Biotechnology and BioengineeringVolume 118, Issue 5 p. 1757-1761 ISSUE INFORMATIONFree Access Biotechnology and Bioengineering: Volume 118, Number 5, May 2021 First published: 14 April 2021 https://doi.org/10.1002/bit.27401AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume118, Issue5May 2021Pages 1757-1761 RelatedInformation

The control of nutrient availability is critical to large-scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time-consuming and thus is difficult to implement for real-time in situ bioprocess control. Genome-scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations. This potential has not been realized due to unresolved challenges: (1) the models assume an optimal and highly efficient metabolism, and therefore tend to underestimate amino acid consumption, and (2) the models assume a steady state, and therefore have a short forecast range. We address these challenges by integrating machine learning with the metabolic models. Through this we demonstrate accurate and time-course dependent prediction of individual amino acid concentration in culture medium throughout the production process. Thus, these models can be deployed to control nutrient feeding to avoid premature nutrient depletion or provide early predictions of failed bioreactor runs.

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells – including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

Chinese Hamster Ovary (CHO) cells are used for the production of therapeutic proteins. This work examines improving passaging growth rate of two CHO clones. Growth rates were significantly improved for both clones with supplementation of the nucleosides cytidine, hypoxanthine, uridine, and thymidine to the culturing media at the optimal concentration of 100 µM of each nucleoside. We investigated supplementing the same combination of nucleosides to seed bioreactors and production fed batch bioreactors. In the seed bioreactors, growth rate and harvest density were improved. However, in the production fed batch bioreactors, no improvements in growth rate or peak viable cell density were observed. Cell cycle analysis of the passaging cells provides evidence that nucleosides can affect the cell cycle. It is not clear from our work how the nucleosides impact the cell cycle regulatory pathways. Overall, nucleoside supplementation in cell culture media is an effective approach for improving growth rate in passaging and seed bioreactors of certain CHO cells.