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The FDA/PhUSE collaboration was created to provide high-quality quantitative analysis of information on product safety, effectiveness and quality. The core infrastructure for FDA's scientific community will help support ongoing efforts in pre-market development, modernization of drug review, post-market safety and drug quality. Key projects within the FDA's Computational Science Center are in areas of developing data standards and expanding the use of electronic review tools.

The collaboration was previously organized into 6 Working Groups:

• Working Group 1 - Data Validation and Quality Assessment
  
• Working Group 2 - Reducing Risk within the Inspection Site Selection Process
  
• Working Group 3 - Challenges of Integrating and Converting Data across Studies
  
• Working Group 4 - Standards Implementation Issues with the CDISC Data Models
  
• Working Group 5 - Development of Standard Scripts for Analysis and Programming
  
• Working Group 6 - Non-Clinical Road-map and Impacts on Implementation

Each of the working group identified their own subgroups, projects, goals and timelines.

To maximize efficiency and organization, the groups will be re-organized into fewer working groups with broader focus. Each working group will have specific, well-defined projects. The working groups that will be discussed at the March meeting are described below. In addition to the projects lists, the group will have open discussions about other areas of issues and gaps that should be addressed. 

Working Groups:

(NEW) Emerging Technologies New challenges in regulatory science and drug, biologic, and device development provide new opportunities for recognizing and leveraging emerging technologies and computational tools.  In recognition of this need, The FDA/PhUSE Computational Science Symposium (CSS) will use the March, 2013 meeting as an opportunity to “launch” a new working group that provides a forum for determining interest in specific computational science topics, tools, technologies, and approaches.  This emerging technologies working group will be an open, transparent forum for sharing pre-competitive means of applying new technologies and is being challenged with creation of well-defined collaborative projects that will describe, prioritize, assess, and assist advancement of these opportunities.  Possible topics include (but are not limited to) semantic web applications, analysis metadata, modeling, simulation, and “The Cloud”.  Projects incorporating these topics might include prioritization, development, and piloting for feasibility and value.

Improving Data Quality This working group will focus on collaborating to develop a robust process to rapidly validate and assess data quality as data moves through product lift cycle across both industry and regulatory review. The group will discuss current pains and potential solutions regarding topics such as current data validation rules, therapeutic specific data issues and improving the quality of the data to support specific analytical needs. Projects: SDTM Validation Rules The purpose of this project is to continue the effort of improving SDTM validation rules, ensuring that all rules are both clear and effective for use by industry and by FDA. The goal is to determine new rules that will add value and manage rules that need clarification. This will involve coordinating with CDISC, OpenCDISC and FDA to propose solutions.

Optimizing the use of Data Standards The development and adoption of data standards over the last decade has shown significant promise in improving efficiencies in the product submission and review process. However, there have also been gaps, issues and challenges in the interpretation and use of the standards. This group will identify specific gaps preventing FDA and industry from optimizing the use of standards and collaborate to close those gaps. Projects: Study Data Reviewer’s Guide (SDRG) The define.xml document does not adequately document mapping decisions, sponsor-defined domains, and other key study components and a SDRG would help to address this documentation gap.  The goal of this project is to develop a SDRG template jointly between CDER, Industry, and CDISC to be used for submissions. Traceability and Data Flow of Study Level Data Traceability challenges arise at a study level when raw data is converted to SDTM after the fact, while analysis datasets and the study report trace back to the original raw data source.  This project will discuss and define traceability considerations and best practices for study level dataset conversion for a variety of different data flow scenarios. (NEW) CDRH Pilot for the Electronic Submission of Medical Device Data in an SDTM-Based Format With the recent development of new SDTM-based device domains, industry and CDRH would like to conduct a Pilot to evaluate the use of the domains for submission. (NEW) Analysis Data Reviewer’s Guide (ADRG) ADaM “provides 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.  Although ADaM provides a robust metadata framework, FDA Reviewers benefit from additional, human-readable, documentation of analysis methods, datasets, and programs that cannot be fully explained within the ADaM metadata.  The development of an Analysis Data Reviewer’s Guide (ADRG) template will ensure this documentation is provided to the agency in a consistent and usable format. (NEW) SDTM Exceptions Sponsors collect data elements to support operational activities such as data cleaning or data reconciliation.  Although these data elements are not analyzed, sponsors frequently tabulate them in SDTM.  As a result, both sponsor analysts and FDA Reviewers spend time differentiating analyzable observations from operational noise.  Documenting data elements of limited utility to data analysis and/or FDA Reviewers provides sponsors and the agency with a common baseline for pre-submission data standards discussions.

Development of Standard Scripts for Analysis and Programming With the development and implementation of industry data standards, there is a great opportunity to develop standard reporting across industry and to support the needs of FDA medical and statistical reviewers. This working group will identify potential standard scripts for data transformations and analyses across an within therapeutic areas. The goal will be to begin the process of standardizing analyses across the industry, but also include examples of what can be done with a standardized data set. Projects: Standard Script Repository The project is currently developing an open source repository to develop, share, and validate scripts.  The group is working to bring together scripts, tools, and programs into a single repository, establish the basic structure and management of the repository, and create template and process for creation, review, and validation of the tools. Standard Scripts Development This project includes the development of approximately six white papers that provide recommended Tables, Figures, and Listings for clinical trial study reports and submission documents.  The intent is to begin the process of developing industry standards with respect to analysis and reporting for measurements that are common across clinical trials and across therapeutic areas.In addition, the project will develop a communication plan that conceptualizes efficient ways to communicate working group progress and results, e.g. white papers, and the call for scripts. It will define target groups, timing, communication channels, and the presentation.

Non-Clinical Road-map and Impacts on Implementation There is a need to improve nonclinical assessment and regulatory science by identifying key needs and challenges in the field and then establish an innovative framework for addressing them in a collaborative manner. The group created a framework for moving certain projects forward to support nonclinical informatics efforts and to develop specific implementation solutions and SEND. Projects: Nonclinical Data Interconnectivity  This project is currently developing use cases for performing valid human to non-human endpoint prediction modeling piloting. Nonclinical Historical Control This project is developing recommendations for high priority projects regarding the use of historical control data. These recommendations include identification of areas where additional aggregation or analysis of historical control data may be useful or identification of data standards to develop and pilot for exchange of historical data. Interorganizational SEND The responsibilities for creating the SEND files for a study is often shared across organizations. Clarity is needed on how these responsibilities can be effectively managed. This project will develop a framework to classify and prioritize scenarios in which data from multiple organizations needs to be aggregated and work together to find solutions to challenges with these scenarios when there are protocol amendments, audits that trigger data changes, changes to the SEND standard, etc. SEND Implementation User Group The SEND Implementation Wiki is a knowledge base on implementing SEND - a series of articles designed to help you understand and implement SEND, including modeling basics, useful links, FAQs, and more. In addition to the wiki, a companion SEND Implementation Forum is being developed by the project for future release, to provide a place for active discussion and to ask questions not already answered in the wiki. Nonclinical Standardization Roadmap Currently, the SEND standard allows for the submission of general tox and carc studies in electronic standardized format. However, there are additional study types that are generally received that have not been standardized. This project is developing a strategy on how to prioritize, maximize, and transform standardization efforts. This plan will take into account resources, complexity, timeline and new approaches and technologies.