(724c) Metabolomics and Network Analysis for Sensitive Physiological Monitoring in Industrial Cell Culture Engineering

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. These controls could provide early warnings of problems in protein productivity and/or final quality before the end of the cultivation. 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. Moreover, utilized in the context of programs for industrial process improvement, which allow for experimentation with various cell lines and physiological conditions, these methods could contribute to enhancing our overall understanding of the protein production and manufacturing processes. Metabolomics, referring to the quantification of the (relative) 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 the environmental changes. By quantifying a complete and accurate metabolic profile is foreseen to have a major positive impact in cell culture engineering research.

To investigate this hypothesis, baby hamster kidney (BHK) cells were cultivated in high cell density perfusion bioreactors. In the first experimental series we examined the effect of the cell age on the metabolic physiology of the cell culture [2], while in a second large experimental process the pH, temperature (T), dissolved oxygen (DO) and/or perfusion rate (CSPR) were varied over the course of the experiments using a Design of Experiments (DOE) strategy. Samples were acquired at the end of each experimental condition and subsequently dried, as described in [2]. The metabolite extracts from the dry biomass samples were obtained by methanol/water extraction and, after derivatization, the metabolic profiles were acquired using the Varian Saturn 2200 Gas Chromatograph (GC) - (ion trap) Mass Spectrometer (MS), as described in [2]. After appropriate normalization and filtering from experimental biases and artifacts [3,4], the profiles were analyzed using multivariate statistical analysis algorithms, as implemented in the TM4 MeV software (www.tm4.org/mev). The results were visualized in the context of a BHK cell line metabolic network that was reconstructed based on the metabolic databases KEGG (www.kegg.com), EXPASY (enzyme.expasy.org), MetaCyc (metacyc.org) and available literature. Multivariate statistical analysis of the metabolomics data showed the differences between the cell culture samples, which were based on cell age and subtle changes in cell culture growth parameters, while these differences are not directly observable based on the conventional monitoring toolbox.

REFERENCES

[1]  Vernardis SI., Goudar CT, Klapa MI (2013) Metabolic profiling reveals that time related physiological changes in mammalian cell perfusion cultures are bioreactor scale Independent  Metabolic Engineering (in press)

[2]   Chrysanthopoulos PK., Goudar CT. and Klapa MI. (2009) Metabolomics for high-resolution monitoring of the cellular physiological state in cell culture engineering. Metabolic Engineering 12: 212-222

[3]   Kanani H., Klapa MI. (2007) Data correction strategy for metabolomics analysis using gas chromatography-mass spectrometry. Metabolic Engineering 9:39 – 51

[4]   Kanani H., Chrysanthopoulos PK., Klapa MI. (2008) Standardizing GC-MS metabolomics. Journal of Chromatography B: Biomedical Applications 871:191 - 201