Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed-batch cell cultures

Glycosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs). The glycan pattern can have a large impact on the immunological functions, serum half-life and stability. The medium components and cultivation parameters are known to potentially influence the glycosylation profile. Mathematical modelling provides a strategy for rational design and control of the upstream bioprocess. However, the kinetic models usually contain a very large number of unknown parameters, which limit their practical applications. In this article, we consider the metabolic network of N-linked glycosylation as a Bayesian network (BN) and calculate the fluxes of the glycosylation process as joint probability using the culture parameters as inputs. The modelling approach is validated with data of different Chinese hamster ovary cell cultures in pseudo perfusion, perfusion, and fed batch cultures, all showing very good predictive capacities. In cases where a large number of cultivation parameters is available, it is shown here that principal components analysis can efficiently be employed for a dimension reduction of the inputs compared to Pearson correlation analysis and feature importance by decision tree. The present study demonstrates that BN model can be a powerful tool in upstream process and medium development for glycoprotein productions.

Automated summary

This article discusses the importance of glycosylation in therapeutic monoclonal antibodies and validates the predictive ability of the modeling approach using Bayesian networks. Principal component analysis is shown to efficiently reduce input dimensions for glycoprotein production. The study demonstrates that a Bayesian network model can be a powerful tool in upstream process and medium development for glycoprotein productions.