Uridine modulates monoclonal antibody charge heterogeneity in Chinese hamster ovary cell fed-batch cultures

Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions

Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models.

Quantifying the impact of cell culture media on CHO cell growth and protein production

In recombinant protein production, cell culture media development and optimization is typically seen as a useful strategy to increase titer and cell density, reduce by-products, as well as improve product quality (with cell density and titer often serving as the primary reported outcome of media studies). However, despite the large number of media optimization studies, there have been few attempts to comprehensively assess the overall effectiveness of media additives. The aim of this review is therefore both to document published media optimization studies over the last twenty years (in the context of Chinese hamster ovary cell recombinant production) and quantitatively estimate the impact of this media optimization on cell culture performance. In considering 78 studies, we have identified 238 unique media components that have been supplemented over the last 20 years. Among these additives, trace elements stood out as having a positive impact on cell density while nucleotides show potential for increasing titer, with commercial supplements benefiting both. However, we also identified that the impact of specific additives is far more variable than often perceived. With relatively few media studies considering multiple cell lines or multiple basal media, teasing out consistent and general trends becomes a considerable challenge. By extracting cell density and titer values from all of the reviewed studies, we were able to build a mixed-effect model capable of estimating the relative impact of additives, cell line, product type, basal medium, cultivation method (flask or reactor), and feeding strategy (batch or fed-batch). Overall, additives only accounted for 3% of the variation in cell density and 1% of the variation in titer. Similarly, the impact of basal media was also relatively modest, at 10% for cell density and 0% for titer. Cell line, product type, and feeding strategy were all found to have more impact. These results emphasize the need for media studies to consider more factors to ensure that reported observations can be generalized and further developed.