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Analytical similarity assessment
Author(s) -
Chow SheinChung,
Song Fuyu
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1407
Subject(s) - biosimilar , critical quality attributes , food and drug administration , product (mathematics) , quality (philosophy) , computer science , similarity (geometry) , new product development , risk analysis (engineering) , medicine , mathematics , business , microbiology and biotechnology , marketing , artificial intelligence , biology , philosophy , geometry , epistemology , image (mathematics)
For regulatory review and approval of biosimilar products, the United States ( US ) Food and Drug Administration ( FDA ) recommended a stepwise approach for demonstrating biosimilarity between a proposed biosimilar product and an innovative (reference) biological product (e.g., an US ‐licensed product). 1–3 The stepwise approach is to provide totality‐of‐the‐evidence for demonstrating biosimilarity between the proposed biosimilar product and the reference product. The stepwise approach starts with analytical studies for functional and structural characterization of critical quality attributes ( CQAs ) at various stages of manufacturing process. For the assessment of analytical similarity of CQAs , FDA suggests, first, identifying the CQAs that are relevant to clinical outcomes, and then classifying the identified CQAs into several tiers depending upon their criticality or risk ranking. FDA also suggests different methods be used to assess similarity for CQAs from different tiers. For example, equivalence test for CQAs from Tier 1, quality range approach for CQAs from Tier 2, and descriptive raw data and graphical comparison for CQAs from Tier 3. In this article, controversial issues regarding the FDA ’s recommended approaches are discussed followed by alternative methods for assessment of similarity for CQAs from Tier 1. WIREs Comput Stat 2017, 9:e1407. doi: 10.1002/wics.1407 This article is categorized under: Applications of Computational Statistics > Clinical Trials