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CDER Risk-based Site Selection Model: An FDA Risk Management Tool

CDER Risk-based Site Selection Model: An FDA Risk Management Tool. Presentation to FDA Science Board November 5, 2004 by Kara Morgan, Ph.D., Office of Planning/Office of the Commissioner. Upgrading FDA Risk Management Tools. FDA is interested in formalizing risk management tools

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CDER Risk-based Site Selection Model: An FDA Risk Management Tool

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  1. CDER Risk-based Site Selection Model: An FDA Risk Management Tool Presentation to FDA Science Board November 5, 2004 by Kara Morgan, Ph.D., Office of Planning/Office of the Commissioner Nov 5, 2004

  2. Upgrading FDA Risk Management Tools • FDA is interested in formalizing risk management tools • Rigorous, science-based methods are available • Commissioner’s Strategic Plan – Efficient Risk Management • Difficult to evaluate efficiency when risk management tools are not formalized • OMB Oversight • Transparency, Consistency, Accuracy • Risk as a meaningful public health metric Nov 5, 2004

  3. Motivation for the CDER Risk-based Site Selection Model • Previous (pre FY05) approach • CDER identified “high-risk” as sterile drugs, Rx drugs, new registrants, exclude medical gas • ORA decided where to go within those categories • Problems • Insufficient resources to inspect all sites • “High-risk” category wasn’t discriminating enough • CDER wanted to incorporate additional information • Solution: a formal model to rank sites by risk Note: Before the use of the model, decisions wererisk-based but not formalized. Risk-related knowledge was incorporated by the field. Nov 5, 2004

  4. Process for Model Development • Working group established under CGMP: CDER, CBER, CVM, ORA and OC • Defined risk; brainstormed risk factors Risk = harm to patient from low quality product • Categorized factors into three bins: product-related, process-related, facility-related • Evaluated available data for assessing these factors • Developed weights for the factors Nov 5, 2004

  5. Product-related Factors • Intrinsic factors • Sterility • Prescription • Recall data • By product class • Includes measure of severity Nov 5, 2004

  6. Facility-related Factors • History • Time elapsed since last inspection • Compliance and Violation record • Estimated production volume • Establishment type (i.e., manufacture, repacking, relabeling, testing, sterilizing) • Source of data: FACTS (Field Accomplishments and Compliance Tracking System) Nov 5, 2004

  7. Process-related Factors • Inherent Process Risk Factors, including potential for contamination • Process Controls and Risk Mitigating Factors • No data available • An expert elicitation was conducted to assess potential for risk by process • Modeled after an expert elicitation by ISPE Nov 5, 2004

  8. Combining the factors • Weights for each factor/subfactor • Empirically derived • Expert judgment as needed • Algorithm developed to combine weights and factors • Output: a Site Risk Potential (SRP) Score for each facility • A higher score means more potential for risk Nov 5, 2004

  9. A Plain Language Summary of the Influence of the Factors • A site will be less frequently selected for inspection if… • It has been inspected recently and has few or no previous violations of GMPs and a smaller volume of product (facility weight) • It make non-sterile, OTC drugs, and/or other product types that are not associated with a high frequency of recalls and for serious defects (product weight) • It makes products judged to be relatively straightforward to manufacture with consistent quality, and not vulnerable to contamination (process weight) Nov 5, 2004

  10. Implementation in FY05 • Model was run for all active sites in FACTS • A list was generated for each District, with the Site Risk Potential Score reported for each site. • The basis for the score in terms of the rating on product factor, facility factor, and process factor was reported • The field were asked to use the list of ranked sites to prioritize their inspections. Nov 5, 2004

  11. Next steps • Continuous Improvement • Provide incentives for better data, including the addition of more factors • Feedback from ORA on utilization of rankings • Sensitivity Analysis of factors and weights • Evaluation underway by Office of Planning • CVM considering use for prioritization of animal drug inspections Nov 5, 2004

  12. Expected Outcomes • Better predictability for industry • Incentives for risk mitigation activities • Increased (measurable!) efficiency and effectiveness in terms of resource use • Higher product quality • Improved public health Nov 5, 2004

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