Pharmacometrics and Biostatistics Interactions at the FDA - PowerPoint Presentation

1. Pharmacometrics and Biostatistics Interactions at the FDAJeffry Florian, Ph.D.Division of PharmacometricsOCP/OTS/CDERU. S. Food and Drug Administration www.fda.govPresented at ASA 2016 Biopharmaceutical Section Regulatory-Industry Statistics Workshop in Washington, D.C. on September 29th, 2016

2. Disclaimerwww.fda.govThe opinions expressed in this presentation are the presenter’s and do not necessarily reflect the official views of the United States Food and Drug Administration (FDA).

3. Sometimes it may feel like….www.fda.govStatisticsPharmacometrics

4. A slightly different analogy?Similar interests and toolsUnderstanding uncertainty and promoting public healthStatistics, drug development, clinical trial design

5. Organizational Locationwww.fda.govOffice of Translational SciencesVacant

6. Geographical Locationwww.fda.gov

7. Divisions Within Each OfficeOffice of Clinical PharmacologyImmediate OfficeDiv. of Clinical Pharmacology I, II, III, IV, and VDivision of PharmacometricsGenomics and Targeted Therapy GroupDivision of Applied Regulatory SciencesOffice of BiostatisticsImmediate OfficeDivision of Biometrics I, II, III, IV, and VDivision of Biometrics VI and VIIIwww.fda.govhttp://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/ucm106189.htmhttp://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/ucm166250.htm#Role

8. NDA/BLAReviewsPre-IND8ProtocolReviews- Dose finding- RegistrationQT reviewsOffices Interact in All ActivitiesRegulatoryResearch EOP1/2/2AmeetingsPre-NDA/BLAmeetingsPediatricsEfficacy SupplementsLabelingGuidance andPolicyPost-MarketingBiosimilars

9. Various Internal/External CollaborationsOCP-OB Collaborative Working Group (2012)Good practices for early and timely interactions Team building activities between OfficesPeriodic Office meetings for scientific exchange (Multi-Disciplinary OB-OCP Scientific Exchange)External groups facilitating interactions

10. 1010Example 1: Sofosbuvir (SOF, GS-7977) Prodrug of a nucleotide analog inhibitor of the hepatitis C virus NS5B RNA-dependent RNA polymeraseFirst-in-class submission (breakthrough designation)Broad genotypic activityProposed indication: in combination with other agents for treatment of chronic hepatitis C (CHC) in adultsSofosbuvir was studied in combination with RBV for GT 2 and 3, and in combination with PEG/RBV for GT 1, 4, 5 and 6.Example contributed by Karen Qi, Jeff Florian, Wen Zeng, and Dionne Price

11. 11Phase 3 Trial Design: GT 1, 4, 5, 60122436WeeksSOF + PEG/RBVN=327SVR12GS-US-334-0110NEUTRINOGT1/4/5/6Treatment-naïveN=Number of subjects; PEG=Pegylated Interferon; RBV=RibavirinTrial NamePopulationRegimen* and DurationGS-US-334-0110 (NEUTRINO)Treatment-NaïveSOF+PEG/RBV 12 Weeks*SOF (400 mg/day) + PEG (180 g/week) + RBV (1000 or 1200 mg/day)

12. Evidence to Support Effectiveness of SOF+PEG/RBV in Genotype 1 PEG/RBV Treatment-experienced SubjectsSourceEvidence to support effectiveness of SOF in PEG/RBV TE subjectsTreatment-Naïve, Phase III trial (GS-US-334-0110)Indirect evidence from previous PEG/RBV trialsIndirect evidence from previous DAA+PEG/RBV trialsPEG/RBV TE subjects are represented within the treatment-naïve populationConfirmation of predictive baseline factors and response rate in populationComparison with other approved products suggest a higher response rateVarious bridging analyses conducted based on baseline factors, relative risk, and odds ratio analyses

13. OutcomeAnalyses were presented and discussed at the sofosbuvir advisory committee meetingMajority of the committee agreed with the review team’s conclusion that this regimen would be no worse than available treatment options for GT 1 PEG/RBV TE subjectsSofosbuvir labeling reflects that SOF+PEG/RBV can be used in GT 1 PEG/RBV TE subjectsMultiple factor analysis is described in the label and HCV treatment guidelines Joint publication of FDA’s analyses and rationale

14. “Of the unsuccessful first-time applications, 24 (15.9%) included uncertainties related to dose selection”“Failure to determine the most appropriate dose for clinical use was a major reason for nonapproval.”Sacks et al, JAMA (2014)Example 2: MCP-ModExample contributed by Lei Nie, Dionne Price, Mohamed Alosh, Dinko Rekić, and Yaning Wang

15. MCP-ModAnalysis of dose-response studies has been divided into two primary strategiesmultiple comparison procedures (MCP)model-based approaches (Mod)When applied separately, each strategy has shortcomings that may impact the decision-making process. Submission by Janssen Pharmaceuticals/Novartis Pharmaceuticals intended to support the use of MCP-Mod as an efficient statistical methodology (combines both strategies)Design stageplausible candidate models are selectedAnalysis stageassess the dose-response signal using MCP and select ‘best’ modelFit the selected model(s) to the data and estimate the target dose

16. MCP-Mod (cont.)16Office of Biostatistics and Office of Clinical Pharmacology, Division of Pharmacometrics jointly reviewed submissionAssess materials and simulations provided by the sponsorAdditional sensitivity analyses identified by the reviewersIdentify advantages/disadvantages of MCP-ModWorked in close collaboration throughout reviewReview team: We conclude that MCP-Mod either has similar power to or outperforms alternative approaches and is fit-for-purpose under the defined context of use

17. For the Determination Letter and Discipline Reviewshttp://www.fda.gov/drugs/developmentapprovalprocess/ucm505485.htm

18. Example 3: Qualification of Total Kidney Volume (TKV) in Autosomal Dominant Polycystic Kidney Disease (ADPKD)Joint FDA-EMA submission from Polycystic Kidney Disease Outcomes Consortium (PKDOC)PKDOC approachExample contributed by John Lawrence, DJ Marathe, James Hung, Sue-Jane Wang

19. TKV Biomarker Qualification submissionObjective: Clinical trial enrichment in Autosomal Dominant Polycystic Kidney Disease (ADPKD)Stage of Drug Development for Use: All clinical stages of ADPKD drug development, including proof of concept, dose-ranging, and confirmatory clinical trials.Proposed Context of Use: Baseline TKV can be applied as a prognostic biomarker that, in combination with patient age, can be used to help identify those ADPKD patients who are at the greatest risk of advancing in the course of their disease to a point where there is substantial decline in renal function as measured by clinically meaningful outcomes (30% worsening of eGFR , 57% worsening of eGFR (equivalent to doubling of serum creatinine), and ESRD). eGFR: Estimated Glomerular Filtration rate; ESRD: End Stage Renal Disease

20. Qualification of TKV in ADPKD Biomarker Qualification Review Team (BQRT) conducted additional analyses and performed model development and cross validationAnalyses limited to patients with an eGFR ≥25 and at least 12 years of age, which represent the population likely to be enrolled in clinical trials (925 subjects with 300 events). Some subjects had imaging performed with more than one modality. FDA reviewers selected MRI data as the first preference, CT data as the second preference and ultrasound data as the last preferenceBQRT also carried out an external/independent validation using a separate internal dataset20

21. Qualification of TKV in ADPKDConclusions: Substantial improvement on predictive performance of event risk (based on a concordance measure for time-to-event data) of a fitted survival model including log (TKV) as compared to not including log (TKV) based on the C-statistics using eithersubmitter’s registry data in model development (with cross validation) in a clinical trial data that is available internally as independent validation.Too few ESRD and 57% decline in eGFR events over the time frame of a feasible clinical trial to perform meaningful analysesUse Statement: TKV, measured at baseline, is qualified as a prognostic enrichment biomarker select patients with ADPKD at high risk for a progressive decline in renal function (defined as a confirmed 30% decline in the patient’s eGFR) for inclusion in interventional clinical trials. May be used in combination with age and baseline eGFR for enrichment21

22. For the Executive Summary and Reviewshttp://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/ucm458492.htm

23. Example 4: QT-IRTUS FDA established the Interdisciplinary Review Team for QT studies (June 2006)Provide reliable, consistent advice to FDA drug review divisionsPromote advances in design of QT studiesMembers include medical officer, clinical pharmacologist, statistician, pharmacologist, project manager, and data managerResponsible for reviewing protocols and study reports related to QT assessment~45 reports per year, ~70 protocols per yearExample contributed by numerous individuals throughout the years

24. 24QT-IRT ResponsibilitiesReviews protocols and study reports related to QT assessmentEnsures that sponsors and review divisions consistently receive the best available advice on these studiesParticipates in internal and sponsor meetings, as neededEstablishes and maintains an administrative tracking system for QT studies

25. Extensive Involvement from QT-IRTPolicy developmentQ&A for ICH E14, 2008: ECG measurement method, assay sensitivity, baseline definitionQ&A Revision 1, 2012: heart rate correction, late stage monitoring, sex differencesQ&A Revision 2, 2014: concentration-QT, large proteins, combination products, special casesQ&A Revision 3, 2015: concentration-QTNumerous external presentations and workshopsDrug Information Association, Cardiovascular Safety Research ConsortiumAdvancing science of cardiac safetyIQ-CSRC study for concentration QT1Comprehensive in vitro Proarrhythmia (CiPA) initiativeDarpo et al. 2015

26. SummaryOur groups interact at the FDA during all stages of drug development Overlapping but complementary skillsetsGreater understanding when working togetherVarious pitfalls, but room for growthStatisticsPharmacometrics

27. AcknowledgementsSofosbuvirKaren Qi, Wen Zeng, Dionne Price, Division of Antiviral ProductsMCP-ModLei Nie, Dionne Price, Mohamed Alosh, Dinko Rekić, and Yaning WangTotal Kidney Volume Biomarker QualificationJohn Lawrence, DJ Marathe, James Hung, Martina Sahre, Sue-Jane Wang, Hobart RogersIRT-QT TeamDevi Kozeli, Norman Stockbridge, Christine Garnett, Jiang Liu, Kevin Krudys, Nitin Mehrotra, Joanne Zhang, Qianyu Dang, John Koerner (and numerous other individuals throughout the years)Yaning WangOffice of Clinical Pharmacology at FDAOffice of Biostatistics at FDA

28. Questions

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