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Using Statistical Analysis for Setting Process Validation Acceptance Criteria for Biotech Products
Author(s) -
Wang Xiangyang,
Germansderfer Abe,
Harms Jean,
Rathore Anurag S.
Publication year - 2007
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp060359c
Subject(s) - set (abstract data type) , acceptance testing , process (computing) , computer science , statistical analysis , point (geometry) , selection (genetic algorithm) , monte carlo method , operations research , data mining , statistics , mathematics , machine learning , software engineering , programming language , operating system , geometry
This paper discusses the challenges of setting process validation acceptance criteria for biotech products for cases where using statistical tools is appropriate. Data are analyzed under three different scenarios that are frequently encountered in biotech applications. Scenario A represents the case when a small data set around center point conditions is available for setting acceptance criteria. Scenario B represents the case when a larger data set within normal operation conditions is available for setting acceptance criteria. Scenario C represents the case when a large characterization data set is available for setting acceptance criteria and it is possible to accurately model the impact of operation conditions on performance of the step. Statistical approaches including mean ± 3SD, tolerance interval analysis, prediction profiler, and Monte Carlo simulation are applied to the different scenarios. Strengths and shortcomings of the different statistical tools are discussed, and the best approach for each scenario is recommended. It is shown that selection of the right statistical approach is a critical first step toward setting appropriate acceptance criteria.