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Title: A platform for the optimisation of metabolic pathways for glycosylation to achieve a narrow and targeted glycoform distribution
Author: Jedrzejewski, Philip
ISNI:       0000 0004 6422 5815
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2015
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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 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. 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. A modelling platform was developed and trained to link 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. An initial proof-of-concept model framework was able to reproduce hybridoma batch cell growth dynamics, extracellular nutrient availability, dynamic intracellular NSD and nucleotide concentrations, product titer and the antibody product glycoform. Refinement of the original model framework and further training was achieved through a two-step CHO cell-based experimental process. In a first set of experiments the cells’ dynamic response to mannose and guanosine feeding was observed. The data allowed a model extension to fed-batch operation as well as inclusion of additional control and inhibitory mechanisms in the in silico representation of the nucleotide and NSD de novo synthesis networks. A second set of experiments probed the dynamic impact of galactose and uridine feeding strategies on antibody product galactosylation. The effects were dynamic perturbations with respect to cell culture dynamics, nucleotide and NSD synthesis as well as the antibody product glycoform. This formed as a basis for further model refinement and to provided a calibrated operating space with respect to galactose and uridine additions to cell culture media. The calibrated model platform was able to produce an optimal dynamic feeding strategy in silico with a predicted increase in galactosylation of 46.9% through pulse feeding of galactose and uridine on even days of culture. This framework is a first step towards a platform for the in silico optimisation of bioprocess conditions with respect to product quality and safety in line with the Quality by Design (QbD) paradigm.
Supervisor: Kontoravdi, Cleo ; Polizzi, Karen M. Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral