DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems. (November 2021)

• Record Type: Journal Article
• Authors: Kotidis, Pavlos; Pappas, Iosif; Avraamidou, Styliana; Pistikopoulos, Efstratios N.; Kontoravdi, Cleo; Papathanasiou, Maria M.
• Is Part Of: Computers & chemical engineering. Volume 154(2021)
• Publication Date: 2021-11
• Subjects: Hybrid modelling -- Bioprocess optimisation -- Galactosylation -- Antibody production; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107460
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Development of a hybrid model for CHO cell culture using in silico training datasets Model development focuses on antibody product synthesis and glycosylation Glycosylation is one of main quality indicators for therapeutic antibodies Hybrid model used as basis for the design of dynamic optimisation studies Hybrid model used for reactive scheduling studies that assure product quality Abstract: Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies.