Statistical Considerations in Design Space Development (Part III of III) - Pharmaceutical Technology

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Statistical Considerations in Design Space Development (Part III of III)
In the final article of a three-part series, the authors discuss how to present a design space and evaluate its graphical representation.


Pharmaceutical Technology
Volume 34, Issue 9, pp. 52-61

* For continuity throughout the series, Figures and Tables are numbered in succession. Figure 1 and Table I appeared in Part I of this article series. Figures 2–8 and Table II appeared in Part II.

** "Factor" is synonymous with "x," input, variable. A process parameter can be a factor as can an input material. For simplicity and consistency, "factor" is used throughout the paper. "Response" is synonymous with "y" and output. Here, "response" is either the critical quality attribute (CQA) or the surrogate for the CQA. For consistency, "response" is used throughout the paper.

See Part I of this article series

Acknowledgments: The authors wish to thank Raymond Buck, statistical consultant; Rick Burdick, Amgen; Dave Christopher, Schering-Plough; Peter Lindskoug, AstraZeneca; Tim Schofield and Greg Stockdale, GSK; and Ed Warner, Schering-Plough, for their advice and assistance with this article.

Stan Altan is a senior research fellow at Johnson & Johnson Pharmaceutical R&D in Raritan, NJ. James Bergum is associate director of nonclinical biostatistics at Bristol-Myers Squibb Company in New Brunswick, NJ. Lori Pfahler is associate director, and Edith Senderak is associate director, scientific staff, both at Merck and Co. in West Point, PA. Shanthi Sethuraman is director of chemical product R&D at Lilly Research Laboratories in Indianapolis. Kim Erland Vukovinsky* is director of nonlinical statistics at Pfizer, MS 8200-3150, Eastern Point Rd., Groton, CT 06340, tel. 860.715.0916,
. At the time of this writing, all authors were members of the Pharmaceutical Research and Manufacturers of America (PhRMA) Chemistry, Manufacturing, and Controls Statistics Experts Team (SET).

*To whom all correspondence should be addressed.

Submitted: Jan. 12, 2010. Accepted: Jan. 27, 2010.

References

1. S. Altan et al., Pharm. Technol. Part I, 34 (7) 66–70 (2010).

2. S. Altan et al., Pharm. Technol. Part II, 34 (8) 52–60 (2010).

Additional reading

1. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Q8(R1), Pharmaceutical Development, Step 5, November 2005 (core) and Annex to the Core Guideline, Step 5, November 2008.

2. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Q9, Quality Risk Management, Step 4 , November 2005.

3. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Q10, Pharmaceutical Quality System, Step 5, June 2008.

4. A Posterior Predictive Approach to Multiple Response Surface Optimization, John Peterson, 2004.

5. Potter C., et al..al.. A Guide to EFPIA #8217;s Mock P.2. Document, Pharm Tech 2006.

6. Glodek, M., Liebowitz, S, McCarthy, R., McNally, G., Oksanen, C., Schultz, T., Sundararajan, M., Vorkapich, R., Vukovinsky, K., Watts, C., and Millili, G. Process Robustness: A PQRI White Paper, Pharmaceutical Engineering, November/December 2006.

7. Box, G.E.P, W.G. Hunter, and J.S. Hunter (1978). Statistics for Experimenters: An Introduction to Design, Analysis and Model Building. John Wiley and Sons.

8. Montgomery, D.C. (2001).). Design and Analysis of Experiments. John Wiley and Sons.

9. Box, G.E.P.,and N. R. Draper (1969). Evolutionary Operation: A Statistical Method for Process Improvement. John Wiley and Sons.

10. Cox, D.R. (1992). Planning for Experiments. John-Wiley and Sons.

11. Cornell, J. (2002). Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, 3rd Edition. John Wiley and Sons.

12. Duncan, A.J. (1974). Quality Control and Industrial Statistics, Richard D. Irwin, Inc., Homewood, IL.

13. Myers, R.H. and Montgomery, D.C. (2002).). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley and Sons.

14. Montgomery, D.C. (2001). Introduction to Statistical Quality Control, 4th Edition. John Wiley and Sons.

15. del Castillo, E. (2007).Process Optimization: A Statistical Approach. Springer. New Yor.k

16. Khuri, A. and Cornell, J. A. (1996.). Response Surfaces, 2nd Edition, Marcel-Dekker, New York.

17. MacGregor, J. F. and Bruwer, M-J. (2008). "A Framework for the Development of Design and Control Spaces", Journal of Pharmaceutical Innovation, 3, 15-22.

18. Mir and#243;-Quesada, G., del Castillo, E., and Peterson, J.J., (2004). "A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables", Journal of Applied Statistics, 31, 251-270.

19. Peterson, J. J. (2004). "A Posterior Predictive Approach to Multiple Response Surface Optimization", Journal of Quality Technology, 36, 139-153.

20. Peterson, J. J. (2008). "A Bayesian Approach to the ICH Q8 Definition of Design Space", Journal of Biopharmaceutical Statistics, 18, 958-974.


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