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The objective of the present study was to investigate the effect of hydrodynamic stress heterogeneity on metabolism and productivity of an industrial mammalian cell line. For this purpose, a novel Lobed Taylor-Couette (LTC) mixing unit combining a narrow distribution of hydrodynamic stresses and a membrane aeration system to prevent cell damage by bubble bursting was developed. A hydrodynamic analysis of the LTC was developed to reproduce, in a uniform hydrodynamic environment, the same hydrodynamic stress encountered locally by cells in a stirred tank, particularly at the large scale, e.g., close and far from the impeller. The developed LTC was used to simulate the stress values near the impeller of a laboratory stirred tank bioreactor, equal to about 0.4 Pa, which is however below the threshold value leading to cell death. It was found that the cells actively change their metabolism by increasing lactate production and decreasing titer while the consumption of the main nutrients remains substantially unchanged. When considering average stress values ranging from 1 to 10 Pa found by other researchers to cause physiological response of cells to the hydrodynamic stress in heterogeneous stirred vessels, our results are close to the lower boundary of this interval.

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.

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