Failure to demonstrate efficacy is a leading reason for phase III attrition
Motivation Overview Disease modeling Case study Conclusions
[email protected] 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
[email protected] 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
[email protected] 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
[email protected] 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
[email protected] 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
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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
[email protected] 30, 2008 Leveraging Prior Knowledge 7
FDA disease model ready to use
Motivation Overview Disease modeling Case study Conclusions
[email protected] 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
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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
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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
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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
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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
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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
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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
[email protected] 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
[email protected] 30, 2008 Leveraging Prior Knowledge 16