Mammalian cell cultures have been widely used for the production of therapeutic proteins. The primary objective of most current programs for the development of therapeutic protein production processes is the rapid development of bioreactor cultures that are characterized by high product yield and consistent product quality. In addition, due to the high manufacturing cost of these processes, the identification and use of accurate and sensitive controls for cell cultivation robustness is desirable. Today, both bioreactor monitoring and process improvements are based primarily on cell growth, metabolic activity and protein productivity data. While useful, the limitations of this cell specific rate-based approach have been recognized. There is a clear need for the development and application of methods that enable the comprehensive characterization of the physiological state of mammalian cell cultures. Metabolomics, referring to quantification of the concentration profile of the free small metabolites, is the most recently introduced high-throughput method for the measurement of the metabolic fingerprint of a biological system. Between the molecular levels of the cell function, metabolism is directly and very dynamically affected by changes in the growth environment of the cultures. Therefore, by quantifying a complete and accurate metabolomic profile is foreseen to have a major positive impact in cell culture engineering research. Our group was the first to publish on the value of metabolomics as sensitive monitoring tool in industrial cell culture engineering. In this study, baby hamster kidney (BHK) cells were cultivated in high cell density perfusion bioreactors in two experiments. In the first experimental series we examined the effect of the cell age on the metabolic physiology of the cell culture, while in a large-scale second the pH, temperature, dissolved oxygen (DO) and/or perfusion rate (CSPR) in the growth environment of the cultures were varied over the course of the experiments using a Design of Experiments (DOE) strategy. The metabolic profiles were acquired using Gas Chromatograph (GC) - (ion trap) Mass Spectrometry (MS) and analyzed using multivariate statistical and network analysis algorithms. Metabolomic analysis allowed for the identification of differences between the cell culture samples not directly observable based on the conventional monitoring toolbox.

industrial cell culture engineering, GC-MS metabolomics, perfusion cultures, high-throughput biomolecular analysis, metabolic network reconstruction