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

2 Advisory 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?

3 Decisions in Drug Development and at FDA: How combining prior knowledge with quantitative-based decisions can improve productivity & quality Bob Powell, PharmD Pharmacometrics Offices of Clinical Pharmacology & Translational Sciences

4 Outline 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

5 Modeling & Simulation Influences 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

6 The Ultimate ‘Learn-Confirm’ Paradigm

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10 Modeling & Simulation Why? Decrease bias & risk in decisions Overcome complexity (simultaneously thinking about many factors influencing outcome) Increase quality Decrease cost Decrease time

11 Modeling & Simulation Process Act Collect Relevant Information Results Organize into Model(s) Simulate Outcomes or Scenarios Decision Prediction Teach Design Entertain Predictive check Complex Multiple dimensions Raw data best Learning ↑ Risk Expensive Important

12 OCP 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)

13 FDA Pharmacometrics 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

14 Driver for Clinical Trial M&S Declining Success Across Clinical Phases Science 309:726, 2005

15 50% Phase 3 Clinical Trial Failure Rate: Root cause? What to do? True +True -False +False - OBJECTIVE: Root Cause Ø Efficacy ↑ Toxicity Placebo Baseline Dropouts Patient Selection

16 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] Relative Risk MI & STROKE RETINOPATHY NEPHROPATHY DISEASE MODEL CLINICAL TRIAL INFO BASELINE PLACEBO EFFECT DROP-OUT RATE ADHERENCE MODEL BASED DRUG DEVELOPMENT Dose [Drug] [HbA1c] [Drug] Toxicity [HbA1c] [TIME (WEEKS)] Toxicity DRUG MODEL [Drug] Time

17 2 Extract Clinical Trial Information BASELINE EFFECT/ MODEL PLACEBO MODEL DROP-OUT MODEL DESIGN PATIENT DEMOGRAPHICS MECHANISM-SYMPTOMS-OUTCOMES 1 Build Disease & Drug Model TIME 4 Plug Sponsor Data, Play & Decide (Go/No Go, trial design) TRIAL DESIGN PATIENT SELECTION DOSAGE REGIMEN SAMPLE SIZE SAMPLING TIMES ENDPOINTS, ANALYSIS 3 Simulate Scenarios UPDATE 1, 2, 3: PUBLIC LIBRARY Modeling Cycle

18 Moerman, D. E. et. al. Ann Intern Med 2002;136: Duodenal Ulcer Healing Rate in Active (Cimetidine or Ranitidine) vs Placebo Duodenal Ulcer Healing Rate in Active (Cimetidine or Ranitidine) vs Placebo (n=83 studies) Good luck Bad luck

19 Placebo Response in Depression JAMA 287: , 2002 ↑ trial failure risk ↑ false positive risk

20 Parkinson’s disease patients Rx with Levodopa + Selegiline or Placebo for 5 years Eur J Neurol 6: 539, 1999

21 Parkinson’s disease patients Rx with Levodopa + Selegiline or Placebo for 5 years Key Questions Entry criteria & baseline effect Detect disease progression change Dropouts ? ? Eur J Neurol 6: 539, 1999

22 Alzheimer’s Disease Natural History & Drug Response (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

23 Clin Pharmacol Ther 54:556, 1993 AZT Response Relationships in Early HIV (Blaschke & Sheiner)

24 Software Plan: Now & Near Future (acquire, assure, analyze, save) Software Plan: Now & Near Future (acquire, assure, analyze, save) Warehouse PKS FDA Database (eg, EDR, CDISC) or analysis datasets SAS, S+ NONMEM WinNonlin FDA Data Viewing Software (e.g., i-review, WebSDM) CDISC Validation CDISC Connector; DMerge QBR Report Data sets NDA models Disease models FDA does not endorse any product

25 Future 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

26 Sharing Knowledge to Improve Clinical Drug Development & Regulatory Decisions: Data/models of Diseases, Drugs, Placebo, Baseline and Dropouts January 24-25, 2007 Washington Marriott Hotel nd Street NW Washington, DC 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.

27 Recommendations 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