Model-based optimization of antibody galactosylation in CHO cell culture

Biotechnology and Bioengineering
Pavlos KotidisCleo Kontoravdi

Abstract

Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses.

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Citations

Mar 17, 2020·Annual Review of Chemical and Biomolecular Engineering·Natalia I MajewskaNitin Agarwal
Jul 15, 2020·Biotechnology and Bioengineering·Evan WellsAnne S Robinson
Sep 6, 2019·Biotechnology Journal·Harini NarayananMichael Sokolov
Apr 4, 2021·International Journal of Molecular Sciences·Sakhr AlhuthaliCleo Kontoravdi
Apr 13, 2021·MAbs·Ohnmar Khanal, Abraham M Lenhoff
May 29, 2021·Current Opinion in Biotechnology·Jerneja ŠtorNicole Borth

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