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The use of machine learning and deep learning has become prominent within various fields of bioprocessing for countless modeling and prediction tasks. Previous reviews have emphasized machine learning applications in various fields of bioprocessing, including biomanufacturing. This comprehensive review highlights many of the different machine learning and multivariate analysis techniques that have been utilized within Chinese hamster ovary cell biomanufacturing, specifically due to their rising significance in the industry. Applications of machine and deep learning within other bioprocessing industries are also briefly discussed. [ABSTRACT FROM AUTHOR]

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 the 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, tricarboxylic acid 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. [ABSTRACT FROM AUTHOR]

Due to the favorable attributes of Chinese hamster ovary (CHO) cells for therapeutic proteins and antibodies biomanufacturing, companies generate proprietary cells with desirable phenotypes. One key attribute is the ability to stably express multi‐gram per liter titers in chemically defined media. Cell, media, and feed diversity has limited community efforts to translate knowledge. Moreover, academic, and nonprofit researchers generally cannot study "industrially relevant" CHO cells due to limited public availability, and the time and knowledge required to generate such cells. To address these issues, a university‐industrial consortium (Advanced Mammalian Biomanufacturing Innovation Center, AMBIC) has acquired two CHO "reference cell lines" from different lineages that express monoclonal antibodies. These reference cell lines have relevant production titers, key performance outcomes confirmed by multiple laboratories, and a detailed technology transfer protocol. In commercial media, titers over 2 g/L are reached. Fed‐batch cultivation data from shake flask and scaled‐down bioreactors is presented. Using productivity as the primary attribute, two academic sites aligned with tight reproducibility at each site. Further, a chemically defined media formulation was developed and evaluated in parallel to the commercial media. The goal of this work is to provide a universal, industrially relevant CHO culture platform to accelerate biomanufacturing innovation. [ABSTRACT FROM AUTHOR]

Human‐induced pluripotent stem cells (iPSCs) hold the promise to improve cell‐based therapies. Yet, to meet rising demands and become clinically impactful, sufficient high‐quality iPSCs in quantity must be generated, a task that exceeds current capabilities. In this study, K3 iPSCs cultures were examined using parallel‐labeling metabolic flux analysis (13C‐MFA) to quantify intracellular fluxes at relevant bioprocessing stages: glucose concentrations representative of initial media concentrations and high lactate concentrations representative of fed‐batch culture conditions, prior to and after bolus glucose feeds. The glucose and lactate concentrations are also representative of concentrations that might be encountered at different locations within 3D cell aggregates. Furthermore, a novel method was developed to allow the isotopic tracer [U‐13C3] lactate to be used in the 13C‐MFA model. The results indicated that high extracellular lactate concentrations decreased glucose consumption and lactate production, while glucose concentrations alone did not affect rates of aerobic glycolysis. Moreover, for the high lactate cultures, lactate was used as a metabolic substrate to support oxidative mitochondrial metabolism. These results demonstrate that iPSCs have metabolic flexibility and possess the capacity to metabolize lactate to support exponential growth, and that high lactate concentrations alone do not adversely impact iPSC proliferation. [ABSTRACT FROM AUTHOR]