Towards Controlling the Glycoform: A Model Framework Linking Extracellular Metabolites to Antibody Glycosylation

Abstract

Glycoproteins represent the largest group of the growing number of biologically-derived medicines. The associated glycan structures and their distribution are known to have a large impact on pharmacokinetics. A modelling framework was developed to provide a link from the extracellular environment and its effect on intracellular metabolites to the distribution of glycans on the constant region of an antibody product. The main focus of this work is the mechanistic in silico reconstruction of the nucleotide sugar donor (NSD) metabolic network by means of 34 species mass balances and the saturation kinetics rates of the 60 metabolic reactions involved. NSDs are the co-substrates of the glycosylation process in the Golgi apparatus and their simulated dynamic intracellular concentration profiles were linked to an existing model describing the distribution of N-linked glycan structures of the antibody constant region. The modelling framework also describes the growth dynamics of the cell population by means of modified Monod kinetics. Simulation results match well to experimental data from a murine hybridoma cell line. The result is a modelling platform which is able to describe the product glycoform based on extracellular conditions. It represents a first step towards the in silico prediction of the glycoform of a biotherapeutic and provides a platform for the optimisation of bioprocess conditions with respect to product quality.

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

“…Previous models have addressed diverse aspects of glycosylation, such as the dynamics governing the initial co-translational oligosaccharide attachment (Shelikoff et al, 1996), sequence motifs correlating with glycosylation site occupancy (Senger and Karim, 2005), and prediction of the total glycan diversity produced by a cell based on enzyme kinetics and expression (Hossler et al, 2007; Kawano et al, 2005; Krambeck and Betenbaugh, 2005). Other models have focused on predicting glycoprofiles on pharmaceutically relevant proteins, such as monoclonal antibodies (del Val et al, 2011; Jedrzejewski et al, 2014; Kaveh et al, 2013). In this work, we follow a similar objective as we aim to address problems arising in the glycoengineering of individual glycoproteins produced in cell culture.…”

O-Glycosylation of the N-terminal Region of the Serine-rich Adhesin Srr1 of Streptococcus agalactiae Explored by Mass Spectrometry

“…The biosynthesis of glycan and their attachment to proteins is dependent on a variety of biochemical parameters like the sugar nucleotide donor availability or the activity of glycosylating enzymes (77). Because glycosylation of proteins is a non-template process, variation of these parameters during cultivation could affect protein glycosylation as it has been shown for eukaryotic proteins (78). The consequences of varying S. agalactiae cultivation conditions (medium, carbon sources, temperature etc.)…”

Multi-level regulation of cellular glycosylation: from genes to transcript to enzyme to structure

“…In this regard, glycosylation reaction networks typically have a large number of reactions and reactants, but relatively few model parameters due to the limited number of glycoenzymes in a given system. Fitting such over-determined systems using deterministic ordinary differential equation (ODE) networks is complicated, though this is commonly done [44,45,47,52]. In such studies, optimization approaches like genetic algorithms are sometimes used to determine the bounds of the enzyme rate constants rather than exact solutions [45].…”