Analytical Similarity Assessment in Biosimilar Product Development

AuthorShein-Chung Chow, Ph.D, is currently an Associate Director at Office ofBiostatistics, Center for Drug Evaluation and Research, United States Foodand Drug Administration (FDA). Prior to joining FDA, Dr. Chow was aProfessor at Duke University School of Medicine, Durham, NC. He wasalso a special government employee (SGE) appointed by the FDA as anAdvisory Committee member and statistical advisor to the FDA. Prior tothat, Dr. Chow also held various positions in the pharmaceutical industrysuch as Vice President at Millennium, Cambridge, MA, Executive Director atCovance, Princeton, NJ, and Director and Department Head at Bristol-MyersSquibb, Plainsboro, NJ. Dr. Chow is the Editor-in-Chief of the Journal ofBiopharmaceutical Statistics and the Editor-in-Chief of the Biostatistics Book Seriesat Chapman and Hall/CRC Press, Taylor & Francis Group. He was electedFellow of the American Statistical Association and an elected member of theISI (International Statistical Institute). Dr. Chow is the author or co-author ofover 300 methodology papers and 29 books including Designs and Analysisof Bioavailability and Bioequivalence Studies, Sample Size Calculations in ClinicalResearch, Adaptive Design Methods in Clinical Trials, Translational Medicine,Design and Analysis of Clinical Trials, and Quantitative Methods for TraditionalChinese Medicine Development.

• 1.1 Background 1.2 Past Experience for In Vitro Bioequivalence Testing 1.2.1 Study Design and Data Collection 1.2.2 Bioequivalence Limit 1.2.3 Statistical Methods 1.2.4 Sample Size Requirement 1.3 Analytical Similarity Assessment 1.3.1 Tier 1 Equivalence Test 1.3.2 Tier 2 Quality Range Approach 1.3.3 Tier 3 Raw Data and Graphical Comparison 1.4 Scientific Factors and Practical Issues 1.4.1 Fundamental Similarity Assumption 1.4.2 Primary Assumptions for Tiered Approach 1.4.3 Fixed Approach for Margin Selection 1.4.4 Inconsistent Test Results between Tiered Approaches 1.4.5 Sample Size Requirement 1.4.6 Relationship between Similarity Limit and Variability