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A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

机译:基于代谢网络的基于基于网络的方法,用于开发CHO细胞增加单克隆抗体生产的饲养策略

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摘要

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, to suggest new ways of increasing production in different cell factories.
机译:中国仓鼠卵巢(CHO)细胞是生物制药工业中的主要作战,用于生产重组蛋白,如单克隆抗体。迄今为止,已经使用各种代谢工程方法来提高CHO细胞的生产率。虽然遗传操作在哺乳动物细胞中可能艰苦艰苦,但CHO细胞培养基或有效的FED批量策略的理性设计是生物过程优化的更受欢迎的方法。在该研究中,使用CHO细胞的基因组级代谢网络模型来设计CHO细胞的饲养策略,以改善单克隆抗体(MAB)生产。通过该模型向细胞培养基中添加到细胞培养基中,提出了许多代谢物,包括苏氨酸和甘草酮。设计的组合物已经通过实验验证,然后使用实验方法设计优化。已经使用该策略观察到总体mAb表达的两倍增加。我们的方法可用于类似的生物过程优化问题,建议在不同细胞工厂中增加产量的新方法。

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