Variability patterns identification of an α-CD20 monoclonal antibody´s
perfusion process by exploratory data analysis: A Case Study
Abstract
Perfusion processes have gained interest as mammalian cell culture mode.
Due to the high complex issues and variability regarding such culture
mode, a Principal Component Analysis (PCA) to the data was used to
characterize such variable process patterns and to achieve a better
understanding. The transfected NS0/1B8 cell line was fermented in a 500
L bioreactor in perfusion culture mode to obtain a desired monoclonal
antibody against CD20 molecule. Given the high variability of the
process, an exploratory data analysis based on a multivariate analysis
technique such as PCA was applied. The variables were selected by a risk
model analysis based on the cause-effect matrix, the experience
accumulated in the process, over the critical quality attributes, and
parameters focus on the fermentation process. As a result, it was
obtained that two main components were able to explain more than 95% of
the total variance, and it was possible to select between the critical
parameters those that have the greatest contribution to the variability
of the fermentation process. Furthermore, the practical experiences of
the specialists matched with the results and new process recommendations
were projected to improve the control strategy for a further Continuous
Process Verification. Keywords: Monoclonal antibody, Principal Component
Analysis, Fermentation, Critical parameters.