Biologics manufacturing typically uses engineered Chinese Hamster Ovary (CHO) cells to produce folded and glycosylated antibodies. Determining the optimum conditions to grow and maintain cell culture often requires considerable time and effort.
A quantitative understanding of cell metabolism through an analysis of cell culture metabolites can enable optimization growth conditions for improved titles or increased perfusion duration. Mass spectrometry is the optimum tool for metabolite measurement, however, transforming raw data into accurate quantitative measurement requires both expertise and extensive sample preparation.
In this poster we demonstrate the ability of simple sample preparation using universal calibrators and a novel machine learning algorithm to rapidly provide biological insight into bioprocessing samples taken from perfusion cell cultures.