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PHARMACOMETRICS AND BIOSTATISTICS INTERACTIONS AT THE FDA Jeffry Florian, Ph.D. Division of Pharmacometrics OCP/OTS/CDER U. S. Food and Drug Administration Presented at ASA 2016 Biopharmaceutical Section Regulatory-Industry Statistics Workshop in Washington, D.C. on September 29th, 2016 www.fda.gov 1 Disclaimer • The 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). www.fda.gov 2 Sometimes it may feel like…. Pharmacometrics www.fda.gov Statistics 3 A slightly different analogy? • Similar interests and tools – Understanding uncertainty and promoting public health – Statistics, drug development, clinical trial design 4 Organizational Location Office of Translational Sciences Vacant www.fda.gov 5 Geographical Location www.fda.gov 6 Divisions Within Each Office Office of Clinical Pharmacology Office of Biostatistics • Immediate Office • Immediate Office • Div. of Clinical Pharmacology I, II, III, IV, and V • Division of Biometrics I, II, • Division of Pharmacometrics III, IV, and V • Genomics and Targeted Therapy Group • Division of Biometrics VI • Division of Applied and VIII Regulatory Sciences http://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedical ProductsandTobacco/CDER/ucm106189.htm www.fda.gov http://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedical ProductsandTobacco/CDER/ucm166250.htm#Role 7 Offices Interact in All Activities Pre-IND Protocol Reviews - Dose finding - Registration NDA/BLA Reviews Regulatory Research QT reviews Pediatrics EOP1/2/2A Pre-NDA/BLA meetings meetings Efficacy Supplements Guidance and Policy Labeling PostMarketing Biosimilars 8 Various Internal/External Collaborations • OCP-OB Collaborative Working Group (2012) – Good practices for early and timely interactions – Team building activities between Offices – Periodic Office meetings for scientific exchange (Multi-Disciplinary OB-OCP Scientific Exchange) • External groups facilitating interactions 9 Example 1: Sofosbuvir (SOF, GS-7977) • Prodrug of a nucleotide analog inhibitor of the hepatitis C virus NS5B RNA-dependent RNA polymerase • First-in-class submission (breakthrough designation) – Broad genotypic activity • Proposed indication: in combination with other agents for treatment of chronic hepatitis C (CHC) in adults • Sofosbuvir 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 10 10 Phase 3 Trial Design: GT 1, 4, 5, 6 Trial Name Population Regimen* and Duration GS-US-334-0110 (NEUTRINO) Treatment-Naïve SOF+PEG/RBV 12 Weeks 0 GS-US-334-0110 NEUTRINO GT1/4/5/6 Treatment-naïve 12 SOF + PEG/RBV N=327 Weeks 24 36 SVR12 N=Number of subjects; PEG=Pegylated Interferon; RBV=Ribavirin *SOF (400 mg/day) + PEG (180 g/week) + RBV (1000 or 1200 mg/day) 11 11 Evidence to Support Effectiveness of SOF+PEG/RBV in Genotype 1 PEG/RBV Treatment-experienced Subjects Source Evidence to support effectiveness of SOF in PEG/RBV TE subjects - Treatment-Naïve, Phase • PEG/RBV TE subjects are represented III trial (GS-US-334-0110) within the treatment-naïve population - Indirect evidence from previous PEG/RBV trials • Confirmation of predictive baseline factors and response rate in population - Indirect evidence from previous DAA+PEG/RBV trials • Comparison with other approved products suggest a higher response rate • Various bridging analyses conducted based on baseline factors, relative risk, and odds ratio analyses 12 Outcome • Analyses were presented and discussed at the sofosbuvir advisory committee meeting • Majority 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 subjects • Sofosbuvir labeling reflects that SOF+PEG/RBV can be used in GT 1 PEG/RBV TE subjects • Multiple factor analysis is described in the label and HCV treatment guidelines • Joint publication of FDA’s analyses and rationale 13 Example 2: MCP-Mod • “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) 14 Example contributed by Lei Nie, Dionne Price, Mohamed Alosh, Dinko Rekić, and Yaning Wang MCP-Mod • Analysis of dose-response studies has been divided into two primary strategies – multiple 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 stage – plausible candidate models are selected • Analysis stage – assess the dose-response signal using MCP and select ‘best’ model – Fit the selected model(s) to the data and estimate the target dose 15 MCP-Mod (cont.) • Office of Biostatistics and Office of Clinical Pharmacology, Division of Pharmacometrics jointly reviewed submission – – – – Assess materials and simulations provided by the sponsor Additional sensitivity analyses identified by the reviewers Identify advantages/disadvantages of MCP-Mod Worked in close collaboration throughout review • Review 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 16 For the Determination Letter and Discipline Reviews http://www.fda.gov/drugs/developmentapprovalprocess/ucm505485.htm 17 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 approach Example contributed by John Lawrence, DJ Marathe, James Hung, Sue-Jane Wang 18 TKV Biomarker Qualification submission • Objective: 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 19 Qualification of TKV in ADPKD • Biomarker Qualification Review Team (BQRT) conducted additional analyses and performed model development and cross validation o Analyses 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). o 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 preference o BQRT also carried out an external/independent validation using a separate internal dataset 20 20 Qualification of TKV in ADPKD Conclusions: • 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 Cstatistics using either – submitter’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 analyses Use 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 enrichment 21 21 For the Executive Summary and Reviews http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificatio nProgram/ucm458492.htm 22 Example 4: QT-IRT • US FDA established the Interdisciplinary Review Team for QT studies (June 2006) - Provide reliable, consistent advice to FDA drug review divisions - Promote advances in design of QT studies • Members include medical officer, clinical pharmacologist, statistician, pharmacologist, project manager, and data manager • Responsible for reviewing protocols and study reports related to QT assessment • ~45 reports per year, ~70 protocols per year Example contributed by numerous individuals throughout the years 23 QT-IRT Responsibilities • Reviews protocols and study reports related to QT assessment • Ensures that sponsors and review divisions consistently receive the best available advice on these studies • Participates in internal and sponsor meetings, as needed • Establishes and maintains an administrative tracking system for QT studies 24 24 Extensive Involvement from QT-IRT • Policy development – Q&A for ICH E14, 2008: ECG measurement method, assay sensitivity, baseline definition – Q&A Revision 1, 2012: heart rate correction, late stage monitoring, sex differences – Q&A Revision 2, 2014: concentration-QT, large proteins, combination products, special cases – Q&A Revision 3, 2015: concentration-QT • Numerous external presentations and workshops – Drug Information Association, Cardiovascular Safety Research Consortium • Advancing science of cardiac safety – IQ-CSRC study for concentration QT1 – Comprehensive in vitro Proarrhythmia (CiPA) initiative Darpo et al. 2015 25 Summary • Our groups interact at the FDA during all stages of drug development • Overlapping but complementary skillsets – Greater understanding when working together – Various pitfalls, but room for growth Statistics Pharmacometrics 26 Acknowledgements • Sofosbuvir – Karen Qi, Wen Zeng, Dionne Price, Division of Antiviral Products • MCP-Mod – Lei Nie, Dionne Price, Mohamed Alosh, Dinko Rekić, and Yaning Wang • Total Kidney Volume Biomarker Qualification – John Lawrence, DJ Marathe, James Hung, Martina Sahre, Sue-Jane Wang, Hobart Rogers • IRT-QT Team – Devi 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 Wang • Office of Clinical Pharmacology at FDA • Office of Biostatistics at FDA 27 Questions 28