Robert J. Seely


Demonstrating the Consistency of Small Data Sets: Application of the Weisberg t-test for Outliers

by Robert J. Seely, Amgen, Inc. Louis Munyakazi, John Haury, Heather Simmerman, W. Heath Rushing, and Thomas F. Curry Determining whether a data point is an "outlier" ? a result that doesn't fit, that is too high or too low, that is extreme or discordant ? is difficult when using small data sets (such as the data from three, four, or five conformance runs). The authors show that the Weisberg t-test is a powerful tool for detecting deviations in small data sets.

Evaluation of Extractables from Product-Contact Surfaces

by the Biopharmaceutical Process Extractables Core Team, including Don Miller, Bayer; Bob Seely, Amgen; John Bennan, ComplianceNet, Inc.; Frank Bing, Abbott Laboratories; Heather Boone, Genentech; Jim Fernandez, Fernandez and Associates; and Harold van Deinse, Baxter Healthcare Corporation Potential interactions between a drug product and its packaging or container closure have always been important considerations for parenteral manufacturers. Now ? at a time of increased regulatory interest in extractables, lower limits of detection, and more biopharmaceuticals reaching commercial stage ? the consequences of not evaluating the extractables in your process stream can be significant. Participants from more than 15 biopharmaceutical companies and data collected for more than 25 years were used to develop the parameters of this article.

Statistical Tools for Setting In-Process Acceptance Criteria

by Robert J. Seely, Louis Munyakazi, and John Haury Regulatory approval and successful manufacturing depend on establishing meaningful and reasonable acceptance criteria for process validations and ongoing monitoring. Three methods are presented here to correct for the likely underestimation of process limits due to small samples.