标题： 通过氨基酸溶液热力学预测增强细胞培养基中营养物沉淀的计算预测 显示英文标题

标题： Computational predictions of nutrient precipitation for intensified cell 1 culture media via amino acid solution thermodynamics

摘要： 大多数市场上治疗性单克隆抗体（mAbs）是使用中国仓鼠卵巢（CHO）细胞在化学定义的细胞培养基中大规模培养生产的。 由于哺乳动物细胞培养相关的高成本，获得高细胞密度以生产高产品滴度是理想的。 这些生物工艺需要基础培养基中高浓度的营养物质，并定期添加浓缩的补料培养基以维持细胞生长和治疗性蛋白的生产能力。 不幸的是，由于溶液中形成的化学复合物沉淀，补料培养基所需的或最优的营养物质浓度通常受到溶解度的限制。 实验筛选包含50到100种化合物的各种细胞培养基配置可能既昂贵又费力。 本文为利用计算工具理解细胞培养基中营养物质的沉淀奠定了基础，通过研究热力学模型中氨基酸之间的两两相互作用来实现。 已使用一种氨基酸在水中的活度系数数据和两种氨基酸在水中的溶解度数据，确定了一组UNIFAC基团相互作用参数，以预测哺乳动物细胞培养基中多组分系统的热力学行为。 据我们所知，本研究收集的数据是迄今为止报道的最大一组三元体系氨基酸溶解度数据。 这些氨基酸沉淀预测已通过实验测得的三元和四元氨基酸溶液进行了验证。 因此，我们展示了我们的模型作为数字孪生的实用性，通过用基于复杂混合物中各个培养基组分热力学的计算预测来替代营养物质沉淀的经验方法，从而识别最佳的细胞培养基组成。 显示英文摘要

摘要： The majority of therapeutic monoclonal antibodies (mAbs) on the market are produced using Chinese Hamster Ovary (CHO) cells cultured at scale in chemically defined cell culture medium. Because of the high costs associated with mammalian cell cultures, obtaining high cell densities to produce high product titers is desired. These bioprocesses require high concentrations of nutrients in the basal media and periodically adding concentrated feed media to sustain cell growth and therapeutic protein productivity. Unfortunately, the desired or optimal nutrient concentrations of the feed media are often solubility limited due to precipitation of chemical complexes that form in the solution. Experimentally screening the various cell culture media configurations which contain 50 to 100 compounds can be expensive and laborious. This article lays the foundation for utilizing computational tools to understand precipitation of nutrients in cell culture media by studying the pairwise interactions between amino acids in thermodynamic models. Activity coefficient data for one amino acid in water and amino acid solubility data of two amino acids in water have been used to determine a single set of UNIFAC group interaction parameters to predict the thermodynamic behavior of the multi-component systems found in mammalian cell culture media. The data collected in this study is, to our knowledge, the largest set of ternary system amino acid solubility data reported to date. These amino acid precipitation predictions have been verified with experimentally measured ternary and quaternary amino acid solutions. Thus, we demonstrate the utility of our model as a digital twin to identify optimal cell culture media compositions by replacing empirical approaches for nutrient precipitation with computational predictions based on thermodynamics of individual media components in complex mixtures.