FDA Data Standards An Update


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FDA Data Standards An Update

  1. 1. Shakul Hameed FDA Data Standards: An Update
  2. 2. Overview  Background on FDA Regulatory Submissions  Challenge and Vision  Data Standards - Exchange Standards - Terminology Standards  The Janus Initiative
  3. 3. Background on FDA Regulatory Submissions  FDA receives an enormous and growing amount of data in a variety of regulatory submissions.  For the last two years (2008 and 2009), FDA has received 11,980 eCTD new applications and 1,44,778 eCTD Sequences  The volume and complexity of drug-related information submitted to FDA for regulatory review enormously increasing.
  4. 4. Number of eCTD submission (2008-2009)
  5. 5. eCTD submission (2005-2009)
  6. 6. Original IND’s and Amendments
  7. 7. Original NDA’s and Amendments
  8. 8. How Regulatory Submissions are Processed in FDA
  9. 9. Submission Reviewing tools used by FDA  Applications used by Reviewers and PMs: - DARRTS (Document Archiving, Reporting & Regulatory Tracking System) - Global Submit Review (eCTD Review Tool) - Adobe Acrobat, MS Office products - Other specialized applications (various statistical programs, JMP, etc)
  10. 10. The Challenge  The FDA receives massive amounts of Regulatory data (clinical research data) –in extremely disparate formats –using a variety of proprietary standards  This makes it extremely difficult for reviewer to review the submissions.
  11. 11. The Challenge  Lack of data standards leads to: – Creates delays and inefficiency in review process – Increases variability in quality of reviews – Reduces transparency and predictability – Each reviewer is creating their own code to answer a standard review question
  12. 12. The Challenge
  13. 13. Standardization needs to speak a common “Language” Study #2 –dmg.xpt Study #1 –demog.xpt Study #3 – axd222.xpt Study #4 – dmgph.xpt
  14. 14. The Vision  FDA has been working towards a standardized approach to capture, receive, and analyze study data  Standardization of study data is vital to integrate pre- marketing study data and post-marketing safety data to improve public health and patient safety  Central to this vision is the creation of an enterprise data infrastructure (Janus) within FDA to improve the management of all structured scientific data
  15. 15. Data Standards  Data standards can be divided into two categories: –Exchange standards –Terminology standards
  16. 16. Exchange Standards  Exchange standards provide a consistent way to exchange information.  Exchange standards help ensure that the sending and the receiving systems both understand unambiguously what information is being exchanged  For example, Structured Product Labeling (SPL) is an exchange of product labeling information.  Study data tabulation Model (SDTM) for exchange of clinical study data this model was developed by CDISC.
  17. 17. Exchange Standards
  18. 18. Terminology Standards  Terminology standards provide a consistent way to describe concepts. - For example, the Unique Ingredient Identifiers (UNII), developed by the FDA, provides a consistent way to describe substances in foods and drugs. - Vocabulary developed National Institute of Cancer to describe terminology related to cancer.
  19. 19. Exchange and Terminology  To develop these data standards, FDA is closely working with HL7
  20. 20. Objectives of Data Standards  Develop well-defined, publicly available data standards  Ensure that data is submitted according to publicized standards  Develop Advanced review tools so that reviewers can use standardized data efficiently and effectively
  21. 21. The Janus Initiative  Janus n : (Roman mythology)  The Roman god of doorways, passages and beginnings; is depicted with two faces on opposite sides of his head, one looking towards the past and the other looking towards the future.
  22. 22. What is Janus?  Collection of smart, interoperable databases - clinical study database - nonclinical study database - other scientific databases  Enterprise program* (project not a specific software application) this would help FDA - To enhance scientific computing - To improve scientific knowledge - To take regulatory decisions
  23. 23. Janus Solutions Components
  24. 24. Janus Solutions Components
  25. 25. FDA Target Flow for Future Submission Review
  26. 26. Current state of FDA Submission Review Process
  27. 27. FDA Target State of Submission Review Process
  28. 28. What will Janus do?  Answer questions regulatory review and research questions e.g., how many women have been enrolled in a particular HIV drug study  Provide common platform for mining data - Across product types - Across applications - Across product lifecycle