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Disease Models Overview and Case Studies

Disease Models Overview and Case Studies. Joga Gobburu Pharmacometrics Office Clinical Pharmacology, Office of Translational Sciences, CDER, FDA. Pharmacometrics Survey. Between 2000-2006, 72 NDAs needed Pharmacometrics Reviews/Analyses

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Disease Models Overview and Case Studies

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  1. Disease ModelsOverview 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 Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review Bhattaram et al. AAPS Journal.  2005; 7(3): Article 51. DOI:  10.1208/aapsj070351

  4. Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review Impact of Pharmacometrics Analyses 2005-2006 DCP=Division of Clinical Pharmacology @=survey pending in 1 case

  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. NDA#2: Establishment of biomarker-outcome relationship allowed more efficient future trial design • 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

  7. 1.6 0.5 NDA#2: Establishment of biomarker-outcome relationship allowed more efficient future trial design Relative risk of the disease event Hazard ratio=10.0 (95% CI 2.5-30.0) p<0.001 Ratio of biomarker level to baseline

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

  9. Females seem to be more sensitive to QT prolongation Slope Slope Slope 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 Information • Biomarker-Endpoint • Time course • Drop-out • Inclusion/Exclusion • criteria (Trial) Placebo & Disease Models • Parkinson’s • Obesity, Diabetes • Tumor-Survival • Rheumatologic condition • HIV • Epilepsy • Pain Knowledge We are referring to such diverse quantitative approach(es) as ‘Disease Modeling’

  12. Core Development Strategy for Testosterone Suppressants IC50 Reporter Gene Assay - Early screening of compounds based on IC50 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 PKPD data Preclinical Disease Model - In vitro IC50 as a guide for preclinical dose selection - Animal models to measure all possible biomarkers e.g. GnRH, LH, T and Drug conc. Clinical Trial Simulation Dose optimization in cancer patients PKPD data - Invitro and preclinical data for clinical dose and regimen selection - Clinical development plan - Pilot study for dose optimization thr’ innovative trial designs Pivotal trial |----*2 mo-----| |----*2 mo-----| |----*2 mo-----| |----*3 mo-----| |---------*12 mo--------------| *Actual execution time.- it does account for time spent accumulating resources. 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 Model Qualification Baseline Body Weight 3000 patients

  15. Patients with small weight loss drop-out Drop-out patients Remaining patients 0-12 36-52 12-24 24-36

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

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

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