2. Overview
Background on FDA Regulatory Submissions
Challenge and Vision
Data Standards
- Exchange Standards
- Terminology Standards
The Janus Initiative
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.
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. 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. 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
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. 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. Data Standards
Data standards can be divided into two categories:
–Exchange standards
–Terminology standards
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.
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.
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. 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. 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
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