1. CDISC Implementation Strategies: Lessons Learned & Future Directions MBC Biostats & Data Management Committee 12 March 2008 Kathleen Greene & A. Brooke Hinkson, BioMedical Operations, Genzyme Corporation

2. Agenda Take you on a journey through time to reflect on Genzyme’s CDISC implementation strategies We will travel  Back in time to “The Past”  Through “The Present”  Into “The Future” Questions & Comments

3. The Past: 2003-2006

4. Past Environment

5. Early SDTM Efforts

6. Introduction to SDTM Submissions  Outsourced end stage SDTM conversion submitted to FDA Data Operations  Submission Datasets: SDTM-like datasets Create SDTM-like datasets from raw EDC data  Data collection: Define new CRF standards Incorporate SDTM variables into eCRFs

7. July 2005 First SDTM Submission Motive: Desire to comply with eCTD Guidance Timeline: September 04 – April 2005  Provided listing CRTs and SDTM datasets to FDA  SDTM datasets & define.xml never used by FDA reviewers Outsourced:  Performed end-stage conversion (mapping & creation of SDTM datasets)  Created define.xml & annotated CRFs Scope: 41 domains; 28 SUPPQUALS

8. Submissions Lessons Learned First SDTM submission effort required significant amount of unanticipated Genzyme effort Valuable lessons regarding implementing SDTM Detailed knowledge of the study data  Mapping exercise required cross functional team Interpretation of standard  There are implementation choices; no universal way all companies should implement SDTM Implementation standards  Genzyme needs to create implementation standards and governance of the standards

9. SDTM Flight Attempts Submission datasets: Create SDTM-like datasets from raw EDC data Data collection: Define new CRF standards Incorporate SDTM variables into CRFs

10. Why Attempts Fizzled Datasets Initiative must be cross-functional  Change cannot be made in isolation; must have up and downstream agreement on new processes and deliverables Did not have infrastructure to work with fully compliant SDTM datasets  Conflicts with project timelines Data Collection Competing with other initiatives  New version of Clintrial, EDC implementations, M&A’s Push submission requirements upstream

11. ODM

12. ODM Experiences Electronic Submissions  define.xml: submit case report tabulation metadata to FDA Metadata Driven Study Authoring  Begin establishing libraries of proprietary and non-proprietary eCRFs  Create vendor extensions to ODM Generate visualizations that mirror EDC vendor’s application user interface & functionality Import Genzyme defined ODM into vendor study architect tools

13. ODM Lessons Learned Metadata Driven Study Authoring  Make decisions regarding horizontal/vertical specifications  Successfully exchange study metadata (forms and workflow) with EDC vendors  Need infrastructure to successfully utilize tool Limited reusability of individual study CRF builds across programs Not just anyone should define studies using the tool  Study modeler should have strong understanding of database design and CDISC SDTM & ODM

14. The Present: 2007 - 2008

15. Present Environment Caption: The scaffolding took longer to assemble than the rocket

16. May 2007 Second SDTM Submission Motive: FDA requested SDTM for all domains  Jan 07 negotiated DM, AE & all SUPPQUALS Timeline: October 2006 – March 2007  Provided listing CRTs, CSR and CRFs to FDA in March  May provided DM, AE, SUPPQUALS, define.xml and annotated CRFs Descriptive documentation of our mapping process  SDTM datasets and define.xml were used by FDA medical reviewer for safety review Outsourced:  Performed end stage conversion (mapping & creation of SDTM datasets)  Created define.xml Scope: 2 domains & 2 SUPPQUALS

17. Lessons Learned FDA requesting SDTM now!! Applied lessons learned from 1 st experience to 2 nd project  Weekly cross-functional meeting with vendor Output failed WebSDM validation  Validation failures identified at Genzyme  We need to incorporate our submission requirements upstream in data collection Not efficient implementation strategy to convert data to SDTM so late in the clinical data lifecycle  Creating extra work for stat. programming, stats. and esub End stage conversion is expensive!!

18. CDISC Roadmap

19. CDISC Roadmap Purpose To present a clear and complete picture of:  Where CDISC standards fit into the entire clinical data lifecycle  What activities must occur to integrate the standards into the processes and sub-processes within each lifecycle stage Provide a common language and reference for further dialog, planning, design, and implementation of CDISC Standards.

20. Series of Initiatives Build a Complete CDISC Standards Implementation Data Flow #1: Late-Stage Conversion Data Flow #2: Mid-Stage Conversion Data Flow #4: CDISC Standards in in Trial Design Provide SDTM data to FDA Data Flow #3: Standards in Collection, Processing & Storage Submit (as SDTM) the collected data on which analysis is based Collect, process & store data according to standards Extend standards-based metadata- driven data flow further upstream into trial design

21. Data Flow Strategy Meet regulatory current requests and soon- to-be requirements as soon as possible Integrate CDISC standards more broadly and deeply into business processes  Develop clinical data based upon CDISC standards instead of converting the data to CDISC standards  Fully gain operational efficiencies from the use of standards

22. Metadata Repository Currently being defined Manage data about the data Serves as a central hub for automation of upstream and downstream processes and tools  i.e. protocol & CRF development, SAS TLF programming Enforces standards Improves efficiency of process flow Enables reusability

23. Data Standards Team & Governance Data Standards Team is essential to successfully implement CDISC standards Data Standards Team will develop, implement, maintain, educate, communicate and govern the standards globally  Standards cannot be viewed as optional Implementation of data standards includes process changes, technology modifications and more subject matter expertise

24. Interim Initiatives

25. Triage Team Charter An interim committee to provide guidance and support to a select number of studies for mapping & programming SDTM datasets  Focus on end & mid-stage conversion activities  Will not be involved with attempts to implement standards at the protocol, CRF or database design lifecycle phases  Will be replaced by the global cross-functional governance body implemented as part of CDISC Roadmap Project

26. Triage Initiative Triage Team* Stat Programming (4) Biostats (4) Data Management (4) CDDS (4) Project Specialists (1) Initiative began Q4 07 will go through 2008 Completed 2 reviews so far Anticipate conducting 10 reviews in Q2 & Q3, with additional studies to be determined in Q4 Currently considering expanding scope to include review of CRF and database design *Include Clinical, Coding, IT & RA as needed

27. Triage Lessons Learned Process works!  Highlights importance of cross-functional communication Need additional cross-functional resources to support initiative Need to operationalize training for new projects going through triage reviews Implementation questions: obtain outside guidance, when needed

28. Parallel Efforts Converge 2008 Phase I: CDISC Roadmap Phase II: Design Phase CRF Standards Triage Review Phase III: Implementation

29. Participation in Standards Activities CDASH HIMSS SDTM Device Sub Team ADaM Working Group WebSDM User Group FDA ODM Pilot HL7 (Q2 2008) CDISC User Networks (BACUN)

30. The Future: 2009 & Beyond

31. Future Environment Visions is evolving Established standards and governance Adoption of a growing list of commercially available standards based products Process improvements enabled by technological advances Technological and operational infrastructure to support a metadata driven end-to-end clinical data lifecycle

32. Sample Future Capabilities Ability to collect, store, analyze/report, compile/submit data to FDA according to SDTM, in conjunction with other CDISC standards Ability to integrate other, non-CDISC, standards ODM XML based interchanges of clinical data with vendors (i.e. EDC vendors, labs, FDA, etc.) Metadata based protocol writing tools that establish the framework for collection, analysis & reporting at the inception of the study design

33. Questions email: Kathleen.Greene@genzyme.com Brooke.Hinkson@genzyme.com