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

Abstract

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.