Designing and selecting cell culture media and their feeding are a key strategy to maximize culture performance in industrial biopharmaceutical processes. However, mammalian cells are very sensitive to their culture environment, requiring specific nutritional needs to grow and produce high-quality proteins such as antibodies, depending on cell lines and operational conditions. In this regard, previously we developed data-driven and in-silico model-guided systematic framework to investigate the effect of growth media on Chinese hamster ovary (CHO) cell culture performance, allowing us to design and reformulate basal media. To expand our exploration for media development research further, we evaluated two chemically defined feed media, A and B, in ambr15 bioreactor runs using a monoclonal antibody-producing CHO K1 cell line. We observed a significant impact of feed media on cell growth, longevity, viability, productivity and toxic metabolites production. Specifically, concentrated feed A was not sufficient to support prolonged cell culture and high titer compared to feed B. The framework systematically characterized the major metabolic bottlenecks in the TCA cycle and its related amino acid transferase reactions, thereby identifying key design components, such as asparagine, aspartate, and glutamate, which are needed for highly productive cell cultures. Based on our results, we subsequently reformulated the feeds by adjusting the amounts of those amino acids and successfully validated their effectiveness in promoting cell growth and/or titer.

Recently, enormous culture profiles and datasets from biomanufacturing processes to produce recombinant therapeutic proteins (RTP) such as monoclonal antibodies (mAbs) could be generated by virtue of the advancement in process analytical techniques and artificial intelligence (AI). Thus, now it is highly necessary to develop AI-based data-driven models (DDMs) and exploit them accordingly in order to further enhance operational efficiency and accelerate reliable product supply. Since bioprocess is a complex and dynamic system, DDMs are practical and particularly useful to describe the intrinsic relationship among biological and process parameters and cell culture conditions by capturing inherent patterns and to produce high-quality RTP under consistent operations as well as to decrease cost and time by predicting incipient or abrupt faults during the cell cultures. In this work, we provide the practical guideline for choosing the best DDM on given mAb-producing Chinese hamster ovary (CHO) cell culture data sets, enabling us to forecast culture performance such as VCD, and mAb titer as well as glucose, lactate and ammonia concentrations in real time manner. Via the case study with 32 fed-batch data sets of CHO cell cultures, we suggested best combination of model elements including AI algorithms and multi-step ahead forecasting strategies, for good prediction in terms of the computational load as well as the model accuracy and reliability, which is applicable to implementation of interactive data-driven model within bioprocess digital twins. We believe this systematic study can help bioprocess engineers to start developing predictive DDMs with their own data and learn how their cell cultures behave in near future, thereby making proactive decision possible.

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their growth, productivity, and product quality, practically media reformulation has been one of key focuses with several technical challenges which are due to the myriad of intricate molecular and regulatory mechanisms underlying the media effects on culture behaviours; it is highly required to systematically characterize metabolic bottlenecks of cell cultures in various media conditions. To do so, we combined multivariate statistical analysis with flux balance analysis of a genome-scale metabolic model of CHO cells based on the culture profiles of CHO-DG44 under one commercial medium and two in-house media. At the outset, we used partial least square regression to identify metabolite exchanges that are correlated to specific growth and productivity. By using a commercial medium as reference, we found sub-optimal level of four nutrients and two metabolic wastes that plausibly hinder cell growth and productivity with in-house media. Subsequently, we elucidated that the recycling of lactate and ammonia wastes to be affected by both glutamine and asparagine metabolisms mechanistically, and further modulated by hitherto unsuspected folate and choline supplements. In summary, the current work successfully demonstrated how multivariate statistical analysis can be synergistically combined with in silico analysis of metabolic models to uncover the mechanistic elements underlying the differing performance of various media. Our approach for the systematic identification of promising nutrient targets thus paves the way for cell culture medium reformulation to enhance cellular growth and recombinant protein production.