Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells
| Autor: | Ioscani Jimenez del Val, Benjamin Strain, Rodrigo Barbosa, Athanasios Antonakoudis, Cleo Kontoravdi |
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| Rok vydání: | 2021 |
| Předmět: |
Glycan
Process modeling Glycosylation Artificial neural network biology Computer science General Chemical Engineering Chinese hamster ovary cell 0904 Chemical Engineering Chemical Engineering Nucleotide sugar Computer Science Applications chemistry.chemical_compound chemistry Critical to quality biology.protein Process control Biological system 0913 Mechanical Engineering |
| Popis: | In-process quality control of biotherapeutics, such as monoclonal antibodies, requires computationally efficient process models that use readily measured process variables to compute product quality. Existing kinetic cell culture models can effectively describe the underlying mechanisms but require considerable development and parameterisation effort. Stoichiometric models, on the other hand, provide a generic, parameter-free means for describing metabolic behaviour but do not extend to product quality prediction. We have overcome this limitation by integrating a stoichiometric model of Chinese hamster ovary (CHO) cell metabolism with an artificial neural network that uses the fluxes of nucleotide sugar donor synthesis to compute the profile of Fc N-glycosylation, a critical quality attribute of antibody therapeutics. We demonstrate that this hybrid framework accurately computes glycan distribution profiles using a set of easy-to-obtain experimental data, thus providing a platform for process control applications. |
| Databáze: | OpenAIRE |
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