IgG1 antibodies produced in Chinese hamster ovary (CHO) cells are heavily a1,6-fucosylated, a modification that reduces antibody-dependent cellular cytotoxicity (ADCC) and can inhibit therapeutic antibody function in vivo. Addition of fucose is catalyzed by Fut8, a a1,6-fucosyltransferase. FUT8À/À CHO cell lines produce completely nonfucosylated antibodies, but the difficulty of recapitulating the knockout in protein-production cell lines has prevented the widespread adoption of FUT8 À/À cells as hosts for antibody production. We have created zinc-finger nucleases (ZFNs) that cleave the FUT8 gene in a region encoding the catalytic core of the enzyme, allowing the functional disruption of FUT8 in any CHO cell line. These reagents produce FUT8 À/À CHO cells in 3 weeks at a frequency of 5% in the absence of any selection. Alternately, populations of ZFN-treated cells can be directly selected to give FUT8 À/À cell pools in as few as 3 days. To demonstrate the utility of this method in bioprocess, FUT8 was disrupted in a CHO cell line used for stable protein production. ZFNderived FUT8 À/À cell lines were as transfectable as wild-type, had similar or better growth profiles, and produced equivalent amounts of antibody during transient transfection. Antibodies made in these lines completely lacked core fucosylation but had an otherwise normal glycosylation pattern. Cell lines stably expressing a model antibody were made from wild-type and ZFN-generated FUT8 À/À cells. Clones from both lines had equivalent titer, specific productivity distributions, and integrated viable cell counts. Antibody titer in the best ZFN-generated FUT8 À/À cell lines was fourfold higher than in the best-producing clones of FUT8 À/À cells made by standard homologous recombination in a different CHO subtype. These data demonstrate the straightforward, ZFN-mediated transfer of the Fut8À phenotype to a production CHO cell line without adverse phenotypic effects. This process will speed the production of highly active, completely nonfucosylated therapeutic antibodies.
Large scale biopharmaceutical production of biologics relies on the overexpression of foreign proteins by cells cultivated in stirred tank bioreactors. It is well recognized and documented fact that protein overexpression may impact host cell metabolism and that factors associated with large scale culture, such as the hydrodynamic forces and inhomogeneities within the bioreactors, may promote cellular stress. The metabolic adaptations required to support the high‐level expression of recombinant proteins include increased energy production and improved secretory capacity, which, in turn, can lead to a rise of reactive oxygen species (ROS) generated through the respiration metabolism and the interaction with media components. Oxidative stress is defined as the imbalance between the production of free radicals and the antioxidant response within the cells. Accumulation of intracellular ROS can interfere with the cellular activities and exert cytotoxic effects via the alternation of cellular components. In this context, strategies aiming to alleviate oxidative stress generated during the culture have been developed to improve cell growth, productivity, and reduce product microheterogeneity. In this review, we present a summary of the different approaches used to decrease the oxidative stress in Chinese hamster ovary cells and highlight media development and cell engineering as the main pathways through which ROS levels may be kept under control.
CHO cells have become the favorite expression system for large scale production of complex biopharmaceuticals. However, industrial strategies for upstream process development are based on empirical results, due to a lack of fundamental understanding of intracellular activities. Genome scale models of CHO cells have been reconstructed to provide an economical way of analyzing and interpreting large-omics datasets, since they add cellular context to the data. Here the most recently available CHO-DG44 genome-scale specific model was manually curated and tailored to the metabolic profile of cell lines used for industrial protein production, by modifying 601 reactions. Generic changes were applied to simplify the model and cope with missing constraints related to regulatory effects as well as thermodynamic and osmotic forces. Cell line specific changes were related to the metabolism of high-yielding production cell lines. The model was semi-constrained with 24 metabolites measured on a daily basis in n = 4 independent industrial 2L fed batch cell culture processes for a therapeutic antibody production. This study is the first adaptation of a genome scale model for CHO cells to an industrial process, that successfully predicted cell phenotype. The tailored model predicted accurately both the exometabolomics data (r ≥ 0.8 for 96% of the considered metabolites) and growth rate (r = 0.91) of the industrial cell line. Flux distributions at different days of the process were analyzed for validation and suggestion of strategies for medium optimization. This study shows how to adapt a genome scale model to an industrial process and sheds light on the metabolic specificities of a high production process. The curated genome scale model is a great tool to gain insights into intracellular fluxes and to identify possible bottlenecks impacting cell performances during production process. The general use of genome scale models for modeling industrial recombinant cell lines is a long-term investment that will highly benefit process development and speed up time to market.
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.
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