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 fedbatch 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.

Fingerprint detection and process prediction by multivariate analysis of fed-batch monoclonal antibody cell culture data.

LEARDI, RICCARDO;
2015-01-01

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 fedbatch 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/865440
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