May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 1

Summary

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

• 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

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 2

Quantitative Clinical Pharmacology Applications for Efficient Drug Development

Pravin R. JadhavTeam Leader, Pharmacometrics Office of Clinical PharmacologyFood and Drug Administration

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 3

Agenda

• Pharmacometrics @ FDA

• Pharmacometrics Applications– Regulatory Decisions

• Safety (Zhu H et. al.)• Efficacy (Jadhav P et. al.)

– Early Drug Development• Knowledge Management (KM) Initiatives• Drug-Trial-Disease Models

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 4

Clinical Pharmacology: The Past

From Gobburu J and Lesko L, Annu. Rev. Pharmacol. Toxicol. 2009. 49:291–301

The Future

The 70’s

FormulationBiopharmaceutics

The 80’s

DrugInteractions

The 90’s

DosingRegimen

The 00’s

Exposure-Response

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 5

Pharmacometrics @ FDA

TasksDecisions

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

NDA

reviews1,2ProtocolReviews

- Dose finding- Registration

Disease

ModelsEOP2/2a

meetings4

QT reviews3

Labeling

Trial design

QuantitativeRisk benefit- Dose optimization- Dose adjustment

Evidence of

Effectiveness

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 6

Agenda

• Pharmacometrics @ FDA

• Pharmacometrics Applications– Regulatory Decisions

• Safety (Zhu H et. al.)• Efficacy (Jadhav P et. al.)

– Drug Development• Knowledge Management (KM) Initiatives• Drug-Trial-Disease Models

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 7

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

pharmacodynamic interaction• Double-blinded, 5-treatment, 5-period, cross-over, thorough QT study.

– Treatment 1: Placebo– 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)• Direct subtraction of drug effects leads to overestimation in the presence of two way

pharmacokinetic interaction between Ketoconazole and test drug

400 mg Keto

Test drug

(therapeutic)Test drug

(supratherapeutic)

Combination

(One-Way PK

interaction)

AB

C

D

D-A

Combination

(Two-Way PK

interaction)

E

E-AOverestimate

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 8

CQT analysis guided regulatory decision that test drug does not prolong QT interval to the

extent to be clinically meaningful

20004000

60008000

1000012000

200400

600

5

10

15

Keto Concentration [ng/mL]

Test Drug Concentration [ng/mL]

ΔΔ QTcF [msec]

0

2

4

6

8

10

12

14

16

• A multivariate mixed effect linear model can be used to perform CQT analysis in the presence of two-way PK and/or PD interaction

• Upper bond of 90% CI of ΔΔQTcF from tested Drug under supra-therapeutic exposure < 10 ms

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 9

Quantitative Clinical Pharmacology Modeling: Decision Making

• Alleviated concerns on QT prolongation for test drug

• Alleviated the need for additional TQT trial(s)

• Avoided more $$ and time

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 10

Agenda

• Pharmacometrics @ FDA

• Pharmacometrics Applications– Regulatory Decisions

• Safety (Zhu H et. al.)• Efficacy (Jadhav P et. al.)

– Drug Development• Knowledge Management (KM) Initiatives• Drug-Trial-Disease Models

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 11

Beta Blocker under Pediatric Exclusivity Study

• Approved in adults for the treatment ofhypertension, angina and heart-failure

• One registration trial and an extension safety study

• Registration trial failed to meet primary endpoint (slope of placebo corrected changes in sitting systolic blood pressure (sSBP) from baseline)

• No future studies expected in pediatrics

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 12

Registration Trial Failed to Establish Effectiveness based on Primary Endpoint (Slope

analysis: p=0.5731 )

Mean changes with SE Placebo corrected mean changesand dose-response line

Placebo Dose-1 Dose-2 Dose-3 Dose-1 Dose-2 Dose-3

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 13

High Inter-individual Pharmacokinetic Variability

1.0

10.0

100.0

Obs

erve

d pl

asm

a tr

ough

con

cent

ratio

n, n

g/m

L

Dose-1 Dose-2 Dose-3

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 14

Evidence of Effectiveness: Exposure Response Analysis

• Exposure (Plasma trough concentration) and response (mean sitting blood pressure)– Systolic (msSBP)– Diastolic (msDBP)

• Blood pressure decreases with increasing plasma trough concentration

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 15

Quantitative Clinical Pharmacology Modeling: Public Health Benefit

• Is this beta-blocker effective in treating pediatric hypertension?– Evidence of effectiveness from overall data and not the

primary endpoint• Prior knowledge (adult pharmacotherapy)• Exposure-response analysis• Other supportive quantitative analysis (extension

study; dose group comparison etc.)• Value to pediatric pharmacotherapy

– Rational dosing recommendations• Not for salvaging failed trials but improve public health

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 16

Agenda

• Pharmacometrics @ FDA

• Pharmacometrics Applications– Regulatory Decisions

• Safety (Zhu H et. al.)• Efficacy (Jadhav P et. al.)

– Drug Development• Knowledge Management (KM) Initiatives• Drug-Trial-Disease Models

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 17

From Gobburu J and Lesko L, Annu. Rev. Pharmacol. Toxicol. 2009. 49:291–301

The Future

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 18

Failure to Demonstrate Efficacy is a Leading Reason for Phase III Attrition

Drivers of Attrition-McKinsey & Co. Report 2008

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 19

Antiviral Information Management System (AIMS)

Create a structured (queriable) database

Leverage Knowledge for Efficient Trial Designs

Vm

c

δ

Vw pw

β

c

δ

pm

β

PRES

FIT

Im

Iw

T

b d

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 20

AIMS will be Efficient to Leverage Prior Knowledge and Aid HCV Drug Development

AIMS Disease model/

data library

In vitro

Assay

Monotherapy

Clinical Trial Simulation

Dose optimization-combination

therapy

Pivotal trial

- Early screening of compounds based on IC50 value.

- High thr’put method to filter thousands of compounds

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

- In vitro IC50 as a guide for early dose selection

- Short term monotherapy data to measure viral load, Drug conc. and resistance data or other markers of disease

- Invitro and monotherapy data for clinical dose and regimen selection

- Clinical development plan

- Pilot study for dose optimization thr’ innovative trial designs

Clinical Trial Simulation

-Clinical dose and regimen selection

Automated Automated

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 21

Ongoing or Completed Disease-Drug-Trial Models

Wang 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

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

-Completed-Clinical Pharmacology AC meeting- March 2008-Draft publication ready

Quantify tumor size and survival relationship to guide future drug development decisions

Non-Small Cell Lung Cancer

(NSCLC)

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

Guide dose selection using quantitative clinical, clinical pharmacology and virology data

Antiviral Information

Management System (AIMS)

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

Derive endpoints to discern disease-modifying and symptomatic effects

Parkinson Disease

StatusObjectiveModel

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 22

Pharmacometrics: The 2020 Vision

Train 20 Pharmacometricians

-Technical track-Disease track-Drug development track

-Develop disease specific data,analysis standards-Expect industry to follow

Develop 5 Disease Models

-Create public disease model library

International Harmonization

-Share expertise between globalregulatory bodies

Integrated Quantitative CP Summary

-All NDAs should have exposure-response analyses

Design by Simulation: 100% Pediatric WRs

-Leverage prior knowledge to designPediatrics Written Request trials

Implement 15 Standard Templates

From Gobburu J, AcoP 2008

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 23

Summary

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

• 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

May 19, 2009 MBSW 2009 Pravin.Jadhav@FDA.HHS.GOV 24

Acknowledgements

Pharmacometrics team