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BRIC DOCTORATE PROGRAMME: Development of a computational tool for predicting the impact of bioprocess conditions on protein glycosylation

Reference	BB/J003808/1
Principal Investigator / Supervisor	Professor Cleo Kontoravdi
Co-Investigators / Co-Supervisors	Dr Alison Mason, Professor Karen Polizzi, Dr Christopher Sellick
Institution	Imperial College London
Department	Chemical Engineering
Funding type	Skills
Value (£)	103,932
Status	Completed
Type	Training Grants
Start date	15/10/2011
End date	14/10/2015
Duration	48 months

Abstract

unavailable

Summary

Most licensed monoclonal antibodies (mAbs) contain a consensus N-linked glycosylation site on their heavy chains. The oligosaccharides attached to this site greatly influence the efficacy of mAbs as therapeutics either by reducing their serum half-life or by directly affecting the mechanisms they trigger in vivo. It has been widely reported that cell culture conditions, such as carbon source type and availability, dissolved oxygen concentration, ammonia concentration, medium pH and culture mode, affect protein glycosylation, thus having great impact on end product quality (1-4). Recently, the US FDA and the European Medicines Agency have proposed the implementation of the Quality by Design (QbD) paradigm to the manufacture of biopharmaceuticals. Its implementation requires the use of all available knowledge of a given product for the design, optimization and control of the manufacturing process. The goal is to ensure that quality is built into the product at every stage of the manufacturing process. It is proposed that detailed mathematical models may play a critical role in the design, control and optimization of biopharmaceutical manufacturing processes under the QbD scope. To our knowledge, there are currently no mathematical models that relate product quality in terms of glycosylation with cell culture conditions. A model with this capability would be useful for bioprocess design and control, culture media formulation and hypothesis testing for genetic engineering strategies. We propose the development of a novel mathematical tool that links bioprocess conditions to protein glycosylation. The tool will encompass four components: transport of nutrients into the cell, cytosolic synthesis of nucleotide sugar donors (NSDs), transport of NSDs from the cytosol to the Golgi apparatus, and, finally, their addition onto the protein of interest. Lack of carbon availability directly affects the intracellular availability of NSDs, without which the glycan metabolism in the Golgi cannot continue. Other studies have shown that high ammonia concentrations (4) and extremes of pH (5) lead to poor glycoprofiles. Process conditions therefore directly affect the synthesis of NSDs, which, in turn, affects the glycosylation process and the final product quality. We plan to validate this model-based tool with experimental data from cell cultures conducted using stable and transient transfectants. Even though manufacturing cell lines are stably transfected and clonal, transient transfections are of particular interest for the rapid provision of material for clinical evaluation. It is therefore of paramount importance that the material provided is similar to what would be produced from the final production cell lines. The project will be organised around the following objectives: a) Experimental analysis of the effect of residual glucose and glutamine concentration, ammonia accumulation and culture pH on the availability of NSDs and the variation of glycoforms produced. b) Comparison of glycoforms produced by stable and transiently transfected industrial CHO cell lines. c) Development and validation of kinetic mathematical model for the transient and stable transfectants using the experimental data generated in objective 1 (We have already begun the development of the modelling tool for the effects of glucose availability). d) Computational design and experimental evaluation of process and genetic engineering strategies for narrowing the gap between the glycomic profiles of transient and stable transfectants. [1] Hayter et al., Biotechnol Bioeng 1992, 39:327. [2] Wong et al., Biotechnol Bioeng 2005, 89:164. [3] Trummer et al., Biotechnol Bioeng 2006, 94:1045. [4] Gawlitzek et al., Biotechnol Bioeng 2000, 68:637. [5] Borys, Nature Biotechnol 1993, 11:720.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	Training Grant - Industrial Case

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