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
Arthur H. Kibbe, Ph.D., Chair
Hilda F. Scharen, M.S., Executive Secretary
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
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.
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.
C O N T E N T S
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
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
C O N T E N T S (Continued)
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
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 actually
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
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
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,
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
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,
In the event that the
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
CHAIRMAN KIBBE: Thank you.
And now we'll hear from the Director of
the Office of Pharmaceutical Sciences, Ms. Helen
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
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
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
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,
It was a very good meeting. And
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,
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.
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
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
some of his staff are going to talk about some
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
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
direction, and it seems to be the answer to
handling some of the enormous workloads that we
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
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
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
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
with the issues that we already have identified
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
So that was only, in my mind, the first
But I thought it would be helpful just to
step back real quickly and look at what
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
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
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
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
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
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
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
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
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
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
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
Risk management--risk management, I had
thought--we did introduce a
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
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
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
CHAIRMAN KIBBE: Thank you, Helen.
We now have a report from the chair of one
of the subcommittees--the Manufacturing
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.
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
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.
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.
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
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
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.
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
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
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
it's not a good topic, but primarily because all
the resources that would address Q10 are tied up
with Q8 and Q9.
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
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.
All right. Now, this topic I'm going to
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,
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.
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.
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
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.
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.
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
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.
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
Quality by design is the desired state.
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.
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,
has nothing to make their decisions on. And the
comparability protocols are an important part of
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
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
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
Supplement need is based on the knowledge
of the risk of the change. And there should be a
clear rationale for the selection of
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.
In the Office of Generic Drugs--generic
industry's focus is on producing a bioequivalent
Often patent issues--to design around.
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
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
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
Okay--some additional committee
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.
Need to avoid implying there are two
different quality concepts. We don't want to say
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
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.
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.
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
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
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
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.
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
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
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
Committee members wanted to know if the
sites are going to know how they are
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.
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.
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.
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
But the committee had comments, as well.
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
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.
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.
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
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
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.
DR. SINGPURWALLA: You are confused, Mr.
CHAIRMAN KIBBE: On a regular basis.
You had a question?
DR. MORRIS: Actually, just one comment to
add to what you'd said, Judy, about the Georgetown
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
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
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
case study also.
So we hope in the next several months we
will have examples and case studies to outline the
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.
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
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
DR. WEBBER: Where they were--just all
products--okay. Thank you.
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
Parametric Tolerance Interval Test for
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
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
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
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.
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
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
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.
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?
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?
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
DR. VENITZ: Maybe.
In your draft proposal--or what you're
considering so far to be a draft
DR. O'NEILL: Yes.
DR. VENITZ: --are you considering the
intended use when you look at statistical
characteristics of your operating curve, for
DR. O'NEILL: Well, certainly that has
been discussed, both from an emergency--a
one-time-only, a chronic use, a medical risk
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
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.
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
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
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
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
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
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
DR. SINGPURWALLA: Let me clarify.
I'm not volunteering because
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--
--from the point of view of simply guiding
a framework, or guiding the concept, and things
like that, rather than get involved with the
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
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
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
The Critical Path Initiative--Challenges
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
I'd like to sort of introduce the topic of
Critical Path Initiative--the challenges and
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
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
enterprise research. But also, I think the follow
on biologics, follow-on proteins, I think is
interconnected to all of these discussions.
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
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.
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.
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
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
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
private and public spending on research.
However, new product submissions have
remained flat--or, some would argue, are on the
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.
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.
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.
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
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.
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
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
our review responsibilities predominantly are on
the quality side, our research programs are
interconnected to every aspect of the drug
So you will hear presentations coming from
all aspects--all three dimensions of the Critical
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
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.
In our White Paper, we defined
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
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
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.
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
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
significant need for support.
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
And, clearly, alignment of research
programs in OPS to meet our goals and objectives.
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
addressing the challenges.
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.
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
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.
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
Determine regulatory processes efficiency
and effectiveness, using metrics similar
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
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
or possible, seek similar information from the
sponsors or company scientists on these same
products that we might select.
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
In the following--I think the three
projects that we're thinking about are not actually
fully independent. They're all connected together.
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
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
long life-cycle time.
So we have to treat it as a complex system
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
So the Project 2 proposal that we're
developing is to use ICH Q8 as the bridge
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.
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
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.
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.
Just to illustrate this point, let me
create an example here. The applicability of
multidisciplinary optimization methods for solving
system-level problems and decision
be explored in an NDA review process. That's what
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
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
So that's describing the information
content in section 2.1.1. that we will
receive whene ICH Q8 is done. So how does this
have a bearing on the "Formulation Development"
I'll skip this and just show you a figure.
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
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
Since my means and standard deviations
have become finger-pointing and so
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.
So the potential deliverables of using
this approach could be significant. Since
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
So that's the project that we hope to
We really want to get some feedback from
you, and develop this as a project under the
Critical Path Initiative.
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
The Bayesian approach may proivde a more
comprehensive approach for regulatory decision
process in dealing with CMC uncertainty
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
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
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
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
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
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
The first argumetn I want to
make on your
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
CHAIRMAN KIBBE: Right.
DR. SINGPURWALLA: The Bayesian approach
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
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
And you also say that there is no common
scientific vocabulary. Well, I claim there is a
common scientific vocabulary, and that is
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
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
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
DR. VENITZ: [Off mike.] Well, other than
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
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,
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.
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
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
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
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,
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
think, from a variability perspective, we're not
very sophisticated in how do we deal with
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
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
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
DR. MORRIS: Thank you.
CHAIRMAN KIBBE: Anybody else? Nozer, you
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
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
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
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
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
here--within my domain.
CHAIRMAN KIBBE: We have a couple more
comments, and then we're going to have to take a
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
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
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
an area of focus in an American college of pharmacy
now. Certainly you won't see any departments of
So I think it's been--and it wasn't that
way 20 years ago. But, I mean, it certainly has
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.
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
He's on the way to the podium.
Those of you walking around with
your hands, and sodas, you want to sit down.
Nozer. Here we go. Good luck. We gave you 10
minutes to do that.
You snooze, you lose, as the old saying
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--
--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
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.
The--I didn't bring a pointer. Is there a
VOICE: [Off mike.]
Just use the mouse.
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
But I believe we have opportunities to
have an impact on this entire process in the
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
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
The goal of industry, as well
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.
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
laboratories where research at the bench is going
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
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
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
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
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
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
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.
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
information from academia, industry and other
egoernment agencies, as well.
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
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.
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
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
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
Informatics and Computational Safety Analysis
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
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
pro quo exchange, with software and other
scientific entities outside the center.
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
enpoints--especially in safety endpoints.
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
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.
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.
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
carcinogenicity, for example, based on the compound
structure. It's being used by the pharmaceutical
companies. It's distributed by small software
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
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.
So we talk about the FDA
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
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.
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
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
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
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
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
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
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
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
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
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.
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
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.
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
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.
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
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.
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
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.
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
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
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.
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
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
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.
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
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?
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
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
DR. CONTRERA: [Laughs.] Well--no fair.
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
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
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
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.
And so--and they have access to data that's
So, using the leverage with a publishing
company, we have a pipeline now to the literature.
DR. SELASSIE: Okay. I have another
DR. CONTRERA: Yes.
DR. SELASSIE: When you're inputting the
structures, do you all ever use the SMILES
DR. CONTRERA: Yes, we use SMILES. There