Development of a computational glycan engineering tool for biologics manufacturers

Biologics are currently the most successful class of pharmaceuticals, with seven of the top 10 drugs marketed in 2018 hailing from this class and grossing a total of $60 bn. The majority of these protein drugs are glycosylated, meaning they are decorated with carbohydrate chains also called glycans. The presence of glycans causes a high degree of variability, which stems from the inherent heterogeneity fostered by the biosynthetic machinery that builds glycans. Variability is a problem for these drugs because proteins with different glycan structures attached to them display functional differences. Consequently, biologics are always sold as a mixture of drug molecules with varying efficiency, and each batch of a biologic can be significantly different. Such batch-to-batch variation, and in particular the inability to systematically control it, does curtail the ability of the pharmaceutical industry to develop new biologics and in particular to generate competing off-patent products.
Glycan heterogeneity is not random, but rather controlled in a non-intuitive way by the organisation of a large number of biosynthetic enzymes in the Golgi apparatus. Through a BBSRC IB catalyst project and a BBSRC Doctoral Training Partnership-funded PhD project, we have recently developed a computational model that can efficiently describe the organisation of glycosylation enzymes in the Golgi. In a proof-of-principle theoretical study (funded by a BBSRC impact accelerator award) we were also able to show how to use this computational tool for predicting how enzyme levels would need to be altered to shift the set of glycan structures produced by a cell line. This computational tool could be used to inform companies how to alter their production cell lines to generate biologics with more beneficial glycan repertoires. The proposed study will validate the use of this modelling tool for predicting which glycan biosynthetic enzymes to overproduce in a biologic-producing cell line, and to verify that this intervention indeed shifts the glycan repertoire to the desired range of glycan structures.
The work will be carried out with an industrial partner to ensure that our validation is performed on examples with industrial relevance. Three biologics with increasing glycan complexity, which have been produced by the partner, will be used. Following modelling of the Golgi composition required for generating the glycans that our partner reports on its products, the model will be challenged with a more desirable set of glycans. The predicted change in enzymes will then be used to create synthetic DNA constructs in York, which can then be transferred into the production cells at the company. The alterations in enzyme levels as well as the new glycan repertoire will be investigated using protein and glycan analytical tools, including western blotting and mass spectrometry. The experimentally obtained results will be compared to those computationally predicted, to assess the potency of the computational modelling tool for the engineering of biologic glycan states. Once fully developed and validated, we intend to customize this computational tool for other pharmaceutical companies as well, and will therefore set up meetings with some of these during the course of the project to establish their specific needs.