Identification of Cell Culture Factors Influencing Afucosylation Levels in Monoclonal Antibodies by Partial Least-Squares Regression and Variable Importance Metrics

Retrospective analysis of historic data for cell culture processes is a powerful tool to develop further process understanding. In particular, deploying retrospective analyses can identify important cell culture process parameters for controlling critical quality attributes, e.g., afucosylation, for...

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Bibliographic Details
Published in:Processes 2023-01, Vol.11 (1), p.223
Main Authors: Rish, Adam J., Huang, Zhuangrong, Siddiquee, Khandaker, Xu, Jianlin, Anderson, Carl A., Borys, Michael C., Khetan, Anurag
Format: Article
Language:English
Subjects:
Cell culture
Composition
Data structures
Datasets
Dependent variables
Experiments
Least squares method
Machine learning
Monoclonal antibodies
Parameter identification
Process parameters
Quality management
Regression analysis
Regression models
Variance analysis
Workflow
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Summary:Retrospective analysis of historic data for cell culture processes is a powerful tool to develop further process understanding. In particular, deploying retrospective analyses can identify important cell culture process parameters for controlling critical quality attributes, e.g., afucosylation, for the production of monoclonal antibodies (mAbs). However, a challenge of analyzing large cell culture data is the high correlation between regressors (particularly media composition), which makes traditional analyses, such as analysis of variance and multivariate linear regression, inappropriate. Instead, partial least-squares regression (PLSR) models, in combination with machine learning techniques such as variable importance metrics, are an orthogonal or alternative approach to identifying important regressors and overcoming the challenge of a highly covariant data structure. A specific workflow for the retrospective analysis of cell culture data is proposed that covers data curation, PLS regression, model analysis, and further steps. In this study, the proposed workflow was applied to data from four mAb products in an industrial cell culture process to identify significant process parameters that influence the afucosylation levels. The PLSR workflow successfully identified several significant parameters, such as temperature and media composition, to enhance process understanding of the relationship between cell culture processes and afucosylation levels.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11010223