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AbstractThe pharmaceutical industry employs various strategies to improve cell productivity. These strategies include process intensification, culture media improvement, clonal selection, media supplementation and genetic engineering of cells. However, improved cell productivity has inherent risk of impacting product quality attributes (PQA). PQAs may affect the products’ efficacy via stability, bioavailability, or in vivo bioactivity. Variations in manufacturing process may introduce heterogeneity in the products by altering the type and extent of N-glycosylation, which is a PQA of therapeutic proteins. We investigated the effect of different cell densities representing increasing process intensification in a perfusion cell culture on the production of an IgG1-κ monoclonal antibody from a CHO-K1 cell line. This antibody is glycosylated both on light chain and heavy chain. Our results showed that the contents of glycosylation of IgG1-κ mAb increased in G0F and fucosylated type glycans as a group, whereas sialylated type glycans decreased, for the mAb whole protein. Overall, significant differences were observed in amounts of G0F, G1F, G0, G2FS1, and G2FS2 type glycans across all process intensification levels. G2FS2 and G2 type N-glycans were predominantly quantifiable from light chain rather than heavy chain. It may be concluded that there is a potential impact to product quality attributes of therapeutic proteins during process intensification via perfusion cell culture that needs to be assessed. Since during perfusion cell culture the product is collected throughout the duration of the process, lot allocation needs careful attention to process parameters, as PQAs are affected by the critical process parameters (CPPs). Key points• Molecular integrity may suffer with increasing process intensity.• Galactosylated and sialylated N-glycans may decrease.• Perfusion culture appears to maintain protein charge structure. 

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. 

Abstract A majority of the biotherapeutics industry today relies on the manufacturing of monoclonal antibodies from Chinese hamster ovary (CHO) cells, yet challenges remain with maintaining consistent product quality from high‐producing cell lines. Previous studies report the impact of individual trace metal supplemental on CHO cells, and thus, the combinatorial effects of these metals could be leveraged to improve bioprocesses further. A three‐level factorial experimental design was performed in fed‐batch shake flasks to evaluate the impact of time wise addition of individual or combined trace metals (zinc and copper) on CHO cell culture performance. Correlations among each factor (experimental parameters) and response variables (changes in cell culture performance) were examined based on their significance and goodness of fit to a partial least square's regression model. The model indicated that zinc concentration and time of addition counter‐influence peak viable cell density and antibody production. Meanwhile, early copper supplementation influenced late‐stage ROS activity in a dose‐dependent manner likely by alleviating cellular oxidative stress. Regression coefficients indicated that combined metal addition had less significant impact on titer and specific productivity compared to zinc addition alone, although titer increased the most under combined metal addition. Glycan analysis showed that combined metal addition reduced galactosylation to a greater extent than single metals when supplemented during the early growth phase. A validation experiment was performed to confirm the validity of the regression model by testing an optimized setpoint of metal supplement time and concentration to improve protein productivity.