Analytical Similarity Assessment in Biosimilar Product Development

This book focuses on analytical similarity assessment in biosimilar product development following the FDA’s recommended stepwise approach for obtaining totality-of-the-evidence for approval of biosimilar products. It covers concepts such as the tiered approach for assessment of similarity of critical quality attributes in the manufacturing process of biosimilar products, models/methods like the statistical model for classification of critical quality attributes, equivalence tests for critical quality attributes in Tier 1 and the corresponding sample size requirements, current issues, and recent developments in analytical similarity assessment.

Author

Shein-Chung Chow, Ph.D, is currently an Associate Director at Office of

Biostatistics, Center for Drug Evaluation and Research, United States Food

and Drug Administration (FDA). Prior to joining FDA, Dr. Chow was a

Professor at Duke University School of Medicine, Durham, NC. He was

also a special government employee (SGE) appointed by the FDA as an

Advisory Committee member and statistical advisor to the FDA. Prior to

that, Dr. Chow also held various positions in the pharmaceutical industry

such as Vice President at Millennium, Cambridge, MA, Executive Director at

Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers

Squibb, Plainsboro, NJ. Dr. Chow is the Editor-in-Chief of the Journal of

Biopharmaceutical Statistics and the Editor-in-Chief of the Biostatistics Book Series

at Chapman and Hall/CRC Press, Taylor & Francis Group. He was elected

Fellow of the American Statistical Association and an elected member of the

ISI (International Statistical Institute). Dr. Chow is the author or co-author of

over 300 methodology papers and 29 books including Designs and Analysis

of Bioavailability and Bioequivalence Studies, Sample Size Calculations in Clinical

Research, Adaptive Design Methods in Clinical Trials, Translational Medicine,

Design and Analysis of Clinical Trials, and Quantitative Methods for Traditional

Chinese Medicine Development.