1. Disease Models Overview and Case Studies Joga Gobburu Pharmacometrics Office Clinical Pharmacology, Office of Translational Sciences, CDER, FDA

2. Pharmacometrics Survey Between 2000-2006, 72 NDAs needed Pharmacometrics Reviews/Analyses For each of the Pharmacometrics Reviews, the ‘customers’ were asked to rate the impact on approval related and labeling decisions: –Pivotal: Decision would not have been the same without Pharmacometrics analysis –Supportive: Decision was well supported by the Pharmacometrics analysis –No Contribution: No need for the Pharmacometrics analysis

3. Impact of Pharmacometrics Analyses 2000-2004 Bhattaram et al. AAPS Journal. 2005; 7(3): Article 51. DOI: 10.1208/aapsj07035110.1208/aapsj070351 ImpactApprovalLabeling Pivotal54%57% Supportive46%30% No Contribution014% Pivotal: Regulatory decision will not be the same without PM review Supportive: Regulatory decision is supported by PM review

4. Impact → Discipline ApprovalLabeling PM Reviewer95%100% DCP Reviewer95%100% DCP TL90%94% Medical Reviewer90% @ DCP=Division of Clinical Pharmacology @=survey pending in 1 case Impact of Pharmacometrics Analyses 2005-2006

5. NDA#1: Approval of monotherapy oxcarbazepine in pediatrics for treating partial seizures using prior clinical data FDA/Sponsor pursued approaches to best utilize knowledge from the previous trials to assess if monotherapy in pediatrics can be approved without new controlled trials

6. The sponsor was pursuing an accelerated approval, for drug to prevent a life- threatening disease, based on a biomarker even though clinical endpoint analysis failed in two pivotal trials NDA#2: Establishment of biomarker- outcome relationship allowed more efficient future trial design

7. Ratio of biomarker level to baseline Hazard ratio=10.0 (95% CI 2.5-30.0) p<0.001 Relative risk of the disease event 0.5 1.6

8. NDA#3: Insights into trial failure reasons will lead to more efficient future trials Mild Baseline Disease Non-Responders Severe Baseline Disease Responders

9. Females seem to be more sensitive to QT prolongation Slope

10. Need/Opportunities for Innovative Quantitative Methods in Drug Development Optimal design to show ‘disease modifying’ effects? Good marker(s) of survival benefit in cancer patients? Maximize the change of success of a 2yr obesity trial? Given 85% of depression trials fail, how to improve success? Best dose for a 26wk trial based on 12 wk data? Providing solutions for these issues calls for efficient use of prior knowledge

11. Manage and Leverage Knowledge Knowledge Placebo & Disease Models InformationInformation Biomarker-Endpoint Time course Drop-out Inclusion/Exclusion criteria (Trial) Parkinson’s Obesity, Diabetes Tumor-Survival Rheumatologic condition HIV Epilepsy Pain We are referring to such diverse quantitative approach(es) as ‘Disease Modeling’

12. Core Development Strategy for Testosterone Suppressants Disease Model Reporter Gene Assay Preclinical Clinical Trial Simulation Dose optimization in cancer patients Pivotal trial |----*2 mo-----| *Actual execution time.- it does account for time spent accumulating resources. |----*2 mo-----| |----*3 mo-----||---------*12 mo--------------| - Early screening of compounds based on IC 50 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 IC 50 PKPD data - In vitro IC 50 as a guide for preclinical dose selection - Animal models to measure all possible biomarkers e.g. GnRH, LH, T and Drug conc. - Invitro and preclinical data for clinical dose and regimen selection - Clinical development plan - Pilot study for dose optimization thr’ innovative trial designs PKPD data From Pravin Jadhav, VCU/FDA

13. Obesity Obesity trials are large, over 1-2 yrs and fraught with challenges due to high drop- out rate Dr. Jenny J Zheng Dr. Wei Qiu Dr. Hae Young Ahn

14. Obesity Baseline Body Weight 3000 patients Model Qualification

15. 0-12 12-2424-36 36-52 Drop-out patients Remaining patients Patients with small weight loss drop-out

16. Obesity: Time Course of Placebo Effect

17. Value to Drug Development Effective use of prior data for designing future registration trials Might lead to alternative dosing considerations –Titration vs. fixed dose –Could lead to increased trial success Allows of designing useful shorter duration trials for future compounds for screening and initial dose range selection

18. Diabetes How to reliably select doses for registration trials based on abbreviated dose finding trials Need arose from an EOP2A meeting –Work in progress: No patient population and drop-out models yet. Drs. Vaidyanathan, Ahn, Yim, Zheng, Wang, Gobburu, Powell, Sahlroot, Orloff

19. Pivotal Trial Dose Selection: Anti- Diabetic Sponsor conducted 12 wk dose ranging trial in diabetics Key Regulatory Question –What is a reasonable dose range and regimen for the pivotal trial(s)? Challenge –Estimate of effect size on HbA1c at 26 wks not available. Effect size on FPG available.

20. FPG HbA1c HbAlc FPG Drug Conc. Time (Week) Cmt 1Cmt 2 1 st order Oral Absorption FPG-HbA1c relationship from historic studies employed to estimate effects on HbA1c of the new compound Jusko et al

21. Biological relationship between FPG- HbA1c bridged information gap + = Drug X (Sponsor) in 72 patients Drug X (other) in 28 patients Hybrid dataset in 100 patients

22. Value to Drug Development More informed dose/regimen selection –Could lead to increased trial success Quantitative analysis was critical Effective use of prior data for predictions Supports conduct of useful shorter duration trials for future compounds

23. Disease Models: Challenges Data Management –How to best maintain an efficient database? Analysis –How to best conduct meta-analysis? –Identify and fill gaps (time-varying biomarkers in survival models)? Inter-disciplinary collaboration –Biologists, Pharmacologists, Statisticians, Disease Experts, Quantitative Clinical Pharmacologists, Engineers need to come together to develop these models as a team.