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


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