Prediction of intracellular nucleotide sugar variation impact on monoclonal antibodies (mAbs) glycan distribution using glycosylation reaction network models
Sha Shaa and Seongkyu Yoona
aDepartment of Chemical Engineering, University of Massachusetts, Lowell, MA, USA
Glycosylation is a critical post-translational process on monoclonal antibodies (mAbs) produced by mammalian cells as the glycan profile on mAbs plays significant impact on the pharmaceuticals' stability, efficacy and half-life. Glycosylation central reaction network (CRN) in cellular Golgi compartment is the branching paths for mAb to be attached with various intermediate or terminal glycan structures via the mediation of a series of enzymes. The recent development of reaction network models is an approach to simulate glycosylation process in Golgi CRN and predict the distribution of glycan on final monoclonal antibodies (including a number of intermediate or terminal glycan structures) through simulation using the model, and thus are potentially of great value in the application in the industrial bioprocess. Reaction network models are formulated by describing the kinetics of reactions in the network and involve various intracellular factors associated with glycosylation as model parameters. The capability of models being built to reflect key intercellular factor variation is one of the priorities for models to ensure proper performance. However, the performance of model prediction for glycan distribution out from intracellular factor (e.g. nucleotide sugar) variation has not been sufficiently evaluated in the past studies. In this work, a reaction network model was developed, describing the CRN by resembling Golgi as a four-compartment continuous stirred-tank reactor (CSTR) and the glycan distribution was estimated by solving the model based on mass balance of all glycan structures in the network at a steady state. The impact of intracellular nucleotide sugar concentration variation on the glycan distribution output was then simulated using the model and further compared with nucleotide sugar- glycan variation correlation being examined by published literature. The model was then optimized to minimize the gap between model prediction and experiments-derived data, therefore becoming a more reliable tool to be utilized in the future for glycosylation prediction.
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