Emerging Data File Initiatives & Standards: Potential Impact on Global Clinical Studies

1. Emerging Data File Initiatives & Standards: Potential Impact on Global Clinical Studies

2. Ginger Clasby Executive VP Business Development gclasby@promedica-intl.com Don Hurst Director Clinical Information Technology dhurst@promedica-intl.com Co-Presenters

3. Corporate Goal Optimize revenue stream by getting products to market as quickly as possible

4. Accomplishing the Goal Enroll subjects as quickly as possible • Sites in US & OUS • Experienced sites • Sophisticated px recruitment programs

5. Accomplishing the Goal Maximize clinical trial operational efficiencies to minimize time from last data in to database lock • Standardization • Internet applications for study management docs • Electronic Data Capture (EDC) • Detailed monitoring of subject exam schedules

6. Accomplishing the Goal Minimize time for statistical analysis & CSR development • Standardization • eXtensible Markup Language (XML) • Develop/validate templates prior to database lock

7. Accomplishing the Goal Minimize regulatory agency review period • Standardization • Electronic submissions • XML

8. Oops! Differing data capture & reporting formats across countries/regions

9. Differing Formats • Data conventions: mo/day/yr or day/mo/yr? • Measurement units: kg or lb? • Variable names: English, French, German or ? • Report content/structure varies widely by country or region

10. CDISC Clinical Data Interchange Standards Consortium

11. CDISC Background • Established 1997 • Assumes use of EDC • Collaboration to produce functional standard data models facilitating data interchange between industry stakeholders • Supports end-to-end data flow within trials, from source document to regulatory submission

12. CDISC “Deliverables” Operational Data Modeling (ODM) Data acquisition Data archiving Submission Data Modeling (SDM) Operating database Reg submission

13. CDISC “Reach” • Biopharmaceutical study focus • Active collaboration with FDA • Active collaboration with analogous regulatory organizations in Europe & Japan

14. CDISC vs. ICH • ICH – working toward global submission standards • CDISC – working on standardization of submissions at the data level

15. CDISC vs. HL7 • HL7 – data standards for all health care operations, including reimbursement & order processing – no clinical trials focus • CDISC – oriented to biopharmaceutical product development only

16. Three Technological Trends Impacting Clinical Study and Data Management Design for Global Trials • EDC: environment manipulating the files • Hardware characteristics • Software & database/file characteristics • XML: a vendor neutral file standard • Brief history • SGML • HTML • XML • CDISC • ODM – Operational Data Model • SDM - Submissions Data Model

17. Electronic Data Capture • Hardware Characteristics: • Web enabled, wired or wireless • Transfer devices • PDA (Palm Pilot) for patient diaries, etc. • Blackberry devices (Raspberry devices??) • IVRS (Interactive Voice Response Systems) • Scanning (CRFs) • Keyboard • Smart phones

18. Electronic Data Capture • Hardware Characteristics: • 3. Servers • Many different types of servers (authentication, mail, database, backup, Citrix, etc.) • Server to server communication • Device to server communication • Communication protocols • TCP/IP • FTP • Bisynchronous • Asynchronous

19. Electronic Data Capture • Software & Database/File Characteristics: • Operating systems • Linux • Windows 2000 • Solaris • Front end (Graphical User Interface) • HTML • JAVA • C+ and other languages

20. Electronic Data Capture • Software & Database/File Characteristics: • 3. Backend database • Flat • SQL/Server (Relational) • UDB/DB2 (Relational) • XML (hierarchical) • 4. Client/server type • Thin (Explorer based only) • Hybrid (Explorer + some client download) • Thick (Major client download…usually for offline followed by scheduled uploads)

21. Electronic Data Capture • Software & Database/File Characteristics: • 5. Types of files used between devices • Only one type of file: flat and sequential • Characteristics of flat file are dependent on what is encoded from the source and how file is interpreted, or reconstructed, by the target. Or another way of saying it, is what association is with the file (icon). • A flat file can be many different types: • SAS transport, Excel, Word, unloaded relational, zipped, Adobe PDF, etc. • XML file

22. XML • What is it? • eXtensible Markup Language • Non-proprietary, platform-independent meta-language for hierarchically structuring information • What is XML’s history? • Subset of the International Standards Organization (ISO) Standard Generalized Markup Language (SGML), ISO 8879:1986 • SGML is an ISO Standard - ISO 8879:1986 • SGML Established Standard for 12 years. • SGML released as ISO8879 in 1986 • At SGML 96 Conference in Boston, XML released by a working group associated with the W3C. • SGML ## was previous name for XML #### • XML 1.0 is W3C recommendation (32 pages) • XML became recommendation in February, 1998

23. XML • 3. Where is it used? • Almost all industry groups • Heavily used in the banking sector • 4. How is XML related to SGML and HTML? • XML + HTML = SGML • SGML – Standard Generalized Markup Language • Context (HTML) • Content (XML) • HTML - HyperText Markup Language • Used in Web pages • Can emphasize text, graphics, links, etc. • XML • Hierarchical in organization • Uses data tags to classify data

24. XML 5. What does XML look like (normal view not indented)?

25. XML • 6.Things to remember about XML • Already an international standard • An ISO standard • Standard for the electronic exchange of information in most industry groups • Although a flat and sequential file, it has structure • “Content” based, while HTML is “context” based

26. CDISC • Clinical Data Interchange Standards Consortium • Developing a common interchange standard for clinical data • Accomplish through the development of meta-data models

27. CDISC • 4. Benefits of standard meta-data models: • Clin. labs do not have to support different formats • CROs no longer have to develop completely new databases to meet unique specifications of each trial • EDC providers do not need to customize exchange format for each new client • Sponsors can receive data in standard format in less time • Regulatory reviewers can more quickly and easily review submission data

28. CDISC

29. CDISC • 5. Current Meta-Data models • ODM – Operational Data Model • SDM - Submissions Data Model • RIM - Reference Information Model • ADaM – Analysis Dataset Model • LAB – Laboratory Standards (LOINS)

30. CDISC • 6.ODM – Operational Data Model Goals • Develop vendor independent models for interchange and archive of clinical data using metadata • Base models on XML technology

31. CDISC 7. ODM – Overview Structure

32. CDISC 8. SDM – Submissions Data Model (Domains) • Demographics • Disposition • Exposure • Labs (Chemistry, Hematology, Urinalysis) • Physical Exam • Medical History • Concomitant Medications • Adverse Events • Vitals (Horizontal) • Vitals (Vertical) • ECG (Horizontal) • EKG (Vertical)

33. CDISC • 9. Other data models receiving top priority • Pharmacogenomics • Microbiology

34. Summary (and peek into the future) • Three trends are merging to bring a drug/device to regulatory review in a faster, more standardized package. • The merging of the trends will help lower one of the major costs of trials and remove one of the traditional bottlenecks prior to submission. • The merging trends have gained such momentum that the SAS Institute has invested and created a new procedure called “PROC CDISC”.