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

DOI

10.3390/pr11010223

Authors

Adam J. Rish, Bristol-Myers Squibb
Zhuangrong Huang, Bristol-Myers Squibb
Khandaker Siddiquee, Bristol-Myers Squibb
Jianlin Xu, Bristol-Myers Squibb
Carl A. Anderson, Duquesne University
Michael C. Borys, Bristol-Myers Squibb
Anurag Khetan, Bristol-Myers Squibb

Document Type

Journal Article

Publication Date

1-1-2023

Publication Title

Processes

Volume

11

Issue

1

Keywords

afucosylation, cell culture, monoclonal antibodies, partial least-squares regression (PLSR), selectivity ratio (SR), significance multivariate correlation (sMC), variable importance in projection (VIP) scores, variable importance metric

Abstract

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.

Open Access

Gold

Repository Citation

Rish, A., Huang, Z., Siddiquee, K., Xu, J., Anderson, C., Borys, M., & Khetan, A. (2023). Identification of Cell Culture Factors Influencing Afucosylation Levels in Monoclonal Antibodies by Partial Least-Squares Regression and Variable Importance Metrics. Processes, 11 (1). https://doi.org/10.3390/pr11010223