Monoclonal antibodies (mAbs) constitute the largest component of the current biopharmaceutical market, generating over $100 billion revenue in the U.S. every year. Recombinant therapeutic mAbs are often produced using Chinese hamster ovary (CHO) cell lines that execute the necessary post-translational modifications (e.g., glycosylation) for the drug to be therapeutically efficacious. Glycosylation is an intracellular, enzymatic process by which glycans are attached to a specific location on the antibody. The structure of the glycans attached to the mAb affects the therapeutic efficacy of the molecule, making glycan distribution a critical quality attribute. Consequently, the ability to predict how variations in process conditions affect both product formation and glycosylation is important from a process development viewpoint. A multiscale, mathematical model describing CHO cell growth, antibody production, and glycosylation was developed in MATLAB to provide a quantitative understanding of how to manipulate a cell culture process to improve antibody titer and control glycosylation effectively. At the macroscopic (bioreactor) scale, the cell culture model uses Monod growth kinetics to describe cell growth, nutrient/metabolite concentrations, and mAb production; at the microscopic (antibody) scale, the glycosylation process in the Golgi apparatus is modeled by a plug flow reactor using a reaction network governed by the Michaelis–Menten kinetics. Model predictions are cross-validated against an independent data set from a representative industrial mammalian cell culture process. The multiscale model is also capable of solving process parameters that would lead to a specified production target for the use in a process development setting. In a proof-of-concept experiment, the model was used to generate initial process conditions to drive changes in the glycosylation pattern of a representative mAb product. These initial conditions were validated across three different cell lines and products.

Multiscale modeling of antibody production and glycosylation for improved upstream process design

2019 Spring National Meeting

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