Objectives for today’s talk

1. When to Use CDISC Standards -A practical experience gained from a US submission with Pharmacokinetic dataBASAS, July 5 2011Kristie Kooken, Scott Bahlavooni, Amy Klopman

2. Objectives for today’s talk • To better understand CDISC guidance for Pharmacokinetic (PK) data • To hear first-hand experience using these guidance on a complex filing • To learn about possible challenges with CDISC models for PK data and solutions we implemented STATISTICAL PROGRAMMING AND ANALYSIS

3. Project Background • New Molecular Entity with large PK piece – filing for New Drug Application (NDA) • Small Molecule (taken orally) • PK piece is comprised of two types of studies: • Healthy Volunteers (HV) • Patient Studies • Retrospectively mapped to SDTM structure • PK piece includes 7 studies • 4 are fully-outsourced • 3 are in-house by Genentech • PK & PD analytes for each study • PK analyses cover: • PK characteristics of the drug • Food effects - label enabling • Exposure-response analyses for efficacy and safety • QTc – label enabling • Population PK modeling to determine covariates that could potentially alter dose-concentration relationships Our Goal Is To Develop Support Datasets STATISTICAL PROGRAMMING AND ANALYSIS

4. Data and Data Handling • PK data (any type of assay data) is complex • Time by drug concentrations is unit of analysis • Concentration data receipt is not aligned with other clinical data • PK analysis (i.e. calculating PK parameters), typically occurs late in the clinical trial • At Genentech, a specialty programming group oversees all PK deliverables from Statistical Programming and Analysis • Allows for a deeper understanding of this data type, the purpose of various analyses and greater contribution to analyses STATISTICAL PROGRAMMING AND ANALYSIS

5. PK Data: time vs. concentration plot STATISTICAL PROGRAMMING AND ANALYSIS

6. CDISC Overview • Mission Statement: To develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare • Study Data Tabulation Model (SDTM): [To] define a standard structure for study data tabulations that are to be submitted as part of a product application to a regulatory authority • Analysis Data Model (ADaM): To provide a framework that enables analysis of the data, while at the same time allowing reviewers and other recipients of the data to have a clear understanding of the data’s lineage from collection to analysis to results STATISTICAL PROGRAMMING AND ANALYSIS

7. CDISC Data Flow STATISTICAL PROGRAMMING AND ANALYSIS

8. SDTM PK Domains • Pharmacokinetic Concentrations (PC) • Data collected about concentrations of analytes from blood samples as function of time after dosing • One record per concentration or sample characteristic • Pharmacokinetic Parameters (PP) • Data describing the parameters of the time-concentration curve for PC data, such as Tmax, HL, AUC, Cmax • One record per PK parameter per time-concentration profile • It is recognized the PP is a derived dataset and may be produced from an analysis dataset STATISTICAL PROGRAMMING AND ANALYSIS

9. SDTM PK Domain Ambiguity At DIA/FDA CDER/CBER Computational Science Annual Meeting, Mar 2011 - Helena Sviglin, CDER, stated the FDA is working to better understand PK domains • Should laboratory/PD assessments be included in PC? • Examples: Albumin, urine volume, time-point based glucose • How to relate records in PC to PP? • PPRFTDTC • Analysis metadata • RELREC • How to implement controlled terminology? • PK CT released in 2010 STATISTICAL PROGRAMMING AND ANALYSIS

10. Practical Experience: SDTM & PK • PC and PP domains mapped retrospectively for 4 of 7 studies • Pharmacologists consulted to align reported parameter names with CDISC PK parameter controlled terminology • Selecting the appropriate method to relate PC and PP • Consulted with internal and external subject matter experts including SDTM experts, leading industry experts, Pharmacologists • Differing opinions for defining reference time point • Protocol defined collection schedule vs. analysis time points • Decision: • Protocol defined collection schedule is represented in PC timing variables. PPRFTDTC is defined as the analysis reference date. • PPGRPID is defined to indicated the concentrations used to calculate the parameter. • The specific linking between dosing datetime and PK concentration datetime are described in the analysis metadata STATISTICAL PROGRAMMING AND ANALYSIS

11. Considerations for SDTM & PK (1) • Sponsor • Define and document a methodology for defining PK SDTM timing variables and relating PC and PP domains • Document (e.g. a datastore) mapping reported parameter names to CDISC PK terminology • Study • Prospectively map PC and PP domains • Obtain "test data" early in the clinical study life-cycle • Ensure time point reference dates are clearly defined and collected • Complicated derivations should not be required to assign time point reference dates • Collaborate with the Pharmacologist to clearly document the concentrations used to calculate each PK parameter • Confirm with the Pharmacologist what PK parameters are reported in the Clinical Study Report(s) STATISTICAL PROGRAMMING AND ANALYSIS

12. Considerations for SDTM & PK (2) • CDISC • Reassess PK domains • Does the SDTM IG provide clear guidance to sponsors? • Do the SDTM IG examples sufficiently document various PK data analyses? • Are the examples in the SDTM IG aligned with the controlled terminology? • Can the relationship between the domains be clearly defined? • Solicit sponsor and (FDA) reviewer feedback • Do PK domains meet sponsor tabulation and analytical needs? • Do PK domains meet (FDA) review needs? STATISTICAL PROGRAMMING AND ANALYSIS

13. CDISC Guidance for ADaM • ADSL • The ADSL dataset contains one record per subject • Population flags, planned and actual treatment variables for each period, demographic information, stratification and subgrouping variables, etc. • ADSL and its related metadata are required in a CDISC-based submission of data from a clinical trial even if no other analysis datasets are submitted • Basic Data Structure (BDS) • Dataset with one or more records per subject, per analysis parameter, per analysis time point • Specific features to map for PK data: • Central set of variables represent the data being analyzed • PARAM – Description of the value being analyzed • AVAL – Actual value being analyzed • Timing variables – ADT (date), ATM (time) STATISTICAL PROGRAMMING AND ANALYSIS

14. ADaM BDS Ambiguity • ADaM Implementation Guide states: Though the BDS supports most statistical analyses, it does not support all statistical analyses... it does not support simultaneous analysis of multiple dependent (response/outcome) variables or a correlation analysis across a range of response variables. The BDS was not designed to support analysis of incidence of adverse events or other occurrence data This version of the implementation guide does not fully cover dose escalation trials or integration of multiple studies. • Use ADaM for PK data where it fits… • How did we determine when and where to use BDS? • ADaM BDS works well • Concentration analysis dataset to support summary outputs • ADaMIG is not clear • Datasets used to support modeling or explore PKPD relationships STATISTICAL PROGRAMMING AND ANALYSIS

15. Practical Experience: ADaM & PK (1) Gathered advice from subject matter experts within & outside of Statistical Programming and Analysis: • Met with Genentech Global Data Standards team • Developed basic mapping for ADaM domains • Clinical Pharmacology group met with Modeling & Simulation expert - Marc Gastonguay (Metrum Research Group) • Determined analysis format for Pharmacometric Reviewer Developed a systematic data plan for each analysis: PART 1: • Clinical Study Reports – 6 studies • ADaM structured datasets to support TLG generation • ADPC – Pharmacokinetic concentrations supporting summary outputs • Mapped assay data in support of TLG (inclusion of PD data collected as Safety Labs) • ADPP – Pharmacokinetic parameters supporting summary outputs STATISTICAL PROGRAMMING AND ANALYSIS

16. Practical Experience: ADaM & PK (2) PART 2: • QTc Analysis • Description: ECG and PK concentrations at matching timepoints • Purpose: QTc changes over drug concentration level could be potentially dangerous (cardiotoxicity) • Significant increase from baseline is a clinical concern • Design: Analysis dataset structured to best fit analysis • ADaM-like dataset, all ADSL variables were present • PKPD analyses • Description: Exposure-response analyses with Efficacy / Safety • Purpose: Explore the relationship between drug exposure and pharmacodynamic measures • Design: Structured for NONMEM or S-Plus software • Population PK (5 studies) • Description: Drug concentration data formatted for statistical modeling • Purpose: Estimate the population PK and explore relationships to study covariates (Biomarkers, age, race, baseline disease severity, etc.) • Design: Structured for NONMEM software STATISTICAL PROGRAMMING AND ANALYSIS

17. Considerations for ADaM & PK • Current guidelines for CDISC ADaM limit PK data to the BDS structure • Works well for “Analysis-Ready” PK datasets supporting TLGs • Otherwise, determine best analysis format • Consider analysis tools FDA will use • Whatever analysis format to use, source to SDTM • Focus on “Fit for analysis” CDISC Analysis Data Model Version 2.1: • The focus of any analysis format is to • Facilitate clear and unambiguous communication • Be readily useable by commonly available software tools • Provide traceability between the analysis data and its source data (ultimately SDTM) STATISTICAL PROGRAMMING AND ANALYSIS

18. Other Considerations • Outsourcing • Four of the 7 studies with a PK data piece were outsourced • Create a plan for how to incorporate outsourced data into your filing • Does outsourced SDTM / ADaM match Sponsor SDTM / ADaM? • Do you have in-house all the pieces you need to create analysis deliverables? • Can be difficult to assess if data is only used for population PK analysis STATISTICAL PROGRAMMING AND ANALYSIS

19. Questions STATISTICAL PROGRAMMING AND ANALYSIS

20. Additional Information • Pharmacology: • LABELING • http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm065010.htm • BIOPHARMACEUTICS • http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm064964.htm • CLINICAL PHARMACOLOGY • http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm064982.htm • MODEL/DATA FORMAT • http://www.fda.gov/AboutFDA/CentersOffices/CDER/ucm180482.htm • SDTM / Define xml: • FDA (data standards) • http://www.fda.gov/ForIndustry/DataStandards/default.htm • CDISC (standards) • http://www.cdisc.org/standards • OpenCDISC (SDTM validator) • http://www.opencdisc.org/projects/validator • SAS (clinical standards toolkit) • http://support.sas.com/rnd/base/cdisc/cst/index.html • FDA Standards for Electronic Submissions: • SAShttp://www.sas.com/industry/government/fda/faq.html STATISTICAL PROGRAMMING AND ANALYSIS

21. Thank you to: • Bei Wang • Michael Ward • Laura Harris • Rick Graham • Jin Jin • Yongcun Zhang • Patty Gerend STATISTICAL PROGRAMMING AND ANALYSIS