Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture

What can mathematical modelling say about CHO metabolism and protein glycosylation?

Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.

Nutrient Regulation by Continuous Feeding for Large-scale Expansion of Mammalian Cells in Spheroids

In this demonstration, spheroids formed from the β-TC6 insulinoma cell line were cultured as a model of manufacturing a mammalian islet cell product to demonstrate how regulating nutrient levels can improve cell yields. In previous studies, bioreactors facilitated increased culture volumes over static cultures, but no increase in cell yields were observed. Limitations in key nutrients such as glucose, which were consumed between batch feedings, can lead to limitations in cell expansion. Large fluctuations in glucose levels were observed, despite the increase in glucose concentrations in the media. The use of continuous feeding systems eliminated fluctuations in glucose levels, and improved cell growth rates when compared with batch fed static and SSB culture methods. Additional increases in growth rates were observed by adjusting the feed rate based on calculated nutrient consumption, which allowed the maintenance of physiological glucose over three weeks in culture. This method can also be adapted for other cell types.

Macroscopic modeling of mammalian cell growth and metabolism

We review major modeling strategies and methods to understand and simulate the macroscopic behavior of mammalian cells. These strategies comprise two important steps: the first step is to identify stoichiometric relationships for the cultured cells connecting the extracellular inputs and outputs. In a second step, macroscopic kinetic models are introduced. These relationships together with bioreactor and metabolite balances provide a complete description of a system in the form of a set of differential equations. These can be used for the simulation of cell culture performance and further for optimization of production.

Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris

Background

Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.

Results

This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R², above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.

Conclusions

The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.