Conclusion
Use of systematic RCA is effective at determining the origin of a defect or problem. DMPQ has made substantial progress in
the initial investigation of selected recall areas using data mining, statistical analysis, and RCA. A few root cause determinations
are presented in this paper to illustrate the use of RCA and the type of data amassed for each incident through data mining.
Future papers in this series will discuss the root causes of specific defect focus areas such as Class I recalls, B. cepacia contamination, subpotency, and dissolution.
1 Please see FDA guidances for additional information on recalls (3).
2 that is, during management review of suitability of the process for launch, or during subsequent process performance monitoring
Richard Friedman is division director, Michael Smedley is branch chief, and Israel Santiago is a compliance officer in the Recalls, Shortages and Certificates Branch; all in the Division of Manufacturing and Product
Quality, Office of Compliance, Center for Drug Evaluation and Research, US Food and Drug Administration. Lynn Torbeck* is the principal statistician at Torbeck and Assoc., 2000 Dempster, Evanston, IL 60202-1017, 847.424.1314, Lynn@Torbeck.org
. He is also a member of Pharmaceutical Technology's editorial advisory board.
*To whom all correspondence should be addressed.
Submitted: Sept. 30, 2010. Accepted Nov. 19, 2010.
References
1. L. Torbeck et al., Pharm.Technol.
33 (8), 42–45 (2009).
2. L. Torbeck, FDA/PDA Joint Regulatory Conference (Washington DC, Sept. 9, 2008).
3. FDA, Guidance for Industry: Product Recalls (Rockville, MD, Feb. 2009).
4. ICH, ICH Q10, Pharmaceutical Quality System, June 2008.
5. FDA, Guidance for Industry: FDA Quality Systems Approach to Pharmaceutical CGMP Regulations (Rockville, MD, Sept. 2006).
This article represents the views of the authors and not of FDA.
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