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Bio‐CoDa: A new analysis class to ensure accurate and precise monoclonal antibody CQA estimation and control
Author(s) -
Lonardo Anthony,
Saxena Parekh Babita,
Srivastava Arvind
Publication year - 2017
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.26318
Subject(s) - coda , computer science , constraint (computer aided design) , class (philosophy) , quality (philosophy) , independence (probability theory) , econometrics , data mining , statistics , mathematics , artificial intelligence , physics , geometry , quantum mechanics , acoustics
Monoclonal antibody production processes control critical quality attributes (CQAs), which are the measures that provide proof of a product's identity and quality. Critical decisions rely on the accuracy and precision of these measures, as well as their appropriate statistical treatment. Many measures require special attention. For example, the charge heterogeneity CQA measured by ionic exchange chromatography reports proportions or percentages of the total integrated peak area of known species. Since proportions sum to a constant (1 or 100%), they fall into a special class of data called compositional data that have a unit sum constraint and therefore an inherent correlation. However, these measures are often analyzed assuming independence which is incorrect. Estimating statistics with incorrect assumptions can lead to inferential failures (e.g., shelf life failures), or can lead to missing important structural patterns in the data. Presented here is a new class of analysis methods for CQAs compositional data called Biologic Compositional Data Analysis (Bio‐CoDa). The method is based on the elegant solution to analysis issues by Aitchison (1986). An introduction to the Bio‐CoDa methods with rational is presented as well as examples demonstrating its strengths. Biotechnol. Bioeng. 2017;114: 2001–2010. © 2017 Wiley Periodicals, Inc.