The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model’s performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

In the biopharmaceutical industry, accurate prediction of the oxygen uptake rate (OUR) by the cells is critical to understanding cell health. Accurate estimation of the oxygen transfer rate (OTR) presents a significant challenge in predicting OUR, and OTR is dependent on the volumetric oxygen mass transfer coefficient (kLa). Often kLa is assumed to be constant throughout a fermentation and estimated from experiments using no cells and in a buffer solutions. Yet, it is well-known that additions (i.e., glucose, base, and antifoam), cell secretions, and stir speed markedly effect kLa. Currently, there are three standard methods used to estimate kLa; however, all three have significant issues. This rapid communication describes a novel, non-disruptive method to estimate kLa by modulating the gas flow rate. This approach allowed accurate kLa estimates at multiple times throughout Escherichia coli batch fermentations using a non-disruptive technique, thus lowering the risk of losing a fermentation due to inadequate oxygenation.