Overcoming Disincentives to Process Understanding in the Pharmaceutical CMC Environment - Pharmaceutical Technology

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Overcoming Disincentives to Process Understanding in the Pharmaceutical CMC Environment
Larger and strategic sampling and testing plans can improve process understanding and characterization.


Pharmaceutical Technology


Finally, in many cases, process understanding is best achieved through proper statistical analysis of the data. The statistical tools that are widely used in research and development are equally useful in the CMC setting. The statistical link between sample size and reliability creates an incentive, rather than a disincentive, for collecting more data.

Laura Foust* is a research scientist at Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, tel. 317.276.3007, fax 317.651.6170,
Myron Diener is an industrial development scientist at sanofi-aventis. Mary Ann Gorko is a principal statistician at AstraZeneca Pharmaceuticals LP. Jeff Hofer is a principal research scientist at Eli Lilly and Company. Greg Larner is a statistics manager at Pfizer Scientific and Laboratory Services. David LeBlond is a principal research statistician at Abbott. Jerry Lewis is a senior principal biostatistician scientist at Wyeth Biotech. Dennis Sandell is an associate principal scientist at AstraZeneca. Tim Schofield is senior director, nonclinical statistics, at Merck Research Laboratories. Kimberly Erland Vukovinsky is director, nonclinical statistics, at Pfizer. Ed Warner is director, statistical services, at Schering Plough Global Quality. All authors are members of the PhRMA CM&C Statistics Expert Team.

*To whom all correspondence should be addressed.

Submitted: Jan 31, 2007. Accepted:Feb. 26, 2007

References

1. US Food and Drug Administration, Pharmaceutical CGMPs for the 21st Century—A Risk-Based Approach (Sep. 2004), http://www.fda.gov/cder/gmp/gmp2004/GMP_finalreport2004.htm.

2. ICH, Q8: Pharmaceutical Development (Step 5, Feb. 2003).

3. L. Torbeck, "In Defense of USP Singlet Testing," Pharm.Technol. 28 (2), 105–106 (2005).

4. J.R. Murphy and K.L. Griffiths, "Zero-Tolerance Criteria Do Not Assure Product Quality," Pharm. Technol. 30 (1), 52–60 (2006).

5. ICH, Q1E: Evaluation for Stability Data (Step 5, Nov. 2005).

6. C. Wen-Jen and Y. Tsong, "Significance Levels for Stability Pooling Test: A Simulation Study," J. Biopharm. Stat. 13 (3), 355–374 (2003).

7. USP General Chapter ‹905› "Uniformity of Dosage Units," USP 28–NF 23 (USP, Rockville, MD 2005), 2505–2510.

8. D. Sandell et al., "Development of a Content Uniformity Test Suitable for Large Sample Sizes," Drug Information J. 40 (3), 337–344 (2006).

9. L. Toothaker, "Multiple Comparisons for Researchers," ICH E9 Points to Consider 49595, Fed. Regis. 63 (179) (Sept. 16, 1998, Notices, Sage Publications, Newbury Park, CA).

10. M.A. Black and R.W. Doerge, "Calculation of the Minimum Number of Replicate Spots Required for Detection of Significant Gene Expression Fold Change in Microarray Experiments," Bioinformatics 18 (12), 1609–1616 (2002).

11. Committee on Professional Ethics, Ethical Guidelines for Statistical Practice of the American Statistical Association, Section II.A.8 (1999), http://www.amstat.org/.

12. T. Tougas, "Considerations of the Role of End Product Testing in Assuring the Quality of Pharmaceutical Products," J. Process Analytical Technology 3 (2), 13–17 (2006).

13. D. Montgomery, Introduction to Statistical Process Control (John Wiley & Sons, 2d ed., New York, NY, 1991), p.105.

14. ANSI–ASQC Z1.4-1993, American National Standard: Sampling Procedures and Tables for Inspection by Attributes (American Society for Quality Control, Milwaulkee, WI, 1993).

15. D. LeBlond, T. Schofield, and S. Altan, "Revisiting the Notion of Singlet Testing Requirements," Pharm. Technol. 29 (6), 85–86 (2005).

16. S. Ruberg and J. Stegeman, "Pooling Data for Stability Studies: Testing the Equality of Batch Degradation Slopes," Biometrics 47, 1059–1069 (1991).


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