Fingerprint detection and process prediction by multivariate analysis of fed‐batch monoclonal antibody cell culture data. (7th October 2015)

• Record Type: Journal Article
• Authors: Sokolov, Michael; Soos, Miroslav; Neunstoecklin, Benjamin; Morbidelli, Massimo; Butté, Alessandro; Leardi, Riccardo; Solacroup, Thomas; Stettler, Matthieu; Broly, Hervé
• Is Part Of: Biotechnology progress. Volume 31:Number 6(2015)
• Page Start: 1633
• Page End: 1644
• Publication Date: 2015-10-07
• Subjects: multivariate data analysis -- principal component analysis -- partial least squares regression -- cell culture process -- quality by design; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.2174
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract : This work presents a sequential data analysis path, which was successfully applied to identify important patterns (fingerprints) in mammalian cell culture process data regarding process variables, time evolution and process response. The data set incorporates 116 fed‐batch cultivation experiments for the production of a Fc‐Fusion protein. Having precharacterized the evolutions of the investigated variables and manipulated parameters with univariate analysis, principal component analysis (PCA) and partial least squares regression (PLSR) are used for further investigation. The first major objective is to capture and understand the interaction structure and dynamic behavior of the process variables and the titer (process response) using different models. The second major objective is to evaluate those models regarding their capability to characterize and predict the titer production. Moreover, the effects of data unfolding, imputation of missing data, phase separation, and variable transformation on the performance of the models are evaluated. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1633–1644, 2015