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Topic 3: Using Disease, Placebo, and Drug Topic 3: Using Disease, Placebo, and Drug

Prior Knowledge to Improve DecisionsPrior Knowledge to Improve Decisions

Objectives:• Context for this work- Bob Powell• Industry perspective- Jaap Mandema• FDA perspective- Joga Gobburu• Parkinson’s disease example- Atul Bhattaram & Ohid Siddiqui

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Advisory Committee QuestionsAdvisory Committee Questions

• Parkinson’s disease model

• Is the overall approach reasonable to quantifying various parts of the disease model?

• Is the approach reasonable for selecting the data to model?

• Is the approach reasonable for quantifying the model?

• How should this information be communicated publicly?

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Decisions in Drug Development and at Decisions in Drug Development and at FDA: FDA: How combining prior knowledge with How combining prior knowledge with quantitative-based decisions can improve quantitative-based decisions can improve

productivity & qualityproductivity & quality

Bob Powell, PharmDPharmacometrics

Offices of Clinical Pharmacology & Translational Sciences

robert.powell@fda.gov

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OutlineOutline

• Modeling & simulation impact• FDA pharmacometrics work• Case for extracting & sharing disease,

treatment, placebo, baseline, and dropout information– FDA– Industry

• Future options– Extracting information– Sharing information

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Modeling & Simulation Modeling & Simulation Influences All Lives TodayInfluences All Lives Today

• Weather forecasting• Global warming scenarios• Engineering

– Plant design– Product design

• Airplanes• Cars-crash testing• Bridges• Microprocessors• Widgets

– Traffic flow-roads

• Homeland Security– Disaster preparedness

scenarios– Plague

• Military• Space• Energy• Medical

– Rx patients• Surgery• Diagnostics (MRI,…)

– Education– Devices (hip, knee,..)– Drugs

• Molecular design/receptor• Formulation• Manufacturing• Marketing

– Forensic reconstruction

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The Ultimate ‘Learn-Confirm’ ParadigmThe Ultimate ‘Learn-Confirm’ Paradigm

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Modeling & SimulationModeling & Simulation Why?Why?

• Decrease bias & risk in decisions• Overcome complexity (simultaneously thinking

about many factors influencing outcome)

• Increase quality

• Decrease cost

• Decrease time

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Modeling & SimulationModeling & SimulationProcessProcess

ActCollect

Relevant Information

ResultsOrganize

into Model(s)

Simulate Outcomes

or Scenarios • Decision

• Prediction

• Teach

• Design

• Entertain

Predictive check

• Complex

• Multiple dimensions

• Raw data best

Learning

• ↑ Risk

• Expensive

• Important

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OCP Pharmacometrics ObjectivesOCP Pharmacometrics Objectives

• Facilitate quantitatively based regulatory decisions focused on efficacy/safety through a dose (exposure)-response lens

• High quality partnerships– FDA (physicians, clinical pharmacology, biostatistics)– Externally

• Companies (pre-competitive)– Knowledge generation (disease, placebo, drug, dropouts)– Tools/software

• Academics– Knowledge generation– Training

• Balance– Opportunistic (NDA reviews, EOP2a meetings)– Planned (solving regulatory problems)

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FDA PharmacometricsFDA Pharmacometrics work work

• NDAs– 42 NDAs + case studies (00-04)

AAPS Journal 7(3): E503-12, 2005– 31 NDAs + case studies (05-06) submitted

• Impact survey (clin pharm, physician, ‘metrics)– NDA approval decision: ≥ 85% Pivotal or Supportive– Labeling: 89% Pivotal or Supportive

• EOP2a meetings. Publication in preparation• Planned

(regulatory question → prior knowledge + modeling & simulation → recommendation)– Parkinson’s disease: ∆ disease progression– Non-small cell lung cancer: imaging prediction– Osteoarthritis: imaging prediction

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Driver for Clinical Trial M&S Driver for Clinical Trial M&S Declining Success Across Clinical PhasesDeclining Success Across Clinical Phases

Science 309:726, 2005

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50% Phase 3 Clinical Trial Failure Rate:50% Phase 3 Clinical Trial Failure Rate:Root cause? What to do?Root cause? What to do?

True + True - False + False -

OBJECTIVE:

Root Cause

• Ø Efficacy

• ↑ Toxicity

• Placebo

• Baseline

• Dropouts

• Patient Selection

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SIMULATE DOSING REGIMEN

• DOSE

• FREQUENCY

• DISEASE SEVERITY

• DRUG INTERACTIONS

• PEDIATRICS

IMPACT OPPORTUNITIES- MODEL & SIMULATE KEY DECISIONS

COMPANY → TRIAL DESIGN (2, 3), GO/NO GO, LABELING, FORMULATION, COMBO’S, PEDS

FDA → TRIAL DESIGN (2, 3, 4), NDA APPROVAL (BENEFIT/RISK, DOSING REGIMEN), LABELING, APPROVAL CRITERIA (GUIDANCE REVISION), FORMULATION, COMBOS,

QT STUDIES, PEDIATRIC WRITTEN REQUESTS

[HbA1c]

Rel

ati

ve

Ris

kMI & STROKE

RETINOPATHY

NEPHROPATHY

DISEASE MODEL

CLINICAL TRIAL INFO• BASELINE• PLACEBO EFFECT• DROP-OUT RATE• ADHERENCE

MODEL BASED DRUG DEVELOPMENTMODEL BASED DRUG DEVELOPMENT

Dose[D

rug

]

[Hb

A1c

]

[Drug]

To

xicity [Hb

A1c

]

[TIME (WEEKS)]

To

xicity

DRUG MODEL

[Dru

g]

Time

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2Extract Clinical Trial Information

• BASELINE EFFECT/ MODEL

• PLACEBO MODEL

• DROP-OUT MODEL

• DESIGN

• PATIENT DEMOGRAPHICS

MECHANISM-SYMPTOMS-OUTCOMES

1Build Disease & Drug Model

TIME

4Plug Sponsor Data,

Play & Decide (Go/No Go, trial design)

• TRIAL DESIGN

• PATIENT SELECTION

• DOSAGE REGIMEN

• SAMPLE SIZE

• SAMPLING TIMES

• ENDPOINTS, ANALYSIS

3Simulate Scenarios

UPDATE

1, 2, 3: PUBLIC LIBRARY

Modeling CycleModeling Cycle

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Moerman, D. E. et. al. Ann Intern Med 2002;136:471-476

Duodenal Ulcer Healing Rate in Active (Cimetidine or Duodenal Ulcer Healing Rate in Active (Cimetidine or Ranitidine) vs PlaceboRanitidine) vs Placebo (n=83 studies)

Good luck

Bad luck

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Placebo Response in DepressionPlacebo Response in Depression

JAMA 287: 1840-7, 2002

↑ trial failure risk

↑ false positive risk

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Parkinson’s disease patients Rx with Parkinson’s disease patients Rx with

Levodopa + Selegiline or Placebo for 5 yearsLevodopa + Selegiline or Placebo for 5 years

Eur J Neurol 6: 539, 1999

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Parkinson’s disease patients Rx with Parkinson’s disease patients Rx with Levodopa + Selegiline or Placebo for 5 yearsLevodopa + Selegiline or Placebo for 5 years

Key Questions

• Entry criteria & baseline effect

• Detect disease progression change

• Dropouts

?

?

Eur J Neurol 6: 539, 1999

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Alzheimer’s Disease Natural History & Alzheimer’s Disease Natural History & Drug Response Drug Response (Holford)(Holford)

Idebenone 90 mg/day

Idebenone 270 mg/day

Donepezil

Eptastigmine

Tacrine

Tacrine + estrogen

Predicted natural history ‘92

Predicted tacrine response

♦

◊

■

∆

●

○

Ann Rev P’col Tox’col 41:625, 2001

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Clin Pharmacol Ther 54:556, 1993

AZT Response Relationships in Early HIVAZT Response Relationships in Early HIV(Blaschke & Sheiner)(Blaschke & Sheiner)

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Software Plan: Now & Near Future Software Plan: Now & Near Future (acquire, assure, analyze, save)(acquire, assure, analyze, save)

Warehouse PKS

FDADatabase(eg, EDR,

CDISC)or analysis

datasetsSAS, S+

NONMEM

WinNonlin

FDA DataViewing Software

(e.g., i-review,

WebSDM)

CDISCValidation

CDISC Connector;

DMerge QBRReport

• Data sets• NDA models• Disease models

FDA does not endorse any product

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Future OptionsFuture Options

• Extracting information & problem solving– FDA & NIH are clinical trial data gold mines

• Disease, placebo, drug, dropout, baseline information • Benefits

– Development strategy & clinical trial design– Endpoint & biomarker evaluation– Unanticipated benefits

• Beneficiaries: Industry, FDA, Academics, Public (waste less patient risk & money & time on failed trials

– Dedicate teams to targeted questions– MD’s, biostatistics, epidemiologists, clinical pharmacologists– Manage deliverables like PDUFA time– Learn efficiency– Great FDA new product & career development opportunity

• Sharing information

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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.

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RecommendationsRecommendations

• Pre-competitive knowledge sharing Better define it & develop mechanism for systematic sharing (work expectation)

• Increase investment allow physicians, statisticians & quantitative pharmacologists mine & share prior knowledge & problem solve

• Develop & implement tools– CDISC– Mining– Modeling– Simulation– What if