page.
[Slide.]
All right. Now, this topic I'm going to
31
be reluctant to say a whole lot about,
but we had
an introduction to Bayesian approaches. Dr. Nozer
Singpurwalla was kind enough to give us
an
introduction to the topic. So, Nozer, I apologize
if I mis-speak when I
summarize--[laughs].
You know--so it's with fear and
trepidation--he's threatened us a quiz--
DR. SINGPURWALLA: You've already done it.
DR. BOEHLERT: Yes, I know. [Laughs.]
That's what I was afraid of. But I didn't think I
could leave it out, or you'd get after me
then,
too.
Okay--Reliability for the
Analysis of
Risk." Reliability--the quantification of
uncertainty. And I'm just going to say a few words
here:
utility--costs and rewards that occur as a
consequence of any chosen decision. These are the
things that Nozer talked to us
about--risk
analysis--process assessing reliabilities
and
utilities, including an identification of
consequences. We talked about scales for measuring
uncertainty--for example, probability.
32
[Slide.]
Now this is a quote, so I have
to be
careful here. "When the quantification of
uncertainty is solely based on
probability and its
calculous, the inference is said to be
Bayesian."
I am not a statistician, so I'm certainly
not a
Bayesian statistician. And then there is
discussion of use of Bayesian approaches
for ICH
Q8, Q9, Q10 and the use of prior
information.
[Slide.]
Industrialization--dimension,
the Critical
Path Initiative. We heard about that this morning.
We'll hear about it in the next two days:
examining innovational stagnation. Everybody needs
to take a look at what we've been doing
in the past
and get things moving forward in a new
environment,
with new technologies.
Critical path--has been
inadequate
attention in areas of new or more
efficient
methodologies and development research.
Industrialization--goes from
the physical
design of prototype up to commercial mass
33
production. And Education and research
infrastructure needs improvement. And this
education and research applies to
industry; the
education also applies to the
agency. We all need
to learn how to go forward in the new
environment.
[Slide.]
FDA has a strong interest in
computational
methodologies to support chemistry and
manufacturing control submissions. They're putting
together a chemometrics group. There's a new FDA
research program focusing on
industrialization
dimension. And there's training needs. AS I
mentioned before, particularly with the
pharmaceutical inspectorate. That's started.
There is an inspectorate now of trained
investigators. There need to be more.
[Slide.]
Manufacturing science and
quality by
design--it's a basis for risk-based CMC
review.
Companies share product-process
understanding with
regulators. And this is a new paradigm, if you
will, that companies will share more of
the
34
information that they have available than
they have
in the past.
Specifications should be based on a
mechanistic understanding of the process;
there
should be continuous improvement; and
real time
quality assurance. You shouldn't have to wait
until the end of the process to know that
your
product is okay.
[Slide.]
Science perspective on
manufacturing--define current and the
desired state
and the steps to go from here to there;
define
terms--and this is going to be important
going
forward--things like "manufacturing
science,"
"manufacturing system,"
"manufacturing
capability"--what do they really
mean?
Real case studies will
help. This came up
time and again in the committee discussions. It's
nice to talk about all these theoretical
concepts,
but give me a real case study that I can
look at
and see what it really means.
Testing is mostly non-value
added.
35
Quality by design is the desired state.
[Slide.]
Risk-based CMC review--from the
Office of
New Drugs--should provide regulatory
relief by
incorporating science-based risk assessment;
more
product or process knowledge shared by
the
industry--and I've said this several
times; more
efficient science-based inspections;
focus
resources on critical issues; and
specifications
are based on a risk-based assessment.
[Slide.]
Quality assessment rather than
a chemistry
review--in the past it's been a strict
chemistry
review:
go down the list and check off the boxes;
conducted by inter--and I see some smiles
on the
parts of agency folks--conducted by
interdisciplinary scientists--so it could
be a team
approach.
It should be a risk-based assessment;
focus on critical quality attributes and
their
relevance to safety and efficacy. They have to
rely on the knowledge provided by
applicants. If
industry doesn't submit the information,
the agency
36
has nothing to make their decisions
on. And the
comparability protocols are an important
part of
this review.
[Slide.]
Role of process capability in
setting
specifications will need to be
addressed. Very
often, those kinds of process controls
that you
have may have no clinical relevance. The knowledge
base at the time of submission can be an
issue,
because very often you don't have that
much
information at the time you submit. It's a
learning process as you go through early
marketability and commercial production.
Specifications should not be
used as a
tool to control the manufacturing
process. And we
might need to expand the Quality Overall
Summary
going forward.
[Slide.]
AS I said before, the extent of
product
knowledge is key. Risk-based decisions should be
based on supportive data. Voluntary--all of these
new initiatives are voluntary. And that needs to
37
be made very clear to the industry. These are not
requirements that everybody drop what
they've been
doing in the past and start over with new
approaches--strictly voluntary.
Supplement need is based on the
knowledge
of the risk of the change. And there should be a
clear rationale for the selection of
specifications.
[Slide.]
Identify critical parameters for product
manufacturing and stability; train FDA
staff and
regulated industry--this came up a number
of times.
We all need to learn what the other is
doing;
should give us--industry--greater flexibility
in
optimizing the process; should lessen the
supplement burden, which is good for
industry and
good for the agency. And, once again, real
examples would be an asset.
[Slide.]
In the Office of Generic
Drugs--generic
industry's focus is on producing a
bioequivalent
product.
Often patent issues--to design around.
38
They may not have the flexibility as the
new drug
folks.
Workload in OGD is a significant issue, and
committee members made a number of
comments on this
when they heard how many submissions
there are, and
how far behind they are. We were impressed by the
workload.
Provide advice to industry on
improving
quality of DMFs--those are "drug
master
files"--very important to the
generic
industry--also to the new drugs, but to a
lesser
extent.
[Slide.]
Desired state--include needed
data in a
filing; process and product design;
identify
critical attributes; identify process
critical
control points. And this is the difference from
the past.
Analyze data to produce meaningful
summaries and scientific rationales; and
reviewers
assess the adequacy of the submission by
asking the
right questions.
[Slide.]
Okay--some additional committee
comments
39
that came out of the Day One
discussion: ICH and
ASTM appear to be synergistic, but ICH
needs to be
very aware of the ASTM focus. There was some
concern they might not be tied into
what's going on
there; some concern that FDA, internally,
themselves, may be getting ahead of
what's
happening on an international basis. So
they may be
a little ahead of ICH Q8, Q9 and
Q10. That's not
necessarily a bad thing, by the way.
Need concrete examples--that
came up time
and time again; need to clearly demarcate
"minimum"
and optional information--you know, just
what do
you mean by "this is the minimum you
need," and
just what is "optional"
information? And
"optional" information comes in
degrees. The more
you make the more you know. So you may not have as
much information at submission as you
will down the
road after you've been in commercial
product for a
number of months or years.
[Slide.]
Need to avoid implying there
are two
different quality concepts. We don't want to say
40
that products made in the conventional
way---the
way we've always done it--are different
than
products that may be made according to
some new
paradigms. Bring in new training programs--and
Helen mentioned we're talking about
forming a
working group under the Manufacturing
Subcommittee
to address some of the issues,
particularly case
studies.
We need to find better terms
than
"minimal" and
"optional;" and focus on process
first, and then the tools that we're
going to need.
[Slide.]
We had some reports on an FDA
research
project that's being done by Georgetown
University
and Washington University, and their goal
is to
identify attributes that impact
inspection
outcomes.
They're compiling and linking FDA
databases. They're looking at variables for
product-process, facility, firm and
FDA. Right now
they're collecting data. CDER is just about
completed, and CBER is ongoing--although
by now it
may be even further down the road. This was July.
41
[Slide.]
Focus--are cGMP violations
related to
managerial, organizational and technical
practice?
And then interviewing manufacturers. They have an
internet-based questionnaire that went
out in the
fall of 2003. They're looking at U.S. and European
manufacturers. And their data collection is near
completion.
[Slide.]
There's concern with just
looking at
numbers of deviations or field alerts,
particularly
when investigation may have shown little
cause for
concern.
You can put in a field alert and then
find out later on that--oh--you know, we
figured it
out.
It really wasn't a problem. So if
you just
look at numbers, you get those as well as
the ones
that are true issues.
Also it was pointed out that if
you're a
company with a very detailed SOP you have
a much
bigger chance for deviating from it than
your
company with a really poor SOP that sort
of allows
you to do anything, where you're hardly
ever going
42
to deviate. But who's to say which one is better?
India and China are not include
in the API
manufacturers. And we saw this as a downside to
that survey, because they are major
manufacturers
of APIs.
[Slide.]
We talked then about risk
ranking and
filtering, where risk ranking is a series
of
decisions to start to rank within a class
or across
classes.
Tools may be customized for each
application. And filters may be used to reflect
resource limitations and/or program
goals.
[Slide.]
There's a pilot risk-ranking
model to
prioritize sites for GMP inspections,
using ICH Q9
concepts to define risk; Site Risk
Potential--a new
term for us--SRP--includes product,
process and
facility components.
Look at probability and
severity
components that make up harm; and look at
other
risk-ranking models, for example those
used by EPA
and USDA; and then using the CDER Recall
database.
43
[Slide.]
Comments--from the
committee--focusing on
volume at a site may be misleading
because, in
fact, when you have a high volume your
process may
be better controlled than if you have
small volume.
We need to also consider the
risk of the
loss of availability. If you're a single-source
drug for a life-threatening condition
perhaps that
needs to come into the equation.
Look at "hard to fabricate"
products, or
products with difficulty controlling
uniformity.
Investigator consistency will be--and has
been--an
issue, but with the pharmaceutical
inspectorate
that should be better. And it was suggested by at
least one member that maybe they should
look at
high personnel turnover in a plant,
because that
might be indicative of problems--although
it was
recognized that that might be hard
information to
come by.
[Slide.]
Committee members wanted to
know if the
sites are going to know how they are
ranked. That
44
would be very useful information for
management to
know about. Right now self-inspections are a
critical part of the quality system but
the value
of these would be diminished if that
information
were to become available to FDA. This has been a
longstanding concern of industry. You know, you
don't want to share your self-inspections
because
then they lose their value to you.
[Slide.]
Next talked about GMP guidance
that's
proposed for the production of Phase I
drugs. CMC
review to ensure the identify, strength,
quality
and purity of the investigational drugs
as they
relate to safety. This draft guidance is in
process.
It's a risk-based approach. No
regular
inspection program, but these Phase I
drugs are
looked at on a "for cause"
basis.
I want to point out that it was
noted
during that discussion that for Phase 2
and Phase
3, those drugs still fall under the GMP
regulations--21 C.F.R. 210 and 211.
[Slide.]
45
Also had an update on the PAT
initiative.
As Helen indicated, that guidance was
recently
finalized, in September. It should be expanded to
cover biotech products. And, of course, it
requires continued training of FDA staff.
[Slide.]
We also talked about--we had a
full
agenda--comparability protocol. We had an update
on guidances, The goal is to provide
regulatory
relief for post approval changes. It requires a
detailed plan describing a proposed
change with
tests and studies to be performed,
analytical
procedures to be used, and acceptance criteria
to
demonstrate the lack of adverse effect on
product.
Many comments have been received from the
public.
That was FDA's comment on this. We did not see
those.
But the committee had comments,
as well.
[Slide.]
Single use protocol has limited
utility.
It's more utility if you're going to have
repetitive changes--if you're only going
to do it
46
once it may not help. Specificity of the protocol
may limit repetitive use. Just how much
specificity is needed? And for a well-defined
protocol, an annual report should be
sufficient.
That really will lessen the regulatory
burden.
[Slide.]
Some general conclusions from
our two
days--and we've heard the first one
several
times--general principles are good, but
case
studies are needed to facilitate
understanding.
That came up time and time again.
Case studies
should cover all industries; for example,
dosage
form, API, pioneer and generic.
The committee expressed concern
on what
appears to be understaffing in OGD.
[Slide.]
Failure Mode &Effect
Analysis can be
linked with risk-based decision-making
wherein the
results feed into decision trees;
training and
education of both regulators and the
industry in
the new approaches is going to be key;
historical
inconsistency in regulator findings may
limit the
47
utility of surveys. In the past, you know, not all
investigators have investigated in the
same manner,
so it's difficult to compare results.
And that's the end of my
presentation. I
thank you for your attention, and would
be happy to
address any comments, now or later.
CHAIRMAN KIBBE: Are there any questions
for Judy?
DR. SINGPURWALLA: I have some comments,
but I probably would wait until all the
presentations are over, and then make
comments.
Would that be acceptable?
CHAIRMAN KIBBE: Whichever way you want to
do it, as long as it's within one of the
two tails
of the Bayesian distribution we're all
right.
[Laughter.]
DR. SINGPURWALLA: You are confused, Mr.
Chairman. [Laughs.]
CHAIRMAN KIBBE: On a regular basis.
[Laughter.]
You had a question?
DR. MORRIS: Actually, just one comment to
48
add to what you'd said, Judy, about the
Georgetown
study.
I think they had made sort of a
plea that
the reason that they hadn't been able to
go to the
Indian and Chinese manufacturers was
strictly a
resource issue. It wasn't that they had ignored
that as an area of concern.
DR. BOEHLERT: Ken, thank you for that
clarification.
CHAIRMAN KIBBE: Go ahead.
DR. KOCH: I guess, looking around on the
schedule, I'm not sure if we're going to
talk any
about training. You mentioned it in several
different ways: the continuation, the inclusion of
industry, etcetera. But will that come up as a
discussion topic at some point?
DR. HUSSAIN: Not in this meeting. I
think we will eventually bring that back
at some
other meetings, though.
MS. WINKLE: Actually, when I talk about
some of the organizational gaps I'm going
to bring
up training as part of that gap. So if you want to
49
comment then, it would be fine.
CHAIRMAN KIBBE: Anybody else?
DR. SINGPURWALLA: Well, maybe I'll speak
now.
I just--we--this is a question more to
Ajaz--about case studies and specifics.
We've been through many
sessions of the
Manufacturing Subcommittee meetings. Has there
been any concrete plan made to start
seriously
undertaking some case studies? And, if so, would
you be kind enough to let me know?
DR. HUSSAIN: Yes.
Dr. Boehlert's
presentation to this committee--she's the
chair of
the subcommittee--and the decision was
made to form
a working group under that. And after this meeting
we'll start populating that working group
and
create a working group under that
committee to
start addressing that.
In addition to that, I think
we're also
looking at other parallel tracks to
create case
studies.
One such case study has just started to
take shape, with Ken Morris, and then
Purdue is
working with our reviewers to actually
develop a
50
case study also.
So we hope in the next several
months we
will have examples and case studies to
outline the
framework.
CHAIRMAN KIBBE: Anything else?
DR. SINGPURWALLA: Yeah.
One other
matter.
After the subcommittee meeting, some
minutes were released, and I had made
some comments
about the minutes. I did not receive an update of
the minutes--update of the revision.
Has--is there any reason for
that?
Because the normal protocol--the normal
protocol is
you put out the minutes, people give
comments on
the minutes. You either incorporate those
comments--and if you don't, you let us
know why.
And then you issue a final document of
the minutes.
And then the entire committee, or whoever
it is,
says "Yes, we go along with these
minutes." And
they should become a part of the record.
I was wondering if this was
done, because
I did not have access to that.
51
CHAIRMAN KIBBE: I think the final draft,
or the final copy of the minutes is
posted on the
web page--FDA website--so that after the
draft goes
out to the members of the committee and
the
corrections come back in, they update to
reflect
the suggestions from each of the members,
and then
they post it.
So if you wanted to check the
website you
could see whether--you know, how well
your
suggestions were incorporated in the
final minutes.
DR. BOEHLERT: I would just add, also,
that I reviewed comments that were made
to the
minutes before I made this presentation,
and I
tried to make sure that they were all
incorporated
in what I said today.
DR. SINGPURWALLA: I thought so.
DR. BOEHLERT: If they were not well
reflected in the minutes, they should
have been
reflected in my comments today. So--
DR. SINGPURWALLA: I thought so, but I
wanted to see what the protocol was.
DR. BOEHLERT: Okay.
Thank you. That's
52
fair.
CHAIRMAN KIBBE: Okay?
DR. WEBBER: One quick question.
CHAIRMAN KIBBE: Go ahead.
DR. WEBBER: That will be okay?
You mentioned the
pharmaceutical
manufacture and research study, and I'm
looking at
the dates there. It seemed like it was
fall of
2003.
And I just wanted to confirm whether or
not--that was during the period of
transition of
products from CBER to CDER. Were our products in
OBP--the biotech products that
transitioned
over--were they--are they completed now
within
CDER?
Or are they considered part of the CBER.
DR. BOEHLERT: Yes, I think Ajaz
DR. HUSSAIN: No, Keith, that's
not--that's an external study that's
focusing on
all of manufacturing. So all products--CDER and
CBER--products are under. It doesn't matter
where--
DR. WEBBER: Where they were--just all
products--okay. Thank you.
53
CHAIRMAN KIBBE: Anybody else?
Good, that
will keep us pretty well on schedule.
I have now a "Parametric
Tolerance
Interval Test for Dose-content
Uniformity"--Robert
O'Neill.
Parametric Tolerance Interval
Test for
Dose-content Uniformity
DR. O'NEILL: Magic button.
There we go.
Good morning. I'm Bob O'Neill. I came
before at the last meeting--I was asked
to be the
chair of a working group that you all
blessed, and
I'm here to give you an update on where
we are on
this issue of addressing the
specifications for the
delivered dose--uniformity of inhaled
nasal drug
products.
[Slide.]
Just to refresh your memory,
the folks on
the left-hand side are the FDA folks who
are part
of this working group, and some are more
active
than others--some of them, in blue, are
part of a
sub-group that has been put together that
is
working on more specific issues that I'll
address
54
in a moment; and the folks on the
right--Michael
Golden, in particular, who is a colleague
on the
industry side, who is coordinating our
efforts in
that area.
[Slide.]
The objective of this working
group--as
you probably know--is to develop a
mutually
acceptable standard delivered dose
uniformity
specification--that's both the test and
the
acceptance criteria--for the orally
inhaled nasal
drug products, with a proposal to come
back to you
all.
And that's the time frame that I'm talking
about right now.
So there's been a lot of work
going on in
the past few months, and that's what I
just wanted
to bring you up on.
[Slide.]
There have been three full
working group
meetings, where the folks on that
previous
slide--and some others--have come
together at FDA
for two, three hour sessions, and to go
through
information that has been presented
to--primarily
55
by the industry--to us to chew on. And we have
spent a lot of time internally talking to
ourselves, and coming up with some
additional
issues and proposals, and we met the last
time with
the working group, and FDA had a proposal
that we
felt was moving in the direction of what
everybody
wanted.
Subsequently, there's been a
working group
that will now be chewing on what was
presented to
the last joint meeting, and they're
meeting
November 4
th. And
there's
a lot of statistical
issues; there's data analysis
issues. But I think
what we're all on the same page with
regard to is
that the need to reassess the FDA--the
past FDA
recommendations, and I think there's--as
we
indicated the last time we briefed
you--that the
parametric tolerance interval approach is
an
improvement in a value-added type of
testing
strategy, over and above the zero
tolerance
interval strategy that's been used for
awhile.
So the next steps are the
following.
[Slide.]
56
This working group is
meeting--the
sub-group is meeting in November, and we
hope that
they will then come back to the full
working group
by the end of the year, and we will
evaluated the
iteration between the FDA modification to
the
proposals that have been made by
IPAC--and this has
a lot to do with the placement of the
operating
characteristic curve for the acceptance
criteria.
Essentially, there have been many
operating
characteristic curves that have been
shown to you,
some of which are more steep, some of
which are
more shallow. But where the proposal is being
evaluated right now is: how good is it at getting
from an acceptance or rejection
perspective, those
assays that essentially are off target
mean. You
can look at the performance
characteristic, or an
operating characteristic curve of a
testing
strategy if you assume that it's 100
percent on
target. But the more you move away from
100 percent
on target, the more you look at how well
does it
grab that, and how robust is it to
allowing you to
be a little off 100 percent?
57
[Slide.]
And so we're in the stages of
looking at
the statistical performance
characteristics of
that, and we hope that the working group
will
evaluate this proposal in more detail,
and come
back to you in the spring of 2005, with a
final
recommendation to discuss with you. So that's sort
of the game plan.
And Michael Golden is
here. He's my
colleague on the working group from the
industry
side, and we'd both be willing to take
any
questions if you have them.
CHAIRMAN KIBBE: Questions?
Nozer?
DR. SINGPURWALLA: Well, I guess Jurgen's
hand went up before mine. So--
DR. VENITZ: Okay, let me go first.
DR. SINGPURWALLA: He may ask the same
question.
DR. VENITZ: Maybe.
In your draft proposal--or what
you're
considering so far to be a draft
proposal--
58
DR. O'NEILL: Yes.
DR. VENITZ: --are you considering the
intended use when you look at statistical
characteristics of your operating curve,
for
example?
DR. O'NEILL: Well, certainly that has
been discussed, both from an emergency--a
one-time-only, a chronic use, a medical
risk
involved--
DR. VENITZ: Right.
DR. O'NEILL: --so, certainly, Dr.
Chowdhury is involved, and others are
involved, in
considering this issue. So--
DR. VENITZ: And I would encourage you to
do that because, obviously, in my mind,
it is
different whether you're looking at
inhaled
insulin--
DR. O'NEILL: Right.
DR. VENITZ: --and you're looking at the
performance of a drug product, versus a
beta
agonist, for example.
DR. O'NEILL: Yes.
59
CHAIRMAN KIBBE: Go ahead.
DR. SINGPURWALLA: Dr. O'Neill, we had
this discussion when you made the first
presentation, so I'm going to back--
DR. O'NEILL: Right.
DR. SINGPURWALLA: --to the same point
again.
I agree with you that tolerance
interval
approach is to be preferred to the zero
tolerance,
or something to that effect.
DR. O'NEILL: Right.
DR. SINGPURWALLA: But in your description
of the next steps, you have talked about
operating
characteristic curves, and performance
characteristic curves. Of course those are not
indicative of any Bayesian thinking towards
this
particular area. And while you're in the process
of formulating your plans, I strongly
encourage you
to incorporate that into your
thinking. You may
not want to adopt towards the end, but at
least it
should be evaluated.
And the second comment I'd like
to make is
60
that--and I'm certainly not volunteering
and, if
asked, I would refuse--the working group
members
consists of individuals from the FDA and
from the
pharmaceutical industry. It would be good to have
some neutral people on the working
group--people
from industry or people from government
agencies
that are not connected with the FDA, so
that you
get some sense of balance. Otherwise, it seems to
be--you know, it seems to be a
self-serving group.
So I would like to encourage
you to expand
your membership.
DR. O'NEILL: Yeah.
DR. SINGPURWALLA: And I want to
emphasize: I'm not available.
DR. O'NEILL: Well--no, the last point--I
mean, this is hard work. The people who are doing
this work are spending a lot of time, and there's a
lot of evaluation--a lot of data
evaluation going
on.
We were presented with information from the
IPAC group that consisted of a huge
database.
And one could look at, well,
how much time
do you want to spend on evaluating a huge
database?
61
I mean, it's an electronic database, and
lots of
different--and where I'm going to on this
is the
Bayesian argument. The Bayesian argument is very
much a sensible argument--or a sensible
framework
when you can look at empirical data that
allows you
to feel pretty comfortable about what
your priors
are, and what the distribution of
information is.
That is not always accessible to the
agency. It
may be accessible to a sponsor.
So the strategy of being
in-process and
out-of-process, and being in control, and
what's
acceptable variability is very much--very
much--a
Bayesian framework, and very much within
the
context of how you may want to be looking
at this,
in terms of looking at in-process
validation, as
well as acceptance criteria.
The extent to which that carries over into
the type of testing we have to be very
clear about.
And it's--at the point we're at right
now, we're
essentially most interesting, or most
concerned
about how far out can you push the
acceptance curve
so that it has a proper balance between
accepting
62
and rejecting--particularly when we don't
have, or
no one can show us empirically, what the
distribution of off-target means are, for
example.
How far away from 100 percent does the
mean have to
be before you want to maybe ratchet in
this
operating characteristic curve?
So, I certainly could see the
value to
external folks' helping us out. The more the
better.
And I believe that this is a
time-intensive effort. And just as, you know, you
would not like to volunteer, we would
have to go
and find folks who could invest the
amount of time
that is necessary, in the time frame that
we're
talking about, so we can get where we
want to be.
That's not to say that more
brains are
not--and independent brains--are--but
this is--I
would say we're pretty much trying to meet
in the
middle of this whole thing with resources
that
we've thrown out it that we feel are fair
and
objective.
DR. SINGPURWALLA: Let me clarify.
I'm not volunteering because
I'm making
63
the suggestion.
DR. O'NEILL: Yes.
Yes.
DR. SINGPURWALLA: And that's the proper
thing to do.
What I would like to encourage you is to
involve at least two Bayesian's on your
group--two,
because they need support--
[Laughter.]
--from the point of view of
simply guiding
a framework, or guiding the concept, and
things
like that, rather than get involved with
the
nitty-gritty.
And the two individuals--or
perhaps
more--need not come from two stratified
groups.
They should come from somewhere else.
So I'm making two
suggestions: one is to
have people with expertise in Bayesian
statistics
involved, and to have people from outside
these two
communities also involved--perhaps in a
limited
way.
This will give you a broader perspective and
will not subject you to criticism two
years down
the line.
64
And that's the suggestion.
DR. O'NEILL: Okay.
CHAIRMAN KIBBE: Anybody else?
Ajaz, do you have something to
say?
Reaching for your mike?
DR. HUSSAIN: I think the point I was
going to make was, I think, at this point
in time
it's going to be difficult to add more
people to
the working group. But the point is well taken
that I think you do need to bring that
perspective.
And I'm hoping this Advisory Committee,
and some
other format, could be sufficient to sort
of bring
that framework for that--that perspective
to bear
on the progress of this working group.
CHAIRMAN KIBBE: No one else?
Thank you Dr. O'Neill. Appreciated your
presentation.
Dr. Ajaz, perhaps you could begin our next
topic, and then we can take a break,
because we're
running slightly ahead, and it will give
us a
little flexibility as we move on.
And so we're going to talk
about Critical
65
Path Initiative.
The Critical Path
Initiative--Challenges
and Opportunities
Topic Introduction and OPS
Perspective
DR. HUSSAIN: Yes, I think I'm pleased
that we have more time, because many of
the
presentations here are very lengthy
presentations--[laughs]--including mine.
I'd like to sort of introduce
the topic of
Critical Path Initiative--the challenges
and
opportunities.
[Slide.]
The goals that we have for the
fiscal year
2005--and the initiatives, and the
strategic goals
at FDA level and the Department level are
shown on
this slide. And the slide is from the "State of
CDER" address by Steve Galson and
Doug
Throckmorton.
Today, our discussions will
primarily
focus on the Critical Path, the cGMP
initiative,
focused on risk management and
innovation. And the
goal at the Department level is to
increase science
66
enterprise research. But also, I think the follow
on biologics, follow-on proteins, I think
is
interconnected to all of these
discussions.
[Slide.]
My focus today is to introduce
you to the
topic of Critical Path, and also outline
a proposal
that we are contemplating at the OPS
immediate
office level as an umbrella proposal for
all the
discussions you'll hear today by
scientists from
different parts of the Office of
Pharmaceutical
Science.
But at the same time, some of
the
discussions in here also impact, say,
counter-terrorism effort and other
efforts that are
ongoing.
And not all projects that we'll discuss
are Critical Path projects today.
[Slide.]
What is Critical Path? It's a serious
attempt to examine and improve the
techniques and
methods used to evaluate the safety,
efficacy and
quality of medical products as they move
from
product selection and design to mass manufacture.
67
[Slide.]
In the continuum of drug
discovery and
development, you really go from basic
research to
prototype design or discovery, to
preclinical
development, clinical development, to an
FDA filing
and approval. You have a focused attempt, say, for
example, at the National Institutes of
Health on
translational research. The Critical Path research
does overlap with some of the aspects of the
NIH
translational research, but it covers
predominantly
the drug development aspects of the
entire
sequence.
In our White Paper, we
identified some of
the challenges for Critical Path. The drug
development process--the "Critical
Path" is
becoming a serious bottleneck to delivery
of new
medical products.
[Slide.]
Our research and development
spending has
been exponentially increasing. And as an index of
1993, you can see the exponential
increase from
1993 to the current 10 years--increase in
both
68
private and public spending on research.
[Slide.]
However, new product
submissions have
remained flat--or, some would argue, are
on the
decline.
[Slide.]
Why is FDA concerned? FDA's mission is
not only to protect but also to advance
public
health by improving availability of safe
and
effective new medical products.
[Slide.]
FDA has a unique role in
addressing the
problem.
FDA scientists are involved in reviewing
during product development--they see the
successes,
failures and missed opportunities. FDA is not a
competitor, and can serve as a crucial
convening
and coordinating role for consensus
development
between industry, academia and
government. FDA
sets standards that innovators must
meet. New
knowledge and applied science tools
needed not only
by the innovators must also be
incorporated into
the agency's review process and policy.
69
[Slide.]
The challenge is how do we
proceed? It
should be a science-driven and shared
effort,
drawing on available data, need to target
specific,
deliverable projects that will improve
drug
development efficiency. It cannot just be an FDA
effort.
We can identify problems and propose
solutions. Solutions themselves require efforts of
all stakeholders. We have issued a Federal
Register notice requesting input from
broad
stakeholders, and we have received a
number of
suggestions, and we are working through
those
suggestions as we formulate our strategy
for a
Critical Path research program.
[Slide.]
This is a significant
initiative, and the
Department of Health and Human Services'
Medical
Technologies Innovation Taskforce is
providing
broad leadership. Dr. Lester Crawford is chair of
this Medical Technologies Innovation
Taskforce, and
it includes CDC, CMS, NIH and FDA.
This taskforce is working on
finding
70
additional funding to meet the needs of
the
Critical Path program. It is meeting with external
stakeholders to identify opportunities,
enlist
allies, and so forth.
[Slide.]
In summary, I think from a
Critical Path
perspective, the present state of drug
development
is not sustainable. We believe FDA must lead
efforts to question any assumptions that
limit or
slow new product development: are these
assumptions justified? Are there more efficient
alternatives? If so, why are the alternatives not
being utilized?
[Slide.]
As we sort of focus on the
discussions
today, I'll remind you that the Office of
Pharmaceutical Science is predominantly
focused on
one aspect: Chemistry Manufacturing Control--or
the initialization dimension. But the Office of
Pharmaceutical Science also supports many
other
aspects, from pharmacology, toxicology to
clinical
pharmacology research and so forth. So, although
71
our review responsibilities predominantly
are on
the quality side, our research programs
are
interconnected to every aspect of the drug
development process.
So you will hear presentations
coming from
all aspects--all three dimensions of the
Critical
Path.
[Slide.]
The three dimensions are: assessment of
safety; how to predict if a potential
product will
be harmful; assessing efficacy; how to
determine if
a potential product will have medical
benefit; and,
finally, industrialization--how to manufacture
a
product at commercial scale with
consistently high
quality.
[Slide.]
Our discussions, to a large
degree, have
focused on the third dimension. And I think you
will see, today, many of the projects
within OPS
that also impact the other two
dimensions.
[Slide.]
In our White Paper, we defined
the three
72
dimensions and the connections to the
Critical Path
as follows: safety, medical utility, and
industrialization. An every aspect--every box that
is there has a need for improvement and
research to
support that improvement.
Applied science is needed to
better
evaluate and predict the three key
dimensions on
the Critical Path development.
I just returned from
Europe--spending a
week there last week--and with respect to
the
industrialization dimension, I came back
somewhat
depressed. The amazing work I saw coming out of
the University of Cambridge in the area
of
industrialization of pharmaceuticals--the
approach
to new technology, in terms of
manufacturing, novel
drug delivery systems and manufacturing
processes
itself, was astounding. I don't see any of that in
the U.S.
So my concern is, much of the
R&D and
innovation is going to come from Europe
and Japan,
probably.
And unless we really improve our
infrastructure, we are going to be
lagging behind
73
in a very significant way. And I think that
concern keeps growing on me, and I think
I do want
to sort of emphasize that.
[Slide.]
Office of Pharmaceutical
Science programs
and Critical Path Initiative--the
discussion today
is to seek input from you and advice, on
aligning
and
prioritizing current OPS regulatory assessment
and research programs, with the goals and
objects
of the Critical Path Initiative. Please note that
not all research programs and laboratory
programs
are intended to focus on "Critical
Path." There
are equally important other
aspects--bio-terrorism
and so forth--which may not be considered
as part
of the Critical Path Initiative, but
they're
equally important. So all of our programs and
projects are not likely--or should not be
part of
the Critical Path. There are aspects. So you have
to distinguish that.
We hope that you'll help us
identify gaps
in our current program; identify
opportunities for
addressing the needs identified by the Critical
74
Path Initiative.
[Slide.]
What I'd like to do today
is--before I
introduce Keith Webber--he took the lead
on putting
this program together--I'll share with
you an OPS
immediate office project that Helen and I
have been
developing. These are our initial thoughts of how
an umbrella project, within the OPS
office, will
help to sort of bring all of this
together.
So let me share some of our
thoughts on a
Critical Path project that OPS--Helen and
I are
sort of developing right now.
An immediate need in OPS is to ensure
appropriate support of general drugs--the
growing
volume and complexity of
applications. That's the
challenge. You saw the numbers increasing.
In the New Drug Chemistry, the
new
paradigm for review assessment and
efforts to
support innovation and continuous
improvement goals
of the cGMP initiative--Office of New
Drug
Chemistry has taken the lead to be the
first office
to sort of implement all of this. So they have
75
significant need for support.
Biotechnology
products--complete
integration into OPS, and the evolving
concept of
"follow-on protein
products"--although I have put
follow-on protein products under this, we
don't
know exactly how the regulatory process
will
evolve.
It could be--let's say, a work in
progress.
And, clearly, alignment of
research
programs in OPS to meet our goals and
objectives.
[Slide.]
So what are our thought
processes, from
our immediate office perspective? To develop a
common regulatory decision framework for
addressing
scientific uncertainty in the context of
complexity
of products and manufacturing processes
in the
Offices of New Drug Chemistry,
Biotechnology
Products, and General Drugs.
Regardless of the regulatory
process,
regardless of regulatory submission
strategies and
so forth, we believe we need a common
regulatory
decision framework--a scientific
framework--for
76
addressing the challenges.
[Slide.]
What are the motivations here?
Uncertainty--whether it's variability or
knowledge
uncertainty--and complexity are two
important
elements of risk-based regulatory
decisions. A
common scientific framework, irrespective
of the
regulatory path or process for these
products, will
provide a basis for efficient and
effective policy
development and regulatory assessment to
ensure
timely availability of these products.
That's the overreaching OPS goal, is
to
provide the common framework. Although the
submission strategies might be different,
the
science should not be different.
[Slide.]
How are we trying to approach
this
challenge? We know that there are no good methods
available for developing a standard
approach for
addressing uncertainty. That means you need
different approaches for different
assessment
situations. [Laughs.] All right, let me
complete my
77
thoughts.
So what we are thinking
about--a decision
framework for selecting an approach for
addressing
uncertainty over the life cycle of
products is what
is needed. So you may have different approaches
and so forth, but a common decision
framework will
help us identify the right approach.
[Slide.]
Project 1 is to create an
"As Is"
regulatory decision process map for the
Office of
New Drug Chemistry, Office of
Biotechnology
Products, and Office of Generic
Drugs. Much of
this work will be done through a
contract--we plan
to have a contractor come in and work
with us on
some of these things.
We think a representative
sample of
product applications could be selected
for mapping
the scientific decision process in the
three
offices.
[Slide.]
Determine regulatory processes
efficiency
and effectiveness, using metrics similar
to that
78
what we have learned from the
manufacturing
initiative; and identify and compare
critical
regulatory review decision points and
criteria in
the three different offices; evaluate,
correlate
and/or establish causal links between
review
process efficiency metrics and critical
decisions
criteria, and available information in
the
submission--that's the mapping process;
and, also,
evaluate the role of reviewer training
and
experience, and how it bears on some of
these
decisions.
[Slide.]
Summarize available information on
selected
products; collect and describe product
and
manufacturing process complexity,
post-approval
change history, and compliance
history--including,
when possible, adverse event reports that
come
through MedWatch and other databases;
describe
product and process complexity and
uncertainty with
respect to current scientific knowledge;
information available in submissions;
reviewer
expert opinions and perceptions; and, if
feasible
79
or possible, seek similar information
from the
sponsors or company scientists on these
same
products that we might select.
[Slide.]
What we hope to do is aim for
the
following deliverables: organize Science Rounds
within our office to discuss and debate
the "As Is"
process map, and the knowledge gained
from the
study; identify "best regulatory
practices" and
opportunities for improvement--these may
include
opportunities for improvement of filling
the
knowledge gap, develop a research agenda
for all
OPS laboratories based on what we learn.
What is, I think, missing today
is a
common scientific vocabulary. There's a need to
develop a common scientific vocabulary to
describe
uncertainty and complexity. There can be--each
come from a very different perspective
right now.
Develop an ideal scientific
process map
for addressing uncertainty and
complexity; adapt an
ideal scientific process map to meet the
different
regulatory processes.
80
In the following--I think the
three
projects that we're thinking about are
not actually
fully independent. They're all connected together.
[Slide.]
Project 2 is to sort of focus on a
systems
approach.
We believe that without a systems
approach to the entire regulatory
process--that is
from IND to NDA--Phase IV commitments and
cGMP
inspection, the broad FDA goals under the
cGMP and
the Critical Path Initiatives will not
really be
realized.
[Slide.]
So the team approach and the
systems
perspective that evolved under the cGMP
Initiative
only addressed a part of the
pharmaceutical quality
system.
Quality by design and process
understanding to a large extent is
achieved in the
research and development organization.
Pharmaceutical product development is a
complex and
a creative design process that involves
many
factors, many unknowns, many disciplines,
many
decision-makers, and has multiple
iterations in the
81
long life-cycle time.
So we have to treat it as a complex
system
optimization problem.
[Slide.]
Significant uncertainty is
created when a
particular disciplinary design team must
try to
connect their subsystem to another
disciplinary
subsystem--for example, clinical versus
chemistry,
or CMC to GMP. When you bring those connections,
there's significant uncertainty.
Each subsystem can have its own
goals and
constraints that must be satisfied along
with the
system-level goals and constraints. It is possible
that goals of one subsystem may not
necessarily be
satisfactory from the view of other
subsystem and
design variables in one subsystem may be
controlled
by another disciplinary subsystem. Impurities is a
good example. Pharmtox, CMC, and how you bring
that together.
[Slide.]
So the Project 2 proposal that
we're
developing is to use ICH Q8 as the bridge
between
82
the cGMP Initiative and the rest of the
regulatory
system, and to develop a knowledge
management
system to ensure appropriate connectivity
and
synergy between all regulatory
disciplines. Can
that be done? I mean, that's the feasibility
project that we are trying to
develop. So--connect
Pharm/Tox, Clinical, Clinical
Pharmacology,
Biopharmaceutics, CMC, Compliance all
together.
[Slide.]
The current thinking is to
approach this
problem as connecting every section
within the ICH
Q8 CTD-Q, within the same document, but
to all
other sections in an NDA, in some way or
form. For
example, each section within the P2 can
have an
impact on the other P2 sections and,
similarly,
other sections of a submission and to
cGMP.
By recognizing this as a
complex design
system that involves multiple attributes,
goals,
constraints, multidisciplinary design
teams,
different levels of uncertainty, risk
tolerances,
etcetera, we wish to find opportunities
to identify
robust designs and design space that provides
a
83
sound basis for risk assessment and
mitigation.
So this would be a scientific
framework.
It was a regulatory tool that could come
out of
this.
And with the case studies and everything
coming together, this might be a way to
bring and
connect all the dots.
[Slide.]
What we have been looking out
is outside
pharmaceuticals. We believe that a significant
body of knowledge exists. Example, in mechanical
engineering, as it applies to the design
of
aircrafts, that addresses some of these
challenging
points that we have discussed. These are three
examples that I have selected as just
illustrative
examples of how multidisciplinary
optimization
methods and system-level problem solving
tools can
be thought about in the drug context.
[Slide.]
Just to illustrate this point,
let me
create an example here. The applicability of
multidisciplinary optimization methods
for solving
system-level problems and decision
trade-offs will
84
be explored in an NDA review
process. That's what
we're proposing.
For example, in the Common
Technical
Document for Quality--the P2 section,
which is what
ICH Q8 will define--critical drug
substance
variables that need to be considered in
section
2.2.1, which is "Formulation
Development" are
described in section 2.1.1. So there's a drug
substance, and there's a
formulation. They're two
different sections.
Information for "Drug
Substance," has a
bearing on that of the "Formulation
Development."
So how do you connect the two together?
For example, the current
language in ICH
Q8 for "Drug Substance,"
states: "Key
physicochemical and biological
characteristics of
the drug substance that can influence the
performance of the drug product and its
manufacturability should be identified
and
discussed."
So that's describing the information
content in section 2.1.1. that we will
hopefully
85
receive whene ICH Q8 is done. So how does this
have a bearing on the "Formulation
Development"
section?
[Slide.]
I'll skip this and just show
you a figure.
[Slide.]
You have the API--or drug
substance
manufacturing process. The X(1.1) is the design
variable; the f(1.1) is the objective
function to
be addressed; and the g(1.1) is the
constraint for
that manufacturing process that delivers
the drug
substance. Okay?
Since this is not part of ICH
Q8, what
will be part of ICH Q8 is section 2.1.1.,
which
will identify what are the critical
variables for
the drug substance, as they relate to the
formulation aspect. But that becomes the input for
what--how it connects to the
"Formulation
Development" aspect. And that link is through a
linking variable.
Since my means and standard
deviations
have become finger-pointing and so
86
forth--[laughs]--so you know--you have a
design
variable, you have a linking variable,
you have an
objective function, you have constraints
around
which you define your design space. You have mean
objective function--that's your
target. You have a
standard deveiation that you sort of
bring to bear
on that.
And deviation range of the design
solution, or the design space.
So all of this sort of has to
come
together for this to be meaningfully
connected.
And, for example, if you start with a
simple design
of experiment, you may have mathematical
models,
which are empirical, but then they
provide that
connectivity. So it's a start of a very formal,
rigorous approach to dealing with
uncertainty,
knowledge gaps and complexity.
So this might be a useful
concept. So
that's the process right now, to see
whether this
could be a feasibility project that we
could do.
[Slide.]
So the potential deliverables
of using
this approach could be significant. Since
we are
87
moving towards electronic submissions, in
conjunction with electronic submissions,
this
project can potentially provide a means
to link
multidisciplinary information to imporve
regulatory
decision--that is, clinical relevance to
CMC
specifications. We may not all have all that
information, but the links--the
structure--will be
there as we grow, as we improve our
knowledge base,
or will it be refined, the links could
get
populated, and this might be an approach
for
knowledge management within the agency.
Creating a means for electronic
review
template and collaboration with many
different
disciplines; provide a ocmmon vocabulary
for
interdisciplinary collaboration; create
an
objective institutional memory and
knowledge base;
a tool for new reviewer training; a tool
for FDA's
quality system--and, clearly, it can help
us
connect cGMP Initiative to the Critical
Path
Initiative.
So that's the project that we
hope to
develop.
We really want to get some feedback from
88
you, and develop this as a project under
the
Critical Path Initiative.
[Slide.]
But the third aspect of
this--it all could
happen in parallel--explore the
feasibility of a
quantitative Bayesian approach for
addressing
uncertainty over the life cycle of a
product. The
most common tool for quantifying uncertainty
is
probability. The frequentists--the classical
statisticians--define probability as
"limiting
frequency, which applies only if one can
identify a
sample of independent, identically
distributed
observation of the phenomenon of
interest."
The Bayesian approach looks
upon the
concept of probability as a degree of
belief, and
includes statistical data, physical
models and
expert opinions, and it also provides a
method for
updating probabilities when new data are
introduced.
The Bayesian approach may
proivde a more
comprehensive approach for regulatory
decision
process in dealing with CMC uncertainty
over the
89
life cycle of a product. It may also provide a
means to accommodate expert opinions.
And I think there's a
connection here.
The evolving CMC review process may be a
means to
incorporate expert opinions. And I think that is a
significant opportunity.
Using the information collected
in Project
1--that I described--you would seek to
develop a
quantitative Bayesian approach for
risk-based
regulatory CMC decision in OPS.
So that would be a project that
will run
in parallel to the other two approaches
that we are
moving forward.
[Slide.]
So, I'll stop my presentation
here with
sort of summarizing, in the sense--I
think OPS,
from its goals and objectives, has to
have an
overreaching project that sort of
connects all the
dots together. And the proposal--the first one
clearly is a process map--"As
Is" and so forth.
But the two others are feasibility
projects that we
want to look at the Bayesian approach and
a complex
90
system optimization problem.
The knowledge exists outside. It's simply
adapting and adopting it in our context.
What you'll hear--after the
break, I
think.
Or--unless you want to start earlier--after
the break, is other immediate office
projects;
Office of Biotechnology projects, Office
of New
Drug Chemistry project, Office of Generic
Drug
projects on Critical Path, and Office of
Testing
and Research.
What we have done is Keith
Webber will
introduce the reset of the talks. You will hear
each group's perspective. And we have requested
Jerry Collins to come back and sort of
summarize--after his talk on the Critical
Path--the
entire Critical Path Initiative from an
OPS
perspective and pose questions to you.
And we have also invited
Professor Vince
Lee, who is now part of FDA--who used to
be the
chair of this committee--who has been
with agency
for almost a year now, to come with his
perspective
on how--what are challenges he sees. So you will
91
hear sort of presentations and some
opinions from
people who have been at the agency and
been looking
at this challenge for some time.
So, again, the discussion today
is to seek
input and advice on ACPS; on how to
align, identify
gaps, and identify opportunities.
I'll stop here and entertain
questions on
my part of the presentation.
CHAIRMAN KIBBE: Are there any questions?
DR. SINGPURWALLA: I have comments.
CHAIRMAN KIBBE: Okay.
Thank you.
DR. SINGPURWALLA: I just--what you say is
music to my ears. You have good vision about some
of the things you want to do. But I think it's now
time that the dance should begin.
We should get back--take
concrete problems
and address them. I've said this before.
But let me just make some
specific
comments on some of the things you've
said. And,
of course, I'm going to question some of
the things
you said.
The first argumetn I want to
make on your
92
slide on page 7, about efficacy and
safety:
generally, those tend to be
adversarial. Drugs
that give you benefit may have side
effects. So
the important issue is to do a
trade-off. For that
you need to talk about assessing
utilities: what
is the utility of the benefit, and what
is the
dis-utility of the harm? That's a part of the
whole package of thinking about these
problems, and
I encourage you to look into it.
Now, I take strong objection to
some of
the things you have said. You have distinguished
uncertainty into stochastic and
epistemic. I have
seen that distinction before. I claim it's totally
unnecessary. Uncertainty is uncertainty, and one
doesn't--one should not pay much
attention to the
source of the uncertainty--
DR. HUSSAIN: Right.
DR. SINGPURWALLA: --whether it is
regulated allatoire uncertainty, or
epistemic, does
not matter.
CHAIRMAN KIBBE: Right.
DR. SINGPURWALLA: The Bayesian approach
93
does not distinguish between the
two. And since
you've been talking about it, I think--
You also say that there are no
good
methods for devleoping standard approach
for
addresing uncertainty. I think that's the wrong
slide to put up. That's liable to do more harm
than good.
DR. HUSSAIN: Okay.
DR. SINGPURWALLA: There are methods
available. So I would not encourage you to put it.
And the other thing is: I don't like your
linking uncertainty and complexity. They're two
different issues.
And you also say that there is
no common
scientific vocabulary. Well, I claim there is a
common scientific vocabulary, and that is
probability.
Now, as far as recommendations
are
concerned: I'd like to suggest--and, again, I'm
not volunteering since I'm making the
suggestion--that you have your people
exposed to a
tutorial on Bayesian methods and Bayesian
ideas, so
94
that you get a better appreciation of
what it's all
about.
And the best way to do this is to take a
simple example and work through it; work
through
your expert opinion notions that you're
saying.
Go through an example, and
you'll get a
better appreciation of what it's all
about. And
once you get that appreciation, you'll be
tempted
to remove some of the other things you've
said.
Those are just comments. Thank you.
DR. HUSSAIN: No--the point's well taken.
And we actually have a project right now
with the
University of Iowa, looking at our stability
data
from a Bayesian perspective. So we're just
starting to put a real-life example on
that. So
that's--
With regard to the utility,
Jurgen and the
Clinical Pharmacology Subcommittee has
been sort of
bringing that up. So we will connect to the
Clinical Pharmacology group.
Jurgen, do you want to say
anything about
that?
DR. VENITZ: [Off mike.] Well, other than
95
the fact that--other than the fact that
we're
discussing it. It is a controversial issue,
because you're really trying to map,
then, a lot of
different things into a uniform
scale. Personally,
I don't see an alternative, and I think
it's
already done. We're just doing it intuitively, as
opposed to expressedly.
So it is being discussed. We have to see
where it goes.
DR. HUSSAIN: And, regarding, I think, the
common vocabulary, I think it's a common
vocabulary
in the context of when we speak from a
pharmacist
to a chemist to an engineer--we have very
different
interpretation--that's what was referred
to.
DR. SINGPURWALLA: That's why you need a
tutorial.
DR. HUSSAIN: That's exactly--
DR. SINGPURWALLA: Put people together.
Because about 15, 20 years ago, the
Nuclear
Regulatory Commission was facing similar
problems.
And one of the things they did is they
had lots of
tutorials to get everyone on board,
talking the
96
same language. Otherwise, you'll have a doctor
talk to an engineer, and those two
talking to a
lawyer--and you know what can happen.
[Laughter.]
VOICE: [Off mike.] Lawsuits.
CHAIRMAN KIBBE: Another question?
DR. KOCH: I guess, just to build on the
last comment--when you get into all those
multidisciplinary functions--and
particular when
the ICH Q8 is going to serve as a group,
together
with the implementing the cGMPs--there's
a couple
of organizations out there I think could
serve as
very valuable resources. One we've heard about a
couple times today in the ASTM 55, as a
body to
help at least standardize the terminology. And the
other one is the ISPE, which could serve
as a
multidisciplinary conduit that, working
together
with ICH, could probably facilitate some
of the
multidisciplinary issues.
DR. HUSSAIN: I think we do plan on
extensive training and team building and
coming on
the same page. If you look at the PAT and the
97
manufacturing signs White Paper that we
issued, we
actually laid out a lot of these things in
there,
including the role of ISPE, ASTM, PQRI,
and so
forth.
So we have been thinking about
this in
that context, and at the ICS meeting in
Yokahama--on Wednesday, I think, the date
is
set--we will be updating on that. So I'll get a
chance to talk about ASTM to ICH in
Yokahama,
Japan, also.
So, we're aligning everything
together.
So that's happening.
There was one point that I
wanted to
respond to: the reason for keeping uncertainty, in
terms of variability in
knowledge--keeping the
distinction, at least as we think about
this,
was--and the link to complexity,
also--clearly,
complexity and uncertainty are two
independent
things.
But, unfortunately--well, the challenge we
face is this--in the sense we have a very
complex
product.
We have simple products--within the same
office, in OPS and different
regions. Yet today, I
98
think, from a variability perspective,
we're not
very sophisticated in how do we deal with
variability.
And, for example, in our
manufacturing
science White Paper, we don't even deal
with
variability of our dissolution test
method. We
don't even know how to handle it. So we have
challenges today where simple
variability--we don't
have a good handle on.
So that was the reason for
keeping
variability and knowledge-based
uncertainty on the
table.
CHAIRMAN KIBBE: Ken?
DR. MORRIS: Just a quick question: on
your identification of the gaps in the
current
programs, are you thinking more in terms
of
technical gaps--as in science that needs
to be
done?
As opposed to logistical gaps within--
DR. HUSSAIN: Both.
Both.
DR. MORRIS: So, with respect to the
scientific gaps, are thinking, then, to
take it one
level more--basically, are you talking
more about
99
new science that needs to be created? Or science
that needs to be communicated more
effectively--within the agency?
DR. HUSSAIN: Well, I think the immediate
need would be to communicate the existing
science
and bring all the existing knowledge to
bear on
that.
And, clearly, in the long term there are
fundamental issues--and most of the new
science
would be needed. So I think it's an issue of
timing.
DR. MORRIS: Thank you.
CHAIRMAN KIBBE: Anybody else?
Nozer, you
wanted to--
DR. SINGPURWALLA: No, I just wanted to
say that this distinction between
allatoire and
epistemic has been artifically created by
frequentist statisticians. And Bayesians don't buy
it.
DR. SELASSIE: I have a question.
CHAIRMAN KIBBE: Yes, please.
DR. SELASSIE: You know, in your graph on
R&D spending--has there ever been a
breakdown in
100
how much of that spending can be
attributed to the
"R" and how much to the
"D?"
DR. HUSSAIN: I don't have that--I'm sure
that information's--I don't have it. So I'm not
aware of it.
DR. SELASSIE: Because would one parallel,
you know, the flatness?
DR. HUSSAIN: One was the public funding;
one was more private funding, so--
DR. SELASSIE: Yes, but they're both going
up.
DR. HUSSAIN: Yes.
DR. SELASSIE: But I'm wonder if, you
know--because you look at your product
submissions
are flat.
Now, is that because there's not been an
increase in development funding? Or--
DR. HUSSAIN: I don't think so. But I
don't have an answer.
DR. SELASSIE: Yes.
DR. HUSSAIN: So let me say that.
CHAIRMAN KIBBE: Marvin?
DR. MEYER: Ajaz, you don't seem like a
101
depressive kind of guy--
DR. HUSSAIN: [Laughs.]
DR. MEYER: --but you said you were
depressed last week.
DR. HUSSAIN: Yes.
DR. MEYER: Can you give us just a real
short synopsis of where you see Europe
doing things
right, and us doing things wrong?
DR. HUSSAIN: [Sighs.] [Laughs.]
No, I mean, again, I'll focus
on what I
see happening in Europe--especially in
the
U.K.--and how they're translating
academic
research--academic finding research--into
entrepreneurial business--in particular
in
manufacturing, in particular in dosage
form
design--the pharmacy-related ones.
Look at Bradford, particle
engineering.
And the one I saw--I saw a beautiful
manufacturing
system for coating. Forget coating pans. This is
electrostatic coating; precise,
automated, complete
on line, and so forth.
Nothing of that sort is
happening
102
here--within my domain.
CHAIRMAN KIBBE: We have a couple more
comments, and then we're going to have to
take a
break.
Go ahead.
DR. MORRIS: Yes, just to follow up on
that.
I think there's--I just came back from
Europe depressed, as well, but I was in
Scandinavia. So maybe that had something to do
with it.
[Laughter.]
Yeah, it's pretty dark up
there.
But, in any case, I agree with
Ajaz in
that there are a couple of caveats and,
in fact, if
you look at our latest hires, they're one
from--via
Bradford, another one via Bath. My post-doc is
from Nijmwegin, another post-doc from
Roger Davies
Group in the U.K.
And we're not training
people--number one.
So, aside from not transferring the
technology
effectively we're not training people to
do it very
much any more. There are few places--represented
103
at the table--that still do it to some
degree.
But that stems back to one of
your earlier
slides, which is trying to muster NIH and
NSF to
fund this sort of research. Because some of you
have been a lot closer to deanships than
I. If
there's no overhead money, it doesn't get
a very
kind reception. And the fact of the matter is is
we haven't had it.
So, this is--I'll stop here,
because this
is my old soapbox. But I lay this at the door, in
part, of NIH and NSF for not recognizing,
in the
face of overwhelming data, that there is
a crisis
that needs to be address.
On the upside, there are some
people in
Europe doing some things--and Japan, as
well.
CHAIRMAN KIBBE: Pat, go ahead.
DR. DeLUCA: Just a quick follow up on
that, too. I know from my trips to Europe, too, if
you just look at the colleges--the
pharmacy schools
in Europe--I mean, they all have
departments of
pharmaceutical technology. I mean you'll be
hard-pressed to find pharmaceutical
technology as
104
an area of focus in an American college
of pharmacy
now.
Certainly you won't see any departments of
pharmaceutical technology.
So I think it's been--and it
wasn't that
way 20 years ago. But, I mean, it certainly has
changed, though.
CHAIRMAN KIBBE: Anybody else?
Good--I think we're at a nice
break point.
And if we could take perhaps a 10 minute
break--because Ajaz has managed to get us--use
up
all of our lead time.
[Laughter.]
And we can get Keith to start
his talk at
about 10:22, that would be great.
[Off the record.]
CHAIRMAN KIBBE: 22 minutes after 10 has
arrived, and one way or another we're
going to get
back on process.
Dr. Webber, are you prepared to
get on
process?
He's on the way to the podium.
Those of you walking around with
cakes in
105
your hands, and sodas, you want to sit
down.
Nozer.
Here we go. Good luck. We gave you 10
minutes to do that.
[Pause.]
You snooze, you lose, as the old saying
goes.
So, Dr. Webber, shall we start
our
Strategic Critical Path?
Research Opportunities and
Strategic Direction
DR. WEBBER: Okay.
I guess we're about
ready to get started on this session,
regarding
research activities and our strategic
goals for the
Office of Pharmaceutical Science.
I'm Keith Webber, with the Office of
Biotechnology
Products.
And let me--
[Slide.]
--there we go.
Ajaz went through a very good
presentation, I think, on the Critical
Path. And
I'm not going to really address very much
about the
Critical Path Initiative itself. But, in my view,
this--I've sort of summarized things into
the Drug
106
Development Path, which begins with
discovery of
potential targets--or potential new
drugs; and then
you
have to have a period where one evaluates the
candidates and makes a selection of what
candidate
you should carry forward into the
pre-clinical
study, where one looks for potential
toxicities and
potential efficacies in an indication of
interest.
If all goes wlel, one moves
into clincal
studies, and if all goes even better,
into
commercialization. And then, once you're on the
market, there's always the period of
post-approval
manufacturing optimization--or we would like
to see
that, from the FDA's perspective, anyway.
And then, often, we get new
indications--we see new indications being
developed
for drugs that are on the market. And that
essentially starts the process back up
again--often
at the clinical studies stage.
[Slide.]
The--I didn't bring a
pointer. Is there a
pointer here?
VOICE: [Off mike.]
Just use the mouse.
107
DR. WEBBER: Just use the mouse. Okay.
That will work. Right there is the mouse. Okay.
I guess, historically, FDA
interactions;
have occurred primarily ion this area
here, from
clinical studies on. Prior to that, we have had
very little influence, I think, until we
receive a
submission which contains information
regarding the
pre-clinical studies.
But I believe we have opportunities
to
have an impact on this entire process in
the
future.
Let's see.
Essentially, I guess, sort of
the essence
of the Critical Path is the--in my
mind--is the
view from empirical versus guided drug
development.
And drug development has to be a learning
process
in order to make intelligent decisions
regarding
such issues such as your candidate
selection; what
dosage form you're going to have and what
the
formulation should be; in choosing
clinical
indications, you need to know what
patient
population is going to be the best
selection for
108
your product. And then when you're evaluating
clinical endpoints, one needs to know
which are the
most appropriate endpoints to evaluate in
the
clinical studies, and are there surrogate
endpoints
that are more appropriate than others, if
you can't
look at an endpoint which is directly
related to
survival or efficacy in the more normal
manner.
And, of course, with adverse
event
monitoring, any clinical trial is going
to monitor
particular parameters, and you need to have a
good
knowledge base in order to understand
which adverse
events we should be looking for, and the
best way
to evaluate those.
And then, finally, the
manufacturing
method certainly is a major concern
because that
has to do with the ability to improve the
manufacturing process post-approval and
pre-approval, as well as avoiding issues
that can
come up with regard to safety and
efficacy of your
product.
[Slide.]
The goal of industry, as well
as the
109
agency, I believe, is to establish a
knowlege base
and the tools that are necessary to
predict the
probable success of any given product,
and the
manufacturing methods that are
appropriate to it,
and then to foster the development of
products that
are going to have a high likelihood of
success,
throughout clinical development and on the
market.
[Slide.]
Now, for this late morning's
presentations
and this afternoon's presentations, we'll
be
hearing from a number of groups within
OPS. One is
the
Informatics and Computational Safety Analysis
staff, which is in--essentially in the
immediate
office of the OPS; and then Office of New
Drug
Chemistry, Office of Generic Drugs. And the first
three here are the groups that do a lot
of
relational and database analyses as part
of their
research activities. There are, in some cases,
collaborative research going on with
laboratories,
per se.
But it's the groups on this--the last two,
the Office of Testing and Research, and
Office of
Biotechnology Products, that have actual
110
laboratories where research at the bench
is going
on.
[Slide.]
Let's see--within OPS's
Critical Path
Research, I think we can address--or can
address
the issues regarding candidate selection,
based
upon an understanding of the structure
and activity
of the relationships that we see, and the
products
that ocme down the line, as well as
what's reported
in the literature.
Dosage form development and
evlauation I
think is an important area that we're
working in.
Toxicity predictions for products
is--we're
amenable to that, so our research can
address that
through, again, structure activity-type
relationships and structure-function
issues, as
well as knowledge of the impacts that a
particular
disease state might have on physiological
function
that may lead to toxicities that wouldn't
be
present in all populations.
Bioavailability and
bioequivalence
predictions are certainly important for
all of our
111
products, but particularly for the Office
of
General Drugs, they're quite
critical. And I think
with regard to the follow-on products as
well, it's
a major area of concern.
Metabolism prediction is
something that
is, I think, crucial because products,
once they
enter the body, as you know, they don't
remain in
their initial state. And the metabolism can impact
toxicity, it can impact efficacy, it can
impact the
bioavailability and biofluence of the
products
themselves.
Immunogenicity is another area
that is of
large concern, particularly for protein
products.
And there we need to evaluate and
understand, not
only the caues of immunogenicity, or the
impacts of
various structures in the proteins on
immunogenicity, but also the impact that
the
patient population has on immunogenicity;
what
impact the indication that's selected can
have on
impacts of immunogenicity as a safety
concern.
Often, as I mentioned earlier,
you have
biomarkers that you're looking at for
112
pharmacodynamic parameters, or for
surrogate
endpoints. And a good knowledge of the validity of
a particular biomarker, and our ability
to evaluate
those, as well as industry's ability to
select
those, is dependent upon the knowledge
that they
have of the biology of the disease that
they're
studying, or that they're trying to cure
or that
they're trying to treat.
The mechanism of action of the
drug is
certainly critical when you're looking at
the
potential. One area is with regard to drug-drug
interactions. Oftentimes we've been looking
primarily at metabolism for drug
reactions, but
certainly there's a concern that I think
is
building for utilization of multiple
drugs that
impact on the same metabolic--not
metabolic
pathways, but the signaling pathways,
let's say, at
the cell surface, which are getting the
treatments--you know, getting a treatment
into the
cell, or that are resulting in the clinical
effect--is what I'm trying to say, in a
very poor
way.
113
Let's see--the pharmacogenomics
is a new
area that we're getting involved in, but
it's very
important with regard to patient
selection, as well
as the potential for certain populations
to be
impacted by drugs in a unique way, that
can impact
not just efficacy, but also the safety.
And manufacturing methodologies
are an
area that we have research programs in
within the
office, and those are important for
developing and
understanding of the robustness of
various
manufacturing processes, and the ability
to
implement new paradigms, such as process
technologies in the manufacturing process
of
pharmaceuticals
[Slide.]
Out strategy here is to
coordinate
cooperative research activities. And, as I
mentioned, we have predictive modeling
programs.
And these are generally based upon
information from
regulatory submissions that we receive,
as well as
from laboratory research that's going on
within the
agency, as well as outside and in the
published
114
literature.
One area which, I think, we
need to build
is our abilities to get information from
industry
that we don't get in a our regulatory submissions,
and that they don't publish, and finding
a means to
have them help us to gain knowledge of
that
information so that we can implement it
into the
decisions we make and share
that--basically the
conclusions that come out of that with
industry as
a whole, to address the Critical Path.
[Slide.]
There's also laboratory
research going
on--you'll hear from the Offices of
Testing and
Research, Applied Pharmacology Researhc,
and
Product Quality Research, and
Pharmaceutical
Analysis--and also from my office,
Biotech
Products, from our divisions of
Monoclonal
Antibodies and Therapeutic Products--it
should be
Therapeutic Proteins. Sorry.
Typo there.
There's also research going on
in other
FDA centers that we can collaborate with,
and do
collaborate with, as well as outside, to
gain
115
information from academia, industry and
other
egoernment agencies, as well.
[Slide.]
Now, I think we can gather all
this
information, but it's critical with regard
to how
we're going to use it, and how we're
going to
disseminate it, such that we can have an
impact on
the Critical Path.
There are a number of avenues
to get to
academia and manufacturers, and those
include the
public forums, where we can present the
conclusions
and recommendations. We certainly write guidance
documetns that can help in this manner,
as well.
And then, when industry comes to meet
with us at
the regulatory meetings, such as pre-IND,
and
pre-NDA meetings--pre-BLA meetings--we
can interact
with them at those points, as well.
But we also need to change, to
some
extent, our review processes within the
agency,
and--so the information has to go to the
reviewers,
as well.
And we can do that via training programs,
as well as the guidance documents that we
do write.
116
They're used a great deal by the
reviewers.
Then, again, mentoring
programs, to bring
up the new reviewers in an understanding
of the new
paradigms and new concerns, or lessen
their
concerns for particular issues that
relate to
pharmaceutical manufacturing, or clinical
issues.
And then all of this together
should help
to enhance the application of your
process from the
reviewer's standpoint, and with regard to
the
manufacturers should help to remove some
of the
hurdles and obstacles we see in the
Critical Path.
[Slide.]
You'll hear the coming
presentations. So
there are some questions we'd like you to
keep in
mind, that we'll be bringing up later for
discussion.
And first is: are we focusing, within the
office, on the appropriate Critical Path
topics?
And are there other topics that we should
be
addressing through our research
programs? And it's
both the database relational type
information or
research programs as well as the
laboratory
117
programs.
And then, in the future,
Critical Path
issues may change. So how should we identify
Critical Path issues in the future. And we'd like
recommendations on how we should
prioritize those.
Because we're really--at this point, we
can't do
everything that needs to be done with the
current
resources, and so we're going to have to
prioritize
now, and in the future we'll need to
prioritize, as
well, and we'll need some guidance on
that.
That ends my presentation. We'll move
into the first talk--to stay on
time--which is
going to be--let's see, I'll bring it up
here--Joe
Contrera.
Informatics and Computational
Safety Analysis
Staff (ICSAS)
DR. CONTRERA: Okay.
I'm the director of
the Informatics and Computational Safety
Analysis
group.
Our main mission, really, is to make better
use of what we already know; material or
safety
information, toxicology information
that's buried
in our archives; and also in the
scientific
118
literature and in industry files.
Our group develops databases
and also
predictive models. You can't develop models
without the databases. So they go together.
We have develop our own
paradigms for
transforming data, because traditional
toxicology
data is textual, and converting into a
weighted
numerical kind of a scale that is
amenable to be
processed by computers, and also to be
modeled.
And we encourage, promote and
also work
with outside entities to develop
QSAR--qualitative
structure activity relationship
software--and data
mining software, for use in safety
analysis.
We don't work alone. And you'll hear more
about this in my talk. We leverage, very much, and
cooperate, and collaborate very much with
outside--with academia, with software
companies and
with other agencies. And we do this through
mechanisms such as the CRADA--the
Cooperative
Research and Development Agrement--which
is really
a buisness agreement--and also we do it
with
Material Transfer Agreements, for an
exchange, quid
119
pro quo exchange, with software and other
scientific entities outside the center.
[Slide.]
The Critical Path
Initiative--you've all
been, and you're going to be hearing more
about it,
and you've heard a lot about it. I'm focusing on
what is relevant to my group, and that
is: the
problem is that we have not created sufficient
tools to better assess safety and
efficacy. We're
still relying on toxicology study designs
that were
designed 50 or sometimes 100 years
ago. And it
doesn't mean that they're inferior, but
maybe there
are better ways of doing this now.
So we need a process to develop
better
regulatory tools. And it was really a controversy,
to some extent: whose misison is this? And in the
past, the agency didn't consider it as
the agency
mission to develop these
tools--necesarily. It was
academia.
And academia said, "No, it's the
industry." It wasn't--it was vague as to who was
actually responsible for developing new
analytic
tools that can be used for regulatory
120
enpoints--especially in safety endpoints.
[Slide.]
So now how d we connect with
the citical
path?
I think we were doing Critical Path research
well before there was a Critical Path
Initiative.
I mean, we've been in operation, in one
form
another, for over a decade in the Center,
at a time
when people were questioning whether this
was the
mission of the agency in the beginning.
We developed databases and then
predictive
tools that are used by the industry--by
the
pharmaceutical industry--more and more to
improve
the lead candidate selection. And the question
was:
why should the agency supply industry with
better tools to select lead
candidates? Well, it's
in our interest that they develop lead
candidates
that have fewer toxicology or safety
problems.
Because when they come to us, in the
review process
and submissions, they can said right
through with
very few issues. Otherwise, they'd bog down the
system.
And we have multiple review cycles, and
there are issues to be addressed. And it would be
121
wonderful if they could just slide
through.
And so also to facilitate the
reiew
process internally, by having reviewers
having a
rapid access to information that is
usable for
"decision support," we call
information; that they
can use to make judgments on a day-to-day
basis.
And we hope that also this could reduce
testing;
reduce the use of animals. And also encourage
industry--software companies--to get into
the
business of developing predictive
modeling tools.
[Slide.]
And we see this
three-dimensional diagram
for the Critical Path. Well, the computational
predictive approaches are identified in
two of the
three pathways. And so we feel we're right in step
with what the future goals of the agency
are.
[Slide.]
What have we accomplished
already? Well,
again, we do two things: databases and predictive
modeling.
And this sort of summarizes some of the
accomplishments; the first being we've
developed
predictive software for predicting rodent
122
carcinogenicity, for example, based on
the compound
structure. It's being used by the pharmaceutical
companies. It's distributed by small software
vendors.
We are also--obviously, we cannot
screen
industry's compounds in the agency. That would be
a conflict of interest. But our software is being
used.
We have an Interagency Agreement with
NIH--NIH has a drug development
program--we have a
contract with NIH. NIH sends us compounds that
they're screening in their drug
development program
for treating addiction. And so we are, in our own
way, practicing what we preach, in terms
of using
our software in lead selection in drug
development.
We also--software is being
used--and we
lay a consulting role, within the Center,
for
evaluating contaminants and degradants in
new drug
products and general drugs, to
determine--to
qualify them, and determine limits. So we feel
that our software could have much more
application
in that realm.
And decision support for review
divisions.
123
We collaborate very closely with the
Center for
Food Safety. And, in fact, we're training their
scientists, and have shared our software
with them,
and they're using our carcinogenicity
predictive
software to screen food contact substances.
Because
they're working under the new FDAMA rules
that
place the burden on the agency; in other
words, the
agency has to, within 120 days, decide
whether
there is a risk. The agency has to give cause why
a substance is a risk. It's a reverse of sort of
what drugs are.
So in order to meet those kinds
of
deadlines, they had to go to predictive
modeling to
ascertain whether there's a potential
risk of a
food contact substance--within 120 days.
EPA is looking at our--and we
work with
them.
And the software also can be used in
deciding whether we have a data set that
is
adequate; whether there are research gaps
that need
to be filled.
[Slide.]
So we talk about the FDA
information. We
124
get submissions, we review them. There's an
approval process, and then the post-approval
process.
We extract information from this process.
We extract proprietary toxicology data,
non-proprietary toxicology and clinical
data. And
we build proprietary and non-proprietary
databases,
so we can keep information that can be
shared with
the public through Freedom of Information
and
information that will not be shared--or
cannot be
shared legally--into two different
databases.
And we use these databases for
a variety
of functions: for guidance development, for
modeling.
And also for decision support fo the
review; and also it feeds back on
industry, because
much of this information can be shared
with the
public, because it's under the Freedom of
Information Act.
[Slide.]
We have leveraging initiatives
in both
realms.
We leverage to get support from outside to
help us develop databases, so that we
don't rely
entirely just on FDA funding.
125
And the objectives are to creat
specific
databases--endpoint specific. They could be mouse
studies, three month, 90-day studies, one
year
studies; the toxicology databases that
people are
interested in.
These database initiatives are
funded and
supported through CRADAs and other
mechanisms. We
have a CRADA with MDL Information
Systems, which is
a part of Reed Elsevier publishing
company. They
are interesting in building a large
information
system, and so they're helping,
supporting, our
effort.
We have CRADAs in the works with Leadscope
that has a wonderful platform for
searching
toxicology data. And also we have a CRADA in
process with LHASA Limited, in
England--University
of Leeds in England--that has a system
also--an
interest in these kinds of databases.
What we--our databases are
constructed--the center of our database
is the
chemical structure. It is a chemical-structure
based database. And the structure is in digital
form so that it can be teased--it's a
126
chemoinformatic database. And the digital form is
called the .mol-file structure, and it's
a common
structure used in industry for over a
decade. So
the chemical structure, as well as the
name is the
center search point.
And then once you have a
structure that's
in digital form, you can not only ask a
simple
question about, "Can I find
substance x," but you
can also query and ask whether--"I'd
like to know
everything--all the compounds that are
like it."
And that's such a powerful
tool--regulatory
tool--that I think is another--puts us in
another
dimension.
It's not that I
want--"Tell me about
acetaminophen," but I want to know
compounds that
are 90 percent like acetaminophen in a
data set.
And we're able to do that now--really
easily--with
the system.
So once we have this system,
then we tie
in--the databases are linked to this
search engine.
We have our clinical databases that we
model--post-marketing adverse event
reporting
127
system, and also the tox databases. And we use all
this--what we're really interested in is
modeling;
computational predictive toxicology.
And the sources of that data on
these
databases come from reviews. We extract
information from the regulatory reviews
and from
other databases.
[Slide.]
So, now, getting into our
modeling
operation, we transform the data. We supply the
chemical structure data, and our
collaborators and
software companies supply the
software. And we
work with them on an iterative basis to
improve and
make these things work, and develop
software for
these endpoints.
We've also, I think, are
probably the
first group that have developed a way of
using
chemical structure to predict dose. And so we have
a paradigm for predicting what the
maximum daily
dose of a compound might be in humans,
within a
statistical, obviously, error bar, in
humans.
So, currently, in our prediction
128
department, you might say, we have access
to five
or six different platforms. And they represent
very different algorithms. And this is the
point we want to have interactions with
software
companies that have approaches that are
different
from one another. And then we evaluate and work
with them to try to develop models, using
our data
sets.
So we have two CRADAs on board
right now,
with multi-case and MD/QSAR, and we have
others in
the works. And we also have interactions with
other prediction approaches from the
statistical.
[Slide.]
In terms of the models that
we're working
on now, the objective is to model every
single test
that's required for drug approval. And so we
started with carcinogenicity, because
that was the
most--the highest profile, in terms of
preclinical
requirement; and teratology would be
next. These
are endpoints that cannot be simulated in
clinical
trials; mutagenicity, gene tox--all these
are
models, either have been created or are
in the
129
process of being created and being worked
on.
We're also attempting to model
human
data--the adverse event reporting system;
post-marketing human data. This is an enormously
difficult data set; very dirty data set,
but it's
enormous, in terms of its size.
[Slide.]
And we have had some success,
preliminary
modeling, of hepatic effects, cardiac
effects,
renal and bladder, and immunological
effects in
humans.
These are still works in progress, but we
have made progress.
And in terms of the dose
related
endpoints, we have made really good
progress. We
were surprised, ourselves, because we
didn't really
think this would work. We've been able to
successfully model the human Maximum
Recommended
Daily Dose--you know, that's the dose on
the bottle
when you get your drug. It says "Don't take more
than 10 milligrams a day for an
adult. Well, we
modeled that, because that comes from
clinical
trial data. That is really human data. And it
130
represents an enormous scale--I don't
want to get
into it--but it's like an eight-block
scale of
doses, and we have 1,300 pharmaceuticals
that are
either--that we've modeled, in our
database. And
we were able to successfully model
this--and I'll
get back to that in a moment.
[Slide.]
The other question that came up
was
proprietary data and sharing industry
data. It
would be nice to get their data,
especially in
areas that we know the industry has a
great deal of
experience in, like gene tox data. Right now we
can't have access to data that was not in
submissions. And so we need a way of doing this.
Chemoinformatics gives you a way of at
least
getting there partially. We're able to share the
results by not disclosing the structure
and name of
a compound. You can disclose the results, but you
say "What good is disclosing
results, or using the
results, without knowing where they came
from?"
Well, you can use descriptors--chemical
descriptors--that can be used in
modeling, but
131
cannot be used to unambiguously
reconstruct the
molecular structure. But they contain enough
information to model.
And so you're sort of at least
halfway
there.
You can share some information that can be
used in modeling. And so this is a feasible
approach and, in fact, it's already being
accomplished--legally. It's gone through our
legal--our staff at the agency and it's
incorporated in some of these softwares.
[Slide.]
And this is an example. This is 74 MDL
QSAR descriptors for the compound
methylthiouracil.
Now, these descriptors are used in
modeling, and
ocntain a great deal of scientific
information, in
terms of modeling. But all of these descriptors
will not unambiguously recreate the
structure of
methylthiouracil, because there's a lot
missing.
It's like a pixel pictures. You know, you have a
photograph--a digital photograph--if
you've only
got 70 pixels, you'll get a rough picture
of what
it is, but you won't know it's your
uncle. It's
132
just a person--you know. But if you had 10,000
pixels, you'd know exactly who it is. It's the
same idea. So you can share this crude image.
[Slide.]
Getting back to modeling the
human maximum
daily dose--at present, we have to go
through many
steps to arrive at a starting, Phase I
clinical
starting dose, in a drug that's never
been into man
for the first time. We start with animal
studies--multiple dose studies in
multiple species.
So already that's a lot of cost. Then you estimate
the no-effect level--has to be estimated
from this.
Then you have to decide which species is
closed to
man by looking at the ADME and, you know,
metabolism and everything. And then you have to
convert that to a human equivalent dose
using
allometric scaling. And then, on top of that, you
use a little--the uncertainty factors,
dealing for
inter-species extrapolations--finally
come up with
a dose that you might try for your first
dose in
human--in clinical trials.
Well, if you could model, on
the basis of
133
structure, the maximum recommended daily
dose, you
get a predicted dose in humans--because
that's
human data. You take one-tenth, or one-hundredth
of that, just to be on the safe side, and
you have
a dose.
And what's the benefit? There's no
testing in animals. There's no lab studies.
There's no inter-species extrapolation,
because
you're using human data. And we think it's more
accurate, because animal studies don't
predict
whether a drug is going to cause nausea,
dizziness,
cognitive dysfunction. Animals can't tell you
that.
But yet that appears in labeling for old
drugs all the time.
So we feel that this is a good
approach.
Everyone acknowledges that the estimation
of the
first dose in clinical trials is a
bad--but it's
the only thing we know how to do. So this has got
to be better, because it's better than
nothing.
You know, because right now what we're
doing is a
very crude approximation.
[Slide.]
134
What's another
application? And--in
conclusion--the two-year rodent
carcinogenicity
study--in mouse and rat. It costs $2 million. It
takes at least three years to do. And there's
always controversy about the outcomes of
these.
Yet it has an enormous effect on the
drug's
marketability.
Is it necessary to do these
studies for
all drugs now? Can computational methods replace
some of them? I'm not saying we're getting rid of
all testing. But if we know a lot about a
particular compound, based on the
experience of the
past, perhaps with predictive modeling
there may be
a subset of compounds in which we don't have
to
test as vigorously. And those which we know very
little about--and the computer can tell
you that;
that the compound is not covered in the
learning
set, and therefore you better do all the
studies.
But if a compound is
another--you know,
antihistamine, maybe there's a lesser
path because
a structure that's so well represented in
the data
set, that it's sort of silly to keep
testing it
135
over again, just to meet a regulatory
requirement.
So we're hoping that this would
reduce
unnecessary testing and put the resources
where
they're needed; testing things that we
really don't
know anything about, and that are
new--that are
really new compounds.
[Slide.]
So the challenges for accepting
predictive
modeling:
we need accurate, validated--and that's
always--you know, what we mean by
"validation" is
always arguable. But we need to develop that.
That's part of our mission.
Standardization of software;
experience
and training--it's not something that's
going to go
on a reviewer's desktop ever, because it
requires
interpretation. It's a really special skill.
We need more databases;
adequate sharing
of proprietary information; the bigger
the
database, the better. But we need, also,
regulatory mangers and scientists that
are willing
to consider new ideas--consider; don't
have to
adopt--consider. That makes a big--you know, opens
136
the door for innovation.
And then the ned for an objective
appraisal of current methods. It's the emperor's
clothes.
How good, really, is what we're doing
now?
And that is something that's painful, but
it's something that needs to be done. Compared to
what?
Is it better, worse--compared to what?
[Slide.]
In PhRMA 2005 meeting that
occurred
several years ago--and I think it was
very
farsighted--Price Waterhouse Coopers had
a
paradigm.
And they said, "Right now you have
primary sciences: the lab-based, patients--you
know, clinical trials; and the secondary
is the
computational--what the call
"e-R&D"--that there
will be a transition where they'll reverse
from
primary to secondary. And the primary science
maybe in the next generation, will be the
modeling
and predictive science, and the lab and
clinical
will be the confirmatory science.
So, with that, I'll end my
talk. We've
published much of what we've done. A lot of it is
137
in press right now. We have a web site: our
maximum recommended daily dose database
is on our
website, and a lot of people are working
with it,
and we're happy to say that they're
getting the
same results--which was nice.
And I'll end my talk here.
CHAIRMAN KIBBE: i'll take the prerogative
of the Chair and ask the first
question. And then
we'll get rolling.
Your database looks wonderful
when you're
dealing with toxicity. Have you also done a
similar thing with clinical
effectiveness, or
utility, of compounds? Some way of looking at the
structure, and then looking at the
effect, and
being able to predict how effective one
structure
is relative to another?
And then follow up with
that--if that's
true, can we plug into the opposite end of
your
program and go back the other way, and
just bypass
drug discovery?
[Laughter.]
DR. CONTRERA: [Laughs.] Well--no fair.
138
I'll start with the last one--but you'll
be only
discovering what we already know. There may be--
CHAIRMAN KIBBE: But I was thinking of
plugging in different parameters--
DR. CONTRERA: Yeah.
CHAIRMAN KIBBE: --in the toxicity and
outcome:
lower toxicity, higher efficacy--
DR. CONTRERA: Oh, yes.
Yes.
CHAIRMAN KIBBE: --and then go backwards.
DR. CONTRERA: Yes, that's possible.
CHAIRMAN KIBBE: Thank you.
DR. CONTRERA: But getting back to
efficacy--yes. In fact--I mean, industry is using
it as an efficacy tool all the time. That wasn't
our mission. But potentially--certainly
applicable. And sometimes we stumble on those
things.
But that isn't our mission.
And you know where
research--we've got
four people in this unit. And then we have
contractors. And then we get students. So we're a
small, tight unit. And you have to be very
focused, in terms of your priorities, and
doing
139
what is feasible first, and less--and so
we didn't
get into efficacy. No.
CHAIRMAN KIBBE: Who have I got down here?
I've got everybody on the right side.
So we'll start it at the end,
and work our
way down.
Go ahead.
DR. SELASSIE: Okay.
I have a couple of
questions for you.
First of all, with your
database, you have
in-house data that you're generating for
your
toxicology?
DR. CONTRERA: Yes.
DR. SELASSIE: Do you ever go to the
literature and get information from it?
DR. CONTRERA: Yes.
Actually, that could
be a much more complicated slide. But we mine
everything. We mine other databases; the NIH
databases; literature. And, in fact, we're
using--we're using our CRADA with
MDL--because MDL
owns almost every journal in the world
now--practically. Elsevier owns almost everything.
140
And so--and they have access to data
that's
enormous.
So, using the leverage with a
publishing
company, we have a pipeline now to the
literature.
Yes.
DR. SELASSIE: Okay.
I have another
question.
DR. CONTRERA: Yes.
DR. SELASSIE: When you're inputting the
structures, do you all ever use the
SMILES
notation?
DR. CONTRERA: Yes, we use SMILES. There
is some ambiguity. In fact, the software will use
either one.
But, the .mol file--you know,
you could
add a lot more: three-dimensional components and
other--you know, .mol file has the capability
of
doing a lot more than SMILE. But the software will
run on both--both systems.
DR. SELASSIE: Okay.
And noticed, in
using your descriptors, or using the
e-state
discriptors--
141
DR. CONTRERA: Yes, e-state.
DR. SELASSIE: Do you ever use log P in
there?
For partition coefficient?
DR. CONTRERA: Oh, yes--log P is part of
the
MBL QSAR package. It's also part of the
MCASE
package.
For carcinogenicity--I will be
frank--for
carcinogenicity predictions, log P
doesn't have any
role at all. We took it out because it didn't do
anything.
It didn't help.
CHAIRMAN KIBBE: Jurgen?
DR. VENITZ: Yes, I wanted to commend you
for your efforts. Obviously, this is exactly where
the FDA can something contribute that
nobody else
can--
DR. CONTRERA: Yes.
DR. VENITZ: --because you're in the
possession of all this proprietary piece
of
information, you can perform meta
analysis using
qualitative methods.
A couple of comments: the first
one--right now toxicity is your main
endpoint.
142
DR. CONTRERA: Right.
DR. VENITZ: You're looking for predicting
toxicity--
DR. CONTRERA: Right.
DR. VENITZ: --or doses.
You might also
want to use similar methods to predict
biopharmaceutical characteristics, such
as
bioavailability, metabolic stability,
permeability.
DR. CONTRERA: Yes.
DR. VENITZ: Because, I mean, in the sense
of the Critical Path method, where you're
trying to
screen out, in silico, potentially bad
candidates--
DR. CONTRERA: Right.
DR. VENITZ: --that's, I think, number
one or number two on the list why drugs
fail. They
don't get absorbed, or they get
metabolized.
DR. CONTRERA: Yes, right.
DR. VENITZ: So that if you wanted to use
your resources, other than toxicology,
that would
be one thing to do.
DR. CONTRERA: Right.
DR. VENITZ: The second comment is maybe a
143
little less--or more farfetched, I
guess: and that
is to look at things like biosimulations,
that
don't use empiric models but, rather,
mechanistic
models to predict what might happen with
new
chemicals. In other words, you're trying to mimic
physiology--and, again, I think is think
this is
still in the infancy, in terms of
predicting
certain kinds of--
DR. CONTRERA: Right.
DR. VENITZ: --toxicity.
But it may come
in handy, in addition to those more
statistical
empiric predictive models.
DR. CONTRERA: Well, in terms of your last
point, with the mechanistic data, that's
why we
have a collaboration with University of
Leeds in
England.
Because they have an enormous amount of
experience with human expert
rule-building, and
LHASA Ltd. And they have a--their Derek program is
used all over Europe for predictive
modeling, and
that's based on getting data and trying
to--and a
human committee coming up with
mechanistisc rules,
based on--and so--but we felt that was
out of our
144
expertise, but it was way--it was exactly
what
they're doing. And that's why we're developing a
CRADA with that group. Because they are probably
one of the best, in terms of taking
statistical
modeling--Bayesian modeling--and teasing
out
rules--mechanistic rules.
And in terms of the ADME--of
bioavailability--you know, Dr. Hussain
has already
brought that up as a wave of the future,
and we
actually had discussions with Simulations
Plus, and
Ray Bolger, to get into that.
But we're going to do that with
those
people--within our group--that have
expertise in
that area.
My group is really, mostly
toxicologists
and chemists. So now we've got--and we don't just
leap into a new area until we develop
alliances
with people that are experts in another
field.
DR. VENITZ: One--can I make one last
comment?
CHAIRMAN KIBBE: Go ahead.
DR. VENITZ: It's not related to chemistry
145
as much as looking at biomarkers; and
that is
relating biomarkers to outcomes--either
pre-clinical or clinical outcomes, where
you could
use similar methods to--
DR. CONTRERA: Yes, I think it can be.
This is--you know, this is in its
infancy, but I
think it's an emerging science. It's great.
It's
really exploding.
CHAIRMAN KIBBE: Dr. Koch?
DR. KOCH: I just wanted to comment that I
think it's a very impressive
approach. Will there
be a follow-up, in terms of using this
type of data
as a way to enhance new drug discovery,
or some
examples when something some
together? Or is there
a possibility that it actually raises the
bar on
new drug discovery, because of
predictions?
Maybe a suggestion--unless
you've already
done it--maybe tie in with what Art has
suggested--but if you put into that model
some
already-approved past generation
pharmaceuticals--maybe some simple
things--
DR. CONTRERA: Yes.
146
DR. KOCH: --like acetaminophen or
aspirin or some steroids--and see how you
would
have predicted their--
DR. CONTRERA: Sure.
DR. KOCH: --present day efficacy.
DR. CONTRERA: Sure.
Sure. To some
extent that's part of what we do--what we
call our
internal validation, where you take
compounds out
of the system, then you have the system
predict
them--and not only predict them, but then
show you
what clusters of compounds that were in
the
database it used to make the judgment of
whether it
was going to be carcinogenic or not.
And, actually, that's the most,
I think,
enlightening tool, in terms of the
scientists.
Really, it's an interface. What we're tryign to do
is develop an automated expert. You know, when you
go to an expert, what does an expert
do? He says
he thinks--he has a good deal of
experience, and he
says, "You know, I've seen that
before in my years
of experience." And also, he goes to the
literature, and he--and so all we're
trying to do
147
is, to some extent, automate that, speed
up that
process.
We're still going to have the
human
interface, but people are so--you know,
get a
little bit suspicious of the machine, but
we're
asking the machine to do what we ask our
human
experts to do. But maybe it can do it a little bit
more thoroughly, you know. But you still have to
evaluate the output of the machine.
So one thing is good about many
of the
softwares is that you get the basis for
the
conclusion. And then you can judge and say, you
know, "This doesn't make sense. It says it's
carcinogenic, but the top 10--the
compounds that it
modeled in the cluster of compounds that
it used to
make the model, none of them
are--"--you know. So
you say, "This is junk. There's something wrong."
So you still--so you need good
trained
operators to be able to interpret.
CHAIRMAN KIBBE: Ken?
DR. MORRIS: Yes, thanks.
This is really
a nice presentation. I think it's pretty exciting.
148
The first thing an expert tells
you, of
course, is their rate--by the way.
[Laughter.]
My question actually deals more
with
mayabe what will be in the future, I
guess, because
at least as I understand from the
presentation,
that your descriptors are all based on
the
molecular structure.
DR. CONTRERA: Yes.
DR. MORRIS: And then responses--
DR. CONTRERA: Right.
DR. MORRIS: --which is the typical QSAR
approach.
I guess--and we were talking about this at
breakfast this morning--the thing that
sort of
comes to mind is the opportunity--or is
there an
opportunity, I guess is the question--to
use the
targets--that is the receptors or whatever
it is
that stimulates it, and do a more--what
would be a
more traditional, I guess, molecular
simulations
approach to actually backing into--the
reason the
rational drug design in many senses
didn't meet its
149
promise was because of the statistics, as
well as
the lack of knowledge of efficacy;
whereas here,
your same database should give you
significantly
more data--if you can identify the
targets, and if
there's--
DR. CONTRERA: The targets aren't
necessarily well-defined. And there are better
laboratories than us out there that are
doing
target, you know--modeling targets. And in the
pharmaceutical industry, that is their
domain.
And what we wanted to do is
what no one
else was doing.
DR. MORRIS: There are people modeling
targets?
DR. CONTRERA: Oh, yeah.
Yeah. They have
three-dimensional modeling of receptor
targets in
order to develop drug molecules--
DR. MORRIS: Oh, no, no, no--I don't mean
to develop drug molecules.
DR. CONTRERA: Oh, okay.
DR. MORRIS: I mean, to use the database--
DR. CONTRERA: Yes?
150
DR. MORRIS: --with targets, particularly
if you have structures for the targets--
DR. CONTRERA: yes.
DR. MORRIS: --to be able to go back and
calibrate this. Because the problem with the
people that you're talking about, and the
problem
they face every day is the vagaries in
their force
fields, as well as some of the other
tools they
use.
So, with this as an anchor, so
that you
actually have the data with which you
could
calibrate those in a sort of
semi-empirical
fashion--
DR. CONTRERA: Yes, that may--
DR. MORRIS: --it seems like you'd have a
big leg up.
DR. CONTRERA: Yes, maybe there would be a
complementary--you know--
DR. MORRIS: Yes--no, I don't think--I'm
not saying you should--
DR. CONTRERA: --yes, we stayed away from
that type of--but you're right. Yes.
151
CHAIRMAN KIBBE: Pat, do you have
anything?
DR. DeLUCA: Just--certainly impressive,
what you're doing. And I guess I'm wondering about
applying it to the product development
part of drug
development, in that once something is,
you know,
discovered--knowing it's a weak base, or
a weak
acide, knowing the PKA, solubility--some
of those
parameters that can be plugged into the
database
that would then a lot right in the
formulation
aspects--is there a salt form, if you're
looking
for a higher concentration that you may
not--is not
soluble in the form, the weak base; what
salt form
might be performed, a drug made?
So if the database can help in
that
product development scheme, to look at
formulation
aspects, I think that would be very
helpful.
DR. CONTRERA: Right.
I think,
again--that's something we got involved
in--I think
we get involved with, because I know it's
a big
problem for industry. It's one of the reasons why
drugs fail, in terms of bioavailability
and
152
solubility.
CHAIRMAN KIBBE: Najer--we're working our
way around the table. So I don't want to--
DR. SINGPURWALLA: Well, this is not a
criticism of you--[laughs]--but it's a
criticism of
the Price Waterhouse Coopers slide that
you put up.
DR. CONTRERA: Yeah?
DR. SINGPURWALLA: I think that slide is
very misleading. And I'd be very reluctant to put
it up.
And it's because of a slide like that that
our Chairman raised the question that he
raised.
The slide seems to give the
impression
that computers are going to address these
issues,
and it's going to make the primary
science
secondary. Now, the reason why I take objection to
this is because of the following: that any
model-building endeavor involves three
elements.
Element number one is the basic
science--that's the
physics, the chemistry, the
pharmacy--whatever have
you.
The second thing it involves is data, if
available. And the third thing it involves is the
judgment of the scientist--even in pure
theoretical
153
physics, the judgment of the scientist
plays a very
important role.
So, what the computer--and then,
there is
a theory, which helps you put all these
together.
So there are two theories: there is the theory of
the science, and the theory of the
fusion--how to
put all these things together. And the computer's
role is simply to facilitate the putting
these
three all together.
So I think one should be very
careful in
trying to highlight the role of the
computer here.
There is a parallel in what you're doing,
and what
is done elsewhere, in the context of
nuclear
weapons.
Similar problems are faced.
DR. CONTRERA: Sure.
DR. SINGPURWALLA: We can't talk much
about them, but I think you may want to
look at
what else is going on in that area, and
downplay
the role of computers, and not use this
Price
Waterhouse Coopers slide, because
obviously they
are a consulting firm, and they're going
to push
computers.
154
DR. CONTRERA: Well, I don't know--they
also are--I imagine, are involved in all
kind of
research beside computer research. They do
everything. They just look at markets in general.
But--and maybe there's--calling
it
"primary" and
"secondary" science, people that are
lab-based would say, "Oh, you've
made me a
secondary citizen" kind of
thing. And you can
change the term.
All we're saying, that the
emphasis is
goign to change. There's going to be more emphasis
on trying to model and predict; before
you spend a
lot of money on an experiment you better
make sure
the
experiment's worth doing--or it hasn't been
done before. And that's what we've been wasting
money for a generation.
CHAIRMAN KIBBE: Marvin Meyer?
DR. MEYER: Have you had any successes
yet, where the computer and the software
predicted
no toxicity, and the agency therefore did
not
require certain toxicological
testing? And I
assume the answer is "No, we
haven't."
155
DR. CONTRERA: We--
DR. MEYER: How close are you to that?
DR. CONTRERA: No, we haven't applied it
that we.
We're very careful about saying
that--we're not using this to make a regulatory
decision.
This is a decision support.
DR. MEYER: But you could.
DR. CONTRERA: But down the road maybe it
will be.
But right now we're not there yet--by any
means there yet.
But right now, it's being used
more and
more heavily by the pharmaceutical
industry, in
terms of their screening process. That's where the
big role is.
And, you know, it's just
like--I don't
know if you're familiar with--but when
Bruce Ames
came out with the Ames test--you
know--for
mutagenicity, all of a sudden everyone
started
using it.
It was an easy test. It was
relatively
inexpensive. The drug companies started mass
screening of all the compounds. And before you
know it--you know, we don't get
Ames-positives
156
anymore in the agency. Whereas we used to get
Ames-positive tests that were
compounds. They're
gone.
So that tells you that a testing paradigm
could have a big effect.
And so these programs that
predict
carcinogenicity will filter out those
rodent
carcinogens that are really--major rodent
carcinogens will disappear. And eventually people
are going to say, "You know, we've
been doing this
test.
We never get much positive anymore.
You
think we should--"--that's where I
want it to go.
It won't happen by fiat, it's going to
happen
by--but it's going to happen, you know.
CHAIRMAN KIBBE: Judy has a quick one.
DR. BOEHLERT: Yes, Judy has a quick
one--going out of order.
When adverse drug experience
reports come
into the agency, is anybody going back to
your
database and saying, "Could this
have been
predicted? Does it look like this is real? Or
could this be a fluke?"--you
know. "I wouldn't
expect it for this molecule."
157
DR. CONTRERA: They do.
Actually, they do
come to us. They come to us a great deal when
there's ambiguity--in test data, and they
can go
either way; you know, there's some slight
positives
in one test, it's like negatives on the
other. And
they'll use it sometimes, again, to try
to come in
and weigh on one side or the other. And that's
what we call "decision
support."
It happens a great deal in the
contaminants and degradants area. Now, a compound
comes up really late in development--all
of a
sudden they scale up, and there it's over
x-percent
that the ICH level, and a company said,
"Oh, it's
harmless--"--you know. And we say, "I don't know.
You've got to lower it."
And then what usually
happens--because I
was a reviewer for 10 years, and I was a
team
leader during that period of time. So I sort of
came up from the review ranks. And many times a
chemist would come running to me and say,
"Oh,
we've got to do something about--tell me
everything
you can do, as a pharm tox. What is it?
And is it
158
bad?" And I said, "How do I know?"--you
know.
And, you know, you look at it
and you say,
"Well, is it like something that's
real bad?" And
then you'll tell the company that they have to do a
tet, because you've got to close the
regulatory
loop.
I'd say, "Oh, do a two-week rodent study,
and if it's clean you can go
on." "And do an Ames
test." If it doesn't show a positive, then they
could probably go with over 2 percent.
Now, that's an answer, but the
chances of
getting any positive toxicity in a
two-week study
is zero to none. And they've already done an Ames
test probably, so you do it again.
So what I'm trying to do is
have a
rational basis for regulation, where you
go to the
computer, where you do a predictive
model; the
model gives you 20 compounds that are 90
percent
sinilar, and what their regulatory or
testing
history is. Now, you bring that to a reviewer and
you say, "You know, I think there
may be a problem
because this compound is like a
teratogen. It's 90
percent similar to a known
teratogen." So now you
159
can go to the company and say,
"Look, either you
can reduce it, because we have reason to
believe,
based on the literature, that it's close
to
teratogen. But if you don't think it is, do
a--"--now I can tell you exactly
which test to do.
"Do a segment 2 teratogenic
study. And if it's
negative, you're clear." Or reduce the level.
But I think that's a rational
basis of
regulation.
CHAIRMAN KIBBE: We need to start to close
this up.
So--because we've been having lots of fun
with this talk.
[Laughter.]
Go ahead.
DR. KARO:
Okay. I havea comment, and
then two questions.
First, I would take exception
to something
that you said early on, that we're still
using
toxicity tests from 50 years ago. You know, as a
toxicologist, we've made a lot of
progress.
DR. CONTRERA: Sure.
DR. KARO: And there are some new
160
tests--especially in sensitization; that
we're not
using the old tests.
The other is that with QSAR,
the quality
of the database is absolutely essential
to know.
How do you evaluate the quality of the
various
databases that you're using?
And, secondly, you mentioned
validation.
And that is, you know, critical. If you have a
human database, how do you validate the
predictions
from the human database?
DR. CONTRERA: Well, human database
validation is probably the--that's the
most
difficult. And we're not sure yet how to best
validate that. We're right now trying to devleop
models that are stable, and we validate
those by
looking at the cluster of compounds on
which the
decision was based to see if a human
expert would
agree that they did represent aspects of
the test
compound that made sense--you know?
In terms of data quality,
that's always a
problem.
And that's why we try to rely on data
that's already been screened by
committee. In the
161
case of--that's why--and one of the good
things
about carcinogenicity data is that we
have a
carcinogenicity assessment committee
within the
agency.
And the committee meets and decides on
what the study said. Because there's a lot of
ambiguity within the studies. And so we base it on
the calls of the CAC committee--calls in
our files,
going back many years.
And in terms of other
databases, we try to
base it on committee-based data sets--you
know.
Teratology--the tera agonist--there's a
lot of
organizations that have already, you know,
reviewed
a lot of this data and have published it.
But often, you know--that is
always a
problem with data mining. And my bottom line is to
predict a performance. Because if there's really a
lot of junk in the database, predictive
of
performance will go down. But if the data set has
good predictive performance, then you
have
somewhat--
DR. KARO: It's primarily prediction?
DR. CONTRERA: Yes, the predictive--and
162
how we validate, we do it two ways. We keep
compounds out. They're never in the learning
set--to use later, to see how well it
predicts.
And also we take compounds out of the
data set a
little out of time--
DR. KARO: Right.
DR. CONTRERA: --model and then, you
know--which is the traditional way QSAR
people do
it.
DR. KARO: Let me share and experience.
DR. CONTRERA: Yes.
DR. KARO: I developed a model for skin
irritation, using a human database--
DR. CONTRERA: Yeah.
DR. KARO: --that, using this internal
validation, was at 90 percent predictive.
DR. CONTRERA: Yeah.
DR. KARO: We then went and tested it on
humans, and it was like 30 percent
predictive.
DR. CONTRERA: Right.
And that's what
we've always been afraid of. And that's why we use
external validations a lot. And that involves--the
163
best external validations come from--in
areas where
there's a lot of data--you know? But most of the
time people try to put all the data they
can find
into the model, and then you have nothing
to test
it with--you know?
But because we're in the
agency, compounds
keep coming in. So we stopped collecting at a
certain point for the database, so we
have 1,200
compounds. We wait two weeks--or a year--we'll
have 24 new carcinogenicity studies. So we'll test
it against those, you know. And they represent new
drugs.
And so that's the best sort of
real-world
kind of testing that we try to do.
DR. KARO: And then you readjust the
model--
DR. CONTRERA: Yeah.
Yeah. And then we
go to the model. And so with our collaborators, we
tell them on a yearly basis, we have to
give them
an updated, you know, software.
CHAIRMAN KIBBE: Nozer is going to get the
last word in--I cant see it. And then we're going
164
to have to move on, or else we'll be here
'til
midnight.
DR. CONTRERA: Okay.
CHAIRMAN KIBBE: You're doing a great job.
We're really enjoying it.
DR. SINGPURWALLA: Well, the comment is:
the new paradigm, you said, is modeling
and
prediction. I would like to suggest that the new
paradigm be fusing of information from
dierse
sources, so that you get good
predictions.
DR. CONTRERA: Yes. Yes.
DR. SINGPURWALLA: I think the focus
should be changed.
DR. CONTRERA: Using it from everywhere
that you could possible find. And that's where
leveraging and collaborations are
essential. You
cannot do this alone. No one can.
CHAIRMAN KIBBE: Thank you.
Okay, thank
you very much.
Keith?
DR. WEBBER: The next speaker is Dr. John
Simmons, who is the Director of the
Division of New
165
Drug Chemistry I, in ONDC. And because we have to
start the open public hearing at 1:00, we
may want
to consider saving the last
speaker--Lawrence
Yu--until after lunch, perhaps.
CHAIRMAN KIBBE: Okay, thank you. John?
DR. SIMMONS: Yes, how much time do I
have?
CHAIRMAN KIBBE: John's slides are being
handed out as we speak. Don't go looking for them.
You have one-and-a-half
milliseconds. But just go
ahead.
[Laughter.]
DR. SIMMONS: I'll try to keep it as
focused as possible.
Office of New Drug
Chemistry
DR. SIMMONS: I guess, just a little
background. You know, the Office of New Drug
Chemistry is really where--is the
incubator for
this journey of change. And we'd like your
constructive comments and your input,
because we
are trying to change some paradigms, and
that's not
always a clear path.
166
[Slide.]
I just wanted to highlight four
things
that I'm going to talk about before I
leave. One
is the Critical Path Initiative, and
where we're
at--what our role is going to be; what
our current
regulatory research is--and I'll explain
that a
little bit more as we get to it; then, as
we look
to the risk-based initiatives, as a
paradigm for
review; and, lastly, what some of our
future goals
are going to be.
[Slide.]
Ajaz did a very good job of
outlining the
basic Critical Path components. And, obviously,
where our biggest impact is is on that
lower arrow.
We can certainly step in and help folks
that are
developing beyond discovery, but all the
way up
through large-scare manufacturing, and
that's going
to be our focus, I think.
Likewise, if you look at
industrialization, down at the bottom,
that's our
home; that's where we feel most
comfortable. The
Office of New Drug Chemistry looks at
small-scale
167
production, manufacturing scale-up,
refinement and
selection of specifications; and then,
finally,
large scale. And after that, post-approval changes
and refinement, once a product is up and
running.
[Slide.]
now, as regulators, and as a
regulatory
body, and as a person that's been
involved in both
the research and review and approval of
drugs,
along this Critical Path, if you looke at
some of
the areas where we can have a large
impact, I'd
like to draw your attention to the
pre-IND phases.
More and more, successful companies are
companies
that shorten their Critical Path by
coming in and
talking with us, and meeting with us.
There are invariably questions
that can be
raised, discussed--scientific issues--that
will
shorten their journey. And we certainly encourage
people to do that.
As you move fruther down the
clinical
development, once the IND is submitted
and the
phases start, certainly the end of Phase
2 meeting
is probably one of the more Critical
Paths along
168
that Critical Path. And a firm that is wise, a
firm that would like to minimize the
amount of work
that's done over and above what's
necessary, would
come in to an end-of-phase meeting and
meet with
all the disciplines--but certainly with
CMC.
Oftentimes I see, on a
day-to-day basis,
oftentimes products that are exciting,
that
companies are trying to develop in a
rapid fashion.
Oftentimes their development gets ahead
of the
manufacturing. And I think this is an area where
firms can come in and meet with us, pose
questions;
we can give some guidance. And I think it helps
them.
Another area would be prior to
submitting
an appliation. There is no way that we can review
and approve a new drug application in a
short
amount of time, unless we have interacted
very
thoroughly and very intimately with firms
along
that path. And I think that's something that I
always enocourage people to do when I
speak at
scientific meetings, and gatherings of
the
regulated industry.
169
Now, the Office of New Drug
Chemistry also
gets involved in research--usually
initiating
research.
And I have to be honest with you,
oftentimes it's very reactive; oftentimes
it's very
inefficient; and oftentimes it's very
focused.
The Office of Pharmaceutical
Sciences has
had the foresight to ut in place a
rapid-response
team, which helps us in that venue. When you're
reviewing an application, or you've just
reviewed
an application, or a problem has arisen
post-approval, oftentimes we need to look
at
scientific issues that the firms simply
no longer
are interested in--or simply aren't
equipped to do,
or simply refuse to do. And our rapid response
team has done a very nice job of being
able to take
very focused regulatory projects, put
them into
place as a research project, report back
the
findings, and help us make a
decision. And that's
something that we want to continue to do,
but I
think we want to do it in a more
proactive way; in
a way that helps us anticipate, rather
than be
reactive.
170
And that's one of the reasons we're
here. If you
drop down to that last point, I think--
we're
seeking your input, we're seeking your
guidance.
This is a journey that we are embarking
on, and I
think that's one of the strengths of a
committee
like this, is to validate and direct.
[Slide.]
Just as an aside, you know
we're currently
developing new paradigms. The office is
reorganizing. We've started a journey where, if
you look at chemistry, manufacturing and
controls,
we are trying to balance CM and C. We've spent an
awful lot of time looking at the
chemistry of
things, and now we're looking more
closely and the
manufacturing and the control of that
manufacturing--as an integral part of
this process.
So that is a journey that we're
not afraid
to take, but it will take some guidance.
We're also looking at a review
focus:
what should our review focus be? And we're also
looking at the research focus: how can the
research be focused to help us make
regulatory
171
decisions in a timely way?
[Slide.]
Just to illustrate some of what
I've been
giving you a prelude to: here are four topics that
have involved either regulatory or
regulatory
research activities. And I'll give you some
illustrations after I walk through some
of the
examples.
Conjugated estrogens--difficult
problem
for us; complex drug, mixture of actives,
not
always consistent. We need to look at ways to
fully characterize and establish criteria
for
pharmaceutical equivalence. And we've gone to our
laboratory research groups--we've got one
in St.
Louis and we've got one here in the
metropolitan
D.C. area--that have been very helpful in
that
area.
And I"ll illustrate that shortly.
Prussion Blue--very recent
example of a
compound that was used as--is to be used
as a
counter-terrorist measure; difficult
problem to get
companies involved with. You know, these are
medications and countermeasures that may
never be
172
used, or may only be used in a
catastrophic
condition. Companies are loath to do all the basic
research that are involved in developing
those
products.
During the review of this
product, we
looked to shorten the crticial path, and
we
involved our rapid-response team to look
at
surrogates--in vitro surrogates--for
binding of
this particular compound. It's a ferric cyanide
compound--a complex salt. It does a nice job of
binding some of the radioactive nuclides
that are
around.
And the company that was--the companies
that were involved in developing these
products
certainly didn't havea lot of
information, or
clinical human experience to go on.
There were issues about the
binding
capacities, and what impacted those
binding
capacities. There were also issues of the release
of free cyanide. What happens to these compounds
upon storage, or use; you know, do we
generate
toxic--is the cure worse than the
prevention.
Inhalation products--another area where
173
comparing products across products is not
always
easy, and we invoked our research teams
to look at:
how do we develop in vitro methods to
establish
pharmaceutical equivalence? How can we look at
particle size, spray pattern and chemical
imaging
as techniques to help us come up with
standards by
which we can evaluate these products?
And lastly--and more of a guidance
venue--we're looking now at the marvelous
combination of drugs and devices. We're looking at
stents that are put in coronary
arteries. We've
got a few on the market already. But in the
process of looking at athat it became
painfully
obvious to us that the roles that the
Center for
Drugs and Center for Devices played, and
how we
could interact, needed refinement, needed
focus,
and needed agreement. And we're working feverishly
on some joint guidances so that these
products can
be approved in a more timely fashion.
[Slide.]
I said I wanted to illustrate a
few
issues.
Conjugated estrogens--when we asked our
174
research laboratories to get involved in
these
products, we asked them to look at
complex--look at
a complex mixture and tell us, in a
systemic way,
how we can actually measure them.
And the laboratory out in St.
Louis did
some marvelous work using LC mass spec
combinations
to do just that. Here is a total ion chromatogram
of all the various components.
[Slide.]
And here are some of the
individual
identities of those particular
components. And
they can be identified and
quantitated. And that
helped us in focusing some of the
questions that we
would, in turn, ask our innovator
companies
non-innovator companies.
[Slide.]
With respect to the Prussian
Blue issue,
this was an area that was not too
familiar to the
center.
You know, Prussian Blue is an inorganic
therapeutic, and it's been a long time
since we've
seen inorganic therapeutics in the
agency.
We needed to have a better
sense of what
175
to do with things that were largely
insoluable; how
to look at those, how to evaluate
those. So we
evoked the laboratory to take a look at
them, and
they gave us a very nice idea of what to
expect
when we look at APIs; what types of variations
could we see with time, as to binding;
what are the
batch-to-batch variations--and, in fact,
we saw
some.
And it helped us focus some of the issues
that were involved in the approval.
[Slide.]
Likewise, this material can be dried.
And, as lots of inorganic salts,
oftentimes water
is trapped in the matrix--in various
matrix holes.
And the level of hydration can have a
marked
difference on the ability to bind a
nuclide.
[Slide.]
On to the issue of looking at
inhalation
products.
Our laboratory set up some very nice
work that helped us focus what plume
dimensions
mean to a product; or what spray
pattern--how could
spray patterns be chemically imaged so
that we
could look, and compare products across
product
176
lines to come up with some ocnsistent
questions to
ask firms.
[Slide.]
Now, I'd like to move on to the
risk-based
CMC review paradigm, and that's something
that's a
little different than what we've been
doing in the
past. In the past we've relied largely on
the
science and the guidance--and by
"guidance," I mean
guidances that we ourselves have writen,
guidances
that have been written by international
bodies,
such as International
Harmonization--ICH. We're
moving away from that paradigm. We're tryign to
move from review by guidance, into review
by
science and review by risk. And there are clearly
some benefits.
To patients, the obvious ones
are faster
approval of products, increased
availability,
continued supply. For the FDA, obviously, there's
more product and process knowledge; more
efficient
allocation of resources. If we do risk-based
review versus guidance-based review,
where does
that lead us? And obviously the one thing that
177
probably is the intangible that is hard
to
evaluate, and that is the increase in
trust and
understanding that occurs between
companies that
are submitting new data to us, and the
reviewers
and people that approve those
products. I think
that's an invaluable aspect. If we keep things on
a risk and a science basis, I think it's
much
easier to talk and come to conclusions.
[Slide.]
To industry, obviously it's
more efficient
and science-based inspections. Now that's an
interesting paradigm, as well. Those of you who
are from the biologics venue have seen
team
biologics, where reviewers and
investigators go out
to sites.
We've been exploring that in CDER for
small molecules, but not nearly to any
organized
fashion.
And I think you will see that in the
future.
And I think there's value to that.
There are faster and more
consistent
reviews.
If the manufacturing and the science and
the chemistry are looked at in a more
balanced
way--not only at headquarters, but also in
the
178
field, there's potential for reduced
regulatory
burden.
The issues of changes and
nonconformance
requires less FDA oversight, if you draw
it to its
extreme.
We can focus resources on critical issues
that way.
We can make judgments asto what's more
important.
And then there's flexibility on
focus as
to what's to be done, rather than what
can be done.
And I think Judy raised that issue. At some point
we have to tell people what we would like
to see,
and that's not always an easy issue to
come to an
agreement on.
And, obviously, it also
improves
communication with the agency. You have to
communicate with the agency if you want
to use a
risk-based approach.
[Slide.]
One of the paradigms that our
Center
Director, at the time--Janet Woodcock,
who is now
up at the Commissioner level--raised the
issue to
us was:
you know, how do we link quality
179
attirbutes to clinical performance? How do we link
values and specifications to safety and
efficacy?
And how do we link our inspectional
process to
those same issues. That's not always an easy line
in which to draw the dots.
[Slide.]
Under the new quality assessment
paradigm
that we're currently lo9oking at,
obviously
risk-based assessment is high on the
list; clinical
relevance is high on the list; safety
considerations is high on the list.
The process capabilities are
also high on
the list.
At what point do process capabilities
become a limiting factor? At what point to process
capabilities give us a venue of
guidance? One of
the problems that often happens in rapid
development of drugs is that firms don't
have the
luxury of making large numbers of batches
of
things.
And I think process capabilities can be
used both as a sword and it can also be
used as a
guide.
And I think we're looking toward that
paradigm--that guidance paradigm.
180
The knowledge gained from
pharmaceutical
development reports--you know, one of the
wonderful
things about ICH is that we're into this
paradigm
of sharing information and explaining how
you came
to the conclusion that this was the
optimum
formulation. And process development reports are a
window into that. And I think we would like to
utilize those better as companies move into
that
paradigm.
And then, obviously, the better
utilization of statistical methodologies.
Statistical proces control, I think, is a
way of
the future. I think companies are implementing it
in small ways now, but I don't think that
firms
have had the luxury of developing it on a
large
scale--at least not the drug industry in
this
country.
We're looking at assessment,
starting from
the comprehensive overall
summary--something that
ICH has given us as a paradigm to look
at. At what
point can we look to the firm to
summarize some of
the issues that are involved, rather than
us
181
looking at all the raw data and coming to
our own
conclusions?
Good review practices, and good
scientific
principles--current good scientific
principles--I
think that's probably going to be
something you'll
hear more and more about.
Increased emphasis on
manufacturing
sciences--as we move into the new
paradigm of the
Office of New Drug Chemistry, we are
building a
manufacturing science team. We're currently
identifying and hiring people that have
had
large-scale, hands-on manufacturing
experience. It
will be very interesting to see how we
incorporate
that into the review process. I'm looking forward
to it.
The use of critical and peer
review of our
evaluations--you know, the paradigm up to
now has
been one reviewer, on review, one
product. I think
we're going to be working more on a team
basis in
the future, and I think we're going to be
looking
at critically evaluating ourselves as to
what
questions were asked and what decisions
were made.
182
And then, lastly, this
integration of
review and inspection--I, for one, have
always
encouraged people in my unit to accompany
investigators whenever possible. But there's a
different between accompanying an
investigator and
being an integral part of making the
scientific
evaluations on that site. And I think that's the
paradigm we're moving towards.
[Slide.]
If--my arrows disappeared. What happened?
These are all connected by
arrows, but I
want to draw your attention to the lower
boxes.
VOICE: [Off mike.] [Inaudible.]
DR. SIMMONS: One more click, you think?
By George, you're right. Let's see how many clicks
it takes.
[Pause.]
Great. Thank you.
Draw your attention to the
lower boxes:
quality by design, product development
report, and
comprehensive overall summary--quality
summary.
We're looking at those to feed into
183
risk-based quality assessment, and reduce
time
review.
And, ultimately, if we want to reduce that
Critical Path we want to move towards
first-cycle
approvals--especially when it comes to
the
manufacturing venue.
We have little control over the
toxicity,
little control over the efficacy, but we
can
control some of the manufacturing
issues--early on.
[Slide.]
What's in the regulatory
future? I think
we see increased CMC-only meetings; by
that, I mean
all disciplines certainly meet as a team
with
manufacturers, but there are issues tha
may involve
only the manufacturing, chemistry or
controls, in
which we can meet with industry and
discuss
specific issues, to shorten that Critical
Path.
Quality by design initiatives;
IND
Guidances--how can we better help firms
formulate
what quality we'd like to see, at what
levels as
you move through the graded phases of
development.
Obviously, we have to be flexible on
things like
this.
And I think the more information that we
184
look at earlier on, the better off we'll
be. But
it puts an awful lot of pressure on
industry to
develop those data.
Process Analytical Technologies
has abeen
a driver in the Center. We're looking more and
more at looking at in-line, on-line--or
at-line--analyses that have feedback
loops to
manufacturing. We're seeing it more and more.
The integration of review and
inspection--I've already talked about
that.
Strategies to facilitat first
cycle
approvals--we'd like your input on that.
Combination products--we're now
entering a
wonderful world in which devices and
drugs are
being approved together; where the device
is either
delivering the drug, or the device is
carrying the
drug to prevent some secondary impact, as
in
drug-eluting stents.
Also, with biological-type
products--so
not that the proteinaceous drugs are
within CDER,
we can look more closely at biological
small-molecule combinations. That's the way
185
they're used in real life, and I think now we
can
start looking at them in a more coherent
fashion.
Nono-particle technology--where
will that
take us?
How will we evaluate the size and shape
and impact of that type of technology on
drugs--not
only how they're manufactured, but what
the
toxicity and efficacy of those drugs
are. We now
have in the pipeline nano-technology
products, and
they present some very, very interesting
questions.
And I don't claim to have all the answers,
and I'm
looking to--I think we're looking to the
committee
to give us some guidance on things like
this.
[Slide.]
Some of our immediate next
steps are
obviously implementation of the PAT
Guidance--Process Analytical
Technology. I've had
the wonderful opportunity to work with
teams of
people that we're training to send out to
look at
these products. You know, we've just come off of a
very long journey where we had
investigators and
compliance officers and reviewers exposed
to the
same type of information, and trained as
to what to
186
look for when you're looking at process
analytical
technologies. And I think we're ready to start
seeing the fruits of that labor.
Revision of CMC guidances--can
we make the
guidances more science based? Can we make them
more commonsense? Can we make them far less
checklist in nature?
Combination prodcut
guidacnes--obviouskly
that's an area that we have to look at
very
closely,
And this integration of review and
inspection--what questions can be asked
here? What
questions have to be asked and answered
on a plant
floor?
[Slide.]
I think the two major future
goals are:
to establish a meaningful regulatory
program that's
science-based, that supports drug
deevelopment and
review.
I think we're partners in this process.
We're not simply a hurdle.
And I think the other one
is: to explore
regulatory mechanisms to speed that
process, or
shorten that Critical Path.
187
So I think I'd like to bring
this to a
close, and open it up for questions, and
ask you to
think broadly about some of those issues.
CHAIRMAN KIBBE: Are there any questions
for our speaker?
Good. Go ahead.
DR. MORRIS: this is a relatively short
question.
I think, when you're talking
about the
integration of review and inspection,
which is a
question I get a lot as I visit the
companies--
DR. SIMMONS: Yes.
DR. MORRIS: --but is the limitation
organizational? Or resources?
DR. SIMMONS: I think both.
I think what
we're seeing is that in the current
paradigm, where
there's one reviewer and one application,
and one
product, scheduling can be a terrible
problem. I
think, as we move to separating
pre-approval from
post-approval, and allowing people to
focus on
developmental and NDA issues, I think we
will see
more and more structural inspections
involving the
188
reviewer.
I think the other issue is the
resources
of the field. Obviously, to put two people or
three people together at a site requires
intense
scheduling, the availability of
resources--and
pre-inspection conferences. You can't go into a
plant without a plan.
DR. MORRIS: Yeah.
DR. SIMMONS: And I think that's the type
of thing that we're up against. And I think we'll
be--I'm pretty confident we'll be able
to--
DR. MORRIS: But there's no inhibition
to--
DR. SIMMONS: I don't think so. I don't
think so.
I think it's only limited by our own
resources and biases. Yes.
CHAIRMAN KIBBE: Joe?
DR. MIGLIACCIO: Just following up on
Ken's question--you talk about what
question's
asked here, what questions on the plant
floor.
Remember the scientists who develop the
formulation
and
the process are not on the plant floor.
189
DR. SIMMONS: Good point.
DR. MIGLIACCIO: So we need--
DR. SIMMONS: [INAUDIBLE] made available.
DR. MIGLIACCIO: Yes.
Yes, they are made
available. But we have to have a good discussion
between industry and FDA about where the
division
is.
DR. SIMMONS: Yes.
DR. MIGLIACCIO: What questions--
DR. SIMMONS: I agree.
DR. MIGLIACCIO: --are appropriate for
the plant floor.
DR. SIMMONS: I agree.
DR. MIGLIACCIO: We don't want to be
having detailed formulation discussions--
DR. SIMMONS: No.
No.
DR. MIGLIACCIO: --with pharmaceutical
engineers on the shop floor.
DR. SIMMONS: No. I
agree with that. But
on the other hand, I think it--a picture
is worth a
thousand words. If you're looking at process
analytical technology development, you're
looking
190
at the placement of sensors.
DR. MIGLIACCIO: Sure.
DR. SIMMONS: I think there's no
substitute for looking and touching those
pieces of
equipemtn.
DR. MIGLIACCIO: And if I could just make
one more comment--you talked about
statistical
process control--not heavily used. Actually,
statistical proces control is somewhat
pervasive in
the industry. The problem is, the statistics are
being applied to data that is being
gathered for
compliance purposes.
DR. SIMMONS: Yeah.
Yeah.
DR. MIGLIACCIO: And I think we're
shifting away from that now; that we're
now willing
to gather data for scientific purposes--
DR. SIMMONS: Right.
DR. MIGLIACCIO: --not compliance
purposes.
DR. SIMMONS: Well, thank you--good
clarification.
CHAIRMAN KIBBE: Anyone else?
191
DR. KOCH: John--I know you participated
in the training with the combination
reviewers and
inpsectors. And that continues to come up. And I
know it's difficult for the scheduling,
but
anything that can be done to encourage
increased
involvement in the training, so that you
have more
of a base to draw from for setting up
the--
DR. SIMMONS: I couldn't agree more. I
think there's no substitute for that
hands-on
experience. I think it's valuable.
CHAIRMAN KIBBE: Anybody else?
If there are no further
questions--
thank you.
I have logistics question. We have one
speaker for the open hearing, and we are
at noon.
And we have one more speaker that fits
with this
set.
So the question really is: shall
we go ahead
and run long, and get Dr. Yu done before
we break,
and come back late? Or do we want to fit him in
after the open hearing, before we start
the next
set?
And what would make more sense?
192
DR. HUSSAIN: I think the open hearing
time cannot change. I mean, that's the
restriction.
CHAIRMAN KIBBE: Well, if we have only one
person on our list--so.
I mean, if we had an open
hearing and the
time is used in 15 minutes and we're
done, and
there's no one else, then we can put him
in there.
DR. HUSSAIN: Yes, definitely.
Definitely.
CHAIRMAN KIBBE: All right.
Okay.
So we will then apologize to
our next
speaker, and have him have to give his
presentation
on a full stomach--
[Laughter.]
--which, hopefully, will make
him more
comfortable.
We will now be at recess until
one
o'clock.
And if the members of the committee will
hang around, we'll discuss with you lunch
plans.
[Off the record.]
CHAIRMAN KIBBE: I see by the clock on the
193
wall that we have rapidly approached the
one
o'clock hour, which means that we will
entertain an
open-hearing presentation.
Open Public Hearing
CHAIRMAN KIBBE: Dr. Saul Shiffman?
Please identify yourself.
DR. SHIFFMAN: I will do.
CHAIRMAN KIBBE: And then you can go ahead
and do your presentation--appreciate it.
DR. SHIFFMAN: Well, thank you for your
time.
I'm just going to take you on a brief
excursion to some fairly different territory
than
what you've covered this morning.
[Music.]
My name is Saul Shiffman. In my day job,
I'm a research professor of
pharmaceutical
sciences, psychiatry and psychology at
the
University of Pittsburgh.
Ooop--but today I'm here as
Chief Science
Officer of invivodata, inc., which
provides
clinical diaries for--electronic diaries
for
clinical trials.
194
And, in a sense, I want to
shift the focus
for a moment from the focus on drug
discovery,
screening and manufacturing, to the
testing of drug
products and devices in human clinical
trial; and
also, in a sense, to shift from the sort
of
ambitious initiatives considered under
the Critical
Path Initiative that require new science,
new
technology, new regulation, toward an
example of
some of the kinds of things that can be
done with
current science, current technology,
current
regulation.
So--briefly, I'm going to talk
about the
use of diaries in human clinical trials,
and the
different methodologies that are in
place,
basically talking about the fact that
paper
diaries, which are in wide use, have
serious both
scientific and regulatory, as well as
operational
problems, whereas newer technologies fall
within
the regulations and solve these
operational and
scientific issues; and that the FDA can
facilitate
the development of those newer
methodologies.
So, briefly, stepping
back--while
195
obviously many clinical trials are run
with hard,
biological endpoints, it's not uncommon
that key
endpoints are what are call "patient
reported
outcomes," either because they're
subjective
states--such as pain, which can't be
gathered any
other way--or because the patient is
often, if you
will, the most privileged observer to
report on
certain events which are objective, but
which the
patient is in the best position to observe.
[Slide.]
And, in fact, patient report
outcomes are
collected in nearly three-quarters of all
trials,
across all four phases of drug
development. An FDA
audit showed that they were present in about
a
third of NDAs. And diaries, in particular, are
used in about a quarter of trials. And, of course,
the function of diaries is to get the
data in real
time in order to avoid the pitfalls of
recall.
The traditional method has been
a paper
diary.
And if you've ever done a diary study, this
may bring back some memories. Operationally, there
are a lot of issues. Diaries often contain errors.
196
They're often illegible and therefore, on
both
accounts, fall under the regulatory
standard as a
problem; but also operationally, in
trials
containing diaries, the diary is usually
the last
source of data that's processed. And so it becomes
literally the item on the Critical Path
that slows
completion of the diary.
A number of academic groups, as
well as
industry providers are providing
electronic
diaries, and audits show that they reduce
errors
and the need for data cleaning very
dramatically--by 98 percent--because of
the ability
to filter the data at its source, and
therefore
provide operational efficiencies.
But what's important is the potential for
the diaries also to provide enhanced
validity.
And, really, the biggest concern about
paper
diaries has always been that they're not
completed
in a contemporaneous way. Anyone who's ever done a
diary study has probably seen patients
filling them
out in the parking lot, or in the waiting
room.
And, in fact, the field has coined a
phrase of
197
"parking lot compliance."
That's been anecdotal. Let me show you
some more formal data.
[Slide.]
We did a study with pain
patients. This
shows you the data that's usually
available from a
paper diary. And it shows that the patients
returned the diary cards reflecting that
90 percent
of the diary cards had been completed in
inappropriately timely way. And the problem is
that all we have is--in other words, this
is what
was noted on the card.
The innovation in this study is
that we
had developed an electronically
instrumented paper
diary that, with photosensors, made a
record of
when the record was actually filled out,
so that we
could try and verify the patients' report
of timely
compliance. And the data were rather
dramatic--which is that if you look at
the actual
records, only 11 percent could
conceivably have
been filled out at the appropriate time;
in other
words, 79 percent of the returned records
were
198
either inaccurate or falsified.
Importantly, we observed
hoarding, which
is to say on one-third of all days, the
diary
wasn't opened the entire day, and yet 96
percent of
the diary cards were returned for those
days.
What we never expected to
observe, but did
observe, was forward filling; that is,
that
patients would--
[Laughter.]
--today, on Tuesday, fill out
their
reports for Wednesday, Thursday and
Friday. It
made me think that I wanted to stock
advice from
these folks--
[Laughter.]
--since they could tell the
future.
So, clearly, there are very
serious
problems that go both to meeting the
regulatory
standard--accuracy and contemporaneous
completion--but also, as you'll see, go
to the
issue of scientific validity.
And, in contrast, we had a
group that had
been assigned to use an electronic
diary. And, in
199
fact, they completed 94 percent of the
entries in a
verifiably timely way. So there is a solution to
this problem of diary completion.
So what is the benefit, then,
for clinical
trials of improving the methodology?
[Slide.]
And, if you will, the
hypothesis--the
compelling hypothesis--is that by getting
data in
real time you reduce error, which makes
trials
statistically more efficient, with
greater power,
and therefore you have both more
efficient--that is
smaller--trials, and essentially more
reliable
trials whose answers can be relied upon
better.
And, in fact, to try and
validate this, a
couple of groups have done analyses
comparing paper
and electronic diaries--of the same
phenomenon;
essentially parallel studies.
[Slide.]
And what you see is, in fact, a
one-third
reduction in error variance; essentially
a damping
out of the noise, which translates into
roughly a
50 percent decrease in the sample size
required for
200
those trials.
So this improvement in
measurement can
produce smaller trials, more reliable
trials, and
possibly fewer trials, in the sense that
trials are
often re-done because the first one
failed.
[Slide.]
So, in essence what we have here
is a
situation where the science, the
technology and the
regulations are already in place. You may not be
familiar with ALCOA--it stands for
"attributability, legibility,
contemporaneousness--"--I forget
what the "O"
is--and accuracy. So, essentially, there are the
existing standards, but they haven't been
applied
very systematically to diaries.
[Slide.]
So, what is needed? Really, what's needed
is not new regulation, but for the FDA to
apply its
existing regulations in a consistent
way. At the
moment, some of the older technologies
are getting
a pass on the regulations, in terms of
accuracy,
originality, all of those criteria that
the FDA has
201
set.
And essentially, it's not so much that FDA
has in any way ruled out electronic
diaries, as it
has left room for FUD--is "fear,
uncertainty and
doubt." Industry regulatory folks are not known
for being adventurous. And so without clear
statements from the FDA of its own
policies, this
has hampered the methodological
development of the
field.
[Slide.]
So, essentially, as I've said,
there's now
not just anecdotal but quantitative and
formal
evidence that paper diaries fail both to
meet
regulatory standards and scientific and
statistical
standards; that methods are available,
and what is
needed, as a small step available today,
is for FDA
to speak clearly about its interest in
newer
methodologies.
[Slide.]
The issue of innovation has
been with us
for
a long time. This is a statement from a
scholarly journal you'll be familiar
with: "That
it will ever come into gneral use,
notwithstanding
202
its value, is extremely doubtful because
its
beneficial application requires much time
and gives
a good bit of trouble, both to the
patient and
practitioner, and its foreign to our hats
and
associations." This statement was made in the
London Times, in 1834, and it referred to the
stethoscope.
So, initially, most innovations
are
resisted, simply out of inertia. And I think part
of the Critical Path Initiative has to be
for the
FDA to facilitate the adoption of
improved
methodologies.
Thank you very much for your
time and
attention.
CHAIRMAN KIBBE: Thank you.
Anybody have any quick
questions--clarify
the information?
Marv?
DR. MEYER: Two questions: one, do most
of the electronic diaries have a
provision for an
open-ended response, or an adverse event
that isn't
in the database?
203
And then, secondly, coming from
the great
state of Florida--
[Laughter.]
--where I see a great hesitancy
to launch
into this modern electronic voting--they
much
prefer having paper--
[Laughter.]
--do some of the recipients of
this device
that are participating in a study have
resistence?
DR. SHIFFMAN: Let me take the questions
in turn.
The diaries can have provisions for
open-ended text. And, literally, you can use
handwriting and record the visual image;
or, more
commonly, you can provide a little
keyboard, and
people can type small comments. It varies with the
protocol whether that provision is made
available
or not.
And to, in essence, amplify
what's behind
your question, sometimes, indeed, one of
the
reasons paper diaries are so messy is
that people
write marginal notes, and a few of those
have some
clinical relevance. You'd like to be able to
204
capture those, as well.
In terms of patient resistence,
that's
really been very little of an issue.
I showed you
the data from this pain study. We replicated those
data in a COPD study, where the average
age of the
patients was in the 60s, and we've done a
study of
medications for prostate cancer, with
average age
in the 70s. And, in general, we get not only good
acceptance, but, if anything, we've done
analyses
to show that the performance of older
patients is
actually better.
So I think we have a bit of
ageist bias,
thinking that this is only going to be
for teenage
computer nerds. But there's just a lot of evidence
that this is well accepted and well used.
CHAIRMAN KIBBE: Okay.
Well, thank you
very much.
MS. SHAFFER: Thank you.
CHAIRMAN KIBBE: We now will finish up our
morning's activities.
Lawrence is ready to give us
his 25-minute
presentation in 12-1/2 minutes--to show
you the
205
level of efficiency, when we apply PAT to
presentations.
Critical Path
Initiative--Challenges
and Opportunities -
Continued
Office of Generic Drugs
(OGD)
DR. YU: I think I have 45 minutes,
right?
Until two o'clock. [Laughs.]
CHAIRMAN KIBBE: I do have a priority
button.
DR. YU: Okay.
I've got it.
After 15 years' graduating from
Ajaz, I
guess I still look at his students.
Good afternoon, everyone. Chair and
members of FDA Advisory Committee for
Pharmaceutical Science, and my FDA
colleagues and
distinguished guests, it give me great
pleasure and
privilege this afternoon to discuss with
you FDA's
Critical Path to medical product
development
opportunities to generic drugs.
[Slide.]
As discussed this morning, the FDA's
Critical Path encompasses three aspects,
namely:
206
safety, efficacy and quality.
I want to emphasize that the
path to new
drug development does not end with the
approval of
the NDAs, but it continues with
monitoring of
post-approval changes, post-approval
manufacturing
optimization, and eventually the
development of the
generic drugs. In fact, the generic drugs is an
integral part of the USA health care
system, as
pointed out by our President Bush, on his
October
8 th second
Presidential debate: "Tahere
are
other
ways to make sure drugs are cheaper. One is to
speed up generic drugs to the markeplace,
quicker."
So U.S. government looking for generic
drugs to
limit increase in drug price, while our
fellow
friends--American consumers--looking for
access to
low cost, high quality, efficient, same
efficacy,
and same safety, generic drugs.
[Slide.]
So let's back to the Critical
Path
Initiative, as Janet Woodcock pointed
out--which
you saw this slide in the morning--the
FDA's
Critical Path Initiative is "A
serious attempt to
207
bring attention and focus to the need for
targeted
scientific efforts to modernize the
techniques and
methods used to evaluate the safety,
efficacy and
quality of medical products as they move
from
product selection and design to mass
manufacture."
So, when we apply this to
generic
drugs--let's define what is a generic
drug.
[Slide.]
The generic drug is basically a
therapeutic equivalent to a brand-name
product. So
it would equivalent is defined as a
pharmaceutical
equivalent and bio-equivalent.
So in more term, is a generic
drug is a
comparable to a brand-name drug products
in dosage
form, strength, route of administration,
quality
and performance characteristics and,
finally,
intended use.
[Slide.]
When the Critical Path
Initiative defined
the safety, efficacy and quality as
applied to
generic drugs, we define as
bioavailability,
bioequivalence and quality. As you know, that
208
generic drugs not only should high
quality but,
more importantly--equal importantly, you
know, make
sure they're equivalent in terms of
pharmaceutical
equivalent and bioequivalent and
eventually
therapeutic equivalent to brand-name
products.
So, therefore, my talk covers
the
following three aspects:
[Slide.]
Bioavailability and
bioequivalence
modeling and prediction; bioequivalence
of locally
acting drugs; product design,
characterization and
in vitro performance testing.
Now let me talk on the first
topic:
bioavailability and bioequivalence
modeling and
prediction.
[Slide.]
Now, this is the sketch which I
made a
couple years away for my talk with Gordon
Research
conference. At this time I swear I think I
invented new term: e-ADME.
One time actually I
asked my son to register e-ADME as a website,
end
up like the web site was registered 24
hours ago.
209
So I lost that opportunity to register
web site for
e-ADME.
The basic fundamental is connect with your
control this morning's talk is the e-R
and
D--e-research and development. Here, ADME means
"absorption, distribution,
metabolism and
elimination." So basically e-ADME is electronic
ADME.
In terms of predicting
bioavailability and
bioequivalence, or bioavailability--if
you look at
the approaches of predicating forecast
the
bioavailability, bioequivalence, there's
two
approaches to get there. One is experimental
approach.
You measure solubility, you measure
permeability, you measure metabolism, you
measure
protein binding, and you measure many,
many others
as development scientists did in their
discovery
stage.
From those pharmaceutical
measurements,
you select the so-called pharmaceutical
leads. The
leads will be--a number of select leads
will go to
animals, hope from animal models to
predict
210
bioavailability information for humans.
Now, another approach--which I
will
highlight here--is computer modeling
approach.
I use red here--biopharmaceutics
classification system; compartment
absorption
transit model--or CAT model--and
quantitative
structure bioavailability
relationships. Now
this--I put this slide basically as those
research
is going on in FDA, by no means
incompatible,
because we know, for example, in this
slide we did
not include one of the very well known
approaches
from Pfizer, and in this case Rule 5.
So let me go through each one
of them very
briefly--with I think Dr. Jugen Venitz
discussed
this mornign.
[Slide.]
First, look at he
biopharmaceutics
classification system. The biopharmaceutics
classification is a scientific framework
to
classify drugs based on solubility and
permeability. These two parameters--solubility and
permeability--each parameter has two
levels, you
211
end up with four classes, namely: class BCS Clsss
I, Class II, Class III and Class IV. Class I is
highest solubility, high permeability;
Class II is
low solubility, high permeability; Class
III is
high solubility, low permeability; and,
finally,
Class IV is low solubility, low permeability.
Four years ago, in 2000, the
FDA issued a
guidance to waiver of bioavailability,
bioequivalence studies for highly
soluble, highly
permeable drugs--those rapidly dissolving,
immediate release dosage forms. With issuing the
guidance, does not necessary mean
investigation
research within FDA stopped. In fact, we are
continually exploring possible bi-waiver
extensions
for BCS Class III drugs, namely high
solubility,
low permeability drugs; we're
investigating the
effect of sepins on absorption. We're
investigating transporters--for example,
p-glycoprotein transporter
absorption. We're
investigating refinement of the BCS
classification
system.
So research is very active
within FDA, as
212
is shown here. We have three publications so far
for this year alone.
We blieve the biopharmaceutics
classification system not only its
utility in
regulations, but also has its utility in
drug
discovery and development. This is because the BCS
system can help you to select a proper
dose form;
can help you design a formulation; can
help you to
see what could be issue down the road in
the
development process.
[Slide.]
So, let's move on to next
topic,
which--next, the model, is what we call
the
"compartmental absorption and
transit model." Now
this model has become a software which
was
mentioned this morning, called
"Assimilation Plus."
I have a disclaimer: I have no financial tie
whatsoever with Assimilation Plus."
This is a basic software based
on this CAT
model, which originally developed by
myself long,
long time ago at the University of
Michigan, under
professor Kodio Miro.
213
This basically, basically as a
mechanistic
model, describes how a drug gets into the
blood;
how much it gets into the blood; and how
fast it
gets into the blood. So it's considering the
impact of gastric emptying--for example,
after
lunch, gastric emptying time's probably
four hours.
Before the lunch, only 20s and half
hours. We look
at--we incorporate the effect of the
small
intestine transit time, blood flow,
volume,
dissolution, permeability, metabolism,
distribution
and conventional pharmacokinetics.
The research going on is
continue to
identify critical bioavailability or
bioequivalence
factors.
For example, if you look at this
beautiful suface here, on left side--or
right
side--this is what we call the
"Surface of
preferable properties as a function of
solubility,
permeability, hepatic clearance and potency." Now
this is surface of purely calculated,
based on
computer model, basically give you some
idea what
potentially bioavailability will be for a
new
molecule which just even have not been
synthesized,
214
based on the solubility and permeability
and
hepatic clearance you get some idea what
to the
degree of bioavailability of the drug
itself, of a
compound above this surface--above this
surface.
This means that bioavailability will
likely below
30 percent; below the surface
bioavailability will
likely higher than 30 percent.
Now this is the calculate of
the
theoretical model has not been validated. We are
planning to use FDA data to validate this
surface
for the benefit of the public health.
[Slide.]
The next--the slides basically
show you
the quantitative structure
bioavailability
relationship model. Now this model, if you look at
the top left, that's basically is the
structure and
bioavailability relationship. It's based on 691
drugs whose human bioavailability
actually is
available within the--in the public
domain. If you
look at structure at the activity
relationships or
bioavailability versus structure, you've
got a
correlation coefficient .71. Now, if you look at
215
it statistically, that's .71 very low.
Now, we look at these 691
compounds--this
model--to predict the drugs which were
approved
around 2002, which we have 18 drugs. These 18
drugs never been utilized to QSBR
models. The
correlation coefficient is 0.62.
Now if you look at the
bottom--look at the
rat and dog, how animal predicts
human? The
correlation coefficient for rat is .41,
while the
correlation coefficient for dog is
.43. So this I
can--for this system, for this drug--for
those
drugs which were evaluated, the computer
model at
least will not be worth at all than the
animal
model.
Now, if you look at the bottom
two
figures, you will say, "Lawrence,
you ought to have
a five or four points. Why was that?" You say,
"N=18." Very simple:
because we use 18 data from
NDA jacket internal FDA database to
verify this
model, but those data were not available
in the
public domain, in the public
literature. That's
why we say FDA's in unique place to do
modeling
216
work, which we have the data that we
believe
probably no one else has so complete
database as we
do.
Well, we're unique place to
develop models
for the benefit of the public
[Slide.]
So, to summarize, the
bioavailability and
bioequivalence prediction--we discussed
the
biopharmaceutics classification
system. We're
continue investigating the bi-waiver
extensions;
we're exploring classification
refinement. We are
continue investigating the impact for
transporters,
such as the p-glycoprotein impact and
absorption,
using compartmental absorption and
transit model.
We use the QSBR model is a quantitative
structure
bioavailability model should be
developed.
Unfortunately, at this point, has not
been widely
used.
We believe FDA is in unique position to do
this work for the benefit of the public.
[Slide.]
So now let me move on to next
topic, it's
the bioequivalence method for locally
acting drugs.
217
We all know the bioequivalence method for
systemic
drugs is well understood, well developed,
well
utilized.
In fact, luckily, we have used them for
generic drugs over 7,000, the drug
products.
However, well understood, well
established, well
used for systematic drugs does not
necessary mean
is well understood, well established,
well applied
for
locally acting drugs. That's key
scientific
challenges, we believe, for those--can be
best used
off of FDA's Critical Path Initiative for
the
benefit of the public.
The key scientific challenges
include the
following:
topical dermatological products; nasal
spray and inhalation; gastrointestinal,
vaginal and
ophthalmic products. Now, those products, because
a lack of the bioequivalence method--the
bioequivalence method often requires the
clinical
testing, the clinical evaluation. The target of
research is to provide a scientific basis
for in
vitro and in vivo bioequivalence method.
[Slide.]
Let's look at--give you example
why is
218
clinical studies sometimes an issue. Now this is
for topical products--I'm sorry, what I
want to say
is for locally acting drugs, why this
issue here?
This is because for systematic drugs, the
plasma
concentration usually relates to the
safety and
efficacy of drugs, while for locally
acting drugs,
the plasma concentration is not usually
relevant to
local delivery of bioequivalence. Because of that,
we have to rely on other alternative
methods; for
example, pharmacodynamics method; for
example, in
vivo clinical comparisons--for example,
in vitro
comparison and certainly any other
scientifically
sound, well established method, which we think
is
appropriate.
[Slide.]
So, as we discuss here, the
clinical
method--clinical evaluation is always
available for
establishing bioequivalence. The question comes
back why this is an issue here. Why?
What's going
on?
Let's look at give example
here. This is
a topic product. If you look at the cure rate,
219
different, if you look at the test, in
the figure
you have n=number of subjects--in fact,
the number
of patients. So 90 percent confidence interval
between test, and reference and cure rate
have to
be plus and minus 20s. Now, clinical evaluation
usually has large variation. In this case
estimated variability is around 100
percent.
Look at the table, in the
center. Utilize
463 subject; even with 463 subjects used,
the
confidence interval is minus 8 and plus
20. It
barely pass; barely pass. Now if this is 400
subject, this study will fail. In fact, we were
told the many clinical trial studies fail
because
improper power; inadequacy of the human
subjects.
So that, in sumamry, for
clinical trial
studies to document bioequivalence
present
tremendous challenge for us; tremendous
challenge
to the industry; tremendous
challenge--certainly
difficult for consumers because the
availability or
lack of availability of appropriate
scientific,
reasonable bioequivalence becomes a
barrier to
generic competition; become a barrier, in
fact, for
220
process improvement, for product
improvement, for
products optimization because many cases
those
changes require documentation of
bioequivalence
method--of reasonable, simple, scientific
front,
bioequivalence method is not available
and it will
be difficult to make any improvement or
significant
changes.
[Slide.]
As we see here, clinical
endpoints have
high variabilities, and we hope--we hope,
here--develop scientifically sound,
reasonable,
simple bioequivalence method to reduce
unnecessary
human evaluation, or human testing.
So this is the developed for
the
discussion of bioequivalence of locally
acting
drugs.
Let me move on to the topics which are also
dear to our heart in the Office of
Generic Drugs:
product design and characterization.
[Slide.]
I said it before. The generic drugs not
only show high quality, but also equally
important
to show equivalent- to the brand-name
products or
221
we could pharmaceutical
equivalence--pharmaceutical
equivalence, this means the same drug
substance,
same dosage form, same route of
administration.
So, with respect to to "same drug
substance," we
need to document that exactly same; for
example, we
have lots, lots issues before with
pharmaceutical
solid polymorphism. This issue is resolved. But
issues still can exist for complex drug
substance.
For topical dosage forms,
sometimes it's
difficult to define whether it's ointment
versus
cream.
So this also presents challenges.
So it's
exceeding--in factors of the
classification dosage
form, if those exceed being inside the
classification dosage form, how do we see
they're
the same?
So, therefore, when you define,
you give a
very clear definition what is called the
dosage
forms.
And product quality--when your
product
quality standards; for example, adhesion
tests for
transdermal products--of course,
appropriate
scientific, predictive, in vitro adhesion
test not
222
only can be applied for generic drugs,
but also can
be applied for innovator brand-name
products.
Equally important, we need
standards for
nasal and inhalation products and a novel
drug
delivery system, such as liposomes, which
was
mentioned by Dr. John Simmons this
morning.
[Slide.]
Another typic that
research--the topic I
wanted to mention is product performance
evlauation. Now, in vitro, dissolution testing has
been around for decades; has been very
successful;
has been utilized for ensure the product
quality--give example, left figure, this
in vitro,
dissolution testing has been around for
decades;
has been very successful; has been
utilized for
ensure the product quality--give example,
left
figure, this in vitro dissolution method
can
usually predict, for example, polymorphic
change;
the top one polymorphic 1, the bottom is
polymorphic 2. So proper dissolution testing
ensures the product quality, able to
detect the
inadvertent changes of pharmaceutical
solid
223
polymorphism.
Nevertheless, it's a very
simple
system--just compare to human
gastrointestinal
tract.
You have stomach, you have duodenum, you
have jejunum, you have ileum. The volume changes
back and forth, in and out. There's 14 leaders in
and out.
There's different pHs, from 1.4 to 2.1.
Before the lunch, average pH is 1.4, 2.1;
now after
lunch average pH is 6, or 4.5.
Look at the duodenum or
jejunum--also more
complex is the transit time is
changed. Sometimes
the gastric emptying time is only two or
five
minutes, under fasting conditions;
sometimes hours.
The fundamental message here is:
dissolution is very simplification of a
human
gastrointestinal tract. That's part of the reason
why the very easy, we see the criticism
say that
dissolution is underestimating,
overestimating, and
in vitro, in in vivo dissolution methods
is
formulation-specific. So on and so forth.
So how do we get from here?
[Slide.]
224
The dissoluation method, beginning
was
used for quality control, lately has been
for in
vivo evaluation, basically the
dissolution test as
a product quality-control tool to monitor
batch-to-batch consistency of drug release
form of
product.
It also has been used in vivo
performance
testing as in vitro surrogate for product
performance that it can guide formulation
development and ascertain the need for
bioequivalence tests.
[Slide.]
When we look at complexity, for
quality
control tool, you want to have a simple
dissolution
test you can use every day for every
batch.
However, those simple tests for quality
control may
not be appropriate for in vivo
systems. That's
part of reason why, where, at the
beginning, we're
asking to ourselves if these two
objectives are
consistent? If it's not, we need
investigator--when you develop a bio-relevant
dissolution method it's predict in
vivo--I want to
225
say it again, dissolution method has been
here, has
been very successful ensure the high
quality for
consumers, but those dissolution methods may be
over simplification of in vivo
system. That's part
of the reason why we believe in make an
effort to
develop bio-relevant in vitro dissolution
method to
be predictive of in vivo dissolution, to
be
predictive in vivo phenomena going on in
complicated system.
[Slide.]
Before concluding my talk, I
want to say a
few words on process identification,
simulation and
optimization tools. You have heard enough--that
hisotrically, pharmaceutical products
involves the
manufacture of the finished products
using batch
processes, followed by excessive
laboratory testing
and analysis to verify its quality.
However, the process
identification,
simulation, and optimization tools need
to be
developed for pharmaceutical batch
processes so
that any manufacturing process failure
can be
readily identified and corrected. When this
226
process means that a formulation has been
defined--has been selected. The product quality
ought to be assured by high quality of
starting
materials, robust manufacturing
processes, and
limited--not excessive--laboratory
confirmation and
test or analysis.
[Slide.]
So when we're look in future,
the Office
of Generic Drugs wants to continue--all
go to
continue building world class scientific
expertise
in predicting bioavailability,
bioequivalence and
process optimization. We face many, many
challenges. We prioritize scientific efforts. We
will pursue collaborations. We cannot do it by
ourselves. Within FDA, we have Office of Testing
and Research. I think this afternoon it's the
Division of Pharmaceutical Analysis,
Cindy is goin
to give a talk. She is providing a lot, lot of
support to Generics, and office of
OTR--also, rapid
response teams.
We had a collaboration already
in place
with academia--for example, University of
Michigan,
227
University of Kentucky, Ohio State
University,
University of Maryland, and Colorado
School of
Mining.
We also have a collaboration in
place with
National Institute of Standard
Technology, while
pursue collaboration with other
government
agencies.
Finally--not least--with industry.
With that, I conclude my
talk. Any
comments are welcome. Thank you.
CHAIRMAN KIBBE: Marvin?
DR. MEYER: Lawrence, two questions on
that slide on page--I guess it was slide
10, the
QSBR model. One--simply, you said you illustrated
the one down on the right-hand corner, I
guess, as
illustrative of the FDA's problem in
presenting
data publicly. And you had four data points shown
from an n of 18.
I wonder why--how revealing
would be the
other 14 data points, if you're just
plotting
percent f, human percent f dog? I mean, I have no
idea whether you're talking about aspirin or
you're
talking about vitamin B-12.
228
DR. YU: Well, I guess, first of all,
Marvin, you have to believe me what I
said, here.
[Laughs.]
DR. MEYER: Okay. [Laughs.]
DR. YU: Secondly, in this indeed is very
simplification modeling, and I can show
you slides
with actually 18 drugs--their specific
name--
DR. MEYER: Okay.
DR. YU: Those 18 drugs were approved in
2001 and 2002. The human bioavailability data for
all those 18 drugs were available,
actually in
public domain--the majority either from
the
Physician Desk Reference. However, for animal
data--for example, if you look at rat, we
only
have--I only was able to find five drugs
whose
animal data--rat bioavailability--that
were
available in the public literature. The
rest--basically, that's 13 drugs--were not
available in the public domain.
DR. MEYER: My statement really deals with
agency paranoia, is: why can't you show us the
data points without saying, "This is
a Pfizer
229
product, this is a Lily product, this is
a Teva
product." Just say, "These are products that are
marketed." Or "These are analgesics." Or "These
are antihistimines," or--
DR. HUSSAIN: I think the key is this:
the animal data may not be in the public
domain.
The human data would be on the label and
so forth.
So if you are able--if you can
trace back
what the drug was. That was the reason.
DR. YU: If I showed all 18 drugs here--
DR. MEYER: Mm-hmm.
DR. YU: --basically, I disclose all the
animal data, because you're able to see
it. And
then--
DR. MEYER: But if you don't tell me what
the drug is--
DR. YU: Yes--
CHAIRMAN KIBBE: You're obviously not a
lawyer, Marv.
DR. MEYER: Oh, okay.
[Laughter.]
DR. MEYER: I'll pass on that.
230
The second question--
DR. YU: I guess I don't want to get
myself in trouble.
DR. MEYER:
Yeah, I know--well, that's
paranoia, isn't it.
[Laughter.]
CHAIRMAN KIBBE: It's only paranoia if
it's unreasonable fear.
DR. MEYER: Yeah.
DR. YU: Marvin, you're SG, you can see
all this data.
DR. MEYER: Well, then I'll have to be
quiet about it. So I don't want to do that.
[Laughter.]
CHAIRMAN KIBBE: And that's really hard to
do, too, eh?
DR. MEYER: Maybe a less philosophical
question:
if I look at the upper left and the
upper right, and I draw a line at, let's
say, 70
percent f--on the y axis--
DR. YU: Mm-hmm.
DR. MEYER: --I have a range that goes
231
anywhere from 30 to 100 percent, as
experimental or
observed--in both cases.
DR. YU: Mm-hmm.
Mm-hmm.
DR. MEYER:
So even though the r-squared
may be acceptable, I say you don't have
very good
predictability--at least at that level of
percent
f, which would be one of interest I would
think--70
percent.
DR. YU: Marvin, you have--indeed, you
have an excellent question.
DR. MEYER: [INAUDIBLE]
[Laughter.]
DR. YU: I guess I can answer it two ways;
twofold.
First of all, that's part of
the reason
that the quantitative structural
relationship, as I
stated, that the FDA follow in Biologics
meeting,
follow-on protein biologic product
meeting that one
professor expert state, it's unrealistic
at this
point--maybe in the future--as you also
point out
this morning, the QSBR alone--alone--can
be provide
for regulatory decision-making. In other words,
232
quantitative structure activity
relationship will
be used for supportive information, but
however
cannot provide a conclusive data for
regulatory
decision-making--at least today.
DR. MEYER: There's kind of a line
between--I tend to agree, it's maybe
better than
nothing--maybe. But if I were in a company, and I
went to management and I said,
"Well, I can predict
the experimental bioavailability,"
and my vice
president says, "Well, what will it
be?" "Well,
somewhere between 30 and 100
percent."
[Laughter.]
I better start looking for
another job, I
would think.
DR. YU: Actually, if you look at it, when
you place 100 drugs--supposedly, at this
point, you
have 100 compounds. You have $1 million. The job
is:
give me maximum information you can with this
$1 million. No, 100 drugs you're available, you
can blindly pick up 100 compounds, you
pick let's
say
10, for example, for human evaluation--okay?
And then probably a couple of them--for
example,
233
the bioavailability is 0 or 5 percent, so
you
failed.
So at least failure rate, instead of
you--your test, you got a 7. However if you use
computer model, you pick the 10 with $1
million,
likelihood you got nine. You're getting a lot with
this simple computer model, you're only
cost
$10,000 versus $1 million, you benefit
tremendously.
CHAIRMAN KIBBE: I think we have some
comments on that.
Ken? And then Nozer.
DR. SINGPURWALLA: I would like to pursue
this slide, and the previous slide. So why don't
you put up number nine first, please?
DR. YU: Okay.
Please.
DR. SINGPURWALLA: I'm a little
intrigued with it. You have four variables:
surface permeable properties as a
function of
solubility, permeability, intrinsic
hepatic
clearance, and potency. You have, actually, five
variables, and you're portraying them in
two
dimensions.
234
So I don't know what's the
purpose of that
particular illustration. I don't get a sense of
what it is supposed to convey.
And the second point is: irrespective of
my first point, what was the basis of
your computer
models?
A computer model is based on some theory,
or previous data, or a combination of
it. So it's
not clear to me what is the basis of that
model?
DR. YU: Well, I'll try and answer the
question.
This bsis of the computer model
is a
mechanistic model--okay? If you look at
absorption, you basically have four
fundamental
processes going on. One is gastric emptying and
the intestinal transit; second is the
dissolution;
third is permeation across membrane;
fourth is
metabolism. So this model consists of about 100
differential equations encompasses all
these
processes going on. Is basically what we call the
physiology model.
And this physiologic model--if
you look at
the key parameters impact those
mathematical
235
equations--you have solubility, you have
permeability, you have clearance, and you
have
dose.
So the reason important your dose is here,
because how much input into the body will
impact
the dissolution.
Now, another I think important
terminology
is bioavailability. So, basocially,
bioavailability is a function of
solubility,
permeability, hepatic clearance, and
effective
dose.
Of course many, many other factors, but
here, simplification is basically theses
four,
five--four basically are fundamental
parameters
which ipact the bioavailability.
So, therefore, when you look
ata those
four parameters, if you know effective
dose, the
potency, your educated guess, if you look
at this
surface, you get some idea what likely
bioavailability will be in humans before
you even
actually doing it.
So the advantage is the same
for the early
stage that leads to selection. If you
have a huge
number of subjects--which when I gave
my--I say
236
100--in fact, we have 1,000, for
example--the
candidates for human evaluation. You need to--for
human evaluation which one you
select? So this
surface will help you, which one has a
likelihood
to be successful--likelihood to be
success.
DR. SINGPURWALLA: But you have three
variables labeled--
DR. YU: Mm-hmm.
DR. SINGPURWALLA: --so this illustration
only pertains to three variables. And you said you
had five variables, and a hundred
differential
equations.
DR. YU: It was--yes, we have a
hundred--the way--do have a hundred
differential
equaltions. But a differential equation is a key
parameter here is solubility,
permeability and
hepatic clearance--and dose. That's why I say dose
is 1.0.
In fact we have a series plot--for
example, dose 0.1, 0.5, 1.0, 5 and
10--a--plot. So
when you select a specific dose, and then
you look
at this plot, and this plot--you have
three
parameters, basically--solubility,
permeability and
237
hepatic clearance. And then from there you see
which is more appropriate candidate for
human
evaluation.
DR. SINGPURWALLA: I think I made my
point.
You see three variables here.
There are
two others--I'm sorry, three parameters
here. You
have two other parameters. You need another
picture to connect these with those. And I won't
pursue the matter.
Let's go to number 10--
DR. YU: I think this talk about hours,
all the mathematics from one stepwise.
DR. SINGPURWALLA: No, there are certain
principles.
DR. YU: Yes.
DR. SINGPURWALLA: You can't show, in two
dimensions, more than three dimensions.
DR. YU: Okay.
Thank you.
DR. SINGPURWALLA: All right.
Number 10--picture number 10.
DR. YU: Okay.
DR. SINGPURWALLA: Now, you know the
238
correlation coefficient, r-squares--
DR. YU: Mm-hmm.
DR. SINGPURWALLA: --only measures a
linear relationship.
DR. YU: Yes.
DR. SINGPURWALLA: You could have two
dependent variables that are non-linear--
DR. YU: Mm-hmm, mm-hmm.
DR. SINGPURWALLA: --and completely
dependent on each other, which r-square
doesn't
capture.
So, I go back to the point
raised by
Marvin, here--and previous people. There are only
four or five points. They don't look linear to me
at all.
And you can't claim a correlation--you
can't claim any meaningful correlation of
point
.43.
It doesn't have any meaning.
DR. YU: Actually, you made excellent
point.
I guess I did not make it clear in my
presentation: the point I want to make here is
animal model are not predictive of all
human being.
DR. SINGPURWALLA: Okay.
So--
239
DR. YU: that's the key.
DR. SINGPURWALLA: Okay.
So don't put
r-square.
Okay? Just put it that way.
And the top one doesn't make
sense--the
r-square of .71.
DR. YU: Uh-huh.
DR. SINGPURWALLA: It seems approximately
linear to me--notwithstanding Marvin's
comment.
[Laughs.]
So the first one does make
sense. The
second one--I don't know how many--you
show a lot
of observations--
DR. YU: Yes, there's 18 points.
DR. SINGPURWALLA: No, the second one--the
QSBR model.
DR. YU: Okay--yes, this is 18 points.
Yes.
DR. SINGPURWALLA: I think you have more
than 18.
DR. YU: 20.
DR. SINGPURWALLA: Okay--whatever it is.
Again, r-square doesn't make sense
there--does it?
240
DR. YU: Well, I guess--you know, I said,
you know, when you look at r-square, .6
or .7,
statistically probably is not meaningful
at all.
But, I guess, from physiological,
pharmaceutical
perspectives, that at least gives us some
indications what could be potentially
correlation
coefficient; that whether it's good or
bad.
I hope I answered your
questions.
[Laughs.]
DR. SINGPURWALLA: Yes.
Fine.
CHAIRMAN KIBBE: Ken, you want to wrap
this up?
DR. MORRIS: A general question, I guess,
Lawrence--you know, the charge of looking
at how
we're adjusting the Critical Path or,
how, you
know, that the Critical Path Initiative
is being
addressed--given that a lot of what
you're talking
about isn't really generic
drug-directed--sort of
taking that as a given for the moment, if
it's
adding to the overall Critical Path, it's
probably
still valuable.
But if you look at the larger
picture, and
241
you look at, like, your CAT slide, which
turns out
to be a popular slide--you don't have to
put it
up--but I guess the thing that jumps
out--and maybe
this is jumping forward to tomorrow a
little bit,
is that this all presupposes that the
dosage form
consistency is there to begin with when
we're
talking about the bioequivalence.
VOICE: [Off mike.]
CHAIRMAN KIBBE: Oh, you're mike's off.
DR. YU: You're absolutely correct. And
this scenario, where I'm not
looking--it's useful,
these slides, we have not looked at how
formulation
impact.
Impact, if you look at formulation impact
for immediate-release dosage form, you
have a
suspension, different particle size. I can talk
hours.
In terms of your first
question, is this
absolutely generic? Probably not. It's actually
apply equall for drug discovery and
development
innovators. I guess my Director at the bureau is
so nice he did not criticize, allow me to
[INAUDIBLE] here. So that's--I have to say it
242
comes out sometimes in my research--not
my mission
to talk about some of the prediction
bioavailability, bioequivalence.
DR. MORRIS: Yeah, I didn't really--
DR. YU:
Well, same mission, which is to
protect and advance public health. I'm sorry--go
ahead.
DR. MORRIS: No, I didn't mean it as a
criticism. I was just saying that--I'm just not
sure that the immediate applicability of
this is
with the generics. But--
DR. YU: Yes, this is equally applied to
innovators. I guess, no matter where I am, whether
it's in the Office of Generic Drugs, or
my previous
position, Office of Testing and Research,
our
mission is to protect and advance public
health.
That's why--is part of the reason, I
guess, why my
director, so he's so nice, did not
correct it.
CHAIRMAN KIBBE: Okay.
I think we need
to--
DR. HUSSAIN: Clarify one point, which I
didn't--
243
CHAIRMAN KIBBE: I guess we don't need to
go on.
[Laughter.]
DR. HUSSAIN: No, I think, in listening to
the talk, the message that Lawrence was
delivering
with respect to dissolution for quality,
and
dissolution for predicting performance,
just to
further clarify what I think I thought
process is,
I think--for the last 20 years we have
sort of
merged the two together. And essentially what
we're looking at is separating those
out. There's
a quality-control function, and there's a
function
for
performance prediction. And those have
to be
addressed differently. That's the message that
Lawrence was giving.
DR. YU: Thank you, yes. That made it
very clear.
Thanks.
CHAIRMAN KIBBE: Thank you.
Thank you,
Lawrence.
Jurgen, you're not going to let
us end
244
here, huh? All right.
DR. VENITZ: Is it on?
Okay.
DR. YU: You have two minutes.
DR. VENITZ: I do?
Okay.
DR. YU: [Laughs.]
DR. VENITZ: Okay, you have to count.
First, again, the same comment
ethat I
made earlier today--I obviously commend
you for
using quantitative methods to predict, as
opposed
to always requiring measure, measure.
DR. YU: Thank you.
DR. VENITZ: I do concur with the
previous--with Ken's basically, statement
that here
you're talking about drug substances when
you do
your quantitative structure activity.
DR. YU: Yes.
DR. VENITZ: Given the fact that you are
at OGD, I think you should also focus on
excipients, and products; in other
wordsthe , what
is formulation effect? And I'm not sure whether
you can have those nice models that you
showed us,
that are very meaningful to come up with
NMEs and
245
figuring out what the chemical structure
may be,
related to bioavailability.
The second--so, excipient
effect and food
effect, to me, is something in terms of Critical
Path that's important--not just
predicting drug
substances.
I do urge you to continue to
work on the
BCS, because I'm pretty sure in a couple
of years
you're going to come to this committee,
or the next
generation of committee members, for
Class III, and
you might make the same recommendation
for Class
III that you just, four years ago, made
for Class I
drugs.
DR. YU: Ajaz made, yeah.
DR. VENITZ: Or Ajaz made.
Two more comments: clinical
bioequivalence--that's obviously
something that
this committee pointed around for quite
some time.
And you made the observation--which is a
true
observation--that clinical bioequivalence
means you
need a large number of patients, because
you have
lots of variability.
246
But I would take that argument
around, and
I'd
say two things: number one, you're now
testing
the product in the intended
population. So you
have the benefit of getting away from
healthy,
usually male, volunteers, where you
assess
bioequivalence.
DR. YU: That's correct.
DR. VENITZ: Number two, what is the magic
rule that requires you to have confidence
in the
value of 80 to 120, or 125--as we do for
areas
under the curve? Why can't you/clinicians define a
minimum difference that is perfectly
acceptable?
We do that all the time for
non-inferiority
trials--in the clinical area. So why can't we use
that to assess this concept of clinical
or
therapeutic bioavailability to get around
this
sample size that is going to go up
exponentially?
The last comment--the question
that you
had on the dissolution testing, where you
asked
what is--is this just monitoring product
performance, or is this something that is
more
meaningful? Well, the short answer is: it
247
depends.
If you have an in vitro-in vivo
correlation, it is not only something
that you can
monitor, but it's something that actually can
be
translated in in vivo performance.
So part of what you--maybe as
part of your
research--want to look at, under what
circumstances
do you have IV, IVC for simple dissolution
test, at
a single pH? And the complex GI tract--actually
we'd use this to a beaker with solution
in it?
Anyway--thank you.
DR. YU: Thank you.
Do I have time for
comment?
CHAIRMAN KIBBE: Thank you, Jurgen.
Yes, you have time for
comment. We are
planning, now, to extend the meeting this
afternoon
to 7:30 p.m., so--
[Laughter.]
DR. YU: [Laughs.] I guess the excipient
effect I will show in my BCS slides, not
show in
bioavailability prediction slides.
In fact, the first publication,
Molecular
Pharmaceutics, 2004, is deal with food
effect. So
248
where I just want to say that we're
investigating
effect of excipients on absorption, on
bioavailability and bioequivalence.
And your--I guess I forgot your
other
comment, so I wouldn't have to comment on
that.
[Laughs.]
CHAIRMAN KIBBE: That's nice.
Let me just throw out that I
agree with
Jurgen's penultimate comment.
VOICE: [Off mike.] Figure out what that
means.
[Laughter.]
CHAIRMAN KIBBE: We have an opportunity
here to show a real sense of cooperation
between
academia, industry and the FDA.
We have a series of speakers,
all of which
are claiming they're going to use 30
minutes.
Lawrence said he was going to take
20. It was 47.
[Laughter.]
If the other speakers are on
the same
track--mostly because we ask lots of
questions--all
really good ones--we will, indeed, be
here 'til
249
7:30.
So, let's try to focus
ourselves on the
talks at hand, move through them
quickly. And
anybody who has more than 25 slides
should be
embarrassed.
[Laughter.]
All right?
We're going to start out with
Dr.
Rosenberg, on the Critical Path
Initiatives--the
Division of Therapeutic Proteins'
perspective.
Office of Biotechnology
Products--Current
Research and Future
Plans
DR. ROSENBERG: It's a pleasure to talk to
you about our perspective on Critical
Path.
So--I think it's important to
start with
why--how the Critical Path Initiative
evolved. And
it evolved, of course, because of the
dramatic
decrease in novel drug and biological
product
license applications.
[Slide.]
so what you can see here is
that, from the
mid-'90s there's been a steady overall
decrease.
250
And more than, I think, just
the decrease
in numbers, we've really had a failure to
develop
therapeutics and vaccines to address
difficult
diseases.
There's some diseases for which there
hasn't been an improvement in therapy for
over 30
years.
So, coupled with this general
decrease in
novel product development, there's really
been, of
course, a high candidate drug failure
rate.
[Slide.]
And it's pretty dismal to look
at these
statistics. So, I mean, the last two--so a drug
entering Phase 1 in the year 2000 is less
likely to
reach market than one entering Phase I in
1985.
And more sobering I think, in fact, is
the fact
that about 50 percent of Phase III
studies fail due
to
lack of efficacy.
So there's really a lot of
uncertainty by
the time many compounds are entering
Phase III
trials.
And Bob Temple will go on about why that
is, and how to improve that. But that's not the
subject topic here.
251
[Slide.]
So this isn't--this sort of
dismal picture
isn't for lack of trying. What you can see in this
slide is that, in fact, there's been an
enormous
amount of money and effort dumped into
research and
development, starting in the early '90s,
and that
it certainly outstrips, dramatically, the
number of
new chemical entity approvals.
[Slide.]
So there are many factors that
contribute
to this decline in new product
applications. And
certainly one that has been cited is the
failure of
novel methodologies and treatments to
achieve
practical application. So, you know, all of the
wonderful technologies that have come up
in the
past 10 or 15 years--many of them have
really not
seen very much in the way of a practical
application.
[Slide.]
And I think getting industry's sort of
post mortem analysis on this is very
important and
interesting. So, a comment from Roche was that "I
252
think we got too enamored of technology
and lost
focus of what to do. The 1990s were really a boon
for in terms of science, but we forgot
that we
needed to link all of that to
disease."
And the second comment--from
Adventis--"We
though we would very quickly validate
targets that
were critical to disease and agonize or
inhibit
them as a way to start to find a
drug...and what we
found, in fact, is that validating
targets takes a
lot of time. And this is one of the big
disappointments of this era."
So, I think, nowhere is this--I
mean, it's
key that we have a sort of naivete about
product
development. And I think this is what the Critical
Path is trying to address--to take this
naivete, to
do some good science, and to perhaps
shorten the
length of time it takes from a great idea
to
commercialization.
[Slide.]
And I think nowhere is this
better
illustrated than in the development of a
product
that we regulate in the Office of
Biotechnology
253
Products, and that is monoclonal antibody
development.
And this timeline is a little bit
warped,
in the sense that it doesn't start at the
beginning; because the beginning of this
timeline
is 1975, when Kohler and Millstein
developed the
hypodermic technology that would make it
possible
to produce monoclonals.
And so what you can see is
there's about a
20-year lag period before you have a real
flowering
of products. And so I think that Critical Path
asks a question, and that question
is: can we
shorten this time?
And it's--I don't think it's an
assured
thing, but I think it is certainly worth
a valient
effort.
[Slide.]
So let's focus a little bit
more now on
biotechnology products, and biological
therapeutics, which is the group of
products that
our office regulates.
So, one of the reasons that
there has been
254
a decrease in numbers of these products
is that
there has been a dramatic increase in the
length of
clinical development time. And you can see here,
from the 1980s, through 2002, you know
just this
linear increase in development time. And that's
coupled with, pretty much, preservation
of the
approval--the length of time it takes to
approve
these products.
[Slide.]
And that differs from the case
of small
molecular drugs, where in both the
clinical phase
and review times have diminished or
pretty much
leveled off since the early 1990s.
[Slide.]
So what is it about biological
therapeutics that has caused such a
length in
development time? Well, for one, there's a
really--a big shift in disease
indications since
the mid-1980s, late 1980s. More and more, chronic
diseases are being assessed. And, of course,
longer trials are necessary in the case
of chronic
diseases, to both the assess the efficacy
of the
255
product, but as well as the durability of
responses
is key.
And even more important, I
think, there's
been a shift to therapeutic products
whose
mechanism of action and toxicities were
less well
understood. So, what was encountered in these
clinical trials were unexpected and
difficult
toxicities, as well as a difficult in
developing
appropriate surrogate endpoints that
would allow
for shortening and greater efficiency of
clinical
trials.
[Slide.]
So how can FDA help? As I said, I think
Critical Path is a great tool to try and
address
the enhancement in product development
efficiency.
But I think it's important to realize
that FDA and
industry still will have different roles.
According to this review, the
ultimate
goal, of course, of FDA and industry is
the same:
to provide patients with access to new,
safe and
effective treatments. And what's really at stress
here is that coordination and cooperation
are
256
required.
And the comment here is that
FDA can only
assist in the process. And I think Critical Path
is trying to take this "only
assist" into "assist
greatly."
[Slide.]
In addition, we're not the only
partners
here--and this has been mentioned
before. There
are other players: disease-specific advocacy
groups, NIH, CDC, etcetera. And the NIH recently
has launched their "Road Map,"
which is very much
targeted for drug development. And they have, you
know, three basic initiatives within this
Road Map.
And so FDA is going to have to work, not
only with
industry, but also with NIH, as well as
other
advocacy groups in moving this--in
enhancing the
efficiency of product development.
[Slide.]
Now, this has also been
mentioned--but FDA
is uniquely positioned to identify and
overcome
challenges to product development. Reviewers can
identify common themes and systematic
weaknesses
257
across similar products, and that based on
such
knowledge, reviewers can formulate
guidance
documents and clearly offer industry sage
advice
about pitfalls.
Now, I think it's worth it to
mention that
guidance documetns have actually be shown
to foster
product development; that they improve
the changes
of an initial success of a marketing
application,
and they shorten time to approval. So there is
research that verifies that, and so I
think it's
very critical to have scientific
personnel that can
promulgate very helpful guidances.
[Slide.]
So what are FDA strategies for
speeding
innovate therapies to market? The first one was
actually in 2002, and it was called
"Improving
Innovation in Medical Technology: Beyond 2002."
And this one particularly highlighted the
importance of guidance documents in
avoiding
multi-cycle reviews.
And, of course now we have
Critical Path.
[Slide.]
258
So the Critical Path, as we all
have
heard, it's a method to develop new
tools, to
imiprove predictions regarding safety and
efficacy
of new products in a faster time at lower
cost.
And it essentially supports
research--clinical and otherwise--for
applied
sciences needed for medical product
development.
[Slide.]
You've all seen this. Critical path goes
to some translational research, through
to product
launch.
But actually, in our view, knowing the
trouble that biological therapeutics can
get into
following marketing, and following licensure,
we
think it goes well beyond that, into
post-licensure
phases.
[Slide.]
Again, Critical Path involves
issues of
safety, efficacy and
industrialization. And our
scientists, in the Office of
Biotechnology
Products, are very expert in all of these
aspects--or certainly in targted areas of
all of
these aspects of product
development. And, as I
259
say, underestimated here is
post-licensure issues.
[Slide.]
So, what sort of personnel does
one need
to negotiate this Critical Path? Well, for
biological therapeutics we think that the
researcher/reviewer is ideally positioned
to
advance the Critical Path. So a
researcher/reviewer is a sort of hybrid
species;
this is a person who does a lot of
regulation.
This person is a producdt expert. They're
absolutely integral to the regulatory
process at
all stages of product development, and
they provide
scientific expertise on multiple
levels: product
manufacture, including inspections--all
of our
reviewers go on inspections;
product--this is an
expert in product characterization,
including
mechanisms of action, in vivo bioactivity
and
toxicities. The researcher reviewer is also an
expert in some analytical methods, and in
some
animal modeling. But the researcher/reviewer also
has a key role in policy formulation and
promulgating guidances.
260
[Slide.]
So the basis for the regulatory
expertise
of
the researcher/reviewer is engagement in a high
quality research program. So the
researcher/reviewer is required to
maintain an
active laboratory reseaerch program in
the field
relevant to the review area. This person must
publish findings in peer reviewed, high
quality
journals, and they must undergo site
visit
evaluations of their program every four
years, and
yearly internal evaluations. And, in fact, our
promotions are promulgated more on our
research
expertise, and our research
accomplishments almost
than our regulatory accomplishments.
[Slide.]
So, interestingly, this
requirement for a
regulator who is intimately familiar with
cutting-edge technology is very much in sync with
findings that a subcommittee of the FDA
Science
Board made back in 1998, when they said,
'It is the
consensus of the Committee that FDA
requires a
strong laboratory research focus and not
a virtual
261
science review process; otherwise we risk
the
potential to damage not only the health
of the
population of the U.S., but also the
health of our
economy."
And I think the health of both
are clearly
in danger when we can't get new products
out.
[Slide.]
So this group also went on to
say that
regulators and policy makers require
expert
knowledge and first-hand experience with
the latest
technology being applied to biological
products;
and that an intramural research porgram
is required
to assess risks of new therapies, to
develop assays
and new approaches to increase efficacy
and safety,
and reduce risks. It sounds a lot like Critical
Path to me.
Moreover, I think a very strong
point they
made was that a strong, well maintained
intramural
research program provides the basis for a
climate
of science and scientific communication
with FDA.
They emphasized retaining high-quality
scientific
staff, but I think the permeation of
science into
262
the review process is absolutely
paramount.
[Slide.]
Okay, let's go on--just skip
this.
[Slide.]
So let's go to my division--the
Division
of Therapeutic Proteins. This may be too small to
read, but the only point I wanted to make
is that
all of our reviwers--and we do have some
full-time
reviewers--are spread among three
laboratories:
the Laboratory of Immunology, the Laboratory
of
Biochemistry, and the Laboratory of
Chemistry. And
we think that this is in keeping with
keeping the
culture of science permeated into the
review
process.
[Slide.]
Our division regulates an
enormous
diversity of products. We have 37 total licensed
products; we have 30 novel molecular
entities. We
have many naturally-derived
products--mostly
recombinants, however; and really very
minimal "me
too" products. We have several interferons, for
example.
263
We regulate many engineered
versions of
prototype products that are designed to
enhance PK
or other product characteristics;
pegylated
products.
Many of our products have site-directed
mutagenesis for hyperglycosylation, as
well as
other enhancements.
Our products are produced in
very diverse
cell substrates; from bacteria, yeast,
insect
cells, rodent cells, human, as well as
transgenic
animals and, soon to be, plants. And the
manufacturing process is unique for each
of our
products.
[Slide.]
So the products that we regulate--I
think
you're familiar with: interferons, interleukins,
thrombolytics, anti-thrombotics,
therapeutic
enzymes; all the ematolic growth factors,
neurotrophic growth factors; chemokines--which
are
a novel area for us; wound healing
products;
toxin-fusion molecules; angiogenesis and
anti-angiogenesis agents;
immunomodulators,
receptor antagonists, lectins; and, most
264
importantly, I left off cosmetics. We also have
botox.
We're very proud of that product.
[Slide.]
So what are the principal
scientific
issues--and regulatory challenges--for
us?
We've got a lot of them in our
division.
Comparability is always a paramount
issue, because
there are no analytical techniques that
will
precisely define the 3-D structure of our
complex
proteins, we have to use a variety of
techniques to
establish comparabilitiy. And sometimes that
actually requires animal studies and
sometimes
clinical trials. And we're engaged in a great
exercise of this right now, in our
follow-on
biologicals initiative.
All proteins are potentially
immunogenic,
and so we have problems with
immunogenicity. We
have hypersensitivity responses, we have
neutralizing antibody responses. And these can
really blow up in product development.
Potency assessments--as I said,
because no
analytical technique--and one--is good at
really
265
defining the 3-D structure, we use a
potency assay,
which is an activity assay which gives
you a clue
about product protein conformation. And that
differs quite a bit, in some respects,
from small
molecule regulation.
Our products have been the
subject of
product counterfeit--on both Neupogen and
Epogen.
And so we're working th the Office of the
Commissioner in formulating responses to
that.
We've also faced novel
transgenically
produced products. We're going to get products
produced in chicken eggs, as well as
plants. And
those raise very novel safety issues--and
efficacy
issues, as well.
And we're always faced with
infectious
disease transmission because of the way
our
products are produced, and the materials
that are
used to produce them.
[Slide.]
So, as product experts, we have
a very
keen knowledge of pitfalls in product
development,
from pre-clinical studies to Phase I and
II
266
studies; immunogenicity, unexpected
adverse events,
lack of appropriate animal models. Certainly,
mechanism of action, when it's not fully
evaluated,
can be very problematic.
[Slide.]
in Phase III, the development
of validated
potency assays are a real pitfuall in
product
development, as well as changing
manufacturing in
the
middle of Phase III studies, which really
wreaks havoc.
And so we really--you know, we
spend a lot
of time with sponsors trying to stear
them away
from these pitfalls. And I think you'll see that
our style of communication is highly
valued by
industry, who feels that it's, in fact,
vital for
more efficient product development.
I'm just going to skip over
some of the
clinical ones.
[Slide.]
So, our Critical Path focus for our
division is basically to support ongoing
Critical
Path projects. And we think of those as pertaining
267
to entry of products with novel
mechanisms of
action--and that would encompass research
that
investigates mechanisms of action of new
products;
research that establishes new animal
models for
assessment of safety and efficacy; and
research
that provides new or improved products to
the
piplines.
[Slide.]
Moreover, we recognize very
well the
barriers and hurdles to product
development,
including immunogenicity and potency
assessment.
And so we value research that overcomes
these
barriers to product development;
moreover,
activities to standardize assays--this is
very
important when you're trying to compare
across
different products.
Moreover, the last type of
research we
think is highly critical-path appropriate
is
identification of surrogate endpoints and
biomarkers for safety and efficacy. And so we
really value research that identifies
novel
biomarkers, as well as activities to gain
consensus
268
on appropriate surrogate markers.
[Slide.]
So, some of the programs that
we have
really very much addressed directly with
Critical
Path issues: one of them is the development of CpG
oligonucleotides as immunomodulators for
infectious
diseases.
Daniela Verthelyi is the principal
investigator, and so she investigates CpG
oligonucleotides as they interact with toll
like
receptor, as well as other potential toll
like
receptor ligands. And she studies primates; she's
interested in identification of surrogate
markers
of immune protection, and development of
novel TLR
agonists.
This project also has high relevance to
bioterrorist situations; can we enhance
the immune
response by fiddling with these toll like
receptors
to bioterrorist agents?
[Slide.]
The second project that
directly addresses
Critical Path issues is a research
project that's
focused on chemokines, which are
chemo-attractant
cytokines. And we're ver increasingly coming to
269
appreciate the fact that these products
are
absolutely critical for cell migration in
the
seetings of inflammation, metastasis,
angiogenesis,
and atherosclerosis. Mike Norcross is the
principal investigator. And, within his research,
he is developing methods to assess the
potency of
these products. Potency, as you can imagine, is
very difficult to assess for a product
that's a
chemo-attractant product. Those are very squishy
assays; very variable. So, this has been a real
problem in product development.
He is, as well, trying to
evaluate and
develop methods for non-clinical
screening of
anti-viral biological products, as well
as the
development and validation of biomarkesr
and
surrogate endpoints for immune-based
therapies for
HIV infection.
[Slide.]
And just to show you a little
bit of a
schematic here--so you have bacterial
products,
such as LPS, or CpG oligos that tickle
toll like
receptors that are present on macrophages
and
270
dendritic cells--antigen-presenting
cells--that
cause them to emit chemokines, such as
IL8,
MIP1-alpha, IP10, and these cause
chemoattraction
of various immune mediators, as well as
cause
trafficking of tumor cells to distant
cites.
So it's a very exciting area,
and I think
having such expertise is critical to the
product
development.
[Slide.]
Dr. Donnelly also has a program
which we
think fits directly into Critical
Path. He is
focusing on signaling pathways of novel
interleukins and inferons; specifically,
he's
defining signal transduction pathways for
new
cytokines, new interleukins, ILs 19, 20
and 22, as
well as defining biological properties of
a new
interferon, which may be significantly
less toxic
than interferon-alpha. It's called
interferon-lambda.
[Slide.]
Dr. Beaucage--who many of you
may
know--world-class chemist--basically has a
program
271
to enhance the specificity and
sensitivity of
oligonucleotide microarrays which, of
course, are
used for myriad purposes. And so he has focused on
detection and quantification of bacterial
and viral
nucleic acid contaminants in biologicals,
including
blood products. This methodology would be helpful
for high-throughput screening of point
mutations,
or single-nucleotide polymorphisms that
might
dispose to human disease. And, of course, these
are used widely as gene expression assays
to
evaluate potentially the safety and
efficacy of
drugs.
[Slide.]
So, those are the projects we
think are
directly relevant to Critical Path.
Others, I think, we conceive of
as being
supportive of Critical Path; perhaps not
as highly
targeted, but nevertheless, absolutely
vital to
product development.
So, Dr. Shacter's program, and
Dr.
Johnson's program are focused on novel
anti-cancer
treatments. With Dr. Shacter, modulation of signal
272
transduction pathways to enhance tumor cell
dealth
in response to chemotherapeutic agency,
and the
investigation of antioxidants as
potential
chemoprotective agents to limit side
effects from
cehmotherapy. And Dr. Johnson is focused on
enzymology of epidermal growth factor
receptor
signaling, as well as identification of
novel
signaling molecules.
[Slide.]
Many of our programs are
immunologically
oriented.
And as I said, immunogenicity is a
critical issue along the Critical Path.
So, all of our proteins are
potentially
immogenic. As I said, we can get hypersensitivity,
anaphylactic-type responses, or IgG
antibodies that
will neutralize a therapeutic protein, or
block the
action of an endogenous homolog of that
therapeutic. And immunogenicity has
killed products
in development; certlain from epoeitin,
CNTF,
GM-CSF-IL-3 fusion molecules, as well, it
limits
the efficacy for many giological
therapeutics, such
as therapeutic enzymes, interferons alpha
and beta,
273
and asparaginase.
And it poes an ongoing concern
for
licensed products followoing changes in
manufacture, packaging and clinical
indication.
And I think most of you are aware of the
situation
with Epo and the induction of pre-red
cell eplasia,
due to changes in the packaging of
Epresx.
AS well, there's a lack of
standardized
assays for comparison across products in
the same
class.
And this is a problem.
[Slide.]
So I think, you know, for immunogenicity
most of us conceive of it as being
capable of doing
the following, which is to block the
development.
Actually, interesting--it was supposed to
blow up.
So my Papa Haydn slide didn't work very
well.
[Slide.]
So, the immunogenicity concerns
and the
projects that address this have to do
with
understanding the mechanism by which
antibody
responses to proteins are switched to
cause
anaphylaxis. And this also will have, I think,
274
some meaning for small molecular drug
development,
because they are not without their
hypersensitivity
response; research to develop better animal
models
to assess immune tolerance and
autoimmunity;
research to dissect immune responses to
embryonic
stem cells; and we are also participating
in
international efforts to standardize
antibody
assays for erythropoietin products.
[Slide.]
Some new Critical Path projects
that we
foresee, looking into the future: nanotechnology
is being highly toutedfor potential
abilities to
deliver productsi n novel ways. This may also
actually present big problems
immunogenicity for
vaccines that many of these approaches
might be
terrific in enhancing immunogenicity, but
they
could be devastating for therapeutic
protein
products.
And we think this is worth investigating
so that this technology--at least for
biological
therapeutics is not stopped prematurely.
For therapeutic enzymes, the
immune
response does limit efficacy,
particularly of
275
life-saving products for patients who
lack some
endogenous enzymes which are critical for
life.
And so we think that tolerance induction
should be
explored in that setting.
Protein aggregates are a perpetual
problem
that induce immunogenicity. However, the
specifications for aggregates are
essentially set
on manufacturing experience, not on
risk. And so
we think it would be critical to evaluate
the risk
of protein aggregates. What level of aggregates?
What kinds of aggregates? And how are they
delivered? What is responsible and what is
important in incurring risk?
And also the development of
buidance
documents we think would be a very valid
Critical
Path project.
[Slide.]
As well, some of our
research--out of some
of our research has come an idea for a
novel
product which would promote treatment of
sepsis,
which is a disease that is notoriously
refractory
to treatment. And Dr. Shacter's lab has identified
276
protein S as being critical for many
functions,
among which are clearance of apoptotic
cells. But
since activated protein C works in
conjunction with
activated protein--with proten S, we
think that our
research suggests that addition of
protein S to the
treatment protocol that uses activated
protein C
will improve efficacy. And so we would like to
develop that as a therapeutic
protein. Of course
we would like to get that to a commercial
entity
that would develop it.
[Slide.]
I'm coming to a close now. But we also
think that communication is a critical
component of
Critical Path. And an industry survey done last
year that looked at good review
management
practices, found that the kidns of
communications
we had--and that were alluded to, I
believe, in an
earlier talk--that is open, honest
communication;
informal communiations; regular status
updates;
timely communication of issues as they arise;
and
clear and concise FDA responses with
explanation of
positions--these were all review
practices while we
277
were in CBER, and we have carried over to
CDER, and
we certainly hope that, given that
communication is
vital, that these will be carried on.
[Slide.]
And so I will skip through
this. You can
read through it yourselves.
[Slide.]
Other DTP Critical Path activities
involve
participation in ICH proceedings; and
particularly
with regard to to comparability
guidance. So Dr.
Cherney, who is the Deputy Division
Director is the
lead on the ICH !5e, and so, again, the
importance
of guidance documents can't be
overemphasized, in
terms of enhancing product development
efficiency.
Another one of our personnel,
Dr.
Kirschner, is involved in standardization
of
antibody assays for erythropoietin
products, which
is an international effort. And, moreover, the
suport of risk-based approaches to GMP
and
inspectional issues is something that we
also think
is a vital Critical Path activity. We need to
switch from checklist approaches to GMP,
to
278
risk-based approaches. And we're strongly
participating in that.
[Slide.]
So, in summary, DTP strongly supports
Critical Path efforts to facilitate
development of
new products. We think that we have some projects
that are doing that now, and should be
better
supported. We have identified new projects that we
think should be funded to enhance this
process.
Other activities, including the
development of guidance, adoption of a
risk-based
approach to GMPs, and maintenance of
communication
form at with industry we also think are
vital.
So--I'll end with that. And I hope I
didn't go too much over time.
CHAIRMAN KIBBE: Thank you very much.
Outstanding! All right.
You actually have allowed us
five minutes
worth of question time. And we'll let Meryl have
it all.
DR. KAROL: Okay, thank you. That's a
very impressive summary of what you're
doing.
279
I wondered if you're placing
emphasis on
development eof biomarkers for not only
immunogenicity, but hypersensitivity,
tolerance as
well?
You know, could you tell us about those
efforts, to develop biomarkers to predict
these
effects?
DR. ROSENBERG: Yes, I think--you know, we
do a lot of animal modeling. And so most of our
programs have to do with rodent models,
and looking
at tolerance, and looking at immunogenicity,
particularly--Dr. Verthelyi's program.
Now, she has taken this one
step higher,
to primate models, in trying to come up
with
surrogate markers. And, you know, I think this is
something that we're putting an emphasis
on. I
don't know that we have a real formal
look at that
at this point, or we can really report on
that.
But that is something that we would like
to
emphasize better.
CHAIRMAN KIBBE: Ken?
You really--
DR. MORRIS: I really have a question. I
really have one.
280
CHAIRMAN KIBBE: well, go ahead.
DR. MORRIS: But it's not as technical, I
don't think.
Given the Tufts projections, as
well as
the statistics you showed on success,
have you
attempted to factor the contribution of
the various
thrusts that you're pursuing to
determine--in terms
of prioritization?
DR. ROSENBERG: So--in terms of--yes. I
mean, I think that what we're trying to
emphasize
are aspects that have been proven to do
something.
So, certainly, communication is a
critical aspect,
and development of guidance is--has been
shown--
DR. MORRIS: Yes, actually, I guess I was
thinking more about your research thrust,
but--
DR. ROSENBERG: Yes--so those are
emphasized.
The research thrusts--yes, I think
that
what--you know, what we're looking at
here is the
research projects that we have that
absolutely
address Critical Path issues we would
like to
expand.
Of course, resources are limited, and
281
there's just a certain amount we can
do. But the
one's we've identified I think are
absolutely
critical for these novel emerging
technologies.
And, as we've seen, you know, people can
be very
naive about what one can expect from
those.
So looking--you know, having
looked at
that, and looking at what's coming ahead,
we would
like to investigate, you know, the immunogenicity
concerns for nanotechnology. We would like to
look--you know--we would like to be able
to look at
that.
I can't do that with the personnel
limitations I have now. We would need funding and
personnel to do that.
So--as well as, you know,
bioterrorism is
a very important factor now, and we think
we have
the ability to address a treatment for
that, which
would be the CpG oligonucleotides, or
some similar
pathogen-associated molecular pattern
ligand.
So those, I think--we have good
models,
and we would like to push forward on
those in
particular.
DR. MORRIS: Thank you.
282
CHAIRMAN KIBBE: Melvin, you want to--
DR. KOCH: Yes, just--excellent
presentation. I was just wondering, in many of the
needs you expressed--and they sound like
ideal
candidates for CRADAs. And has that been explored
at all?
DR. ROSENBERG: Yes.
We certainly try to
develop those where we can. It's a little tough,
given some constraints. Because, of course, as
soon as you develop a CRADA, you know,
you're
limited in what you can participate with,
in terms
of regulatory action. So, you know, you're always
sort of caught between a rock and a hard
place.
But--yes, we are trying to develop
CRADAs--for some
of
those projects--and have been successful.
Dr.
Beaucage has been successful,
particularly, with
the micro-arrays over the years, and
getting
CRADAs.
CHAIRMAN KIBBE: Thank you very much.
DR. KAROL: One more question? How are we
from developing SAR models for protein
allogenicity? Is that at all on the horizon?
283
DR. ROSENBERG: Whoa!
That's a very good
question.
And I don't know the answer to that.
I
really can't tell you. I think I would have to
talk to somebody who's more of--more
focused on
allergy.
CHAIRMAN KIBBE: Thank you very much.
DR. ROSENBERG: Thank you.
CHAIRMAN KIBBE: Next, we have Steve
Koslowski. Sever has 64 slides--
[Laughter.]
DR. KOZLOWSKI: Oh, I'm already in
trouble.
CHAIRMAN KIBBE: You're under the gun,
Steve.
DR. KOZLOWSKI: Well, thank you for having
me speak.
And I will try and move quickly.
[Slide.]
So I'm going to talk about the
Division of
Monoclonal Antibodies, which is the other
biotechnology product division. I'm going to talk
a little bit about quality, and I'm going
to kind
of take the lead from one of Ajaz's
slides, and
284
talk about connecting the dots; then
about a
concept called biological
characterization; the
reserch reviewer model, which we can go
through
quickly, because Amy already covered
that; the
organization of our division--our
products; ongoing
research' Critical Path; and then sort of
summarizing Critical Pathways and
directions.
So I want to put up a slide
that Ajaz gave
me, about a way of looking at integrated
quality.
[Slide.]
The fact that different
disciplines in
review from clinical to manufacturing TO
CGMPs to
PAT all need to be interconnected in a
useful way.
And I'd like to take a little bit of a
slice of
that figure--
[Slide.]
--and actually take a way a lot
of points,
and basically leave the CMC relationship
to
clinical attributes, and talk aout
connecting one
dot to begin with: the chemistry of a product--or,
basically, its complete structure, to
those things
that we control in evaluating it.
285
So, clearly, the
characterization of a
product leads to what we eventually use
as
classical specifications-0-or at least
how we've
talked about productsi n the past.
[Slide.]
And there are ICH guidelines on
this. You
need to characterize a biotech product in
order to
pick the relevant specifications that you
use for
quality control. You choose these specifications
to confirm quality--and obvious, you
don't
recharacterize the product each
time. But what's
critical is those molecular and
biological
characteristics that are necessary for
connecting
to safety and efficacy.
[Slide.]
And I think those are really
the weakest
links, because the connections between
what
structure really matters for clinical
outcome--what
attributes are important--and what
controls in
manufacturing, or what controls in
regular testing,
confirming these things is a very hard
link to
make.
286
And clearly you need to know
these
relevant structural attributes to take
advantage of
this CGMP, or more global way of looking
at things.
So, again, what processes you need to
control; what
structural attributes are important.
[Slide.]
That leads me to what I'll call
biological
characterization.
So I'll start with talking
about our
molecules. Amy certainly referred to the fact that
the biotech proteins--or products--tend
to be very
large.
And this is an example of a third of a
monoclonal antibody--an Fab section
compared to a
statin.
[Slide.]
And so, clearly, the large molecule has
issues, not only of primary sequence, but
higher
order structure, post-translational
modifications,
and it is a very heterogeneous
protein. In fact,
the variability in proteins--in fact in
the desired
product--are greater in size than the
size of a
statin.
287
And, again, comparing molecular
weight:
150,000 to 400.
[Slide.]
So this leads to a problem--as
Amy pointed
to--for complex molecules--as again, in
the ICH
guidance--physiochemical information at
least
presently insufficient to define higher
order
structure.
[Slide.]
And so what we use--and it's an
imperfect
thing--but we use biological activity as
the
surrogate, sosrt of, for full biochemical
characterization.
And so biological actiivty is
specific
capacity of a product to achieve a
particular
effect.
And potency is the way we measure that.
We use a variety of bioassays: animal based, cell
culture based, biochemical--sometimes
receptor
ligand binding.
[Slide.]
There's a whole continuum of
these assays,
which go from very simple assays to ones
that are
288
very complex. The complex assays--like, ideally, a
clinical study--is true potency, but its
reproducibility and its utility as an
assay is very
poor.
On the other hand, simple assays are very
useful from a validation perspective, but
may not
really reflect what you want to look for.
[Slide.]
So how do we choose the
relevant biologic
activity as a surrogate for
structure? So
assessment of bioological properties is
an
essential step in the
characterization. And so, by
characterizing the biological responses
that are
generated by the product, one should be
able to
pick a good assay.
[Slide.]
So just like you characterize
the
structure to pick the physiochemical
attributes,
you need to characterize the biological
effects to
pick a potency assay, and to define those
characters that ensure safety and
efficacy. And,
again, defning those
characteristics--what
attributes really matter--are crucial to
the ideas
289
of risk management, CGMPs for the 21
st century, and
PAT.
Because if you don't know what to control,
you can't control it.
And that makes it also relevant
to small
molecules and achieving this ideal state
where
everything is connected, and you can
avoid a lot of
testing at the end, and you can truly
know your
process.
So I want to touch on two quick
things:
molecular mechanism of action, and
biological
plausibility.
[Slide.]
Molecular mechanism of action
is--again,
you need it for potency assays for
therapeutic
proteins.
But for all CDER products, it will help
you pick relevant physiochemical
properties;
sometimes predict toxicity, drug
interactions and
efficacy; and can be useful in choosing animal
models and clinical monitoring early on,
when you
don't have enough data to really know
what a
protein or product is going to do.
[Slide.]
290
biological plausibility--we
talked about
biomarker development and
validation. You need to
be able to interpret early
pharmacogenomic and
proteomic data. When you have a large enough
study, statistical data may be good
enough. But
when early on you need to make a decision
that
involves product development, biological
plausibility is a critical part of
assessing a
biomarker. And one of the only ways to do
that is
to really understand the mechanistic
issues, and to
say that this marker or this gene makes
sense.
[Slide.]
So if biological
characterization is so
critical to these issues, why is there
such little
guidance on how to do it?
If you look at the guidance of
end-of-Phase 2 meetings, it talks about
having
"adequacy in physiochemical and
biological
characterization." The term is used. However, if
you look in the parentheses: "peptide map,"
"structure,"
glycosylation"--no mention of what
biological characterization is.
291
Later on, it talks about
"bioassays," and
metnions using a variety of materials in
the
bioactivity assay, not just the product
itself--which is the beginning of
biological
characterization.
[Slide.]
What you would ideally
want--and, again,
this is difficult to do, and we're not
saying that
this can be done or should be done--but
binding of
the product; singal transduction
pathways; cell
culture effects; tissue studies; and in
vivo
studies--and sometimes multiple studies, because
the same protein or same product can have
multiple
active sites.
To do this, you need relevant
models.
That means you need the right receptor,
the right
pathway, the right cells, tissues and
species. To
pick those, you need to know the
molecular
mechanism of action. However, if that's how you're
defining it, you have a circular
problem. It
really is difficult to do this. There's no linear
algorithm to really biologically
characterizing
292
something. And so, again--I'll use another term
from Ajaz--you really needs a systems
approach.
You need a way of dealing with this
information to
allow you to get the attributes that can
allow you
regulatory relief from controlling them.
And there's also product
specificity.
There's a lot more variability in a lot
of these
biological assays. And it's very expensive.
[Slide.]
So one approach--and we have
companies
who've actually done this--is to sort of
have a
matrix.
So for--and I'll talk about using one or
so lots, and using many lots for some of
these
things.
So in initial development--the
lots very
early on--you might look at multiple in
vitro
assays, and really get a good feel for
what your
developmental lots do; move on to testing
some of
those in more complex animal
assays--transgenic
models, sophisticated models that really
try and
target the relevant attributes. And then, again,
in the end, when you have a validated
bioassay, it
293
would be good to go back and look at all
the lots.
Stressed lots--similar testing
plan--because this is likely to start
giving you
variants that you can define as important
or
unimportant.
And then for some of those
variants you
might purify them, and then repeat some
of othis
testing.
In clinical lot manufacture,
you're always
going to have some lots that are at the
extremes of
the ranges. And use of those lots in some of these
assays can also help you define this.
Ad, finally, the clinical lots,
you know,
should be looked at in the validated bioassay
and
sosmetimes in some of these other assays.
So having a sort of matrix
approach to
what you're looking at may help define
the
information that you need to help you
avoid
retesting and looking at all these
attributes.
[Slide.]
To do this, there needs to be
biology
expertise. A biological characterization is only
294
as good as the data that supports
it. Regulatory
decisions are impacted by this sort of
characterization. There needs to be a framework
for interpreting this data, interpreting
the
assays, and defining what's needed.
And this expertise is going to
become more
important over time; in fact, it may be
useful to
actually have a guidance for how to
approach
biological characterization for some of
these
materials, and a mechanism for consulting
people
with the right expertise in order to do
this.
And with the recent
consolidation, CDER
has now got some additional expertise in
cell and
molecular biology, which could play a
role in somse
of this.
[Slide.]
And, now, we talked about the
research
reviewer model--basically, research
reviewers do
both jobs. It's challenging. We're judged on
productivity. We have to go through site visits
and tenure committees, and we have the
difficulty
of multiple workloads.
295
On the other hand, the research
reviewer
model can serve as a form of catalysis
and synergy,
because basically we know not all
reviewers can
have active research program. It's economically
unfeasible, and it doesn't make sense.
[Slide.]
But if you have a small nucleus
of
research reviewers, they can help
encourage some
issues in biochemical and biological
characterization, process understanding,
and
mechanism. And they can consult on key decisions,
and they can also network to NIH and
other acadmic
groups, OTR staff, and the full time
review staff.
[Slide.]
Research is organized in funny
ways. So,
if you take disciplines like immunology,
tumor
biology, neuroscience and developmental
biology,
there may be people who have expertise in
cytokines, or cell hormones related to
this;
adhesion related to these cells; and
differentiation or in signal
transduction. And
there's a kind of a matrix. And you really can't
296
cover everything. But if you have a number of
researchers--as Amy talked about in her
division,
and we have in our division--who cover a
lot of
these areas and things, you can often find
points
of intersection. And those points may involve
research related to your question; NIH
journal
clubs that people participate in;
academic
conferences; and, finally, the literature
itself--but this variety of networking
that gives
you access to information.
[Slide.]
So, briefly, about our
organization. So
we have three divisions: Molecular Development and
Immunology--Margie Shapiro's the lab
chief; the
laboratory of Cell Biology--Kathleen
Clouse is the
lab chief; and the laboratory of
Immunobiology--and
I'm the lab chief. And each of these have three
principal investigators. They look at lymphocyte
and monocyte biology; tumor suppressors
and
oncogenes; cell-cell and
cytokine-receptor
interactions; signal transduction; and
antibody
interactions--which are very relevant to
our
297
products; and manufacturing process
validation.
[Slide.]
And our products--if you look
at them in
terms of indication--they tend to be
either
immunology or inflammatory-related or
oncology-related. And therefore having expertise
in immunology and tumor biollogy is very
relvant to
our products.
We have a number of approved
products that
relate to immunology or inflammation;
some of them
that share targets. We have to CD25s; we have a
variety of isotypes--different species of
antibodies, and anti-infective antibody
products
against cancer--again, some of them share
targets
like CD20.
[Slide.]
Our reviewers participate in
inspections.
We have imaging agents that are
radio-labeled, and
we're involved in developing guidance
documents--points to consider for
monoclonal
antibody; plant transgenic products;
orphan drug
status-monoclonal antibodies. And we're also
298
involved in Q5e, although Barry's the
lead on that.
And we're involved in follow-on proteins.
[Slide.]
So, we have a research program. And this
I'm going to have to go through extremely
quickly.
So, we have groups that have
studied
particular chemistry. What I'd like to focus on is
antibody structure.
[Slide.]
This is a schematic, based on
crystal
structure diagrams of an IgG
molecure. The V
region on top, with the DRs are the
variable
region, with a binding site which is a
part of the
antibody that leads to finding its
target.
There are variety of other
regions in the
molecule which bind effector molecules
like Fc
receptors, complemin, and a variety of
other
receptors that mediate effector
function. So the
antibodies have lots of different active
sites.
They may be relevant for some things and
not.
They're also glycosylated, some versions
a lot.
IgG1 tends not to be as much, but this is
also
299
relevant, in some cases, to PK and to
effector
functions.
[Slide.]
So we have Margie Shapiro's lab
that looks
at some of the things that generate
antibody
diversity. There are new technologies in making
antibodies, like phase display,
transgenic animals
that express human antibody genes. They may lead
to different binding sites--different
diversity.
They're not.
[Slide.]
And if you look at immunogenicity
of
antibodies--murine bio-similar, of which
this looks
at 8--more than half of the patients who
get them
develop antibodies against them no matter
what.
They're highly immunogenic.
If you take away the Fc region,
and just
have the top half binding site of the
antibody,
that immunogenicity goes down. If you make the Fc
region human, and you leave the variable
regions
mouse, you find that the immunogenicity
also is
between 1 and 13 percent. Again, as Amy said, it's
300
almost impossible to judge these
comparatively. So
you have to take this with a grain of
salt, because
the assays vary.
But if you humanize the
antibody--you make
all of it human except for the binding
site area,
and some other amino acids, the
immunogenicity also
is low--maybe a bit lower. If you take a fully
human antibody, which is one example of
bi-phage
display, it actually doesn't have a lower
immunogenicity.
So the question really is: is the
technology of making these antibodies
relevant to
how
immunogenic they are. And she--and her
lab is
studying this.
[Slide.]
Antibodies have effoctor
interactions.
Complement recptors play a role. There's a new
family of Fc receptors--it was found
through the
genome--which isn't known how it
functions. And we
have Dr. Mate Tolnay, a new investigator,
who's
going to look at whether or not those Fc
receptors
play a role in antibody function.
301
And Dr. Gerry Feldman has
looked at immune
complexes and how they signal
responsiveness to
cytokines. Again, that may play a role in how
antibodies work.
[Slide.]
Now, in terms of the biological
characterization--I think I'm going to
try and just
skip this. We have a lot of different projects
related to lymphocyte signaling, on HIV,
sustaining
in reservoir; the EGF receptor--and all
these
projects relate to products that we have.
So if you look at adhesion
costimulation
molecules, we have a licensed antibody
AGAINST
LFA-1.
And that information is useful on how its
potency assay was looked at.
[Slide.]
We have antibodies herceptin
against
tumors which signal through a molecule
called Cbl,
and we have someone who works on that.
[Slide.]
And, again, I want to talk
briefly about
Wendy Weinberg's project. She looks as skin as a
302
model of differentiation, and is
interested in
p53--but not classic p52, but new members
of this.
So here's an example of cells growing
under low
calcium, and these cells have not
differentiated.
If you increase the calcium
concentration, they
differentiate; you see there's no more
contact;
they're much less likely to grow; and
there's a
decrease in the amount of
proliferation. The S
phase is down by 43 percent.
But if you add a variant of a
p63 gene,
which is a p53 family member, that no
longer
happens.
They continue to grow, despite the fact
that you're induced differentiation.
And the question about what
these family
of genes do in cancers is relevant. And I'm going
to talk about that in a moment.
[Slide.]
We also have research regarding
controls
and manufacturing process--and relating
to
contaminants and process understanding.
[Slide.]
So the slide here is--you know,
"This is a
303
brain; this is a brain on
prions." You can see the
spongioform degradation. And this is an image of
how the prion protein is changed in
conformation in
order to cause disease.
But it turns out peptide's a
prion signal,
and they signal through the NF-kB
pathway, and they
signal through inducing cytokines. And they have
different effects on different cell
types.
And so information on this is
useful in
designing, potentially in the future,
cell-based
assays for prions--although they're not
nearly
sensitive enough to do that now--and
potentially
looking at the mechanism of the disease
of this
common contaminant.
[Slide.]
Kurt Brorson, who works with
Kathleen
Clouse's group, has made studies on
retroviral
testing, using Q-PCR-based assays, which
are much
easier and faster turnaround town;
process
understanding, in terms of the unit
operations, the
purification, chromatography.
[Slide.]
304
And here's an example of a bioreactor
used
to produce many of our products. The question is:
what things can impact retrovirus
expression? And
his work has shown scale and nutrients
don't seem
to matter, but inducing agents and
temperature do.
And, again, butyrate increases the
expression of
retrovirus and increasing temperature
does.
[Slide.]
Critical path--so, again, three
dimensions--we've all seen that.
[Slide.]
In terms of Critical Path
projects that
should be defined as Critical Path--so
you really
need to define a problem, state the
dimensions, and
point out why the FDA--if the FDA's going
to do
this research--is in a unique position to
do so;
and what benefits go to what industry
segments, and
the role we can play, and the impact of
the
solution.
[Slide.]
So I'm going to go through this
really
quickly, because there are a number of
305
investigators who I think have very clear
Critical
Pathways.
And one of them is the fact that anthrax
toxin is potentially a target for
treating and
prophylaxis of anthrax, which is clearly
a problem
we're all familiar with. But the bioassays for
anthrax toxin tend to be murine cell
lines. And
they die.
And, in fact, human cell lines do not
die from anthrax toxin. So is this r eally the
right model to be looking at the efficacy
of things
that block anthrax toxin?
[Slide.]
And obviously this has medical
utility and
industrialization issues, and also
counterbioterrorism. And the unique position of
the FDA is, is we're the only group that
sees all
the INDs for anthrax therapeutics. And there's
another unique aspect here is the FDA
also plays a
role in talking to the groups--like the
CDC--that
are involved in BioShield. So the FDA's in a sort
of a funny role, because it's not only a
regulator,
it's in some ways a stakeholder, since
the
government is, you know, buying these
things to
306
stockpile at some point in the future.
And, again, so David Fruct,
who's doing
this, has shown anthrax lethal toxin
activates a
particular pro-inflammatory cascade
involving
cytokines. There's been a huge
debate in the
literature of the role of cytokines. And I think
he's provided strong evidence that they
do matter.
And he's also been able to show
some
effects on human cells using this and, in
fact, an
enzyme that drives this.
[Slide.]
And I have a quick
schematic. So anthrax
toxin is composed of three
components: the first
one, PA needs to bind a receptor. It forms a
hexomer.
It then translocates the other toxin
units into the cell--this one, lethal
factor,
effects MAP kinases, which are important
to signal
transduction. And these lead to cell death.
But David Fruct's lab has also
shown they
lead to IL-1b and IL-18 release. How this relates
to pathogenesis is unclear. But it already
represents a potential marker you could
have for an
307
assay, for blocking the effect.
And he's also done some studies
showing
some effects in human cells--which,
again, might
make a more relevant bioassay.
[Slide.]
Wendy Weinberg--I showed you
briefly her
slide, about looking at p53-like
products. And I
think there is a huge lacking, in terms
of goo
preclinical models to predict treatments
for
cancer.
have at least a number of products in
which Phase III studies were done for the
wrong
indication, and later worked when the
indication
was shifted.
So, clearly, the preclinical
models used
to choose that first Phase iII study were
in error.
And making models where you have mice,
where you
have p53 knockouts, p63 knockouts--a
variety of
mice in which you can mimic how human
cancers
develop based on what genes are knocked
out in them
might make a much more powerful way of
picking that
first indication.
[Slide.]
308
And, again, Kurt Brorson, who
looks at
process-related things--and I'm just
going to skip
to a picture--
[Slide.]
--so a lot of our process uses
a viral
removal steps, including
nanofiltration. So we
filter away viruses. So how you test, and how you
validate these filters is tricky. You certainly
don't want to test the filter with a
virus, if you
can avoid it. It's more difficult to do, and you'd
need containment procedures. And it's cumbersome.
But you wouldn't like to depend entirely
on things
like gold particles, or a very poor
surrogate for,
really, the ability of the filter to
remove
viruses.
So Dr. Brorson's involved in
using a
phase--a bacteria phase--which is easy to
grow, in
testing these filters--and a good mimic
for large
viruses.
And so the phase he uses is PR772, has
been purified here by cesium chloride
gradient, so
you don't get clumps. Clumps are very misleading,
because they look like they're cleared
when, in
309
fact, your filter really can't filter out
viruses
of the right size. And, again, this is showing the
purity by cesium chloride preps.
So, again, this has the
potential to make
a better way of testing these filters and
showing
that they really do the job they do.
[Slide.]
And there are a number of other
industrialization-related projects, in
terms of
using gene arrays to look at cell culture
changes.
We're also interested in databases of
some of our
manufacturing experience. We sort of wanted a
comparability database for a long time,
but it's
been a little slow to go.
[Slide.]
So to sort of summarize, in
terms of
Critical Pathways, historically, cell and
molecular
biology have always sat with basic
research. But I
think now they have evolved so they are
involved in
looking at clinical outcomes, in terms of
pharmacogenomics, and proteomics, and
they're
involved in industrialization, because
they offer
310
more sophisticated ways of measuring industrial
processes. And they're clearly important in
choosing the right pre-clinical
development, the
right potency assay, and quality issues.
[Slide.]
And, again, by defining the
biology of a
system better, you can pick the relevant
physiochemical properties--and that's
critical for
cGMP and PAT--certainly, for our complex
proteins.
There's a potential for this to affect
toxicity,
drug interactions and efficacy, and even
pick
early-in-development models. We've had cases
where, based on a biological effect that
we would
predict from basic science about a
protein, we've
talked to the clinical reviewers and
we've said,
"Well, maybe this should be an
exclusion criteria."
Or, "Maybe you should think about
looking at this."
So this information really is
relevant at
all stages of development--and, again,
plays a
critical role in process validation and regulatory,
fewer failed studies.
[Slide.]
311
So, I think a critical
direction is really
to better define "biological
characterization."
Clearly, we're not asking industry to test
every
lot of everything they've ever made, in
every
animal model you can think of. But the point is,
by having good characterization of the
mechanism of
action, using a variety of models, sthat
really
leads into the fact that you can say
"this
parameter," "this
glycoform" doesn't matter, and
therefore avoid problems when you have
comparability issues--you know, in terms
of a
difference there, and reduce,
potentially, in the
future, the actual specifications you
have.
Again, ideally, we'd want a
guidacne on
thsi.
It's--again, because it's non-linear it's
ckind of ocmplicated to think about how
to do this.
And
this plays a critical role for follow-on
proteins.
The better you can characterize the
mechanism of action, the more confidence
you are
that a follow-on protein is going to do
what you
think it's going to do.
Again, we'd like to maintain this
312
biological expertise. We'd like to have research,
you know, across the relevant areas for
out
products--which I'm calling "Critical
Pathways,"
because it doesn't necessary fit the A,
B, C, D, E,
F of Critical Path.
We'd like to facilitate access
to OBP
biologists; interactions with other
offices, with
pharmacology and clinical review groups. We
actually briefly had a conversation
yesterday, in
terms of the pharmacogenomic review
process, could
we play a role, in terms of helping
define
mechanistic questions about correlating
markers?
Again, Biotech Rounds with OBP and other
clinical
groups; and mechanism of action journal
clubs,
potentially, to talk about this;
bioprocessing
journal clubs--and, again, eventually
mechanisms
for consults on these issues.
[Slide.]
So--we also want to extend OBP
into
Critical Path projects, like some of the
ones I've
mentioned.
We talked about computers and
313
e-regulation.
And I think relational databases are
a verty useful thing. We have, in our division, a
database Kurt Brorson is setting up for
viral
clearance; a database that Patrick Swann,
our
acting Deputy Director, has for review
management,
USAN names and targets; one for
specifications; one
for the risk of TSE. We have databases now we're
trying to capture internal meeting
summaries;
workload databases; and, ideally, for
monoclonal
antibodies, wehre there's tremendous
similarity
between them, structural sequence
information that
we could compare between our products,
and link to
adverse events, would be very useful.
So, it seems all these
databases--some of
the Excel spreadsheets, and some of them
more
sophisticated--if we got somebody to make
them a
relational database, where you could work
all the
way through, that would be a very
powerful tool,
and potentially aid in the Critical Path.
And, again, our ultimate goal
would be to
use biological information, our research
and our
regulatory review, to enhance safety and
facilitate
314
regulatory relief.
Thank you.
CHAIRMAN KIBBE: Impressive.
Questions?
[Pause.]
Either we were all so
impressed, or we all
need a break.
DR. KOCH:
Well, I have a quick question.
If I understood correctly,
where you have
the industrialization intersecting with
the
Critical Pathway, you're inferring that's
something
like surface plasma and resonance, or
something
else that will actually be an
interrogation of the
process?
DR. KOZLOWSKI: Well, again, I think--for
instance, surface plasma and resonance,
for
instance, you can sort of do--I guess not
in a
line, but you could look at binding off a process--
DR. KOCH: Right.
DR. KOZLOWSKI: --by filtering your two
to a bio-core chip.
Yes--I think that would
certainly--that
315
would be very PAT-like, to actually look
at--
DR. KOCH: Right--that's what I was--
DR. KOZLOWSKI: --the binding of
something on a biacore--say, straight out
of a
fermentor--
DR. KOCH: Right.
DR. KOZLOWSKI: --and look at what your
conditions do.
DR. KOCH: Right.
It becomes an
analytical or monitoring tool.
DR. KOZLOWSKI: Right.
CHAIRMAN KIBBE: Anybody else?
[No response.]
Okay. Thank you very much.
I would liek to propose a short
break--10
minutes.
And then we'll get started with Jerry
Collins at 3:13.
[Off the record.]
CHAIRMAN KIBBE: We need to get started.
And I see by our colleague at the podium,
who is
now changing the entire proces, that we
are almost
ready.
316
What's wrong?
DR. COLLINS: The cursor was stuck, but
it's back on now.
CHAIRMAN KIBBE: The cursor was stuck.
There's nothing like having a stuck
cursor to kind
of ruin your afternoon.
[Pause.]
All right--Jerry Collins is
going to talk
to us about Critical Path initiatives in
lab-based
bioresearch of small molecules -my
favorite kind of
molecules, because I can draw the
structures.
DR. COLLINS: There will be a structure
quiz at the end, then.
Office of Testing and
Research--Current
Research and Future
Plans
[Slide.]
DR. COLLINS: If you haven't gotten tired
of trying to find something different in
this
diagram each time, my only point here is
to
emphasize that there are some areas of
overlap
between what NIH does in translational
research,
and what we consider Critical Path
Initiative at
317
FDA; areas of overlap and areas of
difference.
[Slide.]
We think about research--at least within
the Office of Testing and Research--along
the same
three cornerstones that exist in drug
development:
that's safety, efficacy and quality. And
throughout this talk and my final one, I'll
try to
align our programs to those cornerstones.
[Slide.]
The divisions--in green, on
your
left--represent our quality side in OTR,
and on
your right, in blue, represent the
biology side--or
mostly safety, a little tiny bit of
efficacy.
About 75 percent of our staff
is in the
left side, under "quality,"
about 25 percent is on
biology.
I'm the director of the Laboratory of
Clinical Pharmacology, and also Acting
Director of
Applied Pharmacology.
[Slide.]
There are three research
programs in the
Laboratory of Clinical Pharmacology. For those of
you who've served on this committee in
past terms,
318
we started a metabolism and drug-drug
interactions
program in the mid-'90s. Our goal was to interpret
what was then a barrage--a virtual
avalanche--of
data from in vitro systems, trying to
predict
interactions between drugs, between drugs
and food,
on the basis of metabolic pathways. This has been
a program in concert with the review
staff that's
resulted in the production of several
guidances--I'll mention that in a minute.
A more recent project is
hepatotoxicity.
Hepatoxicity, or idiosyncratic
hepatotoxicity in
particular, has been a major cause of
drug
withdrawals--we're actually trying to use
our
expertise in metabolic processes and
apply it to an
extension into liver toxicity. We'll come back to
that.
And, finally, the only project,
really, in
the office that's related to efficacy,
we're
looking at PET imaging for early
therapeutic
assessment. It generates a number of interesting
consultation reviews, and it's really and
extension
PK-PD issues that clinical pharmacology
319
subcommittee--this group--deals with
regularly.
[Slide.]
I don't need to tell this
audience that
adverse drug-drug interactions are a
major
headache, and have been a major
problem. I think
we have a very, very simple goal in
approaching
this problem, and that's to improve the
efficiency
and design of human clinical trials to
eliminate--or at least minimize to the
smallest
possible degree--the potential for
drug-drug
interactions.
They're relatively easy to
find. They're
relatively easy to predict in
advance. If you can
triage the worst ones--the potentially
worst ones
in vitro, and study them in vivo, then
you can gain
an incredible amount of confidence,
rather than
looking under every stone for another
drug-drug
interaction.
[Slide.]
In addition to our work in
dealing
applications that come from drug
sponsors, we also
work with the National Cancer Institute,
which has
320
its own drug development pipeline, and we
have a
memorandum of understanding to help them
learn the
technology of drug metabolism so that
they can
apply it in their pipeline. One of their employees
actually works in our laboratory on
theses
techniques, and we've also participated
in some of
their Phase I trials by analyzing the
drug and
metabolism in vivo in their first
in-human studies.
[Slide.]
This is the only flow diagram
I'll show.
This essentially describes a decade-long
process in
which we do metabolism studies using
human liver,
in vitro in our lab. That led, initially, to a
guidance on how to do relevant in vitro
drug
metabolism experiments. That guidance was enhanced
by the review experience, particularly
from the
Office of Clinical Pharmacology and
Biopharmaceutics. We then extended that to the in
vivo situation, giving a guidance for
industry on
in vivo metabolism, drug interactions
designs, and
that also built upon collaborate clinical
studies--when we were able to do them--as
well as
321
the metabolism-based drug-drug
interactions that we
were able to study in the
laboratory. And these
guidances are currently being updated in Clin
Pharm
subcommittee of this parent committee,
has been
active in reviewing it, sort of stage by
stage in
some of the new areas.
[Slide.]
Hepatotoxicity, as I mentioned,
is really
and extension of our expertise in drug
metabolism,
because it's been known for decades, now,
that some
of the most troublesome liver toxicities
arise from
reactive metabolites--not from the
chemical that
was swallowed or injected into the
patient, but
from a metabolite that was formed right
in the
liver, and the liver being the place
where the
metabolite is first form, is also the
first site of
potential injury.
So we're using our expertise in
understanding metabolism, to look
specifically at
reactive metabolites. We aren't doing this in
isolation in the laboratory. John Strong, the PI
on this project is on FDA's steering committee.
322
There's a joint program with PhRMA to
meet
regularly and discuss hepatotoxicity
issues
together.
[Slide.]
Here's an example of the
analytical
procedure that John and his group
use. Glutathione
is the universal sponge for sweeping up
reactive
metabolites as they form. So, by radio-labeling
the intracellular pools of glutathione,
we can look
at what grabs onto it after the end of an
incubation with an unlabeled drug, and
we've
labeled the reactive metabolite by its
linkage to
glutathione--almost an operational
definition of
what is a reactive metabolite: it's something that
wants to get together with glutathione.
Using the prototypical liver
toxin,
acetaminophen, we were a bit surprised
when we did
an inter-species comparison: it's a known
hepatotoxin in homo sapiens and in the
rat. And
what we found is that the rat and the
human have
one very large peak, eluting at about 13
minutes on
the HPLC tracing. The rat also has a second peak.
323
I think the important thing is
not to get
distracted by inter-species differences
in this
case; just a take-home message that,
while you can
see it in rodents, since we have the
ability to do
these experiments in human liver in
vitro, that's
where the focus of our experimental work
ought to
be.
[Slide.]
Just a minute t talk about
efficacy. PET
imaging is intended primarily to look at
an early
evaluation of drug action. And our involvement has
been to try to encourage innovation into
this
process.
Those of you who've looked
through your
background materials in the launch
document from
March of 2004, "Noninvasive
Functional Imaging" was
highlighted in the roll-out as one of the
areas in
which FDA and industry have agreed to
work together
to try to maximize its potential for
finding the
winners and losers relatively early,
streamlining
drug development.
We will be announcing a joint
meeting with
324
BIO and with PhRMA, and with the Drug
Information
Association early in 2005 to convene the
community
of imagers and therapeutic developers to
see how
the two can bring their tools to the
table.
[Slide.]
In terms of why we're doing it,
FDA, CDER,
has a very long history--as many of you
have heard
in your service on this committee--in
using
pharmacokinetic and pharmacodynamic
principles and
their application to regulatory
decision-making.
The disappointing thing,
scientifically,
is we're always looking at extra-cellular
fluid.
We do the absolute best we can with what
we've got
to measure, but when it's just the
circulating
plasma, we're only seeing part of the
problem.
And the mechanism-based
activity of the
drug is inside the cell. And the ability to see
distribution of a drug inside the cell,
its
interaction with receptors, enzymes and
transporters, is more like what's done in
drug
discovery, in terms of figuring out why
this drug
was picked. So if we can find out in vivo [sic]
325
whether we have the right concept being
applied in
vivo and select the dose, it could make
our
downstream work a lot easier.
[Slide.]
A good example of this is a
drug that was
reviewed by FDA's GI drug Advisory
Committee last
year, and recommended for approval. This is a drug
called Emend, or aprepitant, from
Merck. It's
intended for the reduction of
chemotherapy-induced
nausea and vomiting.
And this is the classic curve
that this
committee and other Advisory
Committees--and our
review staff--are usually faced with in
drug
development. The y-axis is a measure of
activity--Phase II data, not Phase III
data--and
the x-axis is some plasma concentration
of that
drug.
So there's an attempt made to link
pharmacokinetics with
pharmacodynamics. And what
was found was that at 40 milligrams there
was some
activity, but it was sub-optimal. At 125
milligrams, we exceeded 90 percent of the
activity;
at 375, of course, we were up on the
shoulder, or
326
the plateau, of the curve.
So it was a molecule that
certainly showed
dose-response, or dose concentration
response. The
question is: did any of that activity relate to
why this molecule was chosen for
development? Or is
just a me-too that acts by the same
mechanism that
other drugs do?
Well, Merck Pharmaceuticals is
one of the
leaders in applying PET imaging to the
study of
drugs in their pipeline. And this is a tracer map,
on your left, of substance P-receptors in
the
living human brain. And the color scale is that
red is the hottest concentration of
receptors,
followed by yellow, followed by dark and
then
lighter blue and then darker blue.
So that's the phenotypic map
that can be
measured prior to treatment, or in a
placebo arm.
And it's consistent with what's seen in
the human
brain at autopsy--except that this
subject is
living.
Subsequently, as you move
across to the
right, the next image is what happens at
40
327
milligrams. 40 milligrams--we would interpret this
image as--is very effective at blocking
the
receptor, so that when we give a probe--a
radio-labeled positron-emitting probe for
substance
P, it no longer can stick to the receptor,
and
therefore we don't detect
it--non-invasively.
As we go up to 125
milligrams--the middle
image--there's a little bit better
blockade. If
you do quantitative analysis, you can see
additional blockade. But clearly we're reaching
the plateau. And 375--and one higher dose, shown
on the far right--don't get you any extra
benefit.
These information are
supportive to
approval.
The drug was recommended for approval by
the
Advisory Committee, and approved by FDA, on the
basis of its activity in randomized Phase
III
controlled trials. But the reason these data were
supportive, and presented to the Advisory
Committee
were twofold. First of all, they relate the
activity of the drug, at a particular
dose, to the
presumptive mechanism of action. And, number two,
they permit the lowest possible dose to
be used.
328
Well, we all are familiar with
that
concept:
to maximize the therapeutic index you
want to minimize the penalty, in terms of
adverse
effects.
It turns out that the higher you go with
this drug--just like others--the more
baggage you
bring in terms of adverse reactions.
In this case, there's a serious
increase
in drug-drug interactions because
aprepitant
induces and inhibits many metabolic
systems. Since
these patients, by definition, are going
to be
taking a bunch of other drugs at the same
time,
minimizing drug-drug interactions by
using the
lowest possible dose, consistent with
preserving as
much anti-emetic potential as possible, helped
in
choosing.
So, on the basis of this
linkage of
imaging studies with Phase II data, the
sponsor
chose 100 milligrams as their dose for
the
randomized Phase III trial, and an add-on
trial,
and it showed superiority in a
placebo-controlled
test--an example of what we think is
generalizable
in many therapeutic areas. So much for Clinical
329
Pharmacology.
[Slide.]
In our Applied clinical
Pharmacology
Lab--which also might be called
"Pre-clinical
Safety"--one of our elements is
molecular
toxicology. And, like other labs at FDA, and in
university labs, we're very interested in
microarrays for their potential to show
us a broad
range of signals, good and bad--in the
case of
pre-clinical safety, early indications of
possible
toxicity.
However, from a regulatory standpoint
we're very concerned--just like most of
the
community is--in the chip-to-chip,
platform-to-platform, reliability and
consistency
of microarrays. So microarrays are very impressive
as an 11,000 gene, one-page readout of
most of the
relevant genome, but it's not the quality
of the
image--the "awe factor" that
we're interested in.
We want to know that if we take that same
sample
and do the next 10 chips, with the same
platform,
will we get the same picture? If we go from an
330
Affymetrix platform to an Agilent
platform, will we
get the same kind of readout?
Those are the questions, if we're
going to
make regulatory decisions on the basis of
these
kinds of data, those kinds of
cross-platform and
chip-to-chip reproducibility are what's
important.
We can't do this by our
own. We have
three people who are involved in this
project. So
we partnered with the platform makers,
the users of
these, and we're doing multi-laboratory ,
inter-laboratory comparisons of
standards. And it
seems to be proceeding at the right pace.
[Slide.]
The second important aspect is
not just
does the picture look the same, but how
do you
analyze the picture? What kind of statistical
tests for robustness can you apply? And there,
we've enlisted a very good partnership
with our
internal CDER statisticians--Bob O'Neill,
who
joined us earlier in this meeting--and
Bob's been a
very effective advocate, among the
statisticians
across all centers at FDA, to form a partnership
331
between statisticians and
biologists. So we'll
generate all the data that will help them
develop
tests for figuring out multiple
comparison,
correction factors, and all the things
that they do
behind the scenes. And they'll help us develop
metrics for figuring out how reproducible
the
quality is.
[Slide.]
In terms of gene markers of
toxicity, not
surprisingly, we're interested in
cardiotoxicity,
renal toxicity and, more recently,
differences in
pediatric toxicity versus adults.
Again, this group is not acting
in
isolation in our ivory tower in the White
Oak
laboratory; closely connected to the
Senior Science
Council--Associate Commissioner
Alderson's group;
and several of us are on the Inter-Center
Working
Group on Pharmacogenomics, chaired by
Larry Lesko.
We've had two joint workshops,
between FDA
and PhRMA, that we've participated in,
and the
third one is in planning for 2005.
[Slide.]
332
In preclinical
biomarkers--"biomarkers"
appears throughout the Critical Path
document--what
we're interested in is trying to zero in
on those
clinical toxicities that are particularly
hard to
monitor or only develop late in the course.
And, traditionally, this has
been
done--this is hardly a new field; we call
it
different things--but biomarkers, in the
past,
because of the technology, have been one
at a time
events.
Well, now that we have, you know,
multi-channel arrays of various sorts,
coming from
olmics, genomics, proteomics, how do we
bridge the
way we did these things in the past to
the way we
do them in accelerating in the present?
[Slide.]
Well, anthrocyclines, such as
doxorubicin
are known to cause cardiotoxicity. The slide at
the left, which is doxorubicin by itself,
compared
to the slide at the right--doxorubicin
plus
dexrazoxane--which is a
cardio-protectant, we don't
need to be a histopathologist to see that
there's a
difference, but we do have to have a
piece of the
333
heart.
And although you can get a piece of the
heart for valid therapeutic reasons, it's
clearly a
difficult way to search through
biomarkers. We'd
much rather have some kind of serum test.
[Slide.]
And, sure enough--for those of
you who've
been following the New England Journal of
Medicine
and other clinical papers--the troponin
series has
been recognized--in fact, has been
declared in
several recent articles--to be one of the
major
breakthroughs in monitoring
cardiotoxicity in human
beings.
Now, this particular study
that's in front
of you this afternoon is looking at
troponin T
levels in rats. So we took the signal from humans,
went backwards, in this case, to see
whether we
would have picked it up a priori, or in
advance,
rather than after the fact. And what we find is a
relationship between the cumulative dose
of
doxorubicin on the x-axis, and the serum
troponin T
circulating in the body.
[Slide.]
334
Well, that level of cardiac
troponin T in
the serum does correlate very well with
the
cardiomyopathy score, scored by a
histopathologist.
So it looks certainly like it has the
characteristics of a good biomarker. But it's one
thing.
Is there some way to generalize
this and
look more broadly?
Well, using expression arrays,
we've
looked at a variety of different pieces
of the
heart--pathways in the heart--that are
known to be
affected by anthrocyclines, or have
unknown effects
of anthrocyclines.
So, certainly, cardiac muscle
function and
structure, you can imagine, is adversely
impacted
by doxorubicin itself, and yet if you
look at the
far right column, dexrazoxane has a
protective
effect there.
We were unsure about fatty acid
metabolism
and glucose metabolism, some aspects of
immune
response.
We get some mixed signals there--all of
which show changes in the treated animals
with
335
doxorubicin, but in animals who get the
same dose
of doxorubicin--in the middle--and get
the
dexrazoxane as well, most of those
changes are
modulated. The control arm is the right arm for
dexrazoxane by itself. And finally, it's not
surprising that something that's done
this much
structural and functional damage also has
stress-induced genes that are highly
overexpressed.
[Slide.]
One last example, from the safety
domain--recently, across many different
therapeutic
areas, the phosphodiesterase inhibitors,
among
sub-families 3, 4 and 5, as well as other
vasoactive drugs, have been shown to have
bleeding
problems:
vasculitis, vascular injury problems.
And although there are species
differences across
the mammalian empire, rats, dogs,
primates and
sometimes mice, have shown this
phenomenon.
However, the only way you can see it is
with
invasive testing. So, in keeping with our mission,
we were looking for biomarkers that might
be
associated with it.
336
And a number of them have been
studied.
Again, we're more into one at a time, or
a few at a
time.
We had a half dozen; here's four that fit in
a slide and might be still readable--
[Slide.]
--in which we can see a
progressive
increase in circulating markers when the
vascular
histopathology score is going up as well.
So, treating rodents with a
variety of
phosphodiesterase inhibitors causes
circulating
biomarkers to go up, and that increase in
marker is
associated with the invasive test, which is
looking
at histopathology.
[Slide.]
I guess the bottom line is that
these
biomarkers represent a potential new tool
for
evaluating preclinical safety, and as
important an
endpoint as that is, I have to ask
whether it could
be extended into humans, as well. And I'll talk
about that later in the day.
[Slide.]
In summary, on the biology side
of the
337
Office of Testing and Research, our
programs in
biomarkers, pharmacogenomics, noninvasive
imaging
and drug interactions are certainly the
template,
or the scaffolding that you could develop
a
Critical Path Initiative around. We feel very well
aligned and prepared to charge into the
Critical
Path Initiative projects that we think
are quite
harmonious with its goals.
CHAIRMAN KIBBE: Questions.
[Pause.]
I don't see anybody jumping to
the
microphone--go ahead.
DR. KOCH: I guess it's always in the
definition of "noninvasive,"
but with the PET, you
still need to inject the radioactive,
short-lived
isotope.
But that's noninvasive?
DR. COLLINS: Well, I guess my FDA
training would have me modify it to
"relatively
noninvasive."
DR. KOCH: Oh, okay.
[Laughter.]
DR. COLLINS: We also included MRI
338
techniques in that regard. And if you don't have
to use a contrast agent--if you're doing
the
standard T1 and T2 kind of paramaterization--you
actually do that--the only invasiveness
is a
magnetic field. And it's--you know, particularly
where we come from, we're certainly not
going to
blow off the risk of these kinds of
things. But we
can quantify those risks in terms of
other everyday
life activities. The radiation in a PET image is
less than that of a conventional chest
x-ray, and
it can be made lower with more specific
detectors
that are being detected now.
I forgot how many airplane
trips back and
forth to Denver it would be equivalent
to; the
radiation that you get at 35,000 feet.
So there are additional,
incremental risks
that are undertaken, but in the context of
everyday
risk, the local IRBs, human subject
committees, and
the FDA have said, well, the benefit to
society
versus the minor risk is okay.
But there are very strict
dosimetry limits
on the amount that we can give as a radio
tracer.
339
CHAIRMAN KIBBE: Anybody else?
[No response.]
You seem to have done a
successful job of
presenting information.
And now we have Dr. Buhse.
DR. BUHSE: Okay.
I'm Cindy Buhse,
Director of Division of Pharmaceutical
Analysis.
And as Jerry mentioned we are on the
quality side
of OTR labs.
My lab is mostly responsible
for looking
at analytical methods that are used to
test drugs.
And so my labs mostly made up of
analytical and
physical chemists. And I'm going to go through
some of the programs we have.
[Slide.]
Let's see. Programs we have to support
the Critical Path Initiative--some of
these you've
heard of this morning from John Simmons
and
Lawrence Yu, because a lot of what we do
supports
ONDC and OGD, in terms of trying to help
them
determine how we can characterize novel
dosage
forms and complex drug substances, not
only to help
340
ensure we have the correct testing to
approve a
generic drug, but also to ensure that we
have the
right testing to approve changes in
manufacturing
in innovator drugs.
We also have programs to
measure and
identify micro and nanoparticles in
drugs,
especially--it's often easy to measure
the size of
a particle before you mix all your
excipients and
drug together, and once you have a drug
all mixed
together, what does that do to the
particle size?
And we need ways to take a look at what's
going on
in actually, final drug formulations.
We also establish--help
establish
appropriate surrogate measurement
techniques.
Lawrence talked quite a bit about this,
and
dissolution is a big thing that goes on
in our lab
in this area.
We also work a lot with the
Office of
Compliance on drug authenticity and
anti-counterfeiting techniques. It's an issue--I
think if you watch the news at all--not
only, like
Amy mentioned, in biologics, but its also
an issue
341
with regular oral dosage form drugs.
And then we also--the last two,
I'll
briefly go over--process analytical
technology,
research we're doing, and some
chemometrics, as
well, that ties into that. We're working with
DPQR--Mansoor Kahn's group--on those
programs.
[Slide.]
To start off with the
characterization of
novel dosage forms--some of the work
that's
currently in our lab are things I think
you've
already heard about from ONDC and
OGD. We have a
program on liposomes, trying to
characterize them
after chemical and physical change;
trying to
determine how to--what analytical
techniques work
best to detect changes in liposomes. And we have a
program with DPQR, as well, to take that
a step
further and see if we can use cell-based
assays to
see how these changes in the liposomes
can be
detected in the cell-based assay.
Looking at transdermals--people
call them
"patches" as well, patch products--and
their
adhesive strength. How can we
characterize the
342
adhesive strength and assure we have an
analytical
method that can be used to no only
compare a
generic to an innovator, but also can
assure the
quality of a patch before it's released
for sale.
John Simmons mentioned
conjugated
estrogens. We have some LCMS techniques we've been
running.
He showed some of that data. And
we're
trying to improve those methods to make
sure
they're very reproducible and can be used
to
compare innovator to generic, or to
compare an
innovator product after a change.
We also do some work with
protein
products, trying to look at different
analytical
methods to detect aggregation and
degradation, and
assure we know the exact molecular weight
and
distribution of protein products and can
characterize those.
Some of the regulatory
accomplishments
we've had in the past in this area
include input
into conjugated estrogen guidance, which
is
currently out.
[Slide.]
343
This is just to give you an
idea of the
kind of work we're doing on the liposome
project.
We're looking at two different types of
liposomes:
Pegylated and the convention--in fact
doxo--the
very drug Jerry was just talking about,
up there in
a liposome form.
We're looking at different
stress
conditions, and then looking at different
analytical methods to determine how the
liposome
was affected. Was the actual drug substance itself
affected?
Or was the liposome affected both in the
lipid composition and in the amount of
drug that's
encapsulated in the liposome?
And we determine what stress
conditions
will give us a small amount of
degradation, and
then Mansoor Khan's group will take those
degraded
liposomes and see how they react in a
cell-based
assay, see if we can see differences in
their
uptake.
[Slide.]
Patches--there are different
types of
patches out there on the market, and
we're taking a
344
look at both kinds when we look at
adhesive
properties.
One has actual drug in the
adhesive--the
adhesive and the drug are mixed together
and you
actually get your dosage by the size of
the patch.
And then there's also reservoir-type
patches. And
if you start looking into adhesive
properties--and
we actually are jointly working with CDRH
on this,
as you can well imagine, with things like
band-aids
and medical tapes. There's a lot of variables to
look at when you're doing test method
development
on adhesives. And I've listed some of the
variables down below that we're looking
at to try
to come up with a method that could be
reproducible
for patches.
[Slide.]
In terms of measurement and ID
of micro
and nanoparticles, some of the projects
in our lab
include looking an some of the sunscreens
that are
currently being marketed as having
nanoparticles.
We're trying to look at seeing what
techniques can
be used to evaluate the size of these
particles
345
once the sunscreen has been formulated.
And likewise, in nasal sprays,
we want to
know what is the particle size of the
active
ingredient once it's been mixed together,
especially nasal spray suspensions. And I'll show
an example of that in a second.
We've also done some evaluation
of
Andersen Cascade Impaction, which is used
to
determine fines--trying to determine how
to improve
that test method. It's very variable, and are
there other options to using Andersen
Cascade
Impaction to get a handle on fines in
nasal sprays.
Some of the regulatory
accomplishments
that have come out of our lab included
input into
the nasal spray BA/BE guidance; and also
we've done
some measurement work with cyclosporine
particles
and helped ONDC and OGD with that.
[Slide.]
This is just an example of some
of the
work we've done on nasal sprays. Here's some raman
chemical imaging. And you'll see--I guess it's all
the way on your left, up at the top, you
have a
346
Brightfield, just microscopic image, of
the nasal
suspension. You can see a lot of different
particles there. It's hard to tell which particles
actually are active. So if you're trying to
determine a particle size of your active
within
this formulation, it's tough to tell just
from that
picture.
You can kind of tell--you look
at MCC,
kind of is that rod shape there. However, if you
actually can take the Rama spectra of
each one of
those particles--which is what's shown
just below
that, you can see that the spectra's very
different
at each one of those particles, and you
can look
for the Raman spectra of your actual
active drug to
determine which one of those particles is
your
active drug. And that's what's shown down at the
right--at the bottom. We're determining from the
Raman which one of those particles in
that image is
actually the active drug. And you can see that
there's two of those particles that are
active
drug, and there's a little bit of an
active drug,
maybe, attached to some of that
excipient.
347
And from that we can then get
the particle
size of the active drug within the
formulation, and
we can also get a feel for maybe if the
active is
maybe sticking to some of the excipients,
which may
actually change its actually size from
what you
think you might have put it, from the
formulation.
[Slide.]
Establishment of surrogate
measurement
techniques--we've done quite a lot in the
last year
on dissolution, trying to do quality of
drugs.
We've worked with Office of Compliance on
the
malaria drug mefloquine to try to figure
out why it
was or wasn't working in the field, for
the
military.
And we've also done some work with
megestrol acetate suspensions, trying to
compare
generic to innovator drugs, and figuring
out the
best dissolution test method to use for
that.
In general, we're taking a look
at
dissolution testing because it is heavily
used--as
Lawrence said--not only for quality
control, but
also to try to--for bioequivalence, as
well. And
so we're trying to make sure that the
actual
348
dissolution test methodology can be as
consistent
as possible.
For those of you who do
dissolution, you
know it can be a very variable method.
[Slide.]
This is just some information on the
calibrator tablets that are issued by USP
to check
set-up of your apparatus. And you can see that the
limits on the calibrator tablets are very
high--28
to 42 percent is the range that you can
get for the
Lot M.
And lot N which was after that, was 28 to
54.
And Lot O is currently out, and is proposed to
be just as wide, if not wider, than Lot
N.
So if you use a calibrator like
this to
test your apparatus set-up, you can see
that any
variability that you're seeing in your
test method
potentially could be due to apparatus
set-up,
because you're not going to be
determining it from
this calibrator tablet, because it's just
too
variable.
And so our lab is looking at alternative
ways to ensure set-up and reliability of
dissolution apparatus, other than using
calibrator
349
tables.
[Slide.]
One of the areas that I think
has become
very important lately is just
anti-counterfeiting
techniques, and ways to ensure that the
drug you're
taking is the actual drug you thought you
bought.
And so our lab takes--keeps a close watch
on
technologies that are out there for
counterfeit,
and even to see how they can apply. We've been
involved in several projects with the
Office of
Compliance to ensure the quality of not
only the
active pharmaceutical ingredients, but
also foreign
Internet samples.
So we've tested both of those
in our lab.
And we used conventional techniques--like
HPLC and
GC--looking for impurities, etcetera, to
see
whether the drugs are the same as the
U.S.
equivalent. But we've also taken a look at new
technologies, because some of these can
be very
powerful, much faster ways to detect
counterfeit,
or can actually show us new--maybe give
us clues as
to where drugs may have come from if they
are
350
counterfeit.
[Slide.]
As an example, I was just going
to show a
little bit ratio mass spectrometry. This is a
technique which uses stable isotopes to
try to
detect where chemicals may have come
from, and also
to determine if things were made in the
same plant
or not.
This is a plot of the stable isotope
of
carbon--which C13, versus C12, and
oxygen, which is
O18 versus O16. And you can see that Naproxen,
manufactured at different places in the
world, and
different plants in the world, cluster
together, in
terms of their stable isotopes, and
that's because
stable isotopes aren't the same around
the world.
And when you manufacture a product, your
stable
isotope composition within that product
is
dependent on the raw materials you use,
where those
raw materials came from in the world, and
also on
your manufacturing pathway. And so it can be a
powerful technique. You can see, if you have a
drug, and you can test it by IRMS, and
then
351
determine potentially which plant it came
from.
[Slide.]
In terms of PAT--as in
anti-counterfeiting, we try to take a
look at the
technologies that are out there, either
new
technologies or maybe new to the
pharmaceutical
industry, and try to determine how they
might be
used for PAT; what some of their
limitations or
benefits might be so we can be in a
position to
advise ONDC or OGD as needed.
We have a couple projects in
our lab
taking a look at coating composition, how
that
affects the ability to see what's going
on within a
tablet, and also taking a look at
excipients and
excipient-drug interactions within
spectroscopy,
and how that affects the ability to use
spectroscopy for PAT.
[Slide.]
As an example I wanted to show
you
Terahertz spectrometry. Terahertz--this is between
infrared and kind of your microwave. You can see
up there on the spectra on the right,
there. And
352
one of the benefits of terahertz, it's
like NIR;
it's non-destructive. But it also is a lot more
penetrating. The NIR can go deeper into a tablet
or into tissues.
So it's being looked at, not
only for
quality control of drugs, but also as
imaging of
biological tissue, especially skin
cancers.
And I just want to show you a
little bit
of the spectra we've gotten. These are
acetaminophen tablets. They're from 65 to 135 mgs,
and you can see that the terahertz
spectra, which
is the one on the left--it's not much
features
there.
I mean, you would probably look at all of
those and say that they looked pretty
similar. But
if you take that data and you run it
through some
parametric programming, and compare it to
content
by near-IR, you can see you get a very
good fit
between the terahertz and the
near-IR. But the
good thing about terahertz, it would have
the
potential to go--to look past a coating,
or to look
deeper into a tablet than near-IR.
The terahertz here was actually
done with
353
transmission. So by detecting the radiation
through the entire tablet, and near-IR
often you do
reflectance.
[Slide.]
Chemometrics is another project
that we're
doing with DPQR, trying to understand the
chemometric software packages that are
out there.
If we're requesting people to use PAT,
and to use
more multivariate techniques, we want to
understand
what their limitations and benefits are,
especially
for model building--pre-treatment of
data, things
like that. We want to be able to provide expertise
in that area.
Just as an example, the kind of
things
that we've been doing. Here's some near-infrared
of those--actually the
same--acetaminophen tables
that I just showed you with
terahertz. But this is
all with near-IR. On the left is near-IR
reflectance, which is the full range of
the
spectrum, from 4,000 to 10,000 forcipical
centimeters in the near-infrared. On the right
side is transmittance--okay? So this is where
354
you're trying to go actually through the
tablet.
You'll see it's a little noisier in
transmittance,
and you can actually only use about 8,600
to 10,000
because of the noise.
However, depending on how you
treat the
data--you can see underneath the
reflectance we
have--we've taken a second derivative,
and we get a
good correlation between the near-IR and
the
content measured by HPLC. However in the
transmittance data, we don't need to do
the second
derivative. We can take the direct spectra and get
the same time of correlation with the
content
measured by HPLC.
[Slide.]
I just wanted to put this up
because
people talk about the St. Louis lab,
sometimes.
That's us, I guess--Division of
Pharmaceutical
Analysis.
We are the only CDER lab located outside
of
Maryland, so we have a small group of people at
White Oak, with Jerry Collins and Mansoor
Khan.
But we also have our larger laboratory in
St.
Louis.
And so a lot of our interactions occur by
355
video-conference and telephone. But we still
manage to get quite a bit done out there.
So--hopefully the Cardinals
will come back
in the next two games because, of course,
everyone's very depressed about that out
in St.
Louis.
So I'm not sure there's much work getting
done in the lab right now, after last
night's
defeat.
[Laughter.]
So--I'm happy to answer any
questions
about the Critical Path Initiative.
CHAIRMAN KIBBE: Questions?
Michael?
DR. KORCZYNSKI: This is more or less a
comment.
And I don't know whether you could
directly answer this--but, pharmaceutical
analysis--as you were speaking I was wondering:
most of the products that we're
discussing are,
indeed, sterile products.
So is there a counterpart to
your
activities in the microbiological areas,
such as a
laboratory investing microbiological
analytical
methods for even investigation of
counterfeit
356
drugs, or bioterrorist activities? Is there some
type of microbiological analytical
counterpart to
pharmaceutical analysis of products?
[Pause.]
Or maybe it's resourced
out. I don't
know.
DR. HUSSAIN: Well, I think much of that
is done in our field labs. And Amy--and I don't
know whether we have a focused effort on
microbiological methods, but counterfeit
efforts on
many of the injectable protects and OBP
are being
carried out, too.
But, we actually do not have a
very
focused broad quality microbiology lab
within OPS.
CHAIRMAN KIBBE: Go ahead--Mike?
DR. KOCH: Yes--question, Cindy--on the
surrogate dissolution--
DR. BUHSE: Mm-hmm.
DR. KOCH: --you know, we heard this
morning of the different pHs, and time
and
different things that go on there.
Over the years has there been,
in addition
357
to the USP standard dilute hydrochloric
acid
approach, has there been a way to
simulate the
process, to try to come up with a
dissolution test
that goes through a low pH, followed by
neutral pH,
etcetera--to actually try to simulate.
DR. BUHSE: There's been a lot of research
done on dissolution, and there's a lot of
research
in the literature and in academics. They have--one
of the dissolution apparatus is like a
flow-through
apparatus, rather than the vessel, and
some of the
studies done on those have been the type
that
you've talked about. There, you don't recirculate
the dissolution media, you just
continue--and you
can continue flowing it through the tube,
and
you've got the actual pharmaceutical
suspended in
the middle of the tube, and you can
change the
media as it goes through--things like
that.
So there are research programs
out there
like that, and we're reviewing those and
seeing how
they might be applicable--or maybe more
applicable
than the vessel method.
CHAIRMAN KIBBE: Go ahead.
358
DR. MORRIS: Yes, just a question on the
chemometrics--looking at the
well-executed, but
relatively traditional chemometric
approaches in
evaluating the packages that are out
there.
Looking at cross-process
chemometrics in
sort of process-vector type work, or
multi-block
systems to try to take into account more
than a
single assessment of a product, as
opposed to
looking at the product train?
DR. BUHSE: I guess--maybe Ajaz, who knows
a little bit more--
DR. HUSSAIN: Right--no, I think much of
the internal work has been focused on
what we can
have.
DR. MORRIS: Sure.
DR. HUSSAIN: Because we're hoping the
CRADA with Pfizer, I think we're just
starting to
get in the process and so forth--I think
our
interest would be to get at process
signatures and
so forth.
But I think for that we need to have a
collaboration where we have that.
Mansoor is actually setting up
the
359
manufacturing lab. And so once that is set, we
will have access to that. But most of the
work
we're doing right now with in-house data is based
on chemometrics for products that we have
our hands
on.
DR. BUHSE: Yes, and we've done a little
bit of that. Some of the data I showed you was for
one--like, for instance, for one
compression rate.
We have similar tablets we've made--exact
same
formulation, at different compressions,
different
excipients.
So we, you know, try to throw
more
variables into it. But I think some of the CRADA
and manufacturing efforts--make it
more--give us
more the ability to do further work in
that area.
I think Judy had a question.
CHAIRMAN KIBBE: Yes, Judy had a question.
DR. BOEHLERT: Well, I'm down here in the
corner.
This is sort of a general
question-comment. It applies to you and to several
of the more recent presentations this
afternoon.
360
You have a number of research
projects--liposomes, characterization,
adhesive
nature transdermal. To what extent do you interact
with industry? Because industry is also working on
these same factors, and looking at
adhesive
strength, looking at the stability and
characterization of liposomes.
And, you know, I don't want to
see people
going in two different directions to come
up with
two
different ways to do the same thing. So
is
there synergy between what you're doing
and what
the industry groups--or maybe even the
academics
are doing?
DR. BUHSE: Yes, some of the projects we
do work extensively with industry; with
the patches
project we've been working with--I think
I
mentioned CDRH, our other center, but
we've also
been working with 3M extensively, because
they have
such a knowledge of adhesives, and they
also
actually manufacture quite a few of the
adhesives
for patches--as it turns out.
So, in some cases, we do work
with
361
industry.
A lot of cases we're not really able to
because what we're doing is trying to
compare,
perhaps, two different products, or a
generic and
an innovator, and there starts to become,
you know,
some issues there where collaborating may
be more
of a problem.
CHAIRMAN KIBBE: Follow-up, Ajaz? Go
ahead.
DR. HUSSAIN: no, not follow-up. I think
I just wanted to sort of emphasize--John
Simmons
had mentioned the rapid response.
A lot of the activities in the
St. Louis
lab are getting to solving problems that
we face.
For example, the adhesive issue came up
through
dramatic failures in adhesive performance
on--we
manage, in the Office of Pharmaceutical
Science, a
Therapeutic Inequivalence Action
Coordinating
Committee. And then from the MedWatch, from the
consumer complaints--we were receiving a
lot of
failures of transdermal systems falling
off.
And then we looked at that and
said we
actually do not have a good method, which
is also
362
part of the stabalating program for many
other
products.
So that was an outgrowth of that.
And liposomes, for example--one
of the
challenges was we were setting
dissolution
specifications on liposomes--I'm not
kidding. So
we said, "Let's understand some of
that," and so
forth.
So, a number of projects that
Cindy
does--immediate answers that are needed,
and that
is a very critical element. So you have to keep
that in mind. So that's a very important lab, from
our perspective, in a sense, because
immediate
answers are needed for John Simmons'
Prussian
Blue-type work, and so forth, and so
forth. So
that's--I just wanted to clarify that.
CHAIRMAN KIBBE: Anybody else?
DR. BUHSE: Quick questions?
[No response.]
CHAIRMAN KIBBE: I guess you're off the
hook.
DR. BUHSE: I guess it's on Mansoor.
CHAIRMAN KIBBE: Dr. Khan.
363
DR. HUSSAIN: Just as he comes on
board--he is new to FDA. So he came from academia.
So he's--
CHAIRMAN KIBBE: You're asking us to be
nice to him? Is that what you're doing?
DR. HUSSAIN:
Yes, that's it.
[Laughter.]
DR. KHAN: Good afternoon. It's quite a
challenge to stay motivated and speak in
the
afternoon, but I'll try to do my best
here.
I'd like to thank Dr. Webber
and his team
for giving me this opportunity. I would like to
thank the Advisory committee for your
leadership
and the important role you play in this
process.
I'd also like to thank the audience, who
have been
extremely patient since morning--I've been
noticing.
So--audience.
Most importantly, I would also
like to
thank my colleagues from the Division of
Product
Quality Research. Some of them are here, and some
of them that are not here, but they have
given me
some of the slides to share with you,
just to show
364
what goes on in the Product Quality
Research.
[Slide.]
I will just briefly go over the outline.
People do ask me--I'm also new here, as
Ajaz just
mentioned--that, you know, they asked me,
"Okay,
what's the mission? What do you do?" So I would
briefly at least outline the mission and
the reason
that we have here, then present to you
the team, so
you'd get an idea of what our division is
about,
and the current needs related to Critical
Path and
the cGMP initiatives; some of the future
directions; and examples of "design
space." It
comes about a lot, and I thinks morning,
also, a
question was asked about the case
study. I may not
be able to provide the case study, but at
least I
can provide some examples of that
one. And then
some questions about that. Okay?
[Slide.]
The teams--sorry, the mission
first.
Advance the scientific basis of
regulatory
policy with comprehensive research and
collaboration; focus/identify low and
high-risk
365
product development and manufacturing
practices;
share scientific knowledge with CDER
review staff
and management through laboratory
support, training
programs, seminars, and consultations;
and foster
the utilization of innovative technology
in the
development, manufacture and regulatory
assessment
of product development. Basically, we would like
to stay aligned with OPS and the CDER
missions.
The vision--we want to be
recognized
leaders in providing support for guidance
based on
science and peer-reviewed data; well
trained staff
and state-of-the-art product quality
laboratories
that is capable of providing any
information sought
by reviewers, industry and the FDA
leadership.
Culture--the way we live and
act--one of
cooperation, mutual respect, synergy,
professional
development with life-long learning
opportunities.
Basically, this slide I derived from some
of the
internal presentations. I just wanted to go over
it so that we are all on the same page.
[Slide.]
The division, we have about 19
scientists
366
currently working on this. We have three teams.
The fourth one is in the making: the
pharmaceutical/analytical chemistry team;
we have a
physical pharmacy team; a
biopharmaceutics team;
and a novel drug delivery systems team.
So I'll briefly go over what
they do, and
share some of their slides with you.
[Slide.]
Pharmaceutical/Analytical
Chemistry
projects--we have team leader Dr. Patrick
Faustino.
He has done some work on this Prussian
Blue--basically, safety, efficacy and
product
quality studies. John has presented to you this
morning some of those studies. And basically that
laboratory work was done in a DPQR.
Then we have the shelf-life
extension
program, where we have the stockpile of
drug with
the U.S. Army, so we look at some of the
stability
issues of those drugs. And, then, very recently
they've also worked on isotretinoin--some
of the
bioanalytical and kinetic studies they
have done.
Basically I'm just going some
of the
367
current work that is being done, and what
we want
to do to make changes, with your
recommendations on
this.
[Slide.]
Just one slide--he has already
shared
these things with you, but I think this
is just the
effect of pH we have seen, because this
is a
compound of high interest--radioactive
decontaminant. It was releasing some cyanides we
have seen that the release of cyanide is
much less
at a certain pH--I was just focusing
here--that the
release of cyanide is much less at
certain pH, so
safety at a certain pH--you know, it's
much safer.
And then the efficacy--we have done some
binding
studies of radioactive cesium. Also we are working
on thallium on this one. So the binding studies we
have seen as the pH goes up, the binding
is more
here on there. So this gave us some idea about his
compound.
[Slide.]
The next team, the
biopharmaceutics team,
headed by Dr. Donna Volpe. And it's a small team.
368
If we want to go in the area of bioavailability
and
other issues, then I think this team
needs to be
expanded a little bit.
They have broad activity on the
BCS
guidance.
A question came up this morning, also,
about the extension of this BC guidance. Donna
Volpe is working actively on these
things.
They have also worked on--there
was some
bioequivalence issues of levothyroxine
sodium
products.
So we have looked at the stability of
this.
It was a huge project. A lot of
people were
involved with this. We have just completed that
project and the final report is about to
come out
on that one.
We are looking at the effect of
cyclodextrin, as well as some other
excipients, on
the permeability of certain drugs. Dr. Volpe has
created a huge database where the
permeability of
certain commonly studied drugs--like
atenolol, some
metopralol--and, you know, we also looked
at the
permeability of mannitol and the various
factors
affecting the permeability of that
drug. So we
369
have created a database of that.
And some uptake studies--I think
Cindy
mentioned just some time ago about some
of those
uptake studies of liposomes. So this is the work
which Donn's work group is doing.
[Slide.]
Just to give you an idea--I
think that
this question came up about, you know,
moving this
Phase II--BCS Class III drugs in the
direction of
getting bio-waiver. This will give you an
illustration that if you have a high
permeability
drug--you have a metapralol drug, a high
permeability drug, you get these two
different
excipients there. There was no difference.
But if you change the
excipient, where we
have this osmotic agent here--the
sorbitol--then
you see there's a tremendous difference
in the
availability. So this needs to be sorted out
more--you know, to seek the
bio-waiver. So this
group has helped us study some of those
things.
[Slide.]
The next group--the physical
pharmacy team
370
that we have: Dr. Lyon, Robbe Lyon has done a work
in the PAT-related issues--the Process
Analytical
Chemistry, I might say, because Chris
Watts keeps
correcting us on this
"PAT"--the terminology of
PAT.
But what I'm talking about is mostly the
chemistry aspects of this PAT. And then Everett
Jefferson is the team leader of that.
And so I will highlight some of
the
slides.
They have given it to me--just to show you
what they are doing with these analytical
sciences
that we have.
[Slide.]
You can see here--this is just
with
near-IR profile of--
[Moves off mike.][Inaudible.]
This is a lot easier. You are right.
Trying to get where the mouse is. It will come
sometime?
Okay. Okay. All right.
Got it here.
So we have these acetaminophen
powder
here, the avicel powder, and then you
have a tablet
here.
So you see--so we look at the contents of
this--the HPLC. We saw the content, we saw the
371
near-IR, we saw a correlation. I think Ken asked
some question as to what you do--what
validation,
basically. You eliminate one thing at a time so
that you get a correlation where you can
rely a bit
more on that. That's what they've done on this
one.
[Slide.]
Same thing we have done with
Raman Spectra
here.
We have it here in the laboratory.
We have
this Raman spectra. I will tell you how we can use
it, later on, in some of the optimization
studies.
We want to employ this. But at least now we have
the procedures in place to do some of
those
studies.
Raman spectra--similarly, you
look at some
of these peaks, and then see the
correlation. You
see the HPLC content we have done with
these
tablets, and we have correlated with
Raman, partial
e-squares.
So you can see the correlation
here. It's
fairly good here in this one, too.
[Slide.]
372
Likewise, we have also looked
at the blend
uniformity. You can see, this is a formulation
that it clearly shows that this is well
blended, as
opposed to this formulation which is not
well
blended.
You see the API, you can see that it is
not as well blended here.
So if you look at the near-IR
spectra, the
spectra is very close to each other. You know,
it's likely that it's a good blend; you
know, they
have mixed well. This is separating out. That
means, you know, they have not really
mixed very
well.
So it gives us some idea of this mixing.
[Slide.]
Now, hydration--this
hydration--Robbe Lyon
has given me--hydration is not the
process of
hydration, it's basically just an
identification of
a product which either anhydrous, or a
monohydrate,
or a some hydrate we can detect--whether
the
product is hydrous, or
anhydrous--anhydrous product
or a hydrated product.
[Slide.]
The next slide will show we
have basically
373
two brands of product. We have brand 1, a capsule,
and then a Brand 2. You can see, this capsule
here, it has Core A and Core B. Basically, it has
two different cores--okay? And no some Brand 2 has
three cores, instead of two cores. Basically they
have two of this B core, and one of them
is core A.
So if you want to detect how
much of it is
anhydrous form, or how much of it is in
the hydrate
form.
So they look at some imaging here, and that
imaging, basically they are showing that
in core A,
there's--these are nitrofurantoin
capsules, by the
way--in core A you have more of the
anhydrous
concentration. And basically they have estimated
it to be 8 percent. And actually, when they have
seen it, it was 9 percent there here in
this one.
And, similarly, when they did
it on this
brand, in core B you have seen this is a
monohydrate concentration, this has more
of the
monohydrate concentration. The estimate was 50
percent, but the actual was 40 percent.
So, you know, it just gives us
some idea
of see the current, and it's not just the
drug.
374
But we can look at the different
polymorphic forms
of the drugs themselves.
[Slide.]
This is the near-IR dissolution
correlation. We have looked at some of the
tablets. The tablets were prepared--I
think Cindy
mentioned some time ago, you know, we had
these
acetaminophen tablets. I think the next one will
show.
These are some tablets. Basically we
looked at the tablets. We predicted.
We trained
the data.
This is the training set.
[Slide.]
The one in the blue, and we
looked at the
correlation. The correlation was .984. And then
we have this test data. We looked at almost 72
different tablet formulations, and we saw
that it
was fairly--especially at a higher
dissolution
profiles, and a lower level IC, that the
curve is
off.
Actually, if you take some of these data
points off, if you take--go for a higher
dissolution, when the amount is higher
the
375
correlation might be much higher, if you
take these
data points off.
[Slide.]
So if I have to summarize as to
what's
going on currently in the DPQR, this is
what it is.
We have the Drug Substance--we are
characterizing--trying to get the--
[Pause.]
Okay. Trying to get the mouse here--the
curser here. Okay.
So, basically, if you have a
look--we have
drug substance. We are--currently, in the DPQR we
have this drug substance. We are characterizing,
and the process analytical tools that we
have. We
have the analytical method. And the
biopharmaceutical groups brings some cell
culture
work.
And the drug product--we can characterize
the drug product that we are doing
here. I have
shown you some of the work here that's
being done.
And as well as the
stability--as I
mentioned to you, the shelf-life
extension program
is going on for some of the drugs of
national
376
importance.
But, as a new kid on the block
here in
FDA--I know, having worked in academia
for a long
period of time--for about 12 years I
worked in
academia--and then after coming here, I
wanted to
see if what we are doing is enough for
us. So what
I did, I started listening to the leaders
here. I
started attending the meetings--some
forums like
this--you know, the Advisory Committee--your
July
Advisory Committee, I was here. And then I
listened to a lot of presentations of the
leaders
of the FDA. I got to see what Dr. Woodcock has to
say.
I got to see what Ms. Helen has to say in HR.
I've gone to a lot of their
presentations, and I've
read a lot of internal reports. I attend a lot of
internal meetings, just to see some of
the
directions.
To give you an idea, you have
already seen
a lot of Critical Path slides, so I
didn't want to
duplicate some of those slides. Initially, I had
that in the presentation, but I took some
of them
out, seeing some of the speakers had
those things.
377
But here--you know--because
this is a
developmental type of research. I think Ken also
alluded to this in the morning, as to who
will fund
this--the level of funding--and who will
do this
research?
If academia doesn't do it, if the NIH
doesn't do it, then who else will do
it? And the
industry doesn't want to share the
information.
So at least we will have some
work--at
least we'll have some data in place so
that the
reviewers don't have to operate in a
total dark
box.
Since you have already heard
Ms. Winkle's
presentation, the support for
understanding of--the
process understanding and the Critical
Path roles
is highlighted here in this slide.
[Slide.]
And the internal efforts have
culminated,
really, in the articulation of this thing
in the
desired state about ICH, as you can
see. I don't
know if any other speaker had this, but
previously,
in the manufacturing subcommittee
Advisory
Committee that you had here, you had a
lot of
378
people presenting this. Basically, product quality
and performance achieved and assured by
the design
of effective and efficient manufacturing
processes;
product specifications based on
mechanistic
understanding; and ability to effect
continuous
improvement continuous real-time
assurance of
quality--that's exactly what we want to
do in the
Drug Product Quality Research. So it becomes
easier to expand, it's easier to
re-orient some of
the programs and expand some of the
current
programs in BPQR.
[Slide.]
And this is what we intend to
do--as I
have shown you before. This is the current work
that we were doing. Although we had this Chemical
Stability here, but we want to look at
some of the
physical changes there, in that one,
too. We want
to do that.
And what else we want to do is
the
manufacturing aspects of this--you know,
the role
of the excipient, the role of the formulation
variables, the process variables, the
mechanistic
379
evaluations, optimization procedure--lot
of it may
already have been done. We can gather it
through
the literature, we can gather it through
the
collaborator, we can do some of the
in-house work.
But we want to provide this
information. We want
to provide this training to our
reviewers, and we
want to update ourselves on these things.
And just to give you an
idea: we are
talking about--you know, if we want to
talk about
the process variables--okay?
Now, just taking tablets in the
picture,
there are so many process variables. You have this
mixing, they have milling, then you have
your
granulation, the drying, compression,
coating,
packing.
Just by mixing you've--you know, the
blend, homogenating problems. And just by
granulation you might see a lot of
problems there.
A lot of prime test data is not provided
to the
reviewer.
So to understand that, we need to have
some internal programs going so we will
have this
understanding.
And this we are talking about
just a
380
tablet dosage problem; a dosage form that
is so
well know--or a capsule dosage form. They are so
well know. And when we talk these novel systems
that we are getting--
[Slide.]
--by the way, the
bioavailability, also,
we wanted to either collaborate and do it
in house.
The Novel drug delivery
systems--we want
to have some of this program going in the
novel--the nanoparticles--there's a huge,
huge
area; the liposomes; the sustained
release, the
modified release; the transdermal
systems; the
nasal pulmonary path; disintegration; the
solid
dispersions--basically, we want to have
information
of going in that direction; we want to
have
information readily available, and the
training
that is needed to evaluate those
applications.
[Slide.]
Some of the newer
projects: the novel
drug delivery systems, including
nanoparticulates,
preparation, characterization,
development of in
vitro procedures in DPQR laboratories--I
will share
381
some of the data with you. We have already done
some work prior to me joining here. I'll share
that with you--some science-based
projects, with
mechanistic understanding.
Process engineering with real
time
monitoring and modeling. We have this particular
equipment--fluid bed--with near-IR probes
attached
to it so we can monitor a lot of process
here in
this one.
The SLEP-stability--and there
are some
repackaging issues. We are working on some of the
stability of those repackagings. We want to work
on those issues. Basically they are
stability-related projects.
Generic drugs--I think Lawrence
highlighted some time ago. Tomorrow there's going
to be a presentation also. If you have some
locally-acting drug, what do you do with
that?
Stents--again, combination
drugs. You
have a device and you have a drug, and
you have
some issues related to that. We can help in some
of thee issues related to that.
382
We already have CRADAs with
companies, and
we are going to have some more CRADAs.
Somebody
asked a question about collaborating with
industry.
So we are collaborating with
industry--and more
coming up. And we are very hopeful that we will
have some more CRADAs coming up pretty
soon, so we
will turn in that direction.
Some permeability of these
drugs.
[Slide.]
Now let me spend some time here
on the
design space. I will give you a couple of
examples, and then I will also highlight
the
importance of it. You have already seen that it is
important, but I will just share some of
the
examples with you.
I will share one or two classic
examples.
This is out of the text--you know, you
have some
good statisticians, you have some good
experts here
in this area. All I'm doing is I've just borrowed
something from the book, that people have
been
discussing, and people have been having
in the text
for a long period of time. So I think the time for
383
us is just to be able to adapt some of
those
things, and show their relevance to the
pharmaceutical product.
So I will present one or two
examples from
the literature, and then I will present
some
examples of a design space in the
laboratory
generated data that we have here in this
one.
Now, here is a scientist--okay? A
scientist is trying to work. He has to
run a
reaction, at a laboratory scale--he has
to run a
reaction.
There are two variables in this one--the
time and the temperature. So this scientist is
trying to--first of all, it does. So if you have
two variables there, first of all it does
this, he
fixes the temperature here at 225
degrees; he fixes
the temperature at 225 degrees. He runs the
reaction for a certain length of
time. He is
basically trying to get the yield of this
particular compound.
So, he fixed it at 225 degrees,
and
then--and he ran it at different times,
that
particular reaction. He got a yield, something
384
like 70 or 71 percent, and then he got
that yield.
And then now that he got the time, then
what he
did, he fixed the time here. The lower one will
show that 1--30 minutes--I can't see very
well from
here, from this angle--but somewhere here
it shows
this one, 30 minutes.
So he fixed the time here. Now he ran a
different temperatures--okay? So he came
up with
different temperatures, and he saw that
at 225
degrees, basically, he has this
yield. So
basically he changed one variable at a
time, and he
got the yield at 71 percent.
But if you have--if you listen
to what the
statisticians tell us, what they show, if
you
follow some of the examples that are
already out
there, we can really perform the very
design sort
of experiment, the same scientist, when
he performs
the design sort of experiments--also I
might argue
that a lot of times you will have less
experiments
than you will have with so many, you
know,
duplicates, and triplicates and
quadruplicates.
So the same experiment, if we
do with a
385
design set of study--
[Slide.]
--look at what he got. He basically
changed the temperature and the time
simultaneously. He was basically here in this
one--in this design--no matter how many
experiments
you perform, no matter how many times you
do it,
you're yield is likely to be around 70,
71 percent,
or somewhere in that neighborhood no
matter how
much time you do. Basically, you are totally out.
And somewhere here you would not have
gotten it.
Somewhere here, you see that he got this
90, 91
percent of the yield for this compound.
[Slide.]
Now I'll stop here for a
moment, and I'll
change the gear a little bit.
Let's assume that we
have an identical situation where instead
of the
yield of this particular compound, we are
looking
at some other response--this response
could be a
dissolution response; some percent
dissolve in
certain amount of time. It could be a
bioavailability area. It could be a hardness of a
386
tablet.
You know, it could be any other response
that we are looking at.
But if he develops this kind of
a
strategy--develop some experiments in the
laboratory, come up with something like
this--but
if I have this particular product, if I
have this
particular response--in the laboratory--I
would
hesitate to go to the scaling up and to
the actual
manufacturing, if I have this much a
narrow window,
then talk about these problems here in
scaling up
and product manufacturing. Then you say, well, you
know, the lab-based data is very
different from the
manufacturing data. We don't want to do
that
because it's variable. Yeah, if you are in such a
narrow window, any slight change you
make, then
it's likely to have variability. Then we might
fall into a lot of difficulties. We might fall
into difficulties of--suppose you have
some
out-of-spec situation. Then what do you do in a
case like this?
Okay? So how do you scale
up? Huge
problem.
387
But if you take something
around this
region--but if our product, if our
optimized
product is somewhere here in this
neighborhood, I
would feel more comfortable taking it for
scaling
up, taking it for the manufacturing,
because later
on you have--you don't really have a lot
of
problems of scaling up. You don't really have a
lot of problems of out-of-specs. And even if you
have some out-of-spec situation, you can
really
play around and improve that situation,
because you
have something to go by.
And once we do this--I think if
you really
look at this cGMP--the White Paper of
cGMP--a lot
of these things are already described
there in this
one.
But if you have this formula, you can take it
for the manufacturing, and then what you
can do, if
you are in manufacturing, then you can
take some of
it and do the
evolutionary--EVOP--basically the
next slide will show you that one. So you can play
around.
You can fine tune and improve your
manufacturing process. That basically provides
some opportunities for continuous
improvement and
388
innovation.
But if you have a product here,
you took
it for the manufacturing, then really,
you cannot
change the variables there, you know; anything, any
slight change in any variable might
change the
product, and you don't know where to
start.
[Slide.]
So, as I mentioned to you--this
is a
different example--again from this
book--this Box,
Hunter and Hunter, 1978, book--basically
once you
have this optimized formulation, and once
you take
this formulation, then here, in this
case, you have
the stirring rate, you have this addition
pan--you
have the solution pan, you can play
around and
gradually you can play around. Because you know if
you are in manufacturing, you cannot
afford to fall
outside the specification range. So your window is
very, very limited. So if you have a design space,
your window--you are well within your
window to
play around a little bit. So you can gradually
work on this and improve the yield. And, finally,
you see in the last one, it doesn't
improve any
389
more, you stop.
So this kind of data should be
extremely,
extremely valuable, extremely
useful. And I will
provide one or two examples of the
laboratory data
that we have. I think one of the graduate students
had worked on it.
[Slide.]
And I have selected this for
two reasons.
First of all, it's an extremely
complicated
preparation; very complex preparation. Here you
have a protein, and you are trying to
develop a
formulation of a protein; a lot of
variables in the
protein, just to decide on this
formulation study
itself, we had to do a lot of
precharacterization
and characterization work. And you will see some
back-to-back--two back-to-back papers in
J. Pharms
this year--February and March, there are
two
publications--just to decide on the
formulation
issue that we have to do.
After doing that, then we have
decided
that, all right, we will try a dosage
form--see
this salmon calcitonin is a peptide that
we have
390
taken--polypeptide--salmon
calcitonin. It was
degrading with enzymes. So what we did, we have
seen some turkey ovomucoids--a lot of
work was
already done on turkey
ovomucoid--basically it was
inhibiting the degradation of salmon
calcitonin,
the different enzymes--you know; trypsin,
the
chymo-trypsin, the elastase. It was inhibiting
their degradation. So we wanted to use this turkey
ovomucoid as one of the excipients to
prevent the
degradation.
We also wanted to use this
glycerotinic
acid, because it's protein, big molecule,
doesn't
go through biological membranes. We have see that
glycerotinic acid--we evaluated--we
screened almost
a hundred compounds. But finally we settled with
glycerotinic acid. We have seen that glycerotinic
acid enhances the permeation of this
protein.
So we wanted to make the dosage
form.
This is a bi-layered preparation, by the
way, and
the top layer is very similar to your
procardia--you see this dosage form--this
bi-layered preparation; procardia, vomax
and, you
391
know, these are osmotically-controlled
bi-layer
tablets.
So here you have a protein, and
then we
have this osmotic agent here. If you look at
it--so we make this--we compressed this
tablet, we
made these bi-layer tables. We drill some opening
here.
We provided some coating to it, so that it
releases drug in a particular
fashion. It's a
dual-controlled release. You have a drug
protein--the polypeptide that's
releasing, as well
as the ovomucoids that's releasing. Extremely
complex preparation.
The idea here is: you can see there are
so many variables here right now. What should be
the coating thickness of this one? What should be
the opening of this one? What should be the level
of the excipients that you use?
So you can see there's a lot of
variability here.
Now, a company that is
manufacturing,
that's making dosage form, a lot of that
information they might have in-house as to, you
392
know, the coating thickness that is
needed; some of
the process variables they might already
have. And
if you don't have it, what you can do,
you can
actually screen--we have just selected
some of
them.
We have screened some of those variables.
We could not do an extensive study--very
expensive
proteins.
We cannot do a lot of experiments.
But
at least we screened those variables
here, at two
levels each.
[Slide.]
And then the dependent--the
response--the
previous one, the example that I gave
you--the
yield of that compound was the
response. But in
this particular case, we have the amount
released--salmon calcitonin release--in
three hours
was our response. And then we can also place
constraints.
[Slide.]
Now, here, in this case we have
placed
constraints at different dissolution time
points,
so you can tailor a release. You can do that. Or
you can place constraints on tables. So you're not
393
interested in a tablet where the hardness
is less
than 4 KP or more than 8KP. So you can place
constraints on hardness, constraints on
some of the
parameters that you're looking for. So, here, we
placed constraints so that we can get the
entire
release profile on this one.
So by placing constraints, we
evaluated
that, and we looked at this--the
development
equation here.
Now, again, as I said, this is
just a
screening design. You cannot see the interaction
effect.
The interaction effects are compounded;
the quadratic effects are
compounded. So a lot of
information we are losing, we are
missing. But we
gathered from here is: of those seven variables
that we looked, what are more important,
what are
less important? Basically we screened those
variables.
So if we have to have a few
experiments
you want to run--so what we did. So we selected
out of these three variables, and that we
studied
at a slightly more detail--I will show
you in the
394
next one.
[Slide.]
We have selected another response
design
in this time. So basically we have seen the amount
of sodium chloride, the osmotic agent
that is
needed in that particular tablet dosage
form, and
the amount of coating, and the amount of
Polyox--it's the polymer that is
required.
Basically, these are three variables, and
we found
that these three variables are more
important--at
least they're likely to have more effect
on the
release of salmon calcitonin than other
variables.
So we selected these variables.
And this
was the dependent variable: salmon calcitonin
release in three hours--okay? And now we developed
this model.
[Slide.]
Now, believe it or not, this one
equation
can talk more than probably 20 pages of
slides, 20
pages of information. Really, it does say a lot.
It says how those variables
affect the
response.
It just shows how X1 changes the
395
response here; how X2--the coating
level--if you
increase the coating level, dissolution
decreases.
I know that. And if you increase or decrease the
coating level a little bit, immediately I
can
calculate the response, without even
doing an
experiment I can calculate the
response. Same
thing, I can see the interaction effects
of all of
them; the quadratic effect.
Basically, by this design sort
of
experiment, finally we have used a
process where we
have actually predicted the levels. We predicted
that.
If you have this much of sodium chloride,
this much of coating thickness, and this
much of
the Polyox levels, then we will get--this
is the
kind of tailored dissolution
profile. We predicted
that.
[Slide.]
And what we did, we performed
an
experiment in triplicate--three, the
proof, and
then with our product that we obtained
was
identical to the product that was
predicted.
So this is the case study that
was done in
396
our laboratory by one of the graduate
students.
I will not go into the
details--oh, by the
way, this is the response-surface. You have
already seen the response-surface for the
yield.
So here you know at what level you can
get the
dissolution that you want.
[Slide.]
I will not go into the detail,
but, you
know, we have also prepared some
nanoparticles. We
have characterized by a lot of different
methods.
You can see this
publication--International Journal
of Pharmaceutics--highlighted all those
characterizations. But this one also--these
nanoparticles, also--we used a design set
of
experiments where we have seen, basically,
just a
formulation variable. We took it at three
different variables. After having gone through the
screening and all that, we have optimized
it.
[Slide.]
And we have seen the dependent
variables
here.
And, again, the observed and the predicted
levels were identical in this particular
one. It
397
just shows the levels, as I mentioned to
you, about
the yield.
And here, if I--after
developing this in
the laboratory--now, certainly, one has
to feel
more comfortable taking it to the
manufacturing,
because they know where they can play
around. If
you select this particular product here
for the
manufacturing, you manufacture it, you
know you
have some room to play around. So you can
do this
evolutionary operation and play around
and improve
the product.
[Slide.]
So that is--with this, certain
questions
that I had for the Advisory Committee.
As I said here, that I'm also
learning.
I'm also just so new. I just want to orient our
programs, or orient our lab in such a way
that it
reflects some of the agency's thinking,
some of the
OPS thinking. We want to go in that direction. So
you are the experts in this. You have been
associated with this for quite some time,
and if
there's anything that we are not doing
you want us
398
to do, just let us know.
Does a systematic study with a
designed
set of experiments provide opportunities
for
reduction of--you know the post-approval, I
did not
mention it at this time. But, you know--scale-up
changes--the post-approval changes--you
want to
make some tiny change, you keep on
getting these
post-approval submission documents. If you have
some window to play around, certainly,
you know, it
can reduce. But if you don't agree, just let us
know.
Do you agree that the
information on
design space, with a designed set of
experiments
will reduce the out-of-spec situations a
whole lot
more?
You know, if you have a very tiny window,
any slight change--the speed of the
machine, the
machine going on and off--just an
operator just
coughed--you know, or you just change the
operator
there, or anything might change that
situation.
Do you agree that the research
with
sell-designed set of experiments on lab
scale with
create opportunities for continuous
improvements
399
and innovations in manufacturing? So industry has
got to apply that and provide the data to
the
reviewer, so that they are not operating
under a
black box.
So, with this, I think you very
much.
I'll be happy to take questions. Thank you very
much.
CHAIRMAN KIBBE: Any questions for--
DR. SINGPURWALLA: Mansoor, I was told to
go easy because you are new. [Laughs.]
[Laughter.]
So I will try and go easy.
DR. KHAN: I can only get something I
know.
DR. SINGPURWALLA: The design of
experiments--the questions you asked--my
answers to
all of them is: yes, yes, yes, yes. Because
design of experiments is, you know, well
recognized
and well accepted--particularly by the
chemical
industry.
The question I have for you
is: how do
you intend to use design-of-experiments
in the
400
regulatory process? What you have described is the
use of design-of-experiments in
manufacturing,
which is what the industry should be
doing. I
suspect they are doing it. If they're not doing
it--shame on them.
[Laughter.]
But I'm sure they're doing it.
So how do you intend to use
this in your
particular role as a regulator is what I'm
eager to
see--or hear?
DR. KHAN: The regulatory questions, I
think--you know, some others will
answer. You
know, it's beyond my understanding at
this time.
But my idea here is to provide this
understanding to our reviewers; to
provide this
understanding to our own scientists so
they utilize
it.
And also if we publish more papers--if we just
provide this information to others, a lot
of others
might be more willing to use it.
And as far as the people in the
industry
using it--you know, some of them are
using, some of
them are not using. And people might be using it,
401
but at least they don't provide the
information to
us at all in any significant way at this
time.
CHAIRMAN KIBBE: Okay--
DR. SINGPURWALLA: I see big daddy is
coming to defense.
[Laughter.]
CHAIRMAN KIBBE: Go, Ajaz, go.
DR. HUSSAIN: Well, I think
design-of-experiments is--what?--a
60-year-old
technology that we're introducing. So it's not new
at all and so forth.
But at FDA, we don't have the
ability to
say that somebody has done the work or
not done the
work and so forth. So we have to assume that what
we see is the limited data that
companies--many
companies do this and they don't share
that.
But at the same time, I think
surveys done
by Professor Shangraw, before he
passed--at the
University of Maryland and so forth--and
more
recent surveys, suggested the use of
design-of-experiments in pharmaceutical
industry is
very low.
About 7 percent of the companies we
402
surveyed through the University of
Maryland said
they actually used design-of-experiment.
So that leads to the concern
that we have:
if you haven't understood even the
critical factors
and so forth, how can we allow them to
change? So
we cannot allow them to change and so
forth.
As a result, we have a static
manufacturing process.
So, for those companies that do
this
routinely, that have this sort of
information, if
this can be summarized as a means to
demonstrate
what are the critical variables, to what
extent the
validation ranges can be justified as
wide as
possible, and so forth--so that provides
a means
for regulatory flexibility--for those
companies
that have this type of information and so
forth.
For other who do not--not get
the benefit
of regulatory relief at all. So--
So how would we use this in the
regulatory
setting?
That has been a continued discussion
internally. My thinking right now is this is not
an FDA policy and so forth. It's--what we would
403
simply need is to focus on the
predictability and
reliability of the predictive power that
you have
developed and so forth. And that should be enough.
We don't have to get into deep--there's
volumes and
volumes and volumes of pages of how was
this done
and so forth, because our job is to
understand what
is critical; what ranges are acceptable;
and then
what is the design space. And how well you know
that is through your predictability.
So it's more of a summary type
of
information I'm looking for.
CHAIRMAN KIBBE: Go ahead, Ken.
DR. MORRIS: Yes, just to follow up--I
think part o this falls into the category
of having
the reviewers understanding the process
well enough
so that if they do get a good rationale
of the
formulation and process design, and
design-of-experiments that they've really
outlined
a real variable space, as Mansoor was
talking
about, that they'll be able to appreciate
it.
So part of that is, I think,
ensuring, or
reassuring the companies that, you know,
generating
404
these sorts of data, they'll receive the
proper
reception when they get here.
CHAIRMAN KIBBE: Joe?
And then I have
Melvin.
Go ahead.
DR. MIGLIACCIO: Well, to dispel any
myths--yes, we do use
design-of-experiments.
Aggressively. Aggressively.
I think the issue is is that
what we then
present is a proven acceptable range;
univariant
proven acceptable range. That's been the
tradition. That's what has been expected.
As we move forward, using
design-of-experiments, coupled with the
technology
we have now to, during those experiments,
to
monitor the critical variables
real-time--we'll
move from submitting a static process--a
process
that is based on a range of time or
temperature or
any
other condition--to a dynamic process that
says:
"If A, then B." And
"if A then B" will be
based on rigorous design-of-experiments,
with the
right multivariate analysis.
405
So I think that's--you want to
respond to
that Ajaz? That's--
DR. HUSSAIN: No, I think--we have one
similar thinking on that. I mean, ICH Q8, I mean
that's the direction I see we're going.
DR. MIGLIACCIO: So it's not going to be a
fixed process.
One more--your third question,
I have a
bit of, I guess--it implies something
that I don't
think we want to imply: "Do you agree that the
information on design space, with a
designated set
of experiments will reduce the OOS
situations?"
You're implying there that
you're going to
use the design space to set
specifications. And
that--you know, specifications have to be
based on
a mechanistic understanding of the
formulation and
the process, and its impact on product
performance--not on the capability of the
process.
And the design-of-experiments
is helping
us
to understand what's critical, and what the
process capability is. It should not be used to
establish finished-product
specifications.
406
And your question there
implies--you know,
if we set the specifications correctly,
and we
understand the variability and the
measurement
system, then yes, good
design-of-experiments should
reduce--and establishing the design
space--should
reduce OOS.
But on its own, it won't.
DR. KHAN: I agree.
CHAIRMAN KIBBE: Great.
DR. KOCH: I guess I'll just make a
comment that even though the field is 60,
70 years
old, in terms of Plackett-Burman and a lot of
those
studies, it is surprising how little it's
used.
And you can go into chemical,
petrochemical and
other industries, and they have not used
it very
well.
The reason behind it is often
the cost of
analysis.
To do a good study, where you're running
a number of variables, you've got a huge
amount of
samples.
And I know, just historically--I got
involved in several what they were called
"big
projects"--that would be eight to 10
variables--and
407
it was always neck-back, based on
perceived cost.
I think, in the future there's
going to be
a
lot more opportunity--addressing your last
question--with the development of better
lab-based
equipment--microreactors, a number of
improvements
in high throughput designs for other
reasons. But
I think the equipment's going to become
available,
and PAT is going to be a vehicle to be
able to
monitor these things.
And, eventually, I think you'll
get down
to where you can very effectively use
these
techniques, often even on continuous
processes,
where you can invoke feedback and
feedforward so
you don't have to run a whole number of
experiments, but you can be analyzing in
real time
and adjusting your parameters and filling
out your
space much more adequately.
But I don't think it's been
used very much
in industry.
CHAIRMAN KIBBE: Nozer?
Ah, we get--you
had something else, there, didn't you?
DR. SINGPURWALLA: Yes, I just wanted to
408
react to Jerry's comment.
I was personally--my prior
probability
that industry uses design-of-experiments
was very
high.
So I'm not surprised. And, basically,
if I
was running an industry, I would use
design-of-experiments to maximize my own
profits
and do my business more efficiently.
Industry A and Industry B can
produce
exactly the same product, but one can do
it very
efficiently by using
design-of-experiments. And
the other can do it completely randomly
and still
come up with the same answer, but you're
spending
money.
So that was the only
comment: that it's
more on the manufacturer who has to take
advantage
of it.
And I'm really surprised that they are not
using it--based on what I hear from you.
CHAIRMAN KIBBE: Judy?
DR. BOEHLERT: Yes. I
mean, I would agree
with Jerry: they are using it. There are many
companies that are not. And another area where
it's used a great deal--particularly the
409
Plackett-Burman design--is in the
optimization of
analytical procedures. And I see that in big
companies and small companies. They know how to do
it.
They save their resources and they come up
with much better methods in the end.
It doesn't mean that everybody's
doing it.
So I think to the extent that, you know,
folks like
you can publish what you're doing, it
helps those
that don't understand to get on the
bandwagon.
But it is used, you know, in
industry. It
hasn't been overlooked. But not everybody.
CHAIRMAN KIBBE: Anybody else?
Comments?
DR. SINGPURWALLA: Well, the only comment
I want to make is I studied
design-of-experiments
as
a student. And perhaps it was the most
boring
subject that I had to go through.
[Laughter.]
It is boring.
[Laughter.]
CHAIRMAN KIBBE: It's always good to have
Nozer's opinion on things.
[Laughter.]
410
CHAIRMAN KIBBE: I think we should more on
to Jerry.
And thank you very much.
Jerry, your colleagues have
managed to
leave you three-and-a-half minutes for
your
20-minute presentation. And that will allow Vince
another 15 for his.
Wrap-up and Integration
DR. COLLINS: This is one person's
perspective on the day's events. And for those of
you who give talks a lot, it is very
difficult to
stand up here without any of my
props. I have no
slides.
I've been scribbling notes all
day, since
9:30 this morning, when Ajaz was talking.
One of the most important thins
on his
third slide was describing the Critical
Path
essentially as not just another fad at
FDA. Some
of us are a little shell-shocked by this management
agenda, or that initiative and so
forth. We have a
commitment from the Commissioner--the
Acting
Commissioner--the Deputy Commissioner for
Operations, and our Center Director, that
this is
411
not something that's going away in six
months. And
to turn the ship around and align it
properly, we
need that kind of commitment from our
leadership,
that we won't be thrown into the gulch to
do
something else later. So, from the perspective of
the worker bees, that's very important.
Secondly, several speakers
across the
board talked about relationships with
NIH, going
back to the in silico talk from Joe
Contrera; both
Steve and Amy talked about their
relationships with
various parts of NIH; and my lab also has
cooperation with NIH. I've been at FDA for 17
years, and I spent 11 years at NIH before
that.
I've never seen a better time for FDA and
NIH to
collaborate and work together.
There's always been a little
bit of "let's
make sure we know what our territory
is." There's
overlap in our interest. There's also things that
are uniquely theirs, and things that are
uniquely
ours.
If we just focus on the overlap, I think we
really ought to take advantage of, again,
what I
would call the golden opportunity here
for
412
collaboration.
The other thing that's sort of
been
missed--I'm surprised there hasn't--maybe
I missed
it because I didn't get here 'til
9:30--but this
week, this month--is really the golden
age of
quality.
I mean, my computer screen didn't have
any disk space left a couple weeks ago,
after
announcements on CMC, GMP, BAC, PAT--I
mean, it was
just--there have been so many
announcements about
the
importance of manufacturing as an initiative
for FDA; about the success of the
two-year
initiative; the roll-out of the
implementation
phase.
This really is a strong part--a strong era
of quality.
I hope it doesn't get lost in
the Critical
Path. The Critical Path mentions quality
issues,
but there are so many efficacy and safety
issues
that we need to be vigilant, and not just
rest on
our laurels.
In addition to getting your
input, we've
asked the public for their input. The docket has
over a hundred responses. It's all in the public.
413
You don't--there's nothing secret. If you submit
something as a comment--we asked in
April--and
there's over a hundred--on the
website. And if you
have a really lot of time--because it's
clunky--over the weekend I looked at them
all--and
it's very interesting. Almost all the comments
actually relate to efficacy. There's a few
comments that relate to safety, and a
very small
number that relate to quality. And most of those
are actually for biological products of
one sort or
another; either vaccines, blood-derived
proteins,
or complex molecules from the OBP domain.
So we need to keep challenging
the public
so that they recognize the importance of
quality.
And we also need to look internally, that
we're
responsive to--you know, our job is to
either
convince them of the importance of
quality, or to
re-align our resources.
As I mentioned, in OTR we're
about 75-25
chemistry to biology. One of the excuses for
having this meeting is so the OTR folks
can listen
to the OBP folks, and vice versa. And I'm still
414
learning about OBP. And I get more of a biological
flavor each time that I hear your
presentations. I
don't know that I can fit your round peg
into my
triangle of safety, efficacy and
quality--but
that's part of the reason why we're here,
so we can
learn each other's language, each other's
culture,
and how it fits.
But I think, certainly, OBP
is--actually,
the "B" is for
"biology"--right? So, you
know,
you're definitely more aligned with the
safety and
efficacy side.
What about gaps in our
program--various
places?
Well, first of all, I mean the OTR-OBP gap
is really just about finding out about
each other.
And one of the things that we probably
discovered
today that would bridge the gap is the
Critical
Path Initiative--is that all of OPS, and
all of
CDER, and all of FDA is committed to
going down
this route. So we all now automatically have
something in common, in that our programs
must be
aligned to the Critical Path.
Now, Steve, I can't do that
polygon stuff
415
that you borrowed from Ajaz, but in terms
of a
bridge, I can think of the Critical Path
Initiative
as something that connects two pieces.
The other thing is that product
quality is
important--as everybody in this room
thinks it is;
needs bridges to the clinical side, to
the pharm
tox side, and to the clin pharm
side. And so when
Ajaz talks about the ICH Q8 principles as
one of
the ways that we can actually bridge
these things,
this is really important. We can't do product
quality in isolation. And a hand-off from one to
the other has been covered in several of
the talks.
But that's an area where we need to
focus: on
making sure there isn't a gap there.
The other thing, in terms of
keeping
reviewers and researchers together--we
have two
distinct models that have been discussed
here this
morning.
John Simmons made a number of comments
bout the way Office of New Drug Chemistry
interacts
with Division of Pharmaceutical Analysis,
and
Division of Product Quality
Research. Lawrence Yu
mentioned several projects that they've
been
416
working on there. And then--and the OBP side, we
have the reviewer-researcher model that
both Amy
and Steve articulated in their talks.
Those are somewhat different
approaches.
In CDER, we have tried the
reviewer-researcher
model, with very minor success. We found that
geography is a terrible burden and
barrier--not to
mention use-fee deadlines and growing
workloads on
the review side. So people who initially could do
both research and review eventually had
their desks
swallowed up with all kinds of electronic
copies of
documents, and found it hard to continue.
For the last 10 months OTR--a
large part
of us--have been out at White Oak. And starting in
April, the immediate office of
OPS--including the
in silico group--the Office of New Drug
Chemistry
will all be there in the adjacent
building. And
there is a physical bridge. It's just not a
conceptual bridge. The second floor of our
laboratory building is connected to the
second
floor of their building. I think that will
facilitate reviewer-researcher models,
because it's
417
location, location and location.
Now, it's not the whole thing. I mean,
the Office of Biotech Products is still
on the NIH
campus for the foreseeable future. And our
laboratory in St. Louis is there for the
foreseeable future. So we don't have a
fully-integrated geographical solution to
our gap
analysis, but it will be an interesting
experiment
to see, particularly, how ONDC and the
first floor
of the lab building interact, and whether
that
improves the situation.
Last comment is that we're
supposed to be
"science-oriented" here. And although the Critical
Path in drug development is a fact, it's
well-documented, it's only a hypothesis
that we can
do anything about improving it.
We've laid out today--throughout
the
day--a number of approaches that we've
been
thinking about implementing, and have
started
implementing, but it's only a hypothesis
that
they'll work. The chances that they will work are
enhanced greatly by getting feedback from
talented
418
people--from the public, from the
industry, from
the Advisory Committee--taking that
advice to
heart, and really giving it its best
shot. Any
initiative fails if it's only a
half-hearted
initiative, or if it's not well designed,
or if we
don't have the right equations, or if
we're 60
years behind in the technology. So--we appreciate
any forward-thinking ideas you may have
in that
regard.
CHAIRMAN KIBBE: Okay.
Ajaz, help me here a little
bit. Would it
be best for us to go ahead and let Vince
do his
presentation and then take the three questions
you
have sitting around here?
DR. HUSSAIN: Right--I mean, Helen and
Keith and I were just discussing that, in
a sense,
because we have received constant
feedback from you
throughout.
CHAIRMAN KIBBE: Right.
DR. HUSSAIN: Maybe after Vince's talk you
could just summarize, instead of getting
into
answering all the questions in
detail. But I
419
think, since we have received so much
feedback, if
you could just summarize the Committee's
thoughts,
it will be fine.
CHAIRMAN KIBBE: That means you're up,
Vince.
Challenges and Implications
DR. LEE: Okay, great.
Maybe I can start
with the questions.
[Laughter.]
How am I going to work this
thing?
[Laughter.]
Thank you.
Okay--thank you, Ajaz, and also
thank you
Helen and Ajaz for giving this
opportunity to work
at the FDA. It's an eye-opening experience, and I
recommend it to everybody. Because you get a
different perspective.
I was changing my talk as I was
going
along, and that's why I was away for the
first hour
of this afternoon; I didn't not that I
would have
to make another copy that corresponds to
my slides.
So that's something that also I learned.
420
Let me be more precise about
what do I see
as the implications and challenges.
[Slide.]
The one thing is always to
increase the
return on investment by fostering an innovation.
"Innovation" is the key
word. And also, along the
way, we hope to improve the quality of
life for the
patients, and lower the costs--the health
care
costs--for society. In fact, as I was sitting
around the room, I wish that maybe
sometime down
the road that we should include
economists in the
committee to give us some assessments.
I wanted to look forward and
see if we
were to follow this Critical Path
Initiative, what
is the benchmark. What do we expect to see?
[Slide.]
And I'm trying to be
cooperative, because
I have no clue about what should we
expect. And I
don't know whether we can assume one
number,
because each drug is different. But let's say that
maybe in five years' time--by 2010--then
let's
commit to lower the development costs by
30
421
percent, shorten the development time by
50
percent, and increase success rates by a
factor of
three.
I have no idea if this is realistic or not,
but maybe we should start thinking about
that.
And what else might
happen? I would
expect that more drugs will be launched
in a
controlled delivery platform when our
sustained-release system is used as a
line
extension. So I'm proposing that his model will be
different.
Here comes the next point, is
that the
sponsors of compounds might be forming a
consortium
to share information and knowledge. This is
something that's not being done
today. Obviously
it's because the conditions don't
encourage that.
But we're in different times. And so maybe perhaps
we should think about different models.
And, moreover, maybe the
sponsors will
subject their science to peer review for
open
access in the global community. I would home that
maybe sometime down the road that
equivalence, or
the genome project, would be reproduced
in the drug
422
development arena. Now, this is something which is
quite naive, you might say. But I just want to put
it out there and see who would challenge
that. And
I would be a bit worry that one reviewer,
to make
judgment on one product--part time editor
at the
same time.
[Slide.]
What else might be
happening? Well, the
era of blockbuster might be over. I don't think at
this point in time few executives would
believe in
it.
And, frankly, I do not know how the agency can
confront this avalanche of applications
if
everybody's looking at just specialized
populations. But I do think that a new era would
arrive where we'll be more realistic to
look at
narrower indications, and then use the
patients--the users--to expand the
knowledge base.
And I'm proposing that perhaps all of us
would be
enticed to participate in a Phase IV
study by using
the chips which are recently approved by
the FDA.
This is subject of another big talk.
And then there will be a
growing of
423
nano-sized assemblies with specialized
functionalities. Now this is something--I'm not
that fascinated by nanosystems. What intrigues me
about nanosystems is the capability, for
the first
time, for the device circulating in our
body,
collecting information, providing
feedback to the
scientists. So I envision that maybe we can look
at nanosystems as satellites. This is something
that the body has never been exposed
to. I have no
idea how the body would respond to
it. But
intriguing to find out. Again, that would the
subject of another long presentation,
talking about
diseases for which we need a way to
assess the
early change. Cancer is very dreadful because by
the time we see the symptoms it's already
too late.
Would it be possible to have a micro-chip
circulating in the blood stream,
collecting
information that would report the
scenario--fingerprints characteristic of
disease--and that information would be
fed into a
computer, and a database on that basis, a
diagnosis
would be made.
424
So what I'm proposing is that
maybe we're
approaching an era of preventive
medicine, where
the patient would be at the center of the
whole
process.
I'm going to just give a few
slides in the
interest of time.
[Slide.]
This is a very intriguing slide
to me,
because the reach limiting step--we
talked about
changes, depending on the time. And depending on
the thinking of science at that
time. 10 years
ago, in 1991, PK was a major
problem. Now
everybody was focusing on PK, and now
something
else popped up, a formulation, which was
not a
major problem in 1991, becomes a major
problem.
Who knows what it's going to be?
So what's the message? The message is
that we have to be always in touch with
the leading
edge of science, and where the leading
edge resides
is in the sponsors.
So what are the
implications? The
implications are in four areas, as I see
it.
425
[Slide.]
In terms of individuals, I
think as
scientists that we can no longer focus on
just one
thing that we're looking at. We have to have a 360
degree vision. And this is along the lines of what
Ajaz talked about--having a common
vocabulary. I
don't know his name--but he's gone.
So the next point is the
infrastructure.
How can we organize the scientists in
such a way
they can respond to new opportunities on
short
notice? I understand there's a
SWAT team already
in place, but we need to have more of
these in the
agency.
There have to be incentives, in
terms of
incentives to reward innovation and
teamwork.
Again, it's different times.
And finally, I see there should
be some
kind of interrelationships--with the
NIH--I agree
with Jerry, I think this is a golden
opportunity
for NIH and FDA both being part of the
HHS to
collaborate, to reinforce one
another. I think
this is--and also, I think that the move
to White
426
Oaks is very symbolic in the sense that
for the
first time the agency's under one roof.
So I think that, whereas in the
past
nobody talked to anybody, it's time for
us to work
together, to exchange information. And certainly I
think the agency might consider
sponsoring
projects.
[Slide.]
So what's next? I think that we need case
studies.
This is easier said than done, but I
think there's a lot of
information--data--in the
FDA archives. I don't where it is. I don't want
to volunteer to go look for it. [Laughs.]
But I
think somehow we need the information,
and
demonstrate that--under what conditions
we can
categorize drugs in the same way as we do
at the
BCS.
And I think we need some kind of organization
to organize our thoughts.
We need some benchmarks, what
should we be
looking for, if the Critical Path
Initiative were
to succeed. I think it has succeeded.
Now, which sectors would apply
this road
427
map to?
Well, it was designed for big PhRMA.
But
what about generics, biotechs and
start-ups? And
who else?
So we need to think about that.
And, finally, which drug class should we
begin with? And here we have no definitive answer.
But this is again a very interesting
summary in the
nature of drug discovery, where it says
that the
success--the percent of success--depends
on drug
class--for obvious reasons. And I think that we
need to look at information such as this
and do a
quick demonstration project to convince
the
skeptics that it is the Critical Path
concept is
viable.
[Slide.]
So what are the challenges to
all this? I
think this is a recapitulation of what
was said
throughout the day--communication. I think
everybody should understand what is meant
by
Critical Path. And we should all follow the same
Critical Path. You go different Critical Path, I
think that we go nowhere. So broad understanding
and shared goal community-wide is
important.
428
I think we should have a
mechanism to
inspire the leaders among the scientists
to create
new paradigms; and also to motivate the
scientists
to adopt a new approach to decision
making--willingness to learn, and to
unlearn, to
relearn--and learn. This is something that I'm
trying to do myself.
[Slide.]
This is Ajaz's favorite: the knowledge
management. When he first talked to me--not 11
months ago, but six months ago--I had no
clue what
he was talking about. But finally I saw the light.
And we're definitely living in
the
knowledge era--and there's no question
about that.
And there was 200 years difference--200
years' span
between the industrial era and the
knowledge era.
And the characteristic of the knowledge
era is very
different from the industrial era. Where, in the
past we focused on single entities, now
we're going
to have the ensembles. And why is that? That's
because in the past, we had no access to
organizing
information; that we tend to think--we
reduced
429
everything to a single entity. This may be the
physical chemistry influence on the
formulation.
But when you find in the real
world that
usually--not only single entity, but the
things
work together as a team, an ensemble.
In terms of scientists, they no
longer can
function as an individual; I mean,
accomplish
everything individually, but has to have
a network.
The success of science depends on the
network of
all our colleagues.
Things are moving very fast in
the
knowledge area. And things are dynamic. And I
think that we always are in view of
sharing
information--sharing knowledge--whereas,
in the
past, rewarded by being proprietary. Now this is
something which is very challenging, in
my opinion,
to convince. Everybody think differently--because
we never know what the outcome will
be. But at the
present time by protecting information,
then we
move forward. But as a scientist, myself, I'm
always troubled by the duplication of
efforts.
Oftentimes, you know, it's the failures
that will
430
be useful, because at least I know that I
will not
go down the same path.
And then it's very clear to me
that--the
thing about my children, when they come
along to
use this benefit of medicine in a major
way, it's
definitely in a consumer-centered
society, where
the consumers will know about
health. And
hopefully, I think our government would
promote
health education in the public.
[Slide.]
So what is the road map? It's very
simple--three things.
One is that we need to provide
incentives
for industry and academia to formulate
and test
alternative drug development
schemes. And there's
no reason why drugs should fail in Phase
III. If
they fail in Phase III, there must be a
reason.
They must not be doing something right.
The second thing is that we
need to think
about coordinating data mining worldwide
for
forecasting hurdles to drug action, delivery,
formulation and manufacture. We can learn a great
431
deal from existing information.
And the third think was talked
about,
again early this morning--is the
computational
tools.
I think that if we have access to
simulation models we can begin to test
the weak
points--the critical parameters--and
design
experiments properly, then we might be
able to do
clinical studies more efficiently.
So this is the three things.
[Slide.]
So what are the--the three last
points I
would like to leave with--the three
areas--one is
outreach.
I think that we definitely should
sponsor retrospective studies on the
value of
sharing knowledge in accelerating drug
development
and rendering it more precise. I think that we
can--although the past is no prediction
of the
future, but at least we know what is the
scientific
foundation.
The second proposal I have is
to think
about convening a summit with industrial
and
academic scientific leaders to identify
the pros
432
and cons of what I proposed in the first,
and to
understand the mechanisms to conduct data
mining
without putting the innovator at a
competitive
disadvantage. So I'm proposing we should think
about a strategic plan for drug
development. This
is very far-fetched, but I think we
should
contemplate this framework.
The second area that we should
focus on is
the process. And, again, summarizing what was
talked about all day today--to examine. the
current
review practices with respect to
fostering
innovation and then propose necessary
changes. And
the second point I would like to propose
to be
looked at is to develop mechanisms for
facilitating
continuous improvement in the quality of
approved
products.
I'm talking about the generics in this
particular point. There may be about eight years'
span between the launch of the innovator,
and the
launch of the generic products. But science has
improved a great deal. Have we learned from it?
And how can we take advantage of these
advances in
science.
433
And the third point is to be
proactive in
identifying cutting-edge research of
pharmaceutical
relevance that would fuel
innovation. So, clearly,
the whole points of Critical Path
Initiative is to
encourage innovation.
The last point is human
resource. I think
it's something that, as a former
academician,
education is of great value. And in fact, my
former university has a regulatory
science program.
I don't think it's appropriate for the
future. And
I dare to say that in front of my former
dean. And
I think that we should do something
differently,
because we should prepare the regulatory
scientists
of the future.
In fact, I think it's very
important for
us to think about the scientists on line
five years
from now, and what do we need five years
from now.
So I think the education of the
regulatory science
programs--most of the programs in the
U.S.--perpetuates what we have
today. So we need
to think differently.
And then the second point is a
point
434
addressed to the agency, is the current
practice of
recruiting scientists and retaining them,
as far as
development of leaders from among the
ranks. I
think this is central, and I do believe
that
science has to drive the process, and
research is
an essential component, and there's a lot
to be
learned from the OBP part--the
CBER--whether you
have research--where there's the
opportunity for
research.
But the research that we do has
to be
different--unique. And there's an unmet need. And
clearly it would be the bridge between
academia and
industry research.
So--these are my thoughts. And certainly
if there's an interest, I will answer
easy
questions.
[Laughter.]
Committee Discussion and
Recommendations
CHAIRMAN KIBBE: I don't have easy
questions. I think you've said some very
interesting and thought-provoking things.
I've been thinking about everything
that's
435
been going on today, and I know Ajaz
suggested that
we might come up with some kind of a
summary for
the questions today. And I really don't have a
good summary, but I have a lot more
questions.
And what I think might be
useful is those
of us who are staying for tomorrow to
spend the
evening thinking about all of the things
that we've
heard, and how it all comes together.
The human mind--as opposed to
the
artificial intelligence that sits on our
desks--works in patterns and pattern
recognition,
instead of sequences of computational
paradigms.
But a couple of things come to
mind that
I'd like to share with you, and then
maybe we
can--I'll let you gentlemen ask questions
if you
have any.
DR. LEE: Maybe I can add two points. One
point I should mention is, as a former
chair of
this committee, that what--how could the
committee
be more--let's see, I don't want to use
the word
"useful," but since it's on the
tip of my tongue,
I'll just say it--more of an asset to the
office.
436
And this is something that I think I
would be
interested to hear from this group, about
how the
committee--how should the committee
function
to--you know, in the Critical Path
Initiative. So
that's one thing.
The second is that I think
sharing
information is critical. And the way that things
work now is that information is passed
from one
module to the next. I think that's in today's
world, the way that the human works is to
multitask. So any time information is available to
all the stakeholders in the enterprise.
CHAIRMAN KIBBE: Let me continue with some
thoughts.
First, the question of the Critical Path
Initiative. Are we focusing on the appropriate
Critical Path?
The question I have is: is the output, in
terms of new and novel chemical drugs a
result of
something that we need to work on in
order to prove
the flow-through, or is it the result of
a paradigm
that was begun early in the 20
th century
and has run
its usefulness? Are we actually at that asymptotic
437
curve where we spend tremendous amounts
of energy
to get a small breakthrough, but unless
we have a
significant paradigm shift we're not going
to get
there.
Are we asking ourselves that we
new drug
entities?
Wouldn't we be better off asking
ourselves that we need new and better
therapeutic
ways of treating disease or preventing
disease?
And maybe the shift that we went through,
away from surgeries and manipulations, to
the use
of chemicals in the last century is over,
and we
need to go into a different therapeutic
thinking.
And if we can't make that paradigm shift,
applying
tremendous amounts of energy to an old
paradigm
that's running out of steam, isn't the
way to get
there.
There's a lot of interesting
new
technology on the horizon: computational power,
and
what Vine talked about--which some people call
nanobots, are coming. And in 10 to 15 years we
will be at what some have characterized
at a
singularity in our understanding of
computational
438
power; a day when the ability of a
desktop computer
to think in patterns and reason--as well
in
patterns as it does in digital
format--will allow
it to acquire data off the internet and
come up
with answers we haven't even asked the
questions
for.
And are we right now at that
juxtaposition
where our traditional way of going about
looking
for new therapeutic moieties is running
into the
wall.
DR. LEE: Well, I think that we are,
because I think we leave the treatment
with a
single compound may be on the way
out. And more
likely that we are beginning to treat
diseases with
combinations. Usually when disease, more than one
thing goes wrong.
Also, I too believe that with
the day come
where you can hand in an application,
then computer
will look at it and say, you know, yes or
no. It
might--because, you know about is the
pattern
recognition.
DR. MEYER: Yes, a couple of comments.
439
The agency loves acronym's, as
we've seen
today.
And I think it's interesting that "Critical
Path Initiative" is the same as
"Consumer Price
Index."
[Laughter.]
They are related. We're trying to save
money, get things out sooner, make people
weller.
So that's interesting.
Jerry used the term
"hypothesis." And I
didn't hear what the hypothesis was
necessarily for
all the things we've been talking
about. And then
Vine, on page 5 said,
"benchmarking." And I
think--well, he made up some--50 percent
this, and
30 percent that in 2010--I think that
would be
worthwhile, to show people where you
intend to go.
Just a couple of comments that
really deal
with the questions: prioritization in the era of
limited resources. Obviously, you have
limited
resources. I think there's an impressive quantity
of work that was presented today. It was much like
going to an AAPS symposium. It was just
high-quality stuff.
440
And certainly I was brought up
to learn
you'll be a more effective teacher if
you're
involved in research--much like you'll be
a more
effective reviewer if you're involved in
research.
That was kind of my fair-and-balanced
part. That
was the fair part. Now let's get to balanced part,
if I were a Senator on the Budget
Committee.
We all know FDA has difficulty--a
difficult time with criticism about
speeding up
approvals; difficult time with recalls of
marketed
products; difficult time with a shortage
of OGD
personnel--and a litany of other things.
So, given that era, I think
it's going to
be critical to prioritize what you're
doing in
terms of the Critical Path Initiative or
any of the
other initiatives.
And let me just pose a couple
of questions
that I would ask if you were telling me
what your
priorities were: who else could do the work?
Could NIH? Could industry? Could academia? Could
CRADAs solve the problem? Who else could do the
research?
Who else should do the research?
Are
441
there really other groups that are better
able to
do the work, rather than you re-inventing
a
laboratory, and a process and equipment
and
personnel etcetera? Are there other people that
should do it?
How can another resource
outside the FDA
be encouraged--with a carrot--or
forced--with a
stick--to undertake some of the things
you're
already doing? I would use an example: you
publish a guidance, and before long
there's all
kinds of people that are willing to
train--for
money--industry; all kinds of people that
are
welling to development instrumentation to
help
industry.
So you put an idea out there:
"Henceforth, in 2005, we will
require that,"
somebody's going to figure out how to do
it with
some piece of equipment, and market it,
and that
will be good for the whole economy, and
you won't
have to do it.
I would ask how does the research
relate
to problems faced by FDA--not globally
but, you
know, right now you have conjugated
estrogens.
442
That's an issue. I don't know really who might do
that work. And then what is the importance of the
problem?
So I'd say: are there others capable of
doing the work? And what is the importance of the
problem?
And how does the problem relate to
something closely involved with FDA.
CHAIRMAN KIBBE: Ajaz, what do you think?
Shall we farm it out? Outsource it?
DR. HUSSAIN: these are very, very
important questions. And I think the
benchmarking--the hypothesis--clearly,
anything
that we do, unless we have a goal in
mind, unless
we have a plan in mind, we're not going
to get
there.
And that's the reason the overreaching OPS
immediate office proposal we said was we
will go
through some of the process, trying to
map this
out, define the metrics and so
forth. That would
be essential.
And clearly, I think, an
initiative
umbrella creates expectations, creates a
benchmark
that I think people will hold us to and
so forth,
443
because nothing is free in life. So any
funding--anything that we get to support
these
activities--will have an associated
accountability
and efficiency in metrics.
So I think those are very
important
questions that I think we will have to
sort of
build into our thinking as we move
forward.
CHAIRMAN KIBBE: Ken?
DR. MORRIS: Yes, just to follow up on a
couple points.
First, I think--to your point,
Art--that
in the future I think therapies are going
to be a
lot different; and, hopefully,
significantly
different. But in the interim, between now and
then--given our 401(k)s and all--the
thing that
strikes me most in your presentation
Vince--other
than the eloquence, of course--is the
Nature
Review's drug discovery article, and
particularly
the attrition for each criterion, versus
the
criteria.
And if you look at those from
the '91 to
2000, what you see is that, in fact, tox
has
444
certainly gone up significantly, but cost
of goods
has gone from zero to 10 percent. Formulation has
gone from zero to 5 percent.
Commercial
-"commercialization," I'm assuming--has
gone from 5 to 22 percent.
So I think those statistics
really are
pretty much in line with a lot of what
the 21
st
Century GMP initiatives, as well as the
Critical
Path Initiatives were pointing out. I think,
overall, this is telling us that those
are the
areas of opportunity.
The statistic you used about,
you know,
decreasing the cost part by 30 percent is
really
very consistent with what G.K. presented
at the
manufacturing subcommittee last time
where, if
you'd look at the current cost of goods
sold as 25
or 26 percent of the current burden--if
you can
reduce that by a third--say 30 percent--and apply
that to the discovery R&D--as long as
we're still
in the paradigm of traditional chemical
discovery--that you can increase the
discovery
budget by 50 percent.
445
DR. LEE: Oh, that's true. Yes.
DR. MORRIS: So that I think your
benchmarking is actually pretty--I mean,
you know,
if not realistic--if it's not realistic
we're in
trouble.
I think it has to be realistic. I
think
those are the goals we have to shoot for
in the
short term, all the while keeping our eye
on the
ball of the new therapies, I think.
DR. LEE: Your on the same lines that we
shift the responsibilities to--well, the
upkeep--the maintenance of the quality to
the
manufacturers. So I would see that there might be
a reduction in the size of the regulatory
program--departments--and more resources
that can
go into research.
CHAIRMAN KIBBE: I guess we're
getting--we're running out of time, and I
think we
probably have--do you have something,
Jurgen?
DR. VENITZ: [Off mike.]I always have
something.
[Laughter.]
CHAIRMAN KIBBE: I mean, do you want to
446
say something.
DR. VENITZ: [Off mike.] A question we
wanted to acknowledge or--
CHAIRMAN KIBBE: I don't know.
Turn on
your mike.
DR. VENITZ: Two comments, then.
One has to do with the fact
that I'm
concerned that we're trying to
overreach. I mean,
FDA has only but so much impact on
attrition rates,
on drug development. And I think the major part
of--not drug development, but the
discovery part,
you have no control over. And you shouldn't have
any control over.
And my reading of those numbers
that we've
seen, if I look at efficacy--30 percent
fail
efficacy now, and they failed 10 years
ago. Well,
maybe the wrong target was picked. Maybe we don't
know what the target does. Maybe we don't know how
the target is related to disease. That has nothing
to do with regulatory science. That has nothing to
do with product quality.
So I do think we have to kind
of step back
447
a little bit and realize there's only so
much of an
impact--no matter what your goals are, no
matter
whether you reach them or not--that you
can have an
impact.
The second one--and that's one
that you've
heard me talk about for whatever--however
many
years I've been on this committee--and
that is to
really embrace this concept of risk; that
risk is
something that is intrinsic to being
alive. Being
alive is a risk because we're all going
to die. So
the question then becomes: how can we quantify
risk?
And how can we link that to--in your
case--product performance? And that, to me, is
really essential.
So all the rules that you come
up with
cannot be driven by the ability to
measure certain
things; certain what you consider to be
critical
attributes. But they have to be really
driven by
the fact that we think there is a
reasonable link
between improving those attributes and
some risk to
the patient; and that the stakes are high
enough
for us to put all the resources in, in
terms of
448
controlling that risk.
So--two comments; one, that
there's going
to be a limited impact of whatever the
Critical
Path Initiative that the FDA proposes
will do;
secondly, that you really have to emplace
this
concept of risk, and feed that back into
your
critical attributes, and the whole cGMP
change.
CHAIRMAN KIBBE: Ajaz has a comment.
DR. HUSSAIN: I think the discussion is
sort of coming together, in terms of
giving us very
valuable insight in sort of the questions
that we
need to pose.
If I may impose on the
committee to--as
the Chair suggested--take the evening to
think
about these.
But what I would sort of build
on Marv's
and Jerry's presentation, and Vince's,
is: I think
the key is the metrics, in the sense, I
do believe
in this, since we don't want to
overreach; we need
to
understand where our impacts will be the most
positive, as Jurgen just sort of pointed
out. And
we need to have some meaningful metrics
to measure
449
whatever path we decide to walk on, and
measure our
progress in that direction.
So if the committee members
could think
about--from that--the discussion
perspective now,
to sort of come back tomorrow to sort of
summarize
some of their thoughts on some guidance
on how we
should move forward here, it would be
very useful.
For example, I think just
building on
Jurgen's comments here, in the
sense: where can
FDA have the maximum impact? And how can we
measure that? For example, I think--I look at this
slide here, and I say all right. Traditionally,
formulation was never an issue. Why is it showing
up as an issue now? Are the drugs more complex
that we're not able to--the product
itself is so
complex?
Or--so there are some indicators here
which were surprising, and so forth.
So if FDA has to have maximum
impact, how
will we measure it? Multiple review cycles is one
measurement that we can look at.
For inhalation products, we
have multiple
review cycles. If I look at our root-cause
450
analysis, the physical characteristics is
a CMC
which leads to multiple review cycles as
soon as
you have a drug and a device
combination--inhalation product. We cannot even
approve a generic product when it's
inhalation
because of that level of complexity.
So--multiple review cycles, and
reduction
of that could be a metric. I'm just asking you to
think about it.
Approval decisions--I think,
with respect
to the example of PET imaging, how some
of these
things impacted on approval decisions
could be an
aspect that we could measure.
Clearly, I think, as we move
towards
follow-on proteins, expand the generic
programs and
so forth, within OPS we have a Congressional
mandated committee that we manage, which
is a very
difficult task. It's the Therapeutic
Inequivalence. We don't have a good means to
manage that--reports that come
in--because our
information is limited.
Keeping an eye on post approval reports
451
that come up--is that a means to measure
that? I
don't know.
So I think if the committee
members could
think about the discussion here, what
metrics, how
can we measure this, and then come back
with their
thoughts tomorrow, that will be
wonderful.
DR. LEE: May I interject, also, I would
like to plead for the funding in the
formulation
area.
I think that was talked about this morning.
There's no department on pharmaceutical
technologies. And I think somebody should make the
case to support formulation in a big way.
CHAIRMAN KIBBE: Marv?
DR. MEYER: Just one suggestion--kind of
passing the buck, I guess--it seems to me
it's a
little more efficient if some
representatives of
FDA threw up a straw man tomorrow
morning, because
they know what the problems are. They know what
potential solutions are. They've come up with the
Critical Path Initiative--throw up a
straw man,
maybe with a couple examples, and let us
hack at
that, rather than have us kind of out in
a blind
452
somewhere try to come up with some
harebrained
ideas that will be in the public record.
CHAIRMAN KIBBE: I plan on doing that
tomorrow, though. That was my whole thing with
tomorrow.
It would be nice to hear from
our industry
reps, too. Because they have to live with the
challenge of finding better ways, and
more
efficient ways of improving the quality
of the
therapeutic moieties on the market, and
doing it in
a constricted economic environment.
DR. MORRIS: Just to follow up--one think
I was thinking is that in some of the
work we've
been doing with Ajaz's folks, and
Helen's, we've
been looking at the CMC review
process. And maybe
some of what we've been doing could be
classified
as dividing it into opportunities for
improving the
reviewing efficiency, versus real
scientific
changes that have to be made to stimulate
the
process--which I think sort of is
reflected in this
slide here.
CHAIRMAN KIBBE: I think it's time to call
453
it a day.
You can turn off the tape, and
then I can
say really weird things.
[Whereupon, the meeting was
adjourned, to
reconvene on October 20, 2004.]
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