Volume 9 Supplement 9
An improved model framework linking the extracellular environment to antibody glycosylation
© Jedrzejewski et al. 2015
Published: 14 December 2015
Glycoproteins make up the bulk of biologically-derived medicines, and are taking up an ever increasing share of the prescription pharmaceuticals market. As opposed to small molecule drugs, glycoproteins are large complex molecules with heterogeneity arising from a multitude of glycan moieties. Glycans are complex post-translation modifications, which result from a number of enzymatic reactions in the ER and Golgi collectively known as glycosylation and play an important role in pharmacokinetics such as drug safety, efficacy and half-life. It is known that the availability of the nucleotide sugar donors (NSDs), which are the co-substrates to the enzymatic glycosylation reactions of the Golgi, can be affected by a number of process conditions such as culture mode, temperature, dissolved oxygen and nutrient availability, as well as the addition of precursor molecules to the culture medium . Consequently, feeding strategies of nucleotide and nucleotide sugar precursors have been explored to exert control over the glycoform [2, 3]. In this work, a mathematical model platform is presented to quantify the impact of nutrient availability and feeding strategies on the glycosylation process with the aim to enable the design of feeding strategies to optimise the product glycoform.
As part of this work the Jedrzejewski et al. modelling platform was developed and trained further for improved model confidence and performance . The framework links the extracellular environment, through the availability of intracellular metabolites in the cytoplasm and the Golgi apparatus, to the glycosylation of the conserved glycan site of the IgG heavy chain. The model platform comprises four parts, which are interlinked through dynamic fluxes and metabolite concentrations:
A modified cell growth model based on Monod kinetics capturing cell culture dynamics and the impact of various hexose and nucleotide precursor additions to the cell culture media;
A semi-structured purine and pyrimidine synthesis network describing the intracellular concentrations of nucleotide triphosphates, which are the co-substrates of NSD synthesis;
A structured and mechanistic representation of the NSD synthesis pathway;
The del Val et al. model describing the N-linked glycosylation process of the conserved glycan structure of the IgG antibody heavy chain .
The main focus of the work has been on the bottom-up mechanistic in silico reconstruction of the NSD synthesis network. The 34 species that make up the metabolic network were represented by means of mass balances that are connected through a network of 60 reactions, which were modelled as saturation rate kinetics based on reaction mechanisms found in the literature. The model platform is able to reproduce cell growth dynamics, extracellular nutrient availability, dynamic intracellular NSD and nucleotide concentrations, product titer and the antibody product glycoform.
We have developed a mathematical model that links the extracellular environment to protein product glycoform for CHO cell cultures grown under batch and fed-batch conditions. The model output is in good agreement with data from a variety of culture conditions and is able to capture the dynamic impact of hexose and nucleotide precursor additions to culture media and their impact on NSD concentrations and product glycoform. Data from an experiment in which galactose and uridine were added to the cell culture media, in particular, extended the dynamic range of the model platform to regimes known to increase antibody galactosylation. Following this extensive model development and training exercise, the platform can now be used as an in silico tool towards designing feeding strategies to alter and drive the galactosylation of mAbs. Lastly, the modular nature of the framework allows it to be coupled with other models to translate the framework to other expression systems, operation modes and culture conditions.
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