PharmacometricsPharmacometrics

Jogarao V. GobburuDirector, Pharmacometrics Services

Office of Translational Sciences / Office of Clinical PharmacologyCenter for Drug Evaluation and Research

U.S. Food and Drug Administration

AcknowledgementAcknowledgement

Drs. Lesko and Powell

NDA#1: Why did 3 Consecutive NDA#1: Why did 3 Consecutive Registration Trials Fail?Registration Trials Fail?

Severe Baseline DiseaseResponders

Mild Baseline DiseaseNon-Responders

0 5 10 15 20 25 30Dose, mg

-40

-20

0

20

40

60

80

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0 5 10 15 20 25 30Dose, mg

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NDA#2: Why did 2 Consecutive NDA#2: Why did 2 Consecutive Registration Trials Fail?Registration Trials Fail?

Lack of biomarker-endpoint relationship,Led to poor dose selection

Failed?Success

R&D Productivity Declines: High Late R&D Productivity Declines: High Late Phase AttritionPhase Attrition

↓ 50%

29 JULY 2005 VOL 726 309 SCIENCE www.sciencemag.org

Low Attrition In Late Development Low Attrition In Late Development Will Massively Reduce CostsWill Massively Reduce Costs

0

100

200

300

400

500

600

PCD I II III ApprovalPhase

Cos

t ($U

Sm)

From Steve Arlington,PriceWaterHouseCoopers

The Solution: SimpleThe Solution: Simple

“We are an industry with a 98% failure rate…..The only thing we have to do to double our success rate is to drop our failure rate by 2%” **

Hank McKinnell, Pfizer CEO, at http://www.bio-itworld.com, 2/14/06

** 45% of failures related to inadequate prediction of efficacy and safety. This failure rate has not changed in over 15 years.

CDER New Molecular Entity & New Biologic* Approvals by Calendar Year

13 12 10

18

9

1619

9 7 7 9

2115

129

19

35

30

1416

1817

1012

15

5

0

10

20

30

40

50

60

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004* 2005*

Calendar Year

Num

ber A

ppro

ved

Priority NME Approvals Standard NME Approvals Number of NMEs Filed

*Beginning in 2004, these figures include new BLAs for therapeutic biologic products transferred from CBER to

7 of 39 withdrawnfrom market

“The future ain’t what it used to be!”

Yogi BerraFormer All-Star Catcher

New York Yankees

The Critical Path: Now that I have your attention, where do we go next?

Phacilitate R&D Leaders ForumPhacilitate R&D Leaders ForumAutumn 2006Autumn 2006October 4, 2006October 4, 2006Berlin, GermanyBerlin, Germany

Joga GobburuOffice of Clinical PharmacologyCenter for Drug Evaluation and ResearchFood and Drug Administration

jogarao.gobburu@fda.hhs.gov

Key PointsKey Points

Root Cause Analysis of High Attrition Rate Necessary

Knowledge Management, Quantitative Pharmacology and Innovative Trial Designs/Analyses Will Become Key Drivers of Drug Development Change– Organizational infrastructure will need to

change

The Original Critical Path Initiative The Original Critical Path Initiative Announced by FDAAnnounced by FDA

“…if accomplished, the new tests and tools developed under the critical path initiative will modernize the drug development process by 2010…”

SafetyMedical UtilityIndustrialization

Note: PhRMA, BIO and 21 Patient Groups Signed on to Support Critical Path

Critical Path Priority FDA Activities

Biomarkers, Disease Models Several disease models, Data mgmt tools, Conferences, Advisory Comm meetings

Clinical trial streamlining EOP2A MeetingsGuidances, eIND

Bioinformatics Data standards, VGDS

Manufacturing Quality by Design, Biologics

Specific Public Health Needs Rapid identification of pathogen→anti-biotic

At Risk Populations Innovation of pediatric products

http://www.fda.gov/bbs/topics/news/2006/NEW01336.htmlhttp://www.fda.gov/bbs/topics/news/2006/NEW01336.html

VGDS Submissions Over Time VGDS Submissions Over Time Have Been ConsistentHave Been Consistent

0

1

2

3

Num

ber o

f Sub

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sion

s

Q1,'04

Q2,'04

Q3,'04

Q4,'04

Q1,'05

Q2,'05

Q3,'05

Q4,'05

Q1,'06

Note: Two VGDS submitted jointly with FDA and EMEA in Q4, ’05 and Q1, ‘06

Why do late trials fail?Why do late trials fail?

Poor dose selectionInadequate designUnanticipated placebo responseInefficient data analysis– Handling missing data (or drop-outs)Truly not a drug Unanticipated toxicity

Critical Path (R&D) OpportunitiesCritical Path (R&D) Opportunities

Manage knowledge efficiently– Utilize prior information to drive future– Effective integration of data across

development programs– Useful tools to archive/summarize data

Innovative trial designs/analysis– Model-based drug development– Increased awareness among inter-

disciplinary scientists towards teamwork

OxcarbazepineOxcarbazepine: Anti: Anti--EpilepticEpileptic Adjunctive Monotherapy Adults Clinical trials Clinical trials

Children (4-16 years of age)

Clinical trial “Model Based Bridging” approach proposed by FDA

FDA/Sponsor pursued approaches to bestutilize knowledge from the positive trials toassess if monotherapy in pediatrics can be approved without new controlled trials

ParkinsonParkinson’’s Disease Databases Disease DatabaseData Source #Patients Trial DurationTrial#1

Trial#2Trial#3

Trial#4

Trial#5 IND 300 1.5yr

NDA 400 1yr + 3yr follow-up

NIH 400 1yr + follow-upNDA 900 9mo + follow-up

NDA 200 9mo + follow-up

This project aims at developing policy to discern symptomatic and disease-modifying drug effects

PLACEBO/DISEASE MODEL

Surrogate

Rel

ativ

e R

isk

Morbidity#1

Morbidity#2

Mortality

Surr

ogat

e

TIMEDose

Expo

sure

Surr

ogat

e

Exposure

Toxicity

DRUG MODEL

CLINICAL TRIAL MODEL

80 140 200

020

4060

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010

30

Oth F l

Patient PopulationBaseline Body Weight TIME

% D

rop-

out

%Adherence%

Pat

ient

s

DiseaseDrugTrial

Models

Core Development Strategy

DesignMoleculeScreening

Patient Population Dose Selection

Approval Criteria

Individualization

Value of Disease

DrugTrial

Models

Core Development Strategy for Testosterone SuppressantsCore Development Strategy for Testosterone Suppressants

Knowledge Mgmt System

Reporter Gene Assay

Preclinical

Clinical Trial Simulation

Dose optimization

in cancer patients

Pivotal trial

|----*2 mo-----|*Actual execution time.- it does account for time spent accumulating resources.

|----*2 mo-----||----*2 mo-----||----*3 mo-----||---------*12 mo--------------|

- Early screening of compounds based on IC50 value.

- High thr’putmethod to filter thousands of compounds

- Based on prior experience, a few potential entities will be selected for the next phase

IC50

PKPD data

- In vitro IC50 as a guide for preclinical dose selection

- Animal modelsto measure all possible biomarkers e.g. GnRH, LH, T and Drug conc.

- Invitro and preclinical data for clinical dose and regimen selection

- Clinical development plan

- Pilot study for dose optimization thr’innovative trial designs

Eff/Safety data

From Pravin Jadhav, VCU/FDA

Sharing Knowledge to Improve Clinical Drug Sharing Knowledge to Improve Clinical Drug Development & Regulatory Decisions:Development & Regulatory Decisions:

Data/models of Diseases, Drugs, Placebo, Baseline and Dropouts

January 24-25, 2007Washington Marriott Hotel

1221 22nd Street NWWashington, DC 20037

Objectives:

• Show prior examples for the advantages of sharing information

• Present examples demonstrating the application of sharing information in Parkinson’s Disease, Diabetes, Depression & Cancer to help make decisions

• Consider how information can be shared in a library-type mechanism

• Consider future actions to progress these ideas.

How Will Critical Path Opportunities How Will Critical Path Opportunities Get Done?Get Done?

FDA will not and cannot do it alone- no budget and no FTEs in 2006- approx $6 million requested in 2007 budget- no expected huge influx of resources

Shift from uncoordinated individual projects to coordinated PPP and consortia

- FDA, NIH, CMS, academic and industry- concept of collaboration for common good- consortia are neutral and noncompetitive space- demonstrated already that it can work- sustainability and rate of progress are real risks

Summary: Critical Path to Drug Approval Summary: Critical Path to Drug Approval Is Often Unpredictable Is Often Unpredictable –– No SurpriseNo Surprise

Many possible opportunities and tools under the FDA Critical Path Initiative that have the potential to make development more efficient and predictable– Exploratory IND guidance, VGDS, EOP2A, model-

based drug development and efficient clinical trial designs ~ unprecedented flexibility for innovation

There are likely many other approaches and critical path opportunities to improve productivity. Industry should take full advantage of them.

Key PointsKey Points

Root Cause Analysis of High Attrition Rate Necessary

Knowledge Management, Quantitative Pharmacology and Innovative Trial Designs/Analyses Will Become Key Drivers of Drug Development Change– Organizational infrastructure will need to

change

Change is not essential…survival is not mandatory