1

DEPARTMENT OF HEALTH AND HUMAN SERVICES

FOOD AND DRUG ADMINISTRATION

CENTER FOR DRUG EVALUATION AND RESEARCH

ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE

Tuesday, October 19, 2004

8:30 a.m.

CDER Advisory Committee Conference Room

5630 Fishers Lane

Rockville, Maryland

PARTICIPANTS

Arthur H. Kibbe, Ph.D., Chair

Hilda F. Scharen, M.S., Executive Secretary

MEMBERS

Patrick P. DeLuca, Ph.D.

Paul H. Fackler, Ph.D.

Meryl H. Karol, Ph.D.

Melvin V. Koch, Ph.D.

Michael S. Korczynski, Ph.D.

Marvin C. Meyer, Ph.D.

Gerald P. Migliaccio, Ph.D. (Industry

Representative)

Kenneth R. Morris, Ph.D.

Cynthia R.D. Selassie, Ph.D.

Nozer Singpurwalla, Ph.D.

Marc Swadener, Ed.D. (Consumer Representative)

Jurgen Venitz, M.D., Ph.D.

SPECIAL GOVERNMENT EMPLOYEES SPEAKERS

Judy Boehlert, Ph.D.

Gordon Amidon, Ph.D., M.A.

FDA Staff

Gary Buehler, R.Ph.

Lucinda Buhse, Ph.D.

Jon Clark, M.S.

Jerry Collins, Ph.D.

Joseph Contrera, Ph.D.

Ajaz Hussain, Ph.D.

Monsoor Khan, R.Ph., Ph.D.

Steven Kozlowski, M.D.

Vincent Lee, Ph.D.

Qian Li, Ph.D.

Robert Lionberger, Ph.D.

Robert O'Neill, Ph.D.

Amy Rosenberg, M.D.

John Simmons, Ph.D.

Keith Webber, Ph.D.

Helen Winkle

Lawrence Yu, Ph.D.

C O N T E N T S

PAGE

Call to Order

Arthur Kibbe, Ph.D. 5

Conflict of Interest Statement

Hilda Scharen 5

Introduction to Meeting

Helen Winkle 8

Subcommittee Reports - Manufacturing Subcommittee

Judy Boehlert, Ph.D. 26

Parametric Tolerance Interval Test for Dose

Content Uniformity 53

Critical Path Initiative

Topic Introduction and OPS Perspective

Ajaz Hussain, Ph.D., 64

Research Opportunities and Strategic Direction

Keith Webber, Ph.D. 105

Informatics and Computational Safety

Analysis Staff

Joseph Contrera, Ph.D. 117

Office of New Drug Chemistry

John Simmons, Ph.D. 165

Open Public Hearing

Saul Shiffman, Ph.D. 192

Critical Path Initiative--Continued

Office of Generic Drugs

Lawrence Yu, Ph.D. 204

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)

PAGE

Office of Testing and Research--Current

Research and Future Plans

Jerry Collins, Ph.D. 316

Lucinda Buhse, Ph.D. 338

Mansoor Khan, R.Ph., Ph.D. 362

Wrap-up and Integration

Jerry Collins, Ph.D. 410

Challenges and Implications

Vincent Lee, Ph.D. 419

Committee Discussion and Recommendations 428

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 eight o'clock 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

tomorrow.

I'd like to call you all to order for my

last go-round as Chairman of this August body. And

the first order of business, of course, is to read

about all of our conflicts.

Conflict of Interest Statement

MS. SCHAREN: Good morning.

The following announcement addresses the

issue of conflict of interest with respect to this

meeting, and is made a part of the record to

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,

United States Code Section 208.

A copy of the waiver statements may be

obtained by submitting a written request to the

Agency's Freedom of Information Office, Room 12A30

of the Parklawn Building.

Because general topics impact so many

entities, it is not practical to recite all

potential conflicts of interest as they may apply

to each member, consultant and guest speaker. FDA

acknowledges that there may be potential conflicts

of interest, but because of the general nature of

the discussions before the committee, these

potential conflicts are mitigated.

With respect to FDA's invited industry

representative, we would like to disclosed that

Paul Fackler and Mr. Gerald Migliaccio are

participating in this meeting as a non-voting

industry representative, acting on behalf of

regulated industry.

Dr. Fackler's and Mr. Migliaccio's role on

this committee is to represent industry interest in

general, and not any one particular company. Dr.

Fackler is employed by Teva Pharmaceuticals,

U.S.A., and Mr. Migliaccio is employed by Pfizer,

Incorporated.

In the event that the discussions involve

any other products or firms not already on the

agenda for which FDA participants have a financial

interest, the participants' involvement and their

exclusion will be noted for the record.

With respect to all other participants we

ask, in the interest of fairness, that they address

any current or previous financial involvement with

any firm whose products they may wish to comment

upon.

Thank you.

CHAIRMAN KIBBE: Thank you.

And now we'll hear from the Director of

the Office of Pharmaceutical Sciences, Ms. Helen

Winkler.

Introduction to Meeting

MS. WINKLE: Good morning, everyone.

All right, I want to welcome everybody

this morning to the Advisory Committee for

Pharmaceutical Science. This is, I think, a very

important meeting, and I"m really looking forward

to the discussion. But before we get there, I want

to welcome all of the members. We have one new

prospective member, Carol Gloff--Dr. Gloff--has

joined us. And we have two other prospective

members who we're having a little complication with

in getting on board. So we're working on that.

We also will have a number of SGE's here

today; Dr. Boehlert, Dr. Amidon and several others

who are going to participate with us in a number of

things. So I want to welcome everybody.

I also want to thank Dr. Kibbe. This is

his last time as Chair. It will break all of our

hearts to see Dr. Kibbe go out of this position.

He has been very, very enthusiastic as the Chair of

this committee, and I think all of us have enjoyed

working with him. But he's not to go very far.

We've already told him that we anticipate him

coming back to a number of meetings and helping us

with some of the discussion in the future. So we

really want to, again, thank him for all he's done.

Dr. Cooney--Charles Cooney--has agreed to

be the chair of the committee for the next two

years. Unfortunately, Dr. Cooney couldn't be

here--after he accepted, he couldn't be here today.

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,

I think. It was a very good meeting. And Judy can

fill us in on some of the highlights of that

meeting. Also Bob O'Neill is going to talk about

the Working Group with IPAC RS, and some of the

accomplishments--or the focus that we've had in

that Working Group.

We're also going to talk about the

Critical Path Initiative. And I think this is a

really important discussion that we can have with

the committee today. Critical Path is, of course,

one of the main initiatives in the agency now, and

what we would like to talk about with the committee

is give you some idea of our thoughts, as far as

Critical Path; some of the things that we're doing

in the Critical Path Initiative, in the office of

Pharmaceutical Science in the various product

areas, and get some input from you as to what

direction we need to go; if there's other things we

need to be thinking about; and if there's other

types of topics that we need to be taking up, we'd

like to do that.

Dr. Woodcock talked about the Critical

Path Initiative when she introduced it, saying that

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

state.

So I think that will be a really

interesting issue, and I think there are a number

of things that the committee can help us with in

identifying how best to address these answers and

to address the gaps.

We also have a number of bio-equivalence

issues that we want to discuss. We want to

continue the conversation from the last Advisory

Committee we had on bio-equivalence. And Dr. Yu

and some of his staff are going to talk about some

recommendations from that. And we're also going to

bring up a new topic on gastroenterology drugs.

So--moving on to OPS in 2005. I think

2004, we had an extremely busy year, mainly focused

on the GMP Initiative, and all of the aspects of

that initiative--especially the areas concerning

manufacturing science and how wee were going to

really address those issues and concerns, and how

we were going to incorporate those into the

regulatory framework.

As we move into 2005, I think we still

have a lot of issues that we have to handle under

Pharmaceutical Quality Initiative. We've already

said that that's going to be some of what we take

up with the Advisory Committee today. But we

really need to pursue those next steps. In doing

that, though, we also need to be looking at

continuing to streamline the review processes. We

continue to get more and more products in for

review, and there's got to be some way to offset

that increasing workload. And streamlining the

review processes seems to be--we're moving in that

direction, and it seems to be the answer to

handling some of the enormous workloads that we

have.

Also, we need to incorporate best

practices. We've added the Office of Biotech

Products in the last year. They joined us in

October of 2003, and they have a lot of practices

in their review that I think can be very helpful as

we move forward in looking at ways to improve--both

in out office of New Drug Chemistry, and our Office

of Generic Drugs.

So we're going to be looking at

incorporating best practices across the entire

organization.

Supporting the Critical Path

Initiative--I've already brought this up. It's a

very important part of where we're going. I think

much of our research is going to be done there, and

I think we're talking about much more than

laboratory research. I think there's a number of

activities that we hope to take on in 2005 where

we're looking at improving on how we do the

regulation, and in actually working through the

Critical Path Initiative to get some of this done.

So we'll talk more about that as we get into

Critical Path and some of those projects that we're

looking at doing.

We're looking at further integrating the

whole Office of Biotech products. There are still

some things that need to be accomplished there. I

think there are still a number of questions that

the Advisory Committee can be very helpful to in

answering. So you will hear more about this in the

next fiscal year.

And, last of all, I think there still

continues to be a number of regulatory on follow-on

proteins, as well as a number of general scientific

issues that we'll want to discuss with the

committee.

So I think we have a lot on our plate

during the year, and I look forward to working

closely with the Advisory Committee in the next

fiscal year to help us identify some of the--other

things that we need to look at, as well as help us

with the issues that we already have identified

ourselves.

Okay. As I said, I'm going to talk real

quickly about the CGMP Initiative for the 21st

Century. I think most of you all have probably

read the background material, which included the

report. We've actually come to the end of the

first two years of the initiative. And I"d like to

emphasize: I don't think that's the end of the

initiative. I think it's just the beginning. I

think that the initiative helped us identify a

number of things that we need to be looking at in

review, that we need to be looking at in

inspection. We still have a lot of changes to

make. I think we've made a lot of progress--and

I'll talk a little bit about some of that progress.

But I think we've got a lot more that we have to

focus on.

So that was only, in my mind, the first

step.

But I thought it would be helpful just to

step back real quickly and look at what the goals

of the initiative were. Because I think you can't

really appreciate the accomplishments without

really understanding what the goals were.

So there were basically six major goals.

The first one was to incorporate the most

up-to-date concepts of risk management and quality

systems approaches; secondly, was to encourage the

latest scientific advances in pharmaceutical

manufacturing and technology, ensure submission

review program and the inspection program operating

in a coordinated in synergistic manner; apply

regulation and manufacturing standards

consistently; encourage innovation in the

pharmaceutical manufacturing sector; and use FDA

resources most effectively and efficiently to

address the most significant health risks.

And you can see, when you look back at

these initiatives, the role OPS has had to play in

all of these goals. I think they're very

important, not only to the agency, but important to

us at OPS, and important to the industry and others

involved in the manufacturing of pharmaceutical

goods.

So, quickly, through the

accomplishments--again, you can read the report.

You'll get a lot more out of the report. But I

just want to emphasize that there was an awful lot

done in the last two years; a lot that will affect

how we move forward in the future, in the 21st

century. So I wanted to highlight those.

The first thing was Part 11. We did a

last in the last two years to clarify the scope and

application of Part 11. There were quite a few

questions; quite a bit of complication in

implementing Part 11. And I think we've moved

forward in trying to eliminate some of that

complexity and complication. We issued two

guidances during the two-year period that have

helped in that clarification.

Technical Dispute Resolution Process--this

was also a very important part of the initiative.

And it really has had a very positive effect, I

think, on the industry, and a positive effect on

how the field has dealt with inspections and has

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

positive effect.

International collaboration--I won't go

into that, but we have spent a lot of effort in

ICH, and Q8, Q9, and hope to do a lot in Q10. And

also one of the things we are planning on doing is

getting more involved with PICS, which is looking

at inspections on a worldwide basis.

Facilitating innovation--including doing

standards and policies--we were very fortunate to

put out a number of different guidances under this

part of the initiative; the aseptic processing

guidance--which industry is very familiar with.

They've been waiting for this guidance for a long

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

system.

Manufacturing science--the desired state

under !8 of ICH has become a very important aspect

of where we're driving to. And, of course, we're

going to talk to that tomorrow morning; continuous

improvement and reduction of variability have been

an important part of manufacturing science, and

areas that we need to explore more in the future,

and assure that we can accomplish that, especially

being able to open up in the agency and allow more

continuous improvement for manufacturers.

Product specialists--this includes

enhancing the interactions between the field and

the review. We're looking at a team approach, in

having our reviewers all out on inspections. And

we're looking at best practices from both the PAT

team and Team Biologics. I think there's a lot of

best practices there that we can incorporate in out

thinking in the future on how we handle review and

inspection.

Integration of approval and

inspection--this is more of that. We have

developed the pharmaceutical inspectorate, and

we're looking also at changes in pre-market

approval program.

Quality management systems--there's a

number of things that we've worked on here. They

take a number of directions. We've developed a

standard quality systems framework; a quality

systems guidance. We've worked on GMP

harmonization, analysis process validation, and

good guidance practices--none of which are going to

go into in detail, but I think all very beneficial

to helping us in the future in the 21

st century.

Risk management--risk management, I had

thought--we did introduce a site-selection model

for inspection under this part of the initiative.

I believe there's a number of other things that we,

especially in Review, need to focus on as far as

risk management, and have a much better idea of

what the risk of products are, and how we're going

to mitigate those risks. And I think this is

something that we will bring up in the future at

the committee.

Team Biologics was to look at a number of

initiatives that were already underway, and adopt a

quality systems approach.

And last of all was the evaluation of the

initiative, which hasn't been completed yet, but

it's a very important part of what we've done.

So that, in a nutshell--I mean, that's a

lot of effort, obviously, that we've done. And if

you, again, will read the report I think you'll get

a much better feel. But I felt like, since we've

talked about it so much during the last few years,

that it was very important to sort of wrap up what

has happened in the last two years with this

committee.

So that's all I have to talk today. I'm

going to give it back to Art, and I look forward to

very lively discussion on a number of these issues,

and look forward to working with you for the next

two days.

Thank you.

CHAIRMAN KIBBE: Thank you, Helen.

We now have a report from the chair of one

of the subcommittees--the Manufacturing

Subcommittee.

Judy?

Subcommittee Reports

Manufacturing Subcommittee

DR. BOEHLERT: Good morning, ladies and

gentlemen. Before I just get started here--I tried

pressing down, and--aha. I need an SOP for how to

operate the slides.

[Slide.]

It's a pleasure for me to be here this

morning to update you on the Manufacturing

Subcommittee. We met in July. And I think you'll

find that a lot of the topics we discussed tie in

very well with what Helen was talking about this

morning, and also with some of the topics that are

going to be on your agenda.

[Slide.]

We met for two days in July. Just a brief

overview of the topics that we discussed: quality

by design--we've heard that this morning;

introduction to Bayesian approaches--and we'll talk

just a little bit about that; research and training

needs--the industrialization dimension of the

Critical Path Initiative--another topic we heard

about this morning; manufacturing science and

quality by design as a basis of risk-based CMC

review; and risk-based CMC review paradigm.

[Slide.]

On the 21

st: introduction to

pharmaceutical industry practices research study; a

pilot model for prioritizing selection of

manufacturing sites for GMP inspection; cGMPs for

the production of Phase I INDs; and applying

manufacturing science and knowledge, regulatory

horizons.

What I'm going to do is just go over,

briefly, some of the topics that were discussed,

and also the comments that were made by committee

members.

[Slide.]

Quality by design: topic updates. This

addressed three guidances that should be coming out

of ICH. The first of ICH Q8, which is a guidance

on pharmaceutical development section of the Common

Technical Document. It's going to describe

baseline expectations and optional information;

requires FDA and industry to think differently.

Industry needs to be more forthcoming with

information in their submissions, and FDA needs to

look at the review process; focuses on process

understanding and predictive ability. And if you

really understand your process, you'll gain

regulatory flexibility. It's a framework for

continuous improvement. And Step 2 is expected in

November this year. That means it will be out for

public review and comment.

[Slide.]

ICH Q9 is quality risk management. It

looks at risk identification--should link back to

the potential risk to the patient, because, after

all, that's what's important; risk assessment--what

can go wrong? What is the likelihood? What are

the consequences?

Risk control--options for mitigating,

reducing and controlling risks; risk

communication--between decision makers and other

shareholders. And this may also reach step two in

November of this year, although that was a bit

questionable.

[Slide.]

And then we're going to talk about quality

systems needed to recognize the potential of !8 and

Q9. And this is ICH Q10: monitor and evaluate

processes with feedback groups in a manner to

identify trends and demonstrate control or the need

for action; manage and rectify undesirable

occurrences; handle improvements; management,

implement and monitor change.

This is currently on hold, not because

it's not a good topic, but primarily because all

the resources that would address Q10 are tied up

with Q8 and Q9.

[Slide.]

We also talked about the ASTM E55

Committee. And Helen mentioned that this morning.

Their involved in the development of standards for

PAT. And the important things here are consensus

standards, with input from industry, academia and

regulators. There's an established process, with

an umbrella set of rules. And ASTM is recognized

worldwide.

They have three functional subcommittees

on management, implementation and practices and

terminology. But one of the concerns expressed by

the committees is are they going to duplicate other

initiatives. There area lot of people right now

working on PAT initiatives, and are they going to

duplicate some of that. So we need to make sure

that everybody gets on the same page.

[Slide.]

All right. Now, this topic I'm going to

be reluctant to say a whole lot about, but we had

an introduction to Bayesian approaches. Dr. Nozer

Singpurwalla was kind enough to give us an

introduction to the topic. So, Nozer, I apologize

if I mis-speak when I summarize--[laughs].

You know--so it's with fear and

trepidation--he's threatened us a quiz--

DR. SINGPURWALLA: You've already done it.

DR. BOEHLERT: Yes, I know. [Laughs.]

That's what I was afraid of. But I didn't think I

could leave it out, or you'd get after me then,

too.

Okay--Reliability for the Analysis of

Risk." Reliability--the quantification of

uncertainty. And I'm just going to say a few words

here: utility--costs and rewards that occur as a

consequence of any chosen decision. These are the

things that Nozer talked to us about--risk

analysis--process assessing reliabilities and

utilities, including an identification of

consequences. We talked about scales for measuring

uncertainty--for example, probability.

[Slide.]

Now this is a quote, so I have to be

careful here. "When the quantification of

uncertainty is solely based on probability and its

calculous, the inference is said to be Bayesian."

I am not a statistician, so I'm certainly not a

Bayesian statistician. And then there is

discussion of use of Bayesian approaches for ICH

Q8, Q9, Q10 and the use of prior information.

[Slide.]

Industrialization--dimension, the Critical

Path Initiative. We heard about that this morning.

We'll hear about it in the next two days:

examining innovational stagnation. Everybody needs

to take a look at what we've been doing in the past

and get things moving forward in a new environment,

with new technologies.

Critical path--has been inadequate

attention in areas of new or more efficient

methodologies and development research.

Industrialization--goes from the physical

design of prototype up to commercial mass

production. And Education and research

infrastructure needs improvement. And this

education and research applies to industry; the

education also applies to the agency. We all need

to learn how to go forward in the new environment.

[Slide.]

FDA has a strong interest in computational

methodologies to support chemistry and

manufacturing control submissions. They're putting

together a chemometrics group. There's a new FDA

research program focusing on industrialization

dimension. And there's training needs. AS I

mentioned before, particularly with the

pharmaceutical inspectorate. That's started.

There is an inspectorate now of trained

investigators. There need to be more.

[Slide.]

Manufacturing science and quality by

design--it's a basis for risk-based CMC review.

Companies share product-process understanding with

regulators. And this is a new paradigm, if you

will, that companies will share more of the

information that they have available than they have

in the past.

Specifications should be based on a

mechanistic understanding of the process; there

should be continuous improvement; and real time

quality assurance. You shouldn't have to wait

until the end of the process to know that your

product is okay.

[Slide.]

Science perspective on

manufacturing--define current and the desired state

and the steps to go from here to there; define

terms--and this is going to be important going

forward--things like "manufacturing science,"

"manufacturing system," "manufacturing

capability"--what do they really mean?

Real case studies will help. This came up

time and again in the committee discussions. It's

nice to talk about all these theoretical concepts,

but give me a real case study that I can look at

and see what it really means.

Testing is mostly non-value added.

Quality by design is the desired state.

[Slide.]

Risk-based CMC review--from the Office of

New Drugs--should provide regulatory relief by

incorporating science-based risk assessment; more

product or process knowledge shared by the

industry--and I've said this several times; more

efficient science-based inspections; focus

resources on critical issues; and specifications

are based on a risk-based assessment.

[Slide.]

Quality assessment rather than a chemistry

review--in the past it's been a strict chemistry

review: go down the list and check off the boxes;

conducted by inter--and I see some smiles on the

parts of agency folks--conducted by

interdisciplinary scientists--so it could be a team

approach. It should be a risk-based assessment;

focus on critical quality attributes and their

relevance to safety and efficacy. They have to

rely on the knowledge provided by applicants. If

industry doesn't submit the information, the agency

has nothing to make their decisions on. And the

comparability protocols are an important part of

this review.

[Slide.]

Role of process capability in setting

specifications will need to be addressed. Very

often, those kinds of process controls that you

have may have no clinical relevance. The knowledge

base at the time of submission can be an issue,

because very often you don't have that much

information at the time you submit. It's a

learning process as you go through early

marketability and commercial production.

Specifications should not be used as a

tool to control the manufacturing process. And we

might need to expand the Quality Overall Summary

going forward.

[Slide.]

AS I said before, the extent of product

knowledge is key. Risk-based decisions should be

based on supportive data. Voluntary--all of these

new initiatives are voluntary. And that needs to

be made very clear to the industry. These are not

requirements that everybody drop what they've been

doing in the past and start over with new

approaches--strictly voluntary.

Supplement need is based on the knowledge

of the risk of the change. And there should be a

clear rationale for the selection of

specifications.

[Slide.]

Identify critical parameters for product

manufacturing and stability; train FDA staff and

regulated industry--this came up a number of times.

We all need to learn what the other is doing;

should give us--industry--greater flexibility in

optimizing the process; should lessen the

supplement burden, which is good for industry and

good for the agency. And, once again, real

examples would be an asset.

[Slide.]

In the Office of Generic Drugs--generic

industry's focus is on producing a bioequivalent

product. Often patent issues--to design around.

They may not have the flexibility as the new drug

folks. Workload in OGD is a significant issue, and

committee members made a number of comments on this

when they heard how many submissions there are, and

how far behind they are. We were impressed by the

workload.

Provide advice to industry on improving

quality of DMFs--those are "drug master

files"--very important to the generic

industry--also to the new drugs, but to a lesser

extent.

[Slide.]

Desired state--include needed data in a

filing; process and product design; identify

critical attributes; identify process critical

control points. And this is the difference from

the past. Analyze data to produce meaningful

summaries and scientific rationales; and reviewers

assess the adequacy of the submission by asking the

right questions.

[Slide.]

Okay--some additional committee comments

that came out of the Day One discussion: ICH and

ASTM appear to be synergistic, but ICH needs to be

very aware of the ASTM focus. There was some

concern they might not be tied into what's going on

there; some concern that FDA, internally,

themselves, may be getting ahead of what's

happening on an international basis. So they may be

a little ahead of ICH Q8, Q9 and Q10. That's not

necessarily a bad thing, by the way.

Need concrete examples--that came up time

and time again; need to clearly demarcate "minimum"

and optional information--you know, just what do

you mean by "this is the minimum you need," and

just what is "optional" information? And

"optional" information comes in degrees. The more

you make the more you know. So you may not have as

much information at submission as you will down the

road after you've been in commercial product for a

number of months or years.

[Slide.]

Need to avoid implying there are two

different quality concepts. We don't want to say

that products made in the conventional way---the

way we've always done it--are different than

products that may be made according to some new

paradigms. Bring in new training programs--and

Helen mentioned we're talking about forming a

working group under the Manufacturing Subcommittee

to address some of the issues, particularly case

studies.

We need to find better terms than

"minimal" and "optional;" and focus on process

first, and then the tools that we're going to need.

[Slide.]

We had some reports on an FDA research

project that's being done by Georgetown University

and Washington University, and their goal is to

identify attributes that impact inspection

outcomes. They're compiling and linking FDA

databases. They're looking at variables for

product-process, facility, firm and FDA. Right now

they're collecting data. CDER is just about

completed, and CBER is ongoing--although by now it

may be even further down the road. This was July.

[Slide.]

Focus--are cGMP violations related to

managerial, organizational and technical practice?

And then interviewing manufacturers. They have an

internet-based questionnaire that went out in the

fall of 2003. They're looking at U.S. and European

manufacturers. And their data collection is near

completion.

[Slide.]

There's concern with just looking at

numbers of deviations or field alerts, particularly

when investigation may have shown little cause for

concern. You can put in a field alert and then

find out later on that--oh--you know, we figured it

out. It really wasn't a problem. So if you just

look at numbers, you get those as well as the ones

that are true issues.

Also it was pointed out that if you're a

company with a very detailed SOP you have a much

bigger chance for deviating from it than your

company with a really poor SOP that sort of allows

you to do anything, where you're hardly ever going

to deviate. But who's to say which one is better?

India and China are not include in the API

manufacturers. And we saw this as a downside to

that survey, because they are major manufacturers

of APIs.

[Slide.]

We talked then about risk ranking and

filtering, where risk ranking is a series of

decisions to start to rank within a class or across

classes. Tools may be customized for each

application. And filters may be used to reflect

resource limitations and/or program goals.

[Slide.]

There's a pilot risk-ranking model to

prioritize sites for GMP inspections, using ICH Q9

concepts to define risk; Site Risk Potential--a new

term for us--SRP--includes product, process and

facility components.

Look at probability and severity

components that make up harm; and look at other

risk-ranking models, for example those used by EPA

and USDA; and then using the CDER Recall database.

[Slide.]

Comments--from the committee--focusing on

volume at a site may be misleading because, in

fact, when you have a high volume your process may

be better controlled than if you have small volume.

We need to also consider the risk of the

loss of availability. If you're a single-source

drug for a life-threatening condition perhaps that

needs to come into the equation.

Look at "hard to fabricate" products, or

products with difficulty controlling uniformity.

Investigator consistency will be--and has been--an

issue, but with the pharmaceutical inspectorate

that should be better. And it was suggested by at

least one member that maybe they should look at

high personnel turnover in a plant, because that

might be indicative of problems--although it was

recognized that that might be hard information to

come by.

[Slide.]

Committee members wanted to know if the

sites are going to know how they are ranked. That

would be very useful information for management to

know about. Right now self-inspections are a

critical part of the quality system but the value

of these would be diminished if that information

were to become available to FDA. This has been a

longstanding concern of industry. You know, you

don't want to share your self-inspections because

then they lose their value to you.

[Slide.]

Next talked about GMP guidance that's

proposed for the production of Phase I drugs. CMC

review to ensure the identify, strength, quality

and purity of the investigational drugs as they

relate to safety. This draft guidance is in

process. It's a risk-based approach. No regular

inspection program, but these Phase I drugs are

looked at on a "for cause" basis.

I want to point out that it was noted

during that discussion that for Phase 2 and Phase

3, those drugs still fall under the GMP

regulations--21 C.F.R. 210 and 211.

[Slide.]

Also had an update on the PAT initiative.

As Helen indicated, that guidance was recently

finalized, in September. It should be expanded to

cover biotech products. And, of course, it

requires continued training of FDA staff.

[Slide.]

We also talked about--we had a full

agenda--comparability protocol. We had an update

on guidances, The goal is to provide regulatory

relief for post approval changes. It requires a

detailed plan describing a proposed change with

tests and studies to be performed, analytical

procedures to be used, and acceptance criteria to

demonstrate the lack of adverse effect on product.

Many comments have been received from the public.

That was FDA's comment on this. We did not see

those.

But the committee had comments, as well.

[Slide.]

Single use protocol has limited utility.

It's more utility if you're going to have

repetitive changes--if you're only going to do it

once it may not help. Specificity of the protocol

may limit repetitive use. Just how much

specificity is needed? And for a well-defined

protocol, an annual report should be sufficient.

That really will lessen the regulatory burden.

[Slide.]

Some general conclusions from our two

days--and we've heard the first one several

times--general principles are good, but case

studies are needed to facilitate understanding.

That came up time and time again. Case studies

should cover all industries; for example, dosage

form, API, pioneer and generic.

The committee expressed concern on what

appears to be understaffing in OGD.

[Slide.]

Failure Mode &Effect Analysis can be

linked with risk-based decision-making wherein the

results feed into decision trees; training and

education of both regulators and the industry in

the new approaches is going to be key; historical

inconsistency in regulator findings may limit the

utility of surveys. In the past, you know, not all

investigators have investigated in the same manner,

so it's difficult to compare results.

And that's the end of my presentation. I

thank you for your attention, and would be happy to

address any comments, now or later.

CHAIRMAN KIBBE: Are there any questions

for Judy?

DR. SINGPURWALLA: I have some comments,

but I probably would wait until all the

presentations are over, and then make comments.

Would that be acceptable?

CHAIRMAN KIBBE: Whichever way you want to

do it, as long as it's within one of the two tails

of the Bayesian distribution we're all right.

[Laughter.]

DR. SINGPURWALLA: You are confused, Mr.

Chairman. [Laughs.]

CHAIRMAN KIBBE: On a regular basis.

[Laughter.]

You had a question?

DR. MORRIS: Actually, just one comment to

add to what you'd said, Judy, about the Georgetown

study.

I think they had made sort of a plea that

the reason that they hadn't been able to go to the

Indian and Chinese manufacturers was strictly a

resource issue. It wasn't that they had ignored

that as an area of concern.

DR. BOEHLERT: Ken, thank you for that

clarification.

CHAIRMAN KIBBE: Go ahead.

DR. KOCH: I guess, looking around on the

schedule, I'm not sure if we're going to talk any

about training. You mentioned it in several

different ways: the continuation, the inclusion of

industry, etcetera. But will that come up as a

discussion topic at some point?

DR. HUSSAIN: Not in this meeting. I

think we will eventually bring that back at some

other meetings, though.

MS. WINKLE: Actually, when I talk about

some of the organizational gaps I'm going to bring

up training as part of that gap. So if you want to

comment then, it would be fine.

CHAIRMAN KIBBE: Anybody else?

DR. SINGPURWALLA: Well, maybe I'll speak

now. I just--we--this is a question more to

Ajaz--about case studies and specifics.

We've been through many sessions of the

Manufacturing Subcommittee meetings. Has there

been any concrete plan made to start seriously

undertaking some case studies? And, if so, would

you be kind enough to let me know?

DR. HUSSAIN: Yes. Dr. Boehlert's

presentation to this committee--she's the chair of

the subcommittee--and the decision was made to form

a working group under that. And after this meeting

we'll start populating that working group and

create a working group under that committee to

start addressing that.

In addition to that, I think we're also

looking at other parallel tracks to create case

studies. One such case study has just started to

take shape, with Ken Morris, and then Purdue is

working with our reviewers to actually develop a

case study also.

So we hope in the next several months we

will have examples and case studies to outline the

framework.

CHAIRMAN KIBBE: Anything else?

DR. SINGPURWALLA: Yeah. One other

matter. After the subcommittee meeting, some

minutes were released, and I had made some comments

about the minutes. I did not receive an update of

the minutes--update of the revision.

Has--is there any reason for that?

Because the normal protocol--the normal protocol is

you put out the minutes, people give comments on

the minutes. You either incorporate those

comments--and if you don't, you let us know why.

And then you issue a final document of the minutes.

And then the entire committee, or whoever it is,

says "Yes, we go along with these minutes." And

they should become a part of the record.

I was wondering if this was done, because

I did not have access to that.

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

fair.

CHAIRMAN KIBBE: Okay?

DR. WEBBER: One quick question.

CHAIRMAN KIBBE: Go ahead.

DR. WEBBER: That will be okay?

You mentioned the pharmaceutical

manufacture and research study, and I'm looking at

the dates there. It seemed like it was fall of

2003. And I just wanted to confirm whether or

not--that was during the period of transition of

products from CBER to CDER. Were our products in

OBP--the biotech products that transitioned

over--were they--are they completed now within

CDER? Or are they considered part of the CBER.

DR. BOEHLERT: Yes, I think Ajaz

DR. HUSSAIN: No, Keith, that's

not--that's an external study that's focusing on

all of manufacturing. So all products--CDER and

CBER--products are under. It doesn't matter

where--

DR. WEBBER: Where they were--just all

products--okay. Thank you.

CHAIRMAN KIBBE: Anybody else? Good, that

will keep us pretty well on schedule.

I have now a "Parametric Tolerance

Interval Test for Dose-content Uniformity"--Robert

O'Neill.

Parametric Tolerance Interval Test for

Dose-content Uniformity

DR. O'NEILL: Magic button. There we go.

Good morning. I'm Bob O'Neill. I came

before at the last meeting--I was asked to be the

chair of a working group that you all blessed, and

I'm here to give you an update on where we are on

this issue of addressing the specifications for the

delivered dose--uniformity of inhaled nasal drug

products.

[Slide.]

Just to refresh your memory, the folks on

the left-hand side are the FDA folks who are part

of this working group, and some are more active

than others--some of them, in blue, are part of a

sub-group that has been put together that is

working on more specific issues that I'll address

in a moment; and the folks on the right--Michael

Golden, in particular, who is a colleague on the

industry side, who is coordinating our efforts in

that area.

[Slide.]

The objective of this working group--as

you probably know--is to develop a mutually

acceptable standard delivered dose uniformity

specification--that's both the test and the

acceptance criteria--for the orally inhaled nasal

drug products, with a proposal to come back to you

all. And that's the time frame that I'm talking

about right now.

So there's been a lot of work going on in

the past few months, and that's what I just wanted

to bring you up on.

[Slide.]

There have been three full working group

meetings, where the folks on that previous

slide--and some others--have come together at FDA

for two, three hour sessions, and to go through

information that has been presented to--primarily

by the industry--to us to chew on. And we have

spent a lot of time internally talking to

ourselves, and coming up with some additional

issues and proposals, and we met the last time with

the working group, and FDA had a proposal that we

felt was moving in the direction of what everybody

wanted.

Subsequently, there's been a working group

that will now be chewing on what was presented to

the last joint meeting, and they're meeting

November 4 th. And

there's a lot of statistical

issues; there's data analysis issues. But I think

what we're all on the same page with regard to is

that the need to reassess the FDA--the past FDA

recommendations, and I think there's--as we

indicated the last time we briefed you--that the

parametric tolerance interval approach is an

improvement in a value-added type of testing

strategy, over and above the zero tolerance

interval strategy that's been used for awhile.

So the next steps are the following.

[Slide.]

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?

[Slide.]

And so we're in the stages of looking at

the statistical performance characteristics of

that, and we hope that the working group will

evaluate this proposal in more detail, and come

back to you in the spring of 2005, with a final

recommendation to discuss with you. So that's sort

of the game plan.

And Michael Golden is here. He's my

colleague on the working group from the industry

side, and we'd both be willing to take any

questions if you have them.

CHAIRMAN KIBBE: Questions?

Nozer?

DR. SINGPURWALLA: Well, I guess Jurgen's

hand went up before mine. So--

DR. VENITZ: Okay, let me go first.

DR. SINGPURWALLA: He may ask the same

question.

DR. VENITZ: Maybe.

In your draft proposal--or what you're

considering so far to be a draft proposal--

DR. O'NEILL: Yes.

DR. VENITZ: --are you considering the

intended use when you look at statistical

characteristics of your operating curve, for

example?

DR. O'NEILL: Well, certainly that has

been discussed, both from an emergency--a

one-time-only, a chronic use, a medical risk

involved--

DR. VENITZ: Right.

DR. O'NEILL: --so, certainly, Dr.

Chowdhury is involved, and others are involved, in

considering this issue. So--

DR. VENITZ: And I would encourage you to

do that because, obviously, in my mind, it is

different whether you're looking at inhaled

insulin--

DR. O'NEILL: Right.

DR. VENITZ: --and you're looking at the

performance of a drug product, versus a beta

agonist, for example.

DR. O'NEILL: Yes.

CHAIRMAN KIBBE: Go ahead.

DR. SINGPURWALLA: Dr. O'Neill, we had

this discussion when you made the first

presentation, so I'm going to back--

DR. O'NEILL: Right.

DR. SINGPURWALLA: --to the same point

again.

I agree with you that tolerance interval

approach is to be preferred to the zero tolerance,

or something to that effect.

DR. O'NEILL: Right.

DR. SINGPURWALLA: But in your description

of the next steps, you have talked about operating

characteristic curves, and performance

characteristic curves. Of course those are not

indicative of any Bayesian thinking towards this

particular area. And while you're in the process

of formulating your plans, I strongly encourage you

to incorporate that into your thinking. You may

not want to adopt towards the end, but at least it

should be evaluated.

And the second comment I'd like to make is

that--and I'm certainly not volunteering and, if

asked, I would refuse--the working group members

consists of individuals from the FDA and from the

pharmaceutical industry. It would be good to have

some neutral people on the working group--people

from industry or people from government agencies

that are not connected with the FDA, so that you

get some sense of balance. Otherwise, it seems to

be--you know, it seems to be a self-serving group.

So I would like to encourage you to expand

your membership.

DR. O'NEILL: Yeah.

DR. SINGPURWALLA: And I want to

emphasize: I'm not available.

DR. O'NEILL: Well--no, the last point--I

mean, this is hard work. The people who are doing

this work are spending a lot of time, and there's a

lot of evaluation--a lot of data evaluation going

on. We were presented with information from the

IPAC group that consisted of a huge database.

And one could look at, well, how much time

do you want to spend on evaluating a huge database?

I mean, it's an electronic database, and lots of

different--and where I'm going to on this is the

Bayesian argument. The Bayesian argument is very

much a sensible argument--or a sensible framework

when you can look at empirical data that allows you

to feel pretty comfortable about what your priors

are, and what the distribution of information is.

That is not always accessible to the agency. It

may be accessible to a sponsor.

So the strategy of being in-process and

out-of-process, and being in control, and what's

acceptable variability is very much--very much--a

Bayesian framework, and very much within the

context of how you may want to be looking at this,

in terms of looking at in-process validation, as

well as acceptance criteria.

The extent to which that carries over into

the type of testing we have to be very clear about.

And it's--at the point we're at right now, we're

essentially most interesting, or most concerned

about how far out can you push the acceptance curve

so that it has a proper balance between accepting

and rejecting--particularly when we don't have, or

no one can show us empirically, what the

distribution of off-target means are, for example.

How far away from 100 percent does the mean have to

be before you want to maybe ratchet in this

operating characteristic curve?

So, I certainly could see the value to

external folks' helping us out. The more the

better. And I believe that this is a

time-intensive effort. And just as, you know, you

would not like to volunteer, we would have to go

and find folks who could invest the amount of time

that is necessary, in the time frame that we're

talking about, so we can get where we want to be.

That's not to say that more brains are

not--and independent brains--are--but this is--I

would say we're pretty much trying to meet in the

middle of this whole thing with resources that

we've thrown out it that we feel are fair and

objective.

DR. SINGPURWALLA: Let me clarify.

I'm not volunteering because I'm making

the suggestion.

DR. O'NEILL: Yes. Yes.

DR. SINGPURWALLA: And that's the proper

thing to do.

What I would like to encourage you is to

involve at least two Bayesian's on your group--two,

because they need support--

[Laughter.]

--from the point of view of simply guiding

a framework, or guiding the concept, and things

like that, rather than get involved with the

nitty-gritty.

And the two individuals--or perhaps

more--need not come from two stratified groups.

They should come from somewhere else.

So I'm making two suggestions: one is to

have people with expertise in Bayesian statistics

involved, and to have people from outside these two

communities also involved--perhaps in a limited

way. This will give you a broader perspective and

will not subject you to criticism two years down

the line.

And that's the suggestion.

DR. O'NEILL: Okay.

CHAIRMAN KIBBE: Anybody else?

Ajaz, do you have something to say?

Reaching for your mike?

DR. HUSSAIN: I think the point I was

going to make was, I think, at this point in time

it's going to be difficult to add more people to

the working group. But the point is well taken

that I think you do need to bring that perspective.

And I'm hoping this Advisory Committee, and some

other format, could be sufficient to sort of bring

that framework for that--that perspective to bear

on the progress of this working group.

CHAIRMAN KIBBE: No one else?

Thank you Dr. O'Neill. Appreciated your

presentation.

Dr. Ajaz, perhaps you could begin our next

topic, and then we can take a break, because we're

running slightly ahead, and it will give us a

little flexibility as we move on.

And so we're going to talk about Critical

Path Initiative.

The Critical Path Initiative--Challenges

and Opportunities

Topic Introduction and OPS Perspective

DR. HUSSAIN: Yes, I think I'm pleased

that we have more time, because many of the

presentations here are very lengthy

presentations--[laughs]--including mine.

I'd like to sort of introduce the topic of

Critical Path Initiative--the challenges and

opportunities.

[Slide.]

The goals that we have for the fiscal year

2005--and the initiatives, and the strategic goals

at FDA level and the Department level are shown on

this slide. And the slide is from the "State of

CDER" address by Steve Galson and Doug

Throckmorton.

Today, our discussions will primarily

focus on the Critical Path, the cGMP initiative,

focused on risk management and innovation. And the

goal at the Department level is to increase science

enterprise research. But also, I think the follow

on biologics, follow-on proteins, I think is

interconnected to all of these discussions.

[Slide.]

My focus today is to introduce you to the

topic of Critical Path, and also outline a proposal

that we are contemplating at the OPS immediate

office level as an umbrella proposal for all the

discussions you'll hear today by scientists from

different parts of the Office of Pharmaceutical

Science.

But at the same time, some of the

discussions in here also impact, say,

counter-terrorism effort and other efforts that are

ongoing. And not all projects that we'll discuss

are Critical Path projects today.

[Slide.]

What is Critical Path? It's a serious

attempt to examine and improve the techniques and

methods used to evaluate the safety, efficacy and

quality of medical products as they move from

product selection and design to mass manufacture.

[Slide.]

In the continuum of drug discovery and

development, you really go from basic research to

prototype design or discovery, to preclinical

development, clinical development, to an FDA filing

and approval. You have a focused attempt, say, for

example, at the National Institutes of Health on

translational research. The Critical Path research

does overlap with some of the aspects of the NIH

translational research, but it covers predominantly

the drug development aspects of the entire

sequence.

In our White Paper, we identified some of

the challenges for Critical Path. The drug

development process--the "Critical Path" is

becoming a serious bottleneck to delivery of new

medical products.

[Slide.]

Our research and development spending has

been exponentially increasing. And as an index of

1993, you can see the exponential increase from

1993 to the current 10 years--increase in both

private and public spending on research.

[Slide.]

However, new product submissions have

remained flat--or, some would argue, are on the

decline.

[Slide.]

Why is FDA concerned? FDA's mission is

not only to protect but also to advance public

health by improving availability of safe and

effective new medical products.

[Slide.]

FDA has a unique role in addressing the

problem. FDA scientists are involved in reviewing

during product development--they see the successes,

failures and missed opportunities. FDA is not a

competitor, and can serve as a crucial convening

and coordinating role for consensus development

between industry, academia and government. FDA

sets standards that innovators must meet. New

knowledge and applied science tools needed not only

by the innovators must also be incorporated into

the agency's review process and policy.

[Slide.]

The challenge is how do we proceed? It

should be a science-driven and shared effort,

drawing on available data, need to target specific,

deliverable projects that will improve drug

development efficiency. It cannot just be an FDA

effort. We can identify problems and propose

solutions. Solutions themselves require efforts of

all stakeholders. We have issued a Federal

stakeholders, and we have received a number of

suggestions, and we are working through those

suggestions as we formulate our strategy for a

Critical Path research program.

[Slide.]

This is a significant initiative, and the

Department of Health and Human Services' Medical

Technologies Innovation Taskforce is providing

broad leadership. Dr. Lester Crawford is chair of

this Medical Technologies Innovation Taskforce, and

it includes CDC, CMS, NIH and FDA.

This taskforce is working on finding

additional funding to meet the needs of the

Critical Path program. It is meeting with external

stakeholders to identify opportunities, enlist

allies, and so forth.

[Slide.]

In summary, I think from a Critical Path

perspective, the present state of drug development

is not sustainable. We believe FDA must lead

efforts to question any assumptions that limit or

slow new product development: are these

assumptions justified? Are there more efficient

alternatives? If so, why are the alternatives not

being utilized?

[Slide.]

As we sort of focus on the discussions

today, I'll remind you that the Office of

Pharmaceutical Science is predominantly focused on

one aspect: Chemistry Manufacturing Control--or

the initialization dimension. But the Office of

Pharmaceutical Science also supports many other

aspects, from pharmacology, toxicology to clinical

pharmacology research and so forth. So, although

our review responsibilities predominantly are on

the quality side, our research programs are

interconnected to every aspect of the drug

development process.

So you will hear presentations coming from

all aspects--all three dimensions of the Critical

Path.

[Slide.]

The three dimensions are: assessment of

safety; how to predict if a potential product will

be harmful; assessing efficacy; how to determine if

a potential product will have medical benefit; and,

finally, industrialization--how to manufacture a

product at commercial scale with consistently high

quality.

[Slide.]

Our discussions, to a large degree, have

focused on the third dimension. And I think you

will see, today, many of the projects within OPS

that also impact the other two dimensions.

[Slide.]

In our White Paper, we defined the three

dimensions and the connections to the Critical Path

as follows: safety, medical utility, and

industrialization. An every aspect--every box that

is there has a need for improvement and research to

support that improvement.

Applied science is needed to better

evaluate and predict the three key dimensions on

the Critical Path development.

I just returned from Europe--spending a

week there last week--and with respect to the

industrialization dimension, I came back somewhat

depressed. The amazing work I saw coming out of

the University of Cambridge in the area of

industrialization of pharmaceuticals--the approach

to new technology, in terms of manufacturing, novel

drug delivery systems and manufacturing processes

itself, was astounding. I don't see any of that in

the U.S.

So my concern is, much of the R&D and

innovation is going to come from Europe and Japan,

probably. And unless we really improve our

infrastructure, we are going to be lagging behind

in a very significant way. And I think that

concern keeps growing on me, and I think I do want

to sort of emphasize that.

[Slide.]

Office of Pharmaceutical Science programs

and Critical Path Initiative--the discussion today

is to seek input from you and advice, on aligning

and prioritizing current OPS regulatory assessment

and research programs, with the goals and objects

of the Critical Path Initiative. Please note that

not all research programs and laboratory programs

are intended to focus on "Critical Path." There

are equally important other aspects--bio-terrorism

and so forth--which may not be considered as part

of the Critical Path Initiative, but they're

equally important. So all of our programs and

projects are not likely--or should not be part of

the Critical Path. There are aspects. So you have

to distinguish that.

We hope that you'll help us identify gaps

in our current program; identify opportunities for

addressing the needs identified by the Critical

Path Initiative.

[Slide.]

What I'd like to do today is--before I

introduce Keith Webber--he took the lead on putting

this program together--I'll share with you an OPS

immediate office project that Helen and I have been

developing. These are our initial thoughts of how

an umbrella project, within the OPS office, will

help to sort of bring all of this together.

So let me share some of our thoughts on a

Critical Path project that OPS--Helen and I are

sort of developing right now.

An immediate need in OPS is to ensure

appropriate support of general drugs--the growing

volume and complexity of applications. That's the

challenge. You saw the numbers increasing.

In the New Drug Chemistry, the new

paradigm for review assessment and efforts to

support innovation and continuous improvement goals

of the cGMP initiative--Office of New Drug

Chemistry has taken the lead to be the first office

to sort of implement all of this. So they have

significant need for support.

Biotechnology products--complete

integration into OPS, and the evolving concept of

"follow-on protein products"--although I have put

follow-on protein products under this, we don't

know exactly how the regulatory process will

evolve. It could be--let's say, a work in

progress.

And, clearly, alignment of research

programs in OPS to meet our goals and objectives.

[Slide.]

So what are our thought processes, from

our immediate office perspective? To develop a

common regulatory decision framework for addressing

scientific uncertainty in the context of complexity

of products and manufacturing processes in the

Offices of New Drug Chemistry, Biotechnology

Products, and General Drugs.

Regardless of the regulatory process,

regardless of regulatory submission strategies and

so forth, we believe we need a common regulatory

decision framework--a scientific framework--for

addressing the challenges.

[Slide.]

What are the motivations here?

Uncertainty--whether it's variability or knowledge

uncertainty--and complexity are two important

elements of risk-based regulatory decisions. A

common scientific framework, irrespective of the

regulatory path or process for these products, will

provide a basis for efficient and effective policy

development and regulatory assessment to ensure

timely availability of these products.

That's the overreaching OPS goal, is to

provide the common framework. Although the

submission strategies might be different, the

science should not be different.

[Slide.]

How are we trying to approach this

challenge? We know that there are no good methods

available for developing a standard approach for

addressing uncertainty. That means you need

different approaches for different assessment

situations. [Laughs.] All right, let me complete my

thoughts.

So what we are thinking about--a decision

framework for selecting an approach for addressing

uncertainty over the life cycle of products is what

is needed. So you may have different approaches

and so forth, but a common decision framework will

help us identify the right approach.

[Slide.]

Project 1 is to create an "As Is"

regulatory decision process map for the Office of

New Drug Chemistry, Office of Biotechnology

Products, and Office of Generic Drugs. Much of

this work will be done through a contract--we plan

to have a contractor come in and work with us on

some of these things.

We think a representative sample of

product applications could be selected for mapping

the scientific decision process in the three

offices.

[Slide.]

Determine regulatory processes efficiency

and effectiveness, using metrics similar to that

what we have learned from the manufacturing

initiative; and identify and compare critical

regulatory review decision points and criteria in

the three different offices; evaluate, correlate

and/or establish causal links between review

process efficiency metrics and critical decisions

criteria, and available information in the

submission--that's the mapping process; and, also,

evaluate the role of reviewer training and

experience, and how it bears on some of these

decisions.

[Slide.]

Summarize available information on selected

products; collect and describe product and

manufacturing process complexity, post-approval

change history, and compliance history--including,

when possible, adverse event reports that come

through MedWatch and other databases; describe

product and process complexity and uncertainty with

respect to current scientific knowledge;

information available in submissions; reviewer

expert opinions and perceptions; and, if feasible

or possible, seek similar information from the

sponsors or company scientists on these same

products that we might select.

[Slide.]

What we hope to do is aim for the

following deliverables: organize Science Rounds

within our office to discuss and debate the "As Is"

process map, and the knowledge gained from the

study; identify "best regulatory practices" and

opportunities for improvement--these may include

opportunities for improvement of filling the

knowledge gap, develop a research agenda for all

OPS laboratories based on what we learn.

What is, I think, missing today is a

common scientific vocabulary. There's a need to

develop a common scientific vocabulary to describe

uncertainty and complexity. There can be--each

come from a very different perspective right now.

Develop an ideal scientific process map

for addressing uncertainty and complexity; adapt an

ideal scientific process map to meet the different

regulatory processes.

In the following--I think the three

projects that we're thinking about are not actually

fully independent. They're all connected together.

[Slide.]

Project 2 is to sort of focus on a systems

approach. We believe that without a systems

approach to the entire regulatory process--that is

from IND to NDA--Phase IV commitments and cGMP

inspection, the broad FDA goals under the cGMP and

the Critical Path Initiatives will not really be

realized.

[Slide.]

So the team approach and the systems

perspective that evolved under the cGMP Initiative

only addressed a part of the pharmaceutical quality

system. Quality by design and process

understanding to a large extent is achieved in the

research and development organization.

Pharmaceutical product development is a complex and

a creative design process that involves many

factors, many unknowns, many disciplines, many

decision-makers, and has multiple iterations in the

long life-cycle time.

So we have to treat it as a complex system

optimization problem.

[Slide.]

Significant uncertainty is created when a

particular disciplinary design team must try to

connect their subsystem to another disciplinary

subsystem--for example, clinical versus chemistry,

or CMC to GMP. When you bring those connections,

there's significant uncertainty.

Each subsystem can have its own goals and

constraints that must be satisfied along with the

system-level goals and constraints. It is possible

that goals of one subsystem may not necessarily be

satisfactory from the view of other subsystem and

design variables in one subsystem may be controlled

by another disciplinary subsystem. Impurities is a

good example. Pharmtox, CMC, and how you bring

that together.

[Slide.]

So the Project 2 proposal that we're

developing is to use ICH Q8 as the bridge between

the cGMP Initiative and the rest of the regulatory

system, and to develop a knowledge management

system to ensure appropriate connectivity and

synergy between all regulatory disciplines. Can

that be done? I mean, that's the feasibility

project that we are trying to develop. So--connect

Pharm/Tox, Clinical, Clinical Pharmacology,

Biopharmaceutics, CMC, Compliance all together.

[Slide.]

The current thinking is to approach this

problem as connecting every section within the ICH

Q8 CTD-Q, within the same document, but to all

other sections in an NDA, in some way or form. For

example, each section within the P2 can have an

impact on the other P2 sections and, similarly,

other sections of a submission and to cGMP.

By recognizing this as a complex design

system that involves multiple attributes, goals,

constraints, multidisciplinary design teams,

different levels of uncertainty, risk tolerances,

etcetera, we wish to find opportunities to identify

robust designs and design space that provides a

sound basis for risk assessment and mitigation.

So this would be a scientific framework.

It was a regulatory tool that could come out of

this. And with the case studies and everything

coming together, this might be a way to bring and

connect all the dots.

[Slide.]

What we have been looking out is outside

pharmaceuticals. We believe that a significant

body of knowledge exists. Example, in mechanical

engineering, as it applies to the design of

aircrafts, that addresses some of these challenging

points that we have discussed. These are three

examples that I have selected as just illustrative

examples of how multidisciplinary optimization

methods and system-level problem solving tools can

be thought about in the drug context.

[Slide.]

Just to illustrate this point, let me

create an example here. The applicability of

multidisciplinary optimization methods for solving

system-level problems and decision trade-offs will

be explored in an NDA review process. That's what

we're proposing.

For example, in the Common Technical

Document for Quality--the P2 section, which is what

ICH Q8 will define--critical drug substance

variables that need to be considered in section

2.2.1, which is "Formulation Development" are

described in section 2.1.1. So there's a drug

substance, and there's a formulation. They're two

different sections.

Information for "Drug Substance," has a

bearing on that of the "Formulation Development."

So how do you connect the two together?

For example, the current language in ICH

Q8 for "Drug Substance," states: "Key

physicochemical and biological characteristics of

the drug substance that can influence the

performance of the drug product and its

manufacturability should be identified and

discussed."

So that's describing the information

content in section 2.1.1. that we will hopefully

receive whene ICH Q8 is done. So how does this

have a bearing on the "Formulation Development"

section?

[Slide.]

I'll skip this and just show you a figure.

[Slide.]

You have the API--or drug substance

manufacturing process. The X(1.1) is the design

variable; the f(1.1) is the objective function to

be addressed; and the g(1.1) is the constraint for

that manufacturing process that delivers the drug

substance. Okay?

Since this is not part of ICH Q8, what

will be part of ICH Q8 is section 2.1.1., which

will identify what are the critical variables for

the drug substance, as they relate to the

formulation aspect. But that becomes the input for

what--how it connects to the "Formulation

Development" aspect. And that link is through a

linking variable.

Since my means and standard deviations

have become finger-pointing and so

forth--[laughs]--so you know--you have a design

variable, you have a linking variable, you have an

objective function, you have constraints around

which you define your design space. You have mean

objective function--that's your target. You have a

standard deveiation that you sort of bring to bear

on that. And deviation range of the design

solution, or the design space.

So all of this sort of has to come

together for this to be meaningfully connected.

And, for example, if you start with a simple design

of experiment, you may have mathematical models,

which are empirical, but then they provide that

connectivity. So it's a start of a very formal,

rigorous approach to dealing with uncertainty,

knowledge gaps and complexity.

So this might be a useful concept. So

that's the process right now, to see whether this

could be a feasibility project that we could do.

[Slide.]

So the potential deliverables of using

this approach could be significant. Since we are

moving towards electronic submissions, in

conjunction with electronic submissions, this

project can potentially provide a means to link

multidisciplinary information to imporve regulatory

decision--that is, clinical relevance to CMC

specifications. We may not all have all that

information, but the links--the structure--will be

there as we grow, as we improve our knowledge base,

or will it be refined, the links could get

populated, and this might be an approach for

knowledge management within the agency.

Creating a means for electronic review

template and collaboration with many different

disciplines; provide a ocmmon vocabulary for

interdisciplinary collaboration; create an

objective institutional memory and knowledge base;

a tool for new reviewer training; a tool for FDA's

quality system--and, clearly, it can help us

connect cGMP Initiative to the Critical Path

Initiative.

So that's the project that we hope to

develop. We really want to get some feedback from

you, and develop this as a project under the

Critical Path Initiative.

[Slide.]

But the third aspect of this--it all could

happen in parallel--explore the feasibility of a

quantitative Bayesian approach for addressing

uncertainty over the life cycle of a product. The

most common tool for quantifying uncertainty is

probability. The frequentists--the classical

statisticians--define probability as "limiting

frequency, which applies only if one can identify a

sample of independent, identically distributed

observation of the phenomenon of interest."

The Bayesian approach looks upon the

concept of probability as a degree of belief, and

includes statistical data, physical models and

expert opinions, and it also provides a method for

updating probabilities when new data are

introduced.

The Bayesian approach may proivde a more

comprehensive approach for regulatory decision

process in dealing with CMC uncertainty over the

life cycle of a product. It may also provide a

means to accommodate expert opinions.

And I think there's a connection here.

The evolving CMC review process may be a means to

incorporate expert opinions. And I think that is a

significant opportunity.

Using the information collected in Project

1--that I described--you would seek to develop a

quantitative Bayesian approach for risk-based

regulatory CMC decision in OPS.

So that would be a project that will run

in parallel to the other two approaches that we are

moving forward.

[Slide.]

So, I'll stop my presentation here with

sort of summarizing, in the sense--I think OPS,

from its goals and objectives, has to have an

overreaching project that sort of connects all the

dots together. And the proposal--the first one

clearly is a process map--"As Is" and so forth.

But the two others are feasibility projects that we

want to look at the Bayesian approach and a complex

system optimization problem.

The knowledge exists outside. It's simply

adapting and adopting it in our context.

What you'll hear--after the break, I

think. Or--unless you want to start earlier--after

the break, is other immediate office projects;

Office of Biotechnology projects, Office of New

Drug Chemistry project, Office of Generic Drug

projects on Critical Path, and Office of Testing

and Research.

What we have done is Keith Webber will

introduce the reset of the talks. You will hear

each group's perspective. And we have requested

Jerry Collins to come back and sort of

summarize--after his talk on the Critical Path--the

entire Critical Path Initiative from an OPS

perspective and pose questions to you.

And we have also invited Professor Vince

Lee, who is now part of FDA--who used to be the

chair of this committee--who has been with agency

for almost a year now, to come with his perspective

on how--what are challenges he sees. So you will

hear sort of presentations and some opinions from

people who have been at the agency and been looking

at this challenge for some time.

So, again, the discussion today is to seek

input and advice on ACPS; on how to align, identify

gaps, and identify opportunities.

I'll stop here and entertain questions on

my part of the presentation.

CHAIRMAN KIBBE: Are there any questions?

DR. SINGPURWALLA: I have comments.

CHAIRMAN KIBBE: Okay. Thank you.

DR. SINGPURWALLA: I just--what you say is

music to my ears. You have good vision about some

of the things you want to do. But I think it's now

time that the dance should begin.

We should get back--take concrete problems

and address them. I've said this before.

But let me just make some specific

comments on some of the things you've said. And,

of course, I'm going to question some of the things

you said.

The first argumetn I want to make on your

slide on page 7, about efficacy and safety:

generally, those tend to be adversarial. Drugs

that give you benefit may have side effects. So

the important issue is to do a trade-off. For that

you need to talk about assessing utilities: what

is the utility of the benefit, and what is the

dis-utility of the harm? That's a part of the

whole package of thinking about these problems, and

I encourage you to look into it.

Now, I take strong objection to some of

the things you have said. You have distinguished

uncertainty into stochastic and epistemic. I have

seen that distinction before. I claim it's totally

unnecessary. Uncertainty is uncertainty, and one

doesn't--one should not pay much attention to the

source of the uncertainty--

DR. HUSSAIN: Right.

DR. SINGPURWALLA: --whether it is

regulated allatoire uncertainty, or epistemic, does

not matter.

CHAIRMAN KIBBE: Right.

DR. SINGPURWALLA: The Bayesian approach

does not distinguish between the two. And since

you've been talking about it, I think--

You also say that there are no good

methods for devleoping standard approach for

addresing uncertainty. I think that's the wrong

slide to put up. That's liable to do more harm

than good.

DR. HUSSAIN: Okay.

DR. SINGPURWALLA: There are methods

available. So I would not encourage you to put it.

And the other thing is: I don't like your

linking uncertainty and complexity. They're two

different issues.

And you also say that there is no common

scientific vocabulary. Well, I claim there is a

common scientific vocabulary, and that is

probability.

Now, as far as recommendations are

concerned: I'd like to suggest--and, again, I'm

not volunteering since I'm making the

suggestion--that you have your people exposed to a

tutorial on Bayesian methods and Bayesian ideas, so

that you get a better appreciation of what it's all

about. And the best way to do this is to take a

simple example and work through it; work through

your expert opinion notions that you're saying.

Go through an example, and you'll get a

better appreciation of what it's all about. And

once you get that appreciation, you'll be tempted

to remove some of the other things you've said.

Those are just comments. Thank you.

DR. HUSSAIN: No--the point's well taken.

And we actually have a project right now with the

University of Iowa, looking at our stability data

from a Bayesian perspective. So we're just

starting to put a real-life example on that. So

that's--

With regard to the utility, Jurgen and the

Clinical Pharmacology Subcommittee has been sort of

bringing that up. So we will connect to the

Clinical Pharmacology group.

Jurgen, do you want to say anything about

that?

DR. VENITZ: [Off mike.] Well, other than

the fact that--other than the fact that we're

discussing it. It is a controversial issue,

because you're really trying to map, then, a lot of

different things into a uniform scale. Personally,

I don't see an alternative, and I think it's

already done. We're just doing it intuitively, as

opposed to expressedly.

So it is being discussed. We have to see

where it goes.

DR. HUSSAIN: And, regarding, I think, the

common vocabulary, I think it's a common vocabulary

in the context of when we speak from a pharmacist

to a chemist to an engineer--we have very different

interpretation--that's what was referred to.

DR. SINGPURWALLA: That's why you need a

tutorial.

DR. HUSSAIN: That's exactly--

DR. SINGPURWALLA: Put people together.

Because about 15, 20 years ago, the Nuclear

Regulatory Commission was facing similar problems.

And one of the things they did is they had lots of

tutorials to get everyone on board, talking the

same language. Otherwise, you'll have a doctor

talk to an engineer, and those two talking to a

lawyer--and you know what can happen.

[Laughter.]

VOICE: [Off mike.] Lawsuits.

CHAIRMAN KIBBE: Another question?

DR. KOCH: I guess, just to build on the

last comment--when you get into all those

multidisciplinary functions--and particular when

the ICH Q8 is going to serve as a group, together

with the implementing the cGMPs--there's a couple

of organizations out there I think could serve as

very valuable resources. One we've heard about a

couple times today in the ASTM 55, as a body to

help at least standardize the terminology. And the

other one is the ISPE, which could serve as a

multidisciplinary conduit that, working together

with ICH, could probably facilitate some of the

multidisciplinary issues.

DR. HUSSAIN: I think we do plan on

extensive training and team building and coming on

the same page. If you look at the PAT and the

manufacturing signs White Paper that we issued, we

actually laid out a lot of these things in there,

including the role of ISPE, ASTM, PQRI, and so

forth.

So we have been thinking about this in

that context, and at the ICS meeting in

Yokahama--on Wednesday, I think, the date is

set--we will be updating on that. So I'll get a

chance to talk about ASTM to ICH in Yokahama,

Japan, also.

So, we're aligning everything together.

So that's happening.

There was one point that I wanted to

respond to: the reason for keeping uncertainty, in

terms of variability in knowledge--keeping the

distinction, at least as we think about this,

was--and the link to complexity, also--clearly,

complexity and uncertainty are two independent

things. But, unfortunately--well, the challenge we

face is this--in the sense we have a very complex

product. We have simple products--within the same

office, in OPS and different regions. Yet today, I

think, from a variability perspective, we're not

very sophisticated in how do we deal with

variability.

And, for example, in our manufacturing

science White Paper, we don't even deal with

variability of our dissolution test method. We

don't even know how to handle it. So we have

challenges today where simple variability--we don't

have a good handle on.

So that was the reason for keeping

variability and knowledge-based uncertainty on the

table.

CHAIRMAN KIBBE: Ken?

DR. MORRIS: Just a quick question: on

your identification of the gaps in the current

programs, are you thinking more in terms of

technical gaps--as in science that needs to be

done? As opposed to logistical gaps within--

DR. HUSSAIN: Both. Both.

DR. MORRIS: So, with respect to the

scientific gaps, are thinking, then, to take it one

level more--basically, are you talking more about

new science that needs to be created? Or science

that needs to be communicated more

effectively--within the agency?

DR. HUSSAIN: Well, I think the immediate

need would be to communicate the existing science

and bring all the existing knowledge to bear on

that. And, clearly, in the long term there are

fundamental issues--and most of the new science

would be needed. So I think it's an issue of

timing.

DR. MORRIS: Thank you.

CHAIRMAN KIBBE: Anybody else? Nozer, you

wanted to--

DR. SINGPURWALLA: No, I just wanted to

say that this distinction between allatoire and

epistemic has been artifically created by

frequentist statisticians. And Bayesians don't buy

it.

DR. SELASSIE: I have a question.

CHAIRMAN KIBBE: Yes, please.

DR. SELASSIE: You know, in your graph on

R&D spending--has there ever been a breakdown in

100

how much of that spending can be attributed to the

"R" and how much to the "D?"

DR. HUSSAIN: I don't have that--I'm sure

that information's--I don't have it. So I'm not

aware of it.

DR. SELASSIE: Because would one parallel,

you know, the flatness?

DR. HUSSAIN: One was the public funding;

one was more private funding, so--

DR. SELASSIE: Yes, but they're both going

up.

DR. HUSSAIN: Yes.

DR. SELASSIE: But I'm wonder if, you

know--because you look at your product submissions

are flat. Now, is that because there's not been an

increase in development funding? Or--

DR. HUSSAIN: I don't think so. But I

don't have an answer.

DR. SELASSIE: Yes.

DR. HUSSAIN: So let me say that.

CHAIRMAN KIBBE: Marvin?

DR. MEYER: Ajaz, you don't seem like a

101

depressive kind of guy--

DR. HUSSAIN: [Laughs.]

DR. MEYER: --but you said you were

depressed last week.

DR. HUSSAIN: Yes.

DR. MEYER: Can you give us just a real

short synopsis of where you see Europe doing things

right, and us doing things wrong?

DR. HUSSAIN: [Sighs.] [Laughs.]

No, I mean, again, I'll focus on what I

see happening in Europe--especially in the

U.K.--and how they're translating academic

research--academic finding research--into

entrepreneurial business--in particular in

manufacturing, in particular in dosage form

design--the pharmacy-related ones.

Look at Bradford, particle engineering.

And the one I saw--I saw a beautiful manufacturing

system for coating. Forget coating pans. This is

electrostatic coating; precise, automated, complete

on line, and so forth.

Nothing of that sort is happening

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here--within my domain.

CHAIRMAN KIBBE: We have a couple more

comments, and then we're going to have to take a

break.

Go ahead.

DR. MORRIS: Yes, just to follow up on

that. I think there's--I just came back from

Europe depressed, as well, but I was in

Scandinavia. So maybe that had something to do

with it.

[Laughter.]

Yeah, it's pretty dark up there.

But, in any case, I agree with Ajaz in

that there are a couple of caveats and, in fact, if

you look at our latest hires, they're one from--via

Bradford, another one via Bath. My post-doc is

from Nijmwegin, another post-doc from Roger Davies

Group in the U.K.

And we're not training people--number one.

So, aside from not transferring the technology

effectively we're not training people to do it very

much any more. There are few places--represented

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at the table--that still do it to some degree.

But that stems back to one of your earlier

slides, which is trying to muster NIH and NSF to

fund this sort of research. Because some of you

have been a lot closer to deanships than I. If

there's no overhead money, it doesn't get a very

kind reception. And the fact of the matter is is

we haven't had it.

So, this is--I'll stop here, because this

is my old soapbox. But I lay this at the door, in

part, of NIH and NSF for not recognizing, in the

face of overwhelming data, that there is a crisis

that needs to be address.

On the upside, there are some people in

Europe doing some things--and Japan, as well.

CHAIRMAN KIBBE: Pat, go ahead.

DR. DeLUCA: Just a quick follow up on

that, too. I know from my trips to Europe, too, if

you just look at the colleges--the pharmacy schools

in Europe--I mean, they all have departments of

pharmaceutical technology. I mean you'll be

hard-pressed to find pharmaceutical technology as

104

an area of focus in an American college of pharmacy

now. Certainly you won't see any departments of

pharmaceutical technology.

So I think it's been--and it wasn't that

way 20 years ago. But, I mean, it certainly has

changed, though.

CHAIRMAN KIBBE: Anybody else?

Good--I think we're at a nice break point.

And if we could take perhaps a 10 minute

break--because Ajaz has managed to get us--use up

all of our lead time.

[Laughter.]

And we can get Keith to start his talk at

about 10:22, that would be great.

[Off the record.]

CHAIRMAN KIBBE: 22 minutes after 10 has

arrived, and one way or another we're going to get

back on process.

Dr. Webber, are you prepared to get on

process?

He's on the way to the podium.

Those of you walking around with cakes in

105

your hands, and sodas, you want to sit down.

Nozer. Here we go. Good luck. We gave you 10

minutes to do that.

[Pause.]

You snooze, you lose, as the old saying

goes.

So, Dr. Webber, shall we start our

Strategic Critical Path?

Research Opportunities and Strategic Direction

DR. WEBBER: Okay. I guess we're about

ready to get started on this session, regarding

research activities and our strategic goals for the

Office of Pharmaceutical Science.

I'm Keith Webber, with the Office of Biotechnology

Products. And let me--

[Slide.]

--there we go.

Ajaz went through a very good

presentation, I think, on the Critical Path. And

I'm not going to really address very much about the

Critical Path Initiative itself. But, in my view,

this--I've sort of summarized things into the Drug

106

Development Path, which begins with discovery of

potential targets--or potential new drugs; and then

you have to have a period where one evaluates the

candidates and makes a selection of what candidate

you should carry forward into the pre-clinical

study, where one looks for potential toxicities and

potential efficacies in an indication of interest.

If all goes wlel, one moves into clincal

studies, and if all goes even better, into

commercialization. And then, once you're on the

market, there's always the period of post-approval

manufacturing optimization--or we would like to see

that, from the FDA's perspective, anyway.

And then, often, we get new

indications--we see new indications being developed

for drugs that are on the market. And that

essentially starts the process back up again--often

at the clinical studies stage.

[Slide.]

The--I didn't bring a pointer. Is there a

pointer here?

VOICE: [Off mike.] Just use the mouse.

107

DR. WEBBER: Just use the mouse. Okay.

That will work. Right there is the mouse. Okay.

I guess, historically, FDA interactions;

have occurred primarily ion this area here, from

clinical studies on. Prior to that, we have had

very little influence, I think, until we receive a

submission which contains information regarding the

pre-clinical studies.

But I believe we have opportunities to

have an impact on this entire process in the

future.

Let's see.

Essentially, I guess, sort of the essence

of the Critical Path is the--in my mind--is the

view from empirical versus guided drug development.

And drug development has to be a learning process

in order to make intelligent decisions regarding

such issues such as your candidate selection; what

dosage form you're going to have and what the

formulation should be; in choosing clinical

indications, you need to know what patient

population is going to be the best selection for

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your product. And then when you're evaluating

clinical endpoints, one needs to know which are the

most appropriate endpoints to evaluate in the

clinical studies, and are there surrogate endpoints

that are more appropriate than others, if you can't

look at an endpoint which is directly related to

survival or efficacy in the more normal manner.

And, of course, with adverse event

monitoring, any clinical trial is going to monitor

particular parameters, and you need to have a good

knowledge base in order to understand which adverse

events we should be looking for, and the best way

to evaluate those.

And then, finally, the manufacturing

method certainly is a major concern because that

has to do with the ability to improve the

manufacturing process post-approval and

pre-approval, as well as avoiding issues that can

come up with regard to safety and efficacy of your

product.

[Slide.]

The goal of industry, as well as the

109

agency, I believe, is to establish a knowlege base

and the tools that are necessary to predict the

probable success of any given product, and the

manufacturing methods that are appropriate to it,

and then to foster the development of products that

are going to have a high likelihood of success,

throughout clinical development and on the market.

[Slide.]

Now, for this late morning's presentations

and this afternoon's presentations, we'll be

hearing from a number of groups within OPS. One is

the Informatics and Computational Safety Analysis

staff, which is in--essentially in the immediate

office of the OPS; and then Office of New Drug

Chemistry, Office of Generic Drugs. And the first

three here are the groups that do a lot of

relational and database analyses as part of their

research activities. There are, in some cases,

collaborative research going on with laboratories,

per se. But it's the groups on this--the last two,

the Office of Testing and Research, and Office of

Biotechnology Products, that have actual

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laboratories where research at the bench is going

on.

[Slide.]

Let's see--within OPS's Critical Path

Research, I think we can address--or can address

the issues regarding candidate selection, based

upon an understanding of the structure and activity

of the relationships that we see, and the products

that ocme down the line, as well as what's reported

in the literature.

Dosage form development and evlauation I

think is an important area that we're working in.

Toxicity predictions for products is--we're

amenable to that, so our research can address that

through, again, structure activity-type

relationships and structure-function issues, as

well as knowledge of the impacts that a particular

disease state might have on physiological function

that may lead to toxicities that wouldn't be

present in all populations.

Bioavailability and bioequivalence

predictions are certainly important for all of our

111

products, but particularly for the Office of

General Drugs, they're quite critical. And I think

with regard to the follow-on products as well, it's

a major area of concern.

Metabolism prediction is something that

is, I think, crucial because products, once they

enter the body, as you know, they don't remain in

their initial state. And the metabolism can impact

toxicity, it can impact efficacy, it can impact the

bioavailability and biofluence of the products

themselves.

Immunogenicity is another area that is of

large concern, particularly for protein products.

And there we need to evaluate and understand, not

only the caues of immunogenicity, or the impacts of

various structures in the proteins on

immunogenicity, but also the impact that the

patient population has on immunogenicity; what

impact the indication that's selected can have on

impacts of immunogenicity as a safety concern.

Often, as I mentioned earlier, you have

biomarkers that you're looking at for

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pharmacodynamic parameters, or for surrogate

endpoints. And a good knowledge of the validity of

a particular biomarker, and our ability to evaluate

those, as well as industry's ability to select

those, is dependent upon the knowledge that they

have of the biology of the disease that they're

studying, or that they're trying to cure or that

they're trying to treat.

The mechanism of action of the drug is

certainly critical when you're looking at the

potential. One area is with regard to drug-drug

interactions. Oftentimes we've been looking

primarily at metabolism for drug reactions, but

certainly there's a concern that I think is

building for utilization of multiple drugs that

impact on the same metabolic--not metabolic

pathways, but the signaling pathways, let's say, at

the cell surface, which are getting the

treatments--you know, getting a treatment into the

cell, or that are resulting in the clinical

effect--is what I'm trying to say, in a very poor

way.

113

Let's see--the pharmacogenomics is a new

area that we're getting involved in, but it's very

important with regard to patient selection, as well

as the potential for certain populations to be

impacted by drugs in a unique way, that can impact

not just efficacy, but also the safety.

And manufacturing methodologies are an

area that we have research programs in within the

office, and those are important for developing and

understanding of the robustness of various

manufacturing processes, and the ability to

implement new paradigms, such as process

technologies in the manufacturing process of

pharmaceuticals

[Slide.]

Out strategy here is to coordinate

cooperative research activities. And, as I

mentioned, we have predictive modeling programs.

And these are generally based upon information from

regulatory submissions that we receive, as well as

from laboratory research that's going on within the

agency, as well as outside and in the published

114

literature.

One area which, I think, we need to build

is our abilities to get information from industry

that we don't get in a our regulatory submissions,

and that they don't publish, and finding a means to

have them help us to gain knowledge of that

information so that we can implement it into the

decisions we make and share that--basically the

conclusions that come out of that with industry as

a whole, to address the Critical Path.

[Slide.]

There's also laboratory research going

on--you'll hear from the Offices of Testing and

Research, Applied Pharmacology Researhc, and

Product Quality Research, and Pharmaceutical

Analysis--and also from my office, Biotech

Products, from our divisions of Monoclonal

Antibodies and Therapeutic Products--it should be

Therapeutic Proteins. Sorry. Typo there.

There's also research going on in other

FDA centers that we can collaborate with, and do

collaborate with, as well as outside, to gain

115

information from academia, industry and other

egoernment agencies, as well.

[Slide.]

Now, I think we can gather all this

information, but it's critical with regard to how

we're going to use it, and how we're going to

disseminate it, such that we can have an impact on

the Critical Path.

There are a number of avenues to get to

academia and manufacturers, and those include the

public forums, where we can present the conclusions

and recommendations. We certainly write guidance

documetns that can help in this manner, as well.

And then, when industry comes to meet with us at

the regulatory meetings, such as pre-IND, and

pre-NDA meetings--pre-BLA meetings--we can interact

with them at those points, as well.

But we also need to change, to some

extent, our review processes within the agency,

and--so the information has to go to the reviewers,

as well. And we can do that via training programs,

as well as the guidance documents that we do write.

116

They're used a great deal by the reviewers.

Then, again, mentoring programs, to bring

up the new reviewers in an understanding of the new

paradigms and new concerns, or lessen their

concerns for particular issues that relate to

pharmaceutical manufacturing, or clinical issues.

And then all of this together should help

to enhance the application of your process from the

reviewer's standpoint, and with regard to the

manufacturers should help to remove some of the

hurdles and obstacles we see in the Critical Path.

[Slide.]

You'll hear the coming presentations. So

there are some questions we'd like you to keep in

mind, that we'll be bringing up later for

discussion.

And first is: are we focusing, within the

office, on the appropriate Critical Path topics?

And are there other topics that we should be

addressing through our research programs? And it's

both the database relational type information or

research programs as well as the laboratory

117

programs.

And then, in the future, Critical Path

issues may change. So how should we identify

Critical Path issues in the future. And we'd like

recommendations on how we should prioritize those.

Because we're really--at this point, we can't do

everything that needs to be done with the current

resources, and so we're going to have to prioritize

now, and in the future we'll need to prioritize, as

well, and we'll need some guidance on that.

That ends my presentation. We'll move

into the first talk--to stay on time--which is

going to be--let's see, I'll bring it up here--Joe

Contrera.

Informatics and Computational Safety Analysis

Staff (ICSAS)

DR. CONTRERA: Okay. I'm the director of

the Informatics and Computational Safety Analysis

group. Our main mission, really, is to make better

use of what we already know; material or safety

information, toxicology information that's buried

in our archives; and also in the scientific

118

literature and in industry files.

Our group develops databases and also

predictive models. You can't develop models

without the databases. So they go together.

We have develop our own paradigms for

transforming data, because traditional toxicology

data is textual, and converting into a weighted

numerical kind of a scale that is amenable to be

processed by computers, and also to be modeled.

And we encourage, promote and also work

with outside entities to develop QSAR--qualitative

structure activity relationship software--and data

mining software, for use in safety analysis.

We don't work alone. And you'll hear more

about this in my talk. We leverage, very much, and

cooperate, and collaborate very much with

outside--with academia, with software companies and

with other agencies. And we do this through

mechanisms such as the CRADA--the Cooperative

Research and Development Agrement--which is really

a buisness agreement--and also we do it with

Material Transfer Agreements, for an exchange, quid

119

pro quo exchange, with software and other

scientific entities outside the center.

[Slide.]

The Critical Path Initiative--you've all

been, and you're going to be hearing more about it,

and you've heard a lot about it. I'm focusing on

what is relevant to my group, and that is: the

problem is that we have not created sufficient

tools to better assess safety and efficacy. We're

still relying on toxicology study designs that were

designed 50 or sometimes 100 years ago. And it

doesn't mean that they're inferior, but maybe there

are better ways of doing this now.

So we need a process to develop better

regulatory tools. And it was really a controversy,

to some extent: whose misison is this? And in the

past, the agency didn't consider it as the agency

mission to develop these tools--necesarily. It was

academia. And academia said, "No, it's the

industry." It wasn't--it was vague as to who was

actually responsible for developing new analytic

tools that can be used for regulatory

120

enpoints--especially in safety endpoints.

[Slide.]

So now how d we connect with the citical

path? I think we were doing Critical Path research

well before there was a Critical Path Initiative.

I mean, we've been in operation, in one form

another, for over a decade in the Center, at a time

when people were questioning whether this was the

mission of the agency in the beginning.

We developed databases and then predictive

tools that are used by the industry--by the

pharmaceutical industry--more and more to improve

the lead candidate selection. And the question

was: why should the agency supply industry with

better tools to select lead candidates? Well, it's

in our interest that they develop lead candidates

that have fewer toxicology or safety problems.

Because when they come to us, in the review process

and submissions, they can said right through with

very few issues. Otherwise, they'd bog down the

system. And we have multiple review cycles, and

there are issues to be addressed. And it would be

121

wonderful if they could just slide through.

And so also to facilitate the reiew

process internally, by having reviewers having a

rapid access to information that is usable for

"decision support," we call information; that they

can use to make judgments on a day-to-day basis.

And we hope that also this could reduce testing;

reduce the use of animals. And also encourage

industry--software companies--to get into the

business of developing predictive modeling tools.

[Slide.]

And we see this three-dimensional diagram

for the Critical Path. Well, the computational

predictive approaches are identified in two of the

three pathways. And so we feel we're right in step

with what the future goals of the agency are.

[Slide.]

What have we accomplished already? Well,

again, we do two things: databases and predictive

modeling. And this sort of summarizes some of the

accomplishments; the first being we've developed

predictive software for predicting rodent

122

carcinogenicity, for example, based on the compound

structure. It's being used by the pharmaceutical

companies. It's distributed by small software

vendors.

We are also--obviously, we cannot screen

industry's compounds in the agency. That would be

a conflict of interest. But our software is being

used. We have an Interagency Agreement with

NIH--NIH has a drug development program--we have a

contract with NIH. NIH sends us compounds that

they're screening in their drug development program

for treating addiction. And so we are, in our own

way, practicing what we preach, in terms of using

our software in lead selection in drug development.

We also--software is being used--and we

lay a consulting role, within the Center, for

evaluating contaminants and degradants in new drug

products and general drugs, to determine--to

qualify them, and determine limits. So we feel

that our software could have much more application

in that realm.

And decision support for review divisions.

123

We collaborate very closely with the Center for

Food Safety. And, in fact, we're training their

scientists, and have shared our software with them,

and they're using our carcinogenicity predictive

software to screen food contact substances. Because

they're working under the new FDAMA rules that

place the burden on the agency; in other words, the

agency has to, within 120 days, decide whether

there is a risk. The agency has to give cause why

a substance is a risk. It's a reverse of sort of

what drugs are.

So in order to meet those kinds of

deadlines, they had to go to predictive modeling to

ascertain whether there's a potential risk of a

food contact substance--within 120 days.

EPA is looking at our--and we work with

them. And the software also can be used in

deciding whether we have a data set that is

adequate; whether there are research gaps that need

to be filled.

[Slide.]

So we talk about the FDA information. We

124

get submissions, we review them. There's an

approval process, and then the post-approval

process. We extract information from this process.

We extract proprietary toxicology data,

non-proprietary toxicology and clinical data. And

we build proprietary and non-proprietary databases,

so we can keep information that can be shared with

the public through Freedom of Information and

information that will not be shared--or cannot be

shared legally--into two different databases.

And we use these databases for a variety

of functions: for guidance development, for

modeling. And also for decision support fo the

review; and also it feeds back on industry, because

much of this information can be shared with the

public, because it's under the Freedom of

Information Act.

[Slide.]

We have leveraging initiatives in both

realms. We leverage to get support from outside to

help us develop databases, so that we don't rely

entirely just on FDA funding.

125

And the objectives are to creat specific

databases--endpoint specific. They could be mouse

studies, three month, 90-day studies, one year

studies; the toxicology databases that people are

interested in.

These database initiatives are funded and

supported through CRADAs and other mechanisms. We

have a CRADA with MDL Information Systems, which is

a part of Reed Elsevier publishing company. They

are interesting in building a large information

system, and so they're helping, supporting, our

effort. We have CRADAs in the works with Leadscope

that has a wonderful platform for searching

toxicology data. And also we have a CRADA in

process with LHASA Limited, in England--University

of Leeds in England--that has a system also--an

interest in these kinds of databases.

What we--our databases are

constructed--the center of our database is the

chemical structure. It is a chemical-structure

based database. And the structure is in digital

form so that it can be teased--it's a

126

chemoinformatic database. And the digital form is

called the .mol-file structure, and it's a common

structure used in industry for over a decade. So

the chemical structure, as well as the name is the

center search point.

And then once you have a structure that's

in digital form, you can not only ask a simple

question about, "Can I find substance x," but you

can also query and ask whether--"I'd like to know

everything--all the compounds that are like it."

And that's such a powerful tool--regulatory

tool--that I think is another--puts us in another

dimension.

It's not that I want--"Tell me about

acetaminophen," but I want to know compounds that

are 90 percent like acetaminophen in a data set.

And we're able to do that now--really easily--with

the system.

So once we have this system, then we tie

in--the databases are linked to this search engine.

We have our clinical databases that we

model--post-marketing adverse event reporting

127

system, and also the tox databases. And we use all

this--what we're really interested in is modeling;

computational predictive toxicology.

And the sources of that data on these

databases come from reviews. We extract

information from the regulatory reviews and from

other databases.

[Slide.]

So, now, getting into our modeling

operation, we transform the data. We supply the

chemical structure data, and our collaborators and

software companies supply the software. And we

work with them on an iterative basis to improve and

make these things work, and develop software for

these endpoints.

We've also, I think, are probably the

first group that have developed a way of using

chemical structure to predict dose. And so we have

a paradigm for predicting what the maximum daily

dose of a compound might be in humans, within a

statistical, obviously, error bar, in humans.

So, currently, in our prediction

128

department, you might say, we have access to five

or six different platforms. And they represent

very different algorithms. And this is the

point we want to have interactions with software

companies that have approaches that are different

from one another. And then we evaluate and work

with them to try to develop models, using our data

sets.

So we have two CRADAs on board right now,

with multi-case and MD/QSAR, and we have others in

the works. And we also have interactions with

other prediction approaches from the statistical.

[Slide.]

In terms of the models that we're working

on now, the objective is to model every single test

that's required for drug approval. And so we

started with carcinogenicity, because that was the

most--the highest profile, in terms of preclinical

requirement; and teratology would be next. These

are endpoints that cannot be simulated in clinical

trials; mutagenicity, gene tox--all these are

models, either have been created or are in the

129

process of being created and being worked on.

We're also attempting to model human

data--the adverse event reporting system;

post-marketing human data. This is an enormously

difficult data set; very dirty data set, but it's

enormous, in terms of its size.

[Slide.]

And we have had some success, preliminary

modeling, of hepatic effects, cardiac effects,

renal and bladder, and immunological effects in

humans. These are still works in progress, but we

have made progress.

And in terms of the dose related

endpoints, we have made really good progress. We

were surprised, ourselves, because we didn't really

think this would work. We've been able to

successfully model the human Maximum Recommended

Daily Dose--you know, that's the dose on the bottle

when you get your drug. It says "Don't take more

than 10 milligrams a day for an adult. Well, we

modeled that, because that comes from clinical

trial data. That is really human data. And it

130

represents an enormous scale--I don't want to get

into it--but it's like an eight-block scale of

doses, and we have 1,300 pharmaceuticals that are

either--that we've modeled, in our database. And

we were able to successfully model this--and I'll

get back to that in a moment.

[Slide.]

The other question that came up was

proprietary data and sharing industry data. It

would be nice to get their data, especially in

areas that we know the industry has a great deal of

experience in, like gene tox data. Right now we

can't have access to data that was not in

submissions. And so we need a way of doing this.

Chemoinformatics gives you a way of at least

getting there partially. We're able to share the

results by not disclosing the structure and name of

a compound. You can disclose the results, but you

say "What good is disclosing results, or using the

results, without knowing where they came from?"

Well, you can use descriptors--chemical

descriptors--that can be used in modeling, but

131

cannot be used to unambiguously reconstruct the

molecular structure. But they contain enough

information to model.

And so you're sort of at least halfway

there. You can share some information that can be

used in modeling. And so this is a feasible

approach and, in fact, it's already being

accomplished--legally. It's gone through our

legal--our staff at the agency and it's

incorporated in some of these softwares.

[Slide.]

And this is an example. This is 74 MDL

QSAR descriptors for the compound methylthiouracil.

Now, these descriptors are used in modeling, and

ocntain a great deal of scientific information, in

terms of modeling. But all of these descriptors

will not unambiguously recreate the structure of

methylthiouracil, because there's a lot missing.

It's like a pixel pictures. You know, you have a

photograph--a digital photograph--if you've only

got 70 pixels, you'll get a rough picture of what

it is, but you won't know it's your uncle. It's

132

just a person--you know. But if you had 10,000

pixels, you'd know exactly who it is. It's the

same idea. So you can share this crude image.

[Slide.]

Getting back to modeling the human maximum

daily dose--at present, we have to go through many

steps to arrive at a starting, Phase I clinical

starting dose, in a drug that's never been into man

for the first time. We start with animal

studies--multiple dose studies in multiple species.

So already that's a lot of cost. Then you estimate

the no-effect level--has to be estimated from this.

Then you have to decide which species is closed to

man by looking at the ADME and, you know,

metabolism and everything. And then you have to

convert that to a human equivalent dose using

allometric scaling. And then, on top of that, you

use a little--the uncertainty factors, dealing for

inter-species extrapolations--finally come up with

a dose that you might try for your first dose in

human--in clinical trials.

Well, if you could model, on the basis of

133

structure, the maximum recommended daily dose, you

get a predicted dose in humans--because that's

human data. You take one-tenth, or one-hundredth

of that, just to be on the safe side, and you have

a dose.

And what's the benefit? There's no

testing in animals. There's no lab studies.

There's no inter-species extrapolation, because

you're using human data. And we think it's more

accurate, because animal studies don't predict

whether a drug is going to cause nausea, dizziness,

cognitive dysfunction. Animals can't tell you

that. But yet that appears in labeling for old

drugs all the time.

So we feel that this is a good approach.

Everyone acknowledges that the estimation of the

first dose in clinical trials is a bad--but it's

the only thing we know how to do. So this has got

to be better, because it's better than nothing.

You know, because right now what we're doing is a

very crude approximation.

[Slide.]

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What's another application? And--in

conclusion--the two-year rodent carcinogenicity

study--in mouse and rat. It costs $2 million. It

takes at least three years to do. And there's

always controversy about the outcomes of these.

Yet it has an enormous effect on the drug's

marketability.

Is it necessary to do these studies for

all drugs now? Can computational methods replace

some of them? I'm not saying we're getting rid of

all testing. But if we know a lot about a

particular compound, based on the experience of the

past, perhaps with predictive modeling there may be

a subset of compounds in which we don't have to

test as vigorously. And those which we know very

little about--and the computer can tell you that;

that the compound is not covered in the learning

set, and therefore you better do all the studies.

But if a compound is another--you know,

antihistamine, maybe there's a lesser path because

a structure that's so well represented in the data

set, that it's sort of silly to keep testing it

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over again, just to meet a regulatory requirement.

So we're hoping that this would reduce

unnecessary testing and put the resources where

they're needed; testing things that we really don't

know anything about, and that are new--that are

really new compounds.

[Slide.]

So the challenges for accepting predictive

modeling: we need accurate, validated--and that's

always--you know, what we mean by "validation" is

always arguable. But we need to develop that.

That's part of our mission.

Standardization of software; experience

and training--it's not something that's going to go

on a reviewer's desktop ever, because it requires

interpretation. It's a really special skill.

We need more databases; adequate sharing

of proprietary information; the bigger the

database, the better. But we need, also,

regulatory mangers and scientists that are willing

to consider new ideas--consider; don't have to

adopt--consider. That makes a big--you know, opens

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the door for innovation.

And then the ned for an objective

appraisal of current methods. It's the emperor's

clothes. How good, really, is what we're doing

now? And that is something that's painful, but

it's something that needs to be done. Compared to

what? Is it better, worse--compared to what?

[Slide.]

In PhRMA 2005 meeting that occurred

several years ago--and I think it was very

farsighted--Price Waterhouse Coopers had a

paradigm. And they said, "Right now you have

primary sciences: the lab-based, patients--you

know, clinical trials; and the secondary is the

computational--what the call "e-R&D"--that there

will be a transition where they'll reverse from

primary to secondary. And the primary science

maybe in the next generation, will be the modeling

and predictive science, and the lab and clinical

will be the confirmatory science.

So, with that, I'll end my talk. We've

published much of what we've done. A lot of it is

137

in press right now. We have a web site: our

maximum recommended daily dose database is on our

website, and a lot of people are working with it,

and we're happy to say that they're getting the

same results--which was nice.

And I'll end my talk here.

CHAIRMAN KIBBE: i'll take the prerogative

of the Chair and ask the first question. And then

we'll get rolling.

Your database looks wonderful when you're

dealing with toxicity. Have you also done a

similar thing with clinical effectiveness, or

utility, of compounds? Some way of looking at the

structure, and then looking at the effect, and

being able to predict how effective one structure

is relative to another?

And then follow up with that--if that's

true, can we plug into the opposite end of your

program and go back the other way, and just bypass

drug discovery?

[Laughter.]

DR. CONTRERA: [Laughs.] Well--no fair.

138

I'll start with the last one--but you'll be only

discovering what we already know. There may be--

CHAIRMAN KIBBE: But I was thinking of

plugging in different parameters--

DR. CONTRERA: Yeah.

CHAIRMAN KIBBE: --in the toxicity and

outcome: lower toxicity, higher efficacy--

DR. CONTRERA: Oh, yes. Yes.

CHAIRMAN KIBBE: --and then go backwards.

DR. CONTRERA: Yes, that's possible.

CHAIRMAN KIBBE: Thank you.

DR. CONTRERA: But getting back to

efficacy--yes. In fact--I mean, industry is using

it as an efficacy tool all the time. That wasn't

our mission. But potentially--certainly

applicable. And sometimes we stumble on those

things. But that isn't our mission.

And you know where research--we've got

four people in this unit. And then we have

contractors. And then we get students. So we're a

small, tight unit. And you have to be very

focused, in terms of your priorities, and doing

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what is feasible first, and less--and so we didn't

get into efficacy. No.

CHAIRMAN KIBBE: Who have I got down here?

I've got everybody on the right side.

So we'll start it at the end, and work our

way down.

Go ahead.

DR. SELASSIE: Okay. I have a couple of

questions for you.

First of all, with your database, you have

in-house data that you're generating for your

toxicology?

DR. CONTRERA: Yes.

DR. SELASSIE: Do you ever go to the

literature and get information from it?

DR. CONTRERA: Yes. Actually, that could

be a much more complicated slide. But we mine

everything. We mine other databases; the NIH

databases; literature. And, in fact, we're

using--we're using our CRADA with MDL--because MDL

owns almost every journal in the world

now--practically. Elsevier owns almost everything.

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And so--and they have access to data that's

enormous.

So, using the leverage with a publishing

company, we have a pipeline now to the literature.

Yes.

DR. SELASSIE: Okay. I have another

question.

DR. CONTRERA: Yes.

DR. SELASSIE: When you're inputting the

structures, do you all ever use the SMILES

notation?

DR. CONTRERA: Yes, we use SMILES. There