FDA Experience with End of Phase IIa Meetings: An Attempt to Improve Drug Development Decisions

1. FDA Experience with End of Phase IIa Meetings: An Attempt to Improve Drug Development Decisions Acknowledge: Larry Lesko, Don Stanski, Joga Gobburu, Peter Lee, Yaning Wang, Jenny Zheng and many others Bob Powell, Pharm.D. Office of Clinical Pharmacology FDA powellr@cder.fda.gov (301) 796-1589

2. INFLUENCE SWEET SPOT When to Influence Clinical Drug Development ? • Flexibility • Learning • $ R&D Expense • $ Revenue NDA Market Phase 1 Discovery Phase 2 Phase 3 Preclinical

3. True + True - False + False - OBJECTIVE: 50% Clinical Trial Failure Rate:Is it true? What to do? • Root Cause • Ø Efficacy • ↑ Toxicity • Placebo • Baseline • Dropouts • Patient Selection Account for known failure sources (prior information) in clinical trial design

4. Reasons for Poor Decisions(Definition: an outcome which should/could have been anticipated) • Conspiracy of optimism • Framing the problem too narrowly to bring it inside my own comfort zone • Not involving the right people • Avoiding uncertainty • Ignoring information I do not understand • Being attached to ‘sunk costs’ – high spent development costs • Ignoring risks • Assuming no uncertainty in potential outcomes • Making decision alone BIAS Hammond, Keeney, Raiffa. Smart Choices Harvard Business School Press, 1999

5. Model Based Drug DevelopmentWhat is it? • Objective: improve decision quality by employing drug-disease models & clinical trial simulation • Model: mathematical explanation of relationships thought to explain outcome over time period of interest • Drug-Disease Model (empiric & mechanistic) • Disease model: relationship of patient (e.g., gender, age, genotype), biomarker (e.g., biochemical, imaging) relationship to disease morbidity and mortality • Drug-Disease model: addition of drug (dose, concentration, combination, placebo) and patient (e.g., size, age, adherence, dropout) effects and adverse effects to the disease model • Simulation- Target • Clinical trial design- optimal • New designs-enrichment, randomized withdraw, adaptive • Dosage regimen(s) selection • Go/No go- Sponsor &/or FDA • Labeling- Sponsor &/or FDA

6. End of Phase 2a Meetings • Purpose: ↓ Late phase clinical trial (2b, 3) unnecessary failure • Format: non-binding scientific interchange. Marketing issues should be in the development plan, not at this meeting. • Deliverables: • Perform modeling (relevant phase 1/2a data) & simulation of next trial design employing • Mechanistic or empirical drug-disease model • Literature estimates for comparative drug effects if relevant • Placebo effect (magnitude & time-course) • Rates for dropout and compliance. (prior FDA experience) • Recommendation on sponsors trial design + alternative including patient selection, dosage regimen,… • Code from FDA work, Sponsor can extend work (EOP2, NDA) • Answers to other questions from the clinical and clinical pharmacology development plan • Time-course: ~ 6 weeks • Key sponsor & FDA participants: physician, biostatistician, clinical pharmacology (pharmacometrics), project management

7. EOP2a Meeting Process Meeting Request Letter & Questions FDA Evaluation & Approval Sponsor Phone Meeting: Explain Process & Data Needed FDA Receive Briefing Package, Data, Next Trial Design 6 week start • Final Meeting • Focus on Drug-disease modeling & Clinical trial simulations • 30-40 min Presentation • 1 hour dialogue focused on trial design, dosage regimens, patient selection • Simulation Strategy • Trial Design Alternatives • Dosage Regimens • Sample Size • Patient Selection • Sponsor or FDA? FDA begin data analysis Sponsor Questions Answer other question in writing before meeting

8. Roles & Responsibilities • Project manager • Sponsor communication • FDA meetings • Documentation • Physician • Primary endpoints • Disease information • Trial design • Draft guidance • Clinical Pharmacology/ Pharmacometrics • Drug-disease modeling • Dosage regimens • Drug interactions • Simulations • Statistician • Trial Design • Prior trial information • Placebo • Dropout rates • Simulation

9. Case Study: HIV Phase 2a Meeting Key Questions for ‘Drug X’ • Is the target AUC of 950 ng∙h/mL (based on relationship to viral load suppression at day 11 of Phase IIa trial) reasonable to select the best dose for Phase III? • Is testing BID and QD regimens appropriate? • Are 4 weeks adequate to select the dosage regimen for Phase III?

10. 1.5 Day 11 HIV change from baseline (log10 copies/mL) 950 Drug X AUC (ng-hr/mL) RNA Change from BL vs AUC(0-24) An AUC(0-24) of ~950 ng-h/mL is predicted to achieve a 1.5 log10 decrease in HIV-1 RNA from BL

11. A Mechanistic PK/PD Model • Mechanistic viral dynamic model offers potential advantages over empirical models: • Time course of virus load described • Schedules (bid vs. qd) can be differentiated • Drug-drug interactions • Different design scenarios can be evaluated • Adherence • Pharmacodynamic interactions • Drop out • Resistance

12. Proposed Phase IIb Design Cohort 1 (N=50): ‘Drug X’ 1 mg BID + LPV/r 400/100mg BID Cohort 2 (N=50): ‘Drug X’ 2 mg BID + LPV/r 400/100mg BID Cohort 3 (N=50): ‘Drug X’ 4 mg QD + LPV/r 400/100mg BID Standard of Care (SOC) (N=25): COMBIVIR (ZDV/3TC 300/150mg BID) + LPV/r 400/100mg BID

13. Virus Dynamic Model (virus load vs time) p d2 PI Active Infected l:production rate of target cell d1: dying rate of target cell c: dying rate of virus b: infection rate constant d2: dying rate of active cells d3: dying rate of latent cells p: production rate of virus l fAbVT CD4+ Cells (N)NRTI Virus a + fLbVT Latent Infected (N)NRTI d1 c d3 fA=0.96 and fL=0.03 J Acquir Immun Defic Syndr 26:397, 2001

14. ‘Drug X’ Observed and Model Predicted Mean Virus Load vs Time & Dose 2 mg QD 4 mg QD 2 mg BID 6 mg BID

15. Individual Patients Fit for Drug X

16. Drug-Disease Model ComponentsApplication • Adherence ↑Dose → GI Adverse Effects → ↓Adherence • Dropout rate: biphasic Prior experience from other drugs • Drug-drug interaction • PK • PD • Verified model with data from prior drugs…did not share with sponsor

17. Viral Dynamics During 4 Weeks‘Drug X’ + Kaletra Kaletra BID HIV RNA (Log10 copies/mL) 2Log Drop Drug X 0.5, 1, 2 mg BID and 4QD Log10(50) Time (week)

18. Dropout Model: Prior Study Submission

19. 20 Simulated trials (2 log drop, 90% Adherence, No Drop-out) suggest 2 BID is most likely the winner 100% 90% 80% 70% EQUAL 60% 4 QD Chances of Being the Winner 50% 2 BID 40% 1 BID 30% 20% 10% 0% 4 8 12 16 20 24 28 32 36 40 44 48 Week

20. HIV Phase 2a Meeting Key FDA Response to Questions for ‘Drug X’ • Is the target AUC of 950 ng∙h/mL reasonable? • Concentration targeted dose selection is more appropriate to compare schedule (BID vs QD). • Is testing BID and QD regimens appropriate? • BID regimen is preferable • 0.5 mg BID, instead of 4 mg QD, is worth considering • Are 4 weeks adequate to select the dose? • Low power to discern dose-viral load response thr’ 96wks. Selection of doses based on toxicity might be possible. • No, weeks 12-16 acceptable for preliminary assessment (pick dose for Phase III trial) and week 24 for confirmation based upon prior experience. Continue trial through week 48 for all doses. • In addition: • Kaletra effect is so strong that it may be difficult to demonstrate ‘Drug X’ dose-response in combination • Phase IIb trial was adequately designed to determine dose-response

21. EOP2a Meeting Metrics • Completed 5 over past year, 3 in progress • Therapeutic area: problem: • HIV: new mechanism, dosing • Prostate Ca: Formulation/dosing • Type 2 Diabetes: Genotype, dosing • Anticonvulsant: New mechanism • VMS (hot flashes): New mechanism, dosing • Pain: receptor specificity/adr’s, dosing • Weight-loss: new mechanism, dosing • Workload: 5-7 person-months/project • Sponsor evaluation (post-meeting): 4.1-4.3 (1=worthless, 5=pivotal)

22. Sponsor’s Comments on the Experience (Abstracted from their Senior R&D Meeting slides) • FDA’s Clinical Pharmacologists are very serious about leveraging Clin Pharm to: • Aid selection of dosage regimens for Phases 2b & 3 • Reduce attrition in Phase 3 • Design better Phase 3 studies • FDA is inviting sponsors to participate for certain drugs • Drugs with reliable, quantitative measures of response and reasonable pk-pd models • Projects in early Phase 2 • Particular interest in novel compounds • FDA’s preparation was very extensive • Analyzed our exposure-response data • Applied pk-pd models (based on literature) • Had very detailed feedback • Our preparation must be extensive • Need high caliber Clin Pharm expertise • Be ready to submit datasets & programs from all pk-pd studies - be ready for urgent queries • Work with our scientific team to prepare thoroughly for the meeting

23. IND/NDA Data Review & AnalysisFDA Clinical Pharmacology Work Plan NDA Quantitative Analysis & CP Review Report (Disease specific) • Benefit/Risk • Dose-Response • Drug Interaction • Special Populations • Pharmacogenomic Data Input • EDR • CDISC • SAS data set • (Janus) NDA Disease Modules Data Analysis • Nonmem • SAS • S-Plus EOP2a Disease Models Data Visualization & Data Set Creation (I-Review) Data Warehouse (PKS) Clinical Trial Simulation (TS2) End of Phase 2a Recommendation (2b/3 trial design)

24. GREEK FRENCH FARSI Māori DUTCH Zulu MANDARIN VIETNAMESE SWEDISH DUTCH Swahili CANTONESE • UNITED NATIONS • WORLD BANK • INSEAD • NOVARTIS MONGOLIAN German RUSSIAN ARABIC PORTUGESE ENGLISH ITALIAN NORWEGIAN TURKISH Japanese HINDI ENGLISH SPANISH KOREAN

25. Ferring GENENTECH MERCK GENZYME AMGEN GSK ROCHE Elan NovoNordisk J&J FDA Barr Millenium Daiichi IDEC Berlex Eli Lilly CDISC AstraZeneca Biogen MYLAN Clinical Data Interchange Standards Consortium www.cdisc.org MEDIMMUNE ALLERGAN PFIZER ABBOTT Sanofi

26. Drug-disease models at FDA • Primary sources: literature, scientists, prior NDA’s • Types • Mechanistic • Empirical • Diseases over past year • HIV • Diabetes Mellitus • Parkinson’s Disease • Vasomotor Symptoms (Hot Flashes) • SLE-renal flare • Prostate Cancer- chemical castration • Kidney transplant rejection • In Development • Osteoporosis • Non-small cell lung cancer • Considering • How to share models & some data on public website. Public dialogue on growing models

27. Diabetes 1st order Oral Absorption FPG Cmt 1 Cmt 2 HbA1c Drug Conc. FPG HbAlc Time (Week)

28. Modeling Results for FPG & HBA1CDrug X in 1,000 patients FPG HbA1c

29. ‘Save a Disease’ Prototype • Objective: Quantitative library → ↑ trial outcome • 3 levels: clinical trial data, derived quantitative information, drug-disease models • Derived quantitative information (mean, variability, time-course) Create Standards • Disease, clinical trial, patient description • Biomarker/1° endpoint(s) • Placebo • Dropout rate • Drug (biomarker, 1° endpoint(s), adverse effects) • Covariates • 2 diseases chosen for prototype • Process: Prototype → small to larger groups (FDA>Sponsors>Academics) Test value, set standards, fund

30. In Summary • End of Phase 2a Meetings pilot started • Program evaluation & progression request within 2 months • Draft guidance publication • Literature publication(s) • Model based drug development • ‘Save a Disease’ prototype • Send an academic friend to FDA to create a disease model (funding available) • Software system for Clinical Pharmacology • CDISC standard needs to accelerate. This is a rate limiting step! • Your comments & recommendations?