Failure to demonstrate efficacy is a leading reason for phase III attrition

Motivation Overview Disease modeling Case study Conclusions

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 1

Drivers of Attrition-McKinsey & Co. Report 2008

Summary

Motivation Overview Disease modeling Case study Conclusions

• Knowledge management and quantitative pharmacology will become key drivers of future drug development (hypothesis) and y g ( y )enhance drug development efficiency (hypothesis)

FDA is actively developing quantitative disease models with• FDA is actively developing quantitative disease models, with external input

• Pharmacometrics analyses play a major role in regulatory decision making

• Drug dose or exposure and response analysis are often used to lead or support approval and labeling-related decisions

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 2

Motivation Overview Disease modeling Case study Conclusions

Leveraging Prior Knowledge in Guiding Drug Development and Regulatory Decisions

Pravin JadhavPharmacometricsPharmacometrics

Office of Clinical Pharmacology, Office of Translational SciencesFood and Drug Administration

8th Kitasato University - Harvard School of Public Health SymposiumSeptember 29-30, 2008p ,

Tokyo, Japan

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 3

The opinions expressed in this presentation do not represent official FDA policy

Outline

Motivation Overview Disease modeling Case study Conclusions

• Motivation

• Overview

• Disease modeling

Case study 1: Tetrabenazine approval• Case study 1: Tetrabenazine approval

• Case study 2: Concentration-QT analysisCase study Co ce t at o Q a a ys s

• Conclusions

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 4

Pharmacometrics scope

Motivation Overview Disease modeling Case study Conclusions

Tasks

NDA

reviews1,2ProtocolR iReviews

- Dose finding- Registration

Disease Labeling

ModelsEOP2/2a

Trial design

Quantitativemeetings4

QT reviews3

QuantitativeRisk benefit- Dose optimization- Dose adjustment

Evidence ofEvidence of

Effectiveness

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 5

1. Bhattaram et al. AAPS Journal. 2005 2. Bhattaram et al. CPT. Feb 2007

3. Garnett et al. JCP. Jan 2008 4. Wang et al. JCP. Jan 2008

Quantitative disease-drug-trial models for efficient learning

Motivation Overview Disease modeling Case study Conclusions

DiverseExpertise

Disease Drug Trial

FDA Data Physiology

DiseaseModel

Drug Model

TrialModel

Biology Pharmacology Patient PopulationBiologyBiomarker-outcomeNatural Progression

Placebo

PharmacologyEffectivenessSafety

Early-Late

Patient PopulationDrop-outCompliance

Preclinical-Healthy-Patient

1. Powell R, Gobburu J. CPT, 2007

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 6

1. Powell R, Gobburu J. CPT, 20072. Gobburu J. 2008. Disease models. Clin Adv Hematol Oncol. 6:241-23. Gobburu J, Lesko L. Quant D-D-T models. Ann.Rev.Pharm.Tox (submitted)

Leveraging prior knowledge: disease modeling

Motivation Overview Disease modeling Case study Conclusions

Model Objective Status

Parkinson Derive endpoints to -Completed; provided input to sponsorsParkinson Disease

Derive endpoints to discern disease-modifying and symptomatic effects

Completed; provided input to sponsors-Public meeting- April, 2008-Draft publication ready

Bhattaram A. Demonstrating Disease-modifying Effects for Parkinson's Disease: Drug Development and Regulatory Issues: AAPS, M.J.Fox Workshop April 2008

Non-Small Cell L C

Quantify tumor size and survival relationship to

-Completed-Clinical Pharmacology AC meeting- March 2008Lung Cancer

(NSCLC)

pguide future drug development decisions

Clinical Pharmacology AC meeting March 2008-Draft publication ready

Wang Y and Bruno R Proceedings of the Clinical Pharmacology Sub Committee Advisory Committee MeetingWang Y and Bruno R. Proceedings of the Clinical Pharmacology Sub-Committee Advisory Committee Meeting. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1-01-FDA.pdf

Antiretroviral Information

Guide dose selection using quantitative clinical, clinical pharmacology

-Ongoing-HIV/HCV model parameters and data will be

hi d i t ti i bl t idInformation Management

System (AIMS)

clinical pharmacology and virology data

archived in systematic queriable manner to guide future development programs-Simulations will be used to justify dose and dosing regimen

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 7

FDA disease model ready to use

Motivation Overview Disease modeling Case study Conclusions

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 8

AIMS will be efficient to leverage prior knowledge and aid HCV drug development

Motivation Overview Disease modeling Case study Conclusions

In vitro

AIMS

Assay

Monotherapy- Early screening of AIMS

Disease model/data library

Monotherapy

Clinical Trial

screening of compounds based on IC50 value.

- In vitro IC50 as Simulation

Dose optimization-

- High thr’put method to filter thousands of compounds

a guide for early dose selection

- Short term- Invitro and monotherapy data p

combination therapy

compounds

- Based on prior experience, a few potential

Short term monotherapy data to measure viral load, Drug conc. and resistance data

monotherapy data for clinical dose and regimen selection

- Clinical - Pilot study for dose optimization

Clinical Trial Simulation

Pivotal trialfew potential entities will be selected for the next phase

resistance data or other markers of disease

development plan

pthr’ innovative trial designs

-Clinical dose and regimen selection

Automated Automated

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 9

Motivation Overview Disease modeling Case study Conclusions

Case study 1: Tetrabenazine approval

More case studies can be found in1. Bhattaram et al. AAPS Journal. 2005 2. Bhattaram et al. CPT. Feb 2007

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 10

Equivocal evidence of effectiveness for tetrabenazine (TBZ) was derived from pivotal studies and extension studies

Motivation Overview Disease modeling Case study Conclusions

o p ota stud es a d e te s o stud es• TBZ was proposed for Huntington’s chorea with no approved treatment at

the time of review• One trial was successful and other failed• One trial was successful and other failed

– Failure likely due to trial execution errors– Primary variable: Change in symptom score

Key question

Agency at this point can ask for more

• Key question– Is there adequate evidence of effectiveness?

DB-1 Agency at this point can ask for moreevidence (one or more studies)

ORInvestigate further across the clinical

DB 1Dbl-blind (DB)Randomized

PBO ControlledDose Titration

N=75

OL-1Open label (OL)

WithdrawalDose Titration

trial database whether there is a consistent signal of effectiveness or not

N=75P<0.05

(withdrawal)

DB-2

Dose TitrationN=75

Significant Dose-ResponseDB-2Dbl-blind (DB)Randomized

PBO ControlledDose Withdrawal

N 30

OL-2Open label (OL)

Continue ‘old’ doseN=30

Significant Dose-Response Relationship – DB-1, OL-1

Significant and Consistent Drug

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 11

N=30P>0.05

N=30Significant and Consistent Drug Effects Across Studies

Motivation Overview Disease modeling Case study Conclusions

Case study 2: Concentration-QT (CQT) analysis

More information on CQT methodology can be found inGarnett et al. JCP. Jan 2008

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 12

E14 analysis cannot separate effect of two drugs in the presence of two way pharmacokinetic &/or pharmacodynamic interaction

Motivation Overview Disease modeling Case study Conclusions

ay p a aco et c &/o p a acody a c te act o

• Double-blinded, 5-treatment, 5-period, cross-over, thorough QT study. – Treatment 1: Placebo

T t t 2 M ifl i (400 )– Treatment 2: Moxifloxacin (400 mg)– Treatment 3: Ketoconazole (400 mg)– Treatment 4: Test Drug (Therapeutic)– Treatment 5: Combination Group-Test Drug + Ketoconazole (400 mg) (Supratherapeutic)Treatment 5: Combination Group Test Drug + Ketoconazole (400 mg) (Supratherapeutic)

• Direct subtraction of drug effects leads to overestimation in the presence of two way pharmacokinetic interaction between Ketoconazole and test drug

E

A C

D

D A

E

E-AOverestimate

AB

C D-A

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 13

CQT analysis guided regulatory decision that test drug does not prolong QT interval to the extent to be clinically meaningful

Motivation Overview Disease modeling Case study Conclusions

Q te a to t e e te t to be c ca y ea g u

16

15 12

14

A lti i t i d ff t

10

15

10

12 • A multivariate mixed effect linear model can be used to perform CQT analysis in the presence of two-way PK

5

ΔΔ QTcF [msec] 6

8p yand/or PD interaction

• Upper bond of 90% CI of ΔΔQTcF from tested Drug

800010000

12000400

6002

4

gunder supra-therapeutic exposure < 10 ms

20004000

60008000

200400

Keto Concentration [ng/mL]

Test Drug Concentration [ng/mL] 0

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 14

The value pharmacometrics added

Motivation Overview Disease modeling Case study Conclusions

In TBZ approval– Alleviated the need for additional trial(s) to demonstrate effectiveness– Availability of drug sooner, especially given no approved treatments

(debilitating disease)– Efficient solution to challenging patient enrollment– Fewer review cycles (because of this issue alone)– Avoided more $$ and time– Ultimately might lead to lower drug costs

In characterizing QTc effect of test drug– Alleviated concerns on QT prolongation for test drugAlleviated concerns on QT prolongation for test drug– Alleviated the need for additional TQT trial(s)– Avoided more $$ and time

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 15

Summary

Motivation Overview Disease modeling Case study Conclusions

• Knowledge management and quantitative pharmacology will become key drivers of future drug development (hypothesis) and y g ( y )enhance drug development efficiency (hypothesis)

FDA is actively developing quantitative disease models with• FDA is actively developing quantitative disease models, with external input

• Pharmacometrics analyses play a major role in regulatory decision making

• Drug dose or exposure and response analysis are often used to lead or support approval and labeling-related decisions

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 16

Acknowledgements

Pharmacometrics team

Pravin.Jadhav@fda.hhs.govSeptember 30, 2008 Leveraging Prior Knowledge 17