A Risk-Management Approach to Cleaning-Assay Validation - Pharmaceutical Technology

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A Risk-Management Approach to Cleaning-Assay Validation
The authors recommend a strategy for classifying similar nonstainless-steel surfaces into three groups based upon the analytical recovery that was observed in this study.


Pharmaceutical Technology
Volume 6, Issue 34, pp. 48-55

Conclusion

The authors' data-driven risk-management approach to cleaning verification methods uses analytical-recovery values for a model compound to place product-contact surfaces into groupings for analytical-method validation. The data generated during the studies supported the formation of three recovery groups to validate analytical swab methods. Groups 1–3 were represented by stainless steel 316L, cast iron, and Type III hard anodized aluminum, respectively. This approach allowed all surfaces to be considered during analytical-method validation and provided an objective mechanism to incorporate new surfaces into the strategy.

The benefits of this strategy are numerous. First, only three surfaces must be validated on each compound, which drastically minimizes the number of recovery values established to support the entire portfolio. Second, the strategy includes a way to add new materials of construction to the cleaning program if new equipment is purchased. Traditionally, all swab methods must be revalidated to incorporate the new surface. With this strategy in place, a model compound is evaluated, the new surface is grouped, and no changes to existing methods are required. Third, the strategy allows for a constant state of compliance. A relative recovery value is known for any material of construction for all equipment.

Because the grouping strategy is applied to a small fraction of the total surface area, no surface material of construction is ignored, each molecule undergoes a typical method validation, and the strategy places surfaces into groups conservatively. The authors believe that the strategy controls risks appropriately and that the data set given in this study scientifically supports the strategy of grouping materials of construction to support analytical methods within the cleaning program.

Acknowledgments

The authors would like to acknowledge the following colleagues at Eli Lilly: Gifford Fitzgerald, intern, for generating the swab-recovery data; Ron Iacocca, research advisor, for the SEM data; Sarah Davison, consultant chemist; Mike Ritchie, senior specialist; Mark Strege, senior research scientist; Matt Embry, associate consultant chemist; Kelly Hill, associate consultant for quality assurance; Bill Cleary, analytical chemist; and Laura Montgomery, senior technician, for their contributions and insightful suggestions throughout the project. In addition, Leo Manley, associate consultant engineer, provided the roughness measurements in support of this project.

Brian W. Pack* is a research advisor for analytical sciences research and development, and Jeffrey D. Hofer is a research advisor for statistics, discovery and development, both at Eli Lilly and Company, Indianapolis, IN, tel. 317.422.9043,
.

*To whom all correspondence should be addressed.

Submitted: Oct. 12, 2009. Accepted: Dec. 22, 2009.

References

1. ICH, Q9 Quality Risk Management, Step 5 version (2005).

2. Code of Federal Regulations, Title 21, Food and Drugs (Government Printing Office, Washington, DC), Part 211.67.

3. ICH, Q7 Good Manufacturing Practice Guide for Active Pharmaceutical Ingredients, Step 5 version (2000).

4. FDA, Guideline to Inspection of Validation of Cleaning Processes (Rockville, MD, July 1993).

5. L. Ray et al., Pharm. Eng. 26 (2), 54–64 (2006).

6. PDA, Technical Report 29, "Points to Consider for Cleaning Validation" (PDA, Bethesda, MD, Aug. 1998).

7. G.L. Fourman and M.V. Mullen, Pharm. Technol. 17 (4), 54–60 (1993).

8. ICH, Q2 Validation of Analytical Procedures: Text and Methodology, Step 5 version (1994).

9. R.J. Forsyth, J.C. O'Neill, and J.L. Hartman, Pharm. Technol. 31 (10), 102–116 (2007).


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