Literature DB >> 26743760

Dynamics of immature mAb glycoform secretion during CHO cell culture: An integrated modelling framework.

Ioscani Jimenez Del Val1, Yuzhou Fan2,3, Dietmar Weilguny3.   

Abstract

Ensuring consistent glycosylation-associated quality of therapeutic monoclonal antibodies (mAbs) has become a priority in pharmaceutical bioprocessing given that the distribution and composition of the carbohydrates (glycans) bound to these molecules determines their therapeutic efficacy and immunogenicity. However, the interaction between bioprocess conditions, cellular metabolism and the intracellular process of glycosylation remains to be fully understood. To gain further insight into these interactions, we present a novel integrated modelling platform that links dynamic variations in mAb glycosylation with cellular secretory capacity. Two alternative mechanistic representations of how mAb specific productivity (qp ) influences glycosylation are compared. In the first, mAb glycosylation is modulated by the linear velocity with which secretory cargo traverses the Golgi apparatus. In the second, glycosylation is influenced by variations in Golgi volume. Within our modelling framework, both mechanisms accurately reproduce experimentally-observed dynamic changes in mAb glycosylation. In addition, an optimisation-based strategy has been developed to estimate the concentration of glycosylation enzymes required to minimise mAb glycoform variability. Our results suggest that the availability of glycosylation machinery relative to cellular secretory capacity may play a crucial role in mAb glycosylation. In the future, the modelling framework presented here may aid in selecting and engineering cell lines that ensure consistent mAb glycosylatio.
Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  CHO cells; Dynamic glycosylation model; In silico glycoengineering; Pharmaceutical bioprocessing; Therapeutic protein glycosylation

Mesh:

Substances:

Year:  2016        PMID: 26743760     DOI: 10.1002/biot.201400663

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


  8 in total

1.  Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

Authors:  Philipp N Spahn; Anders H Hansen; Stefan Kol; Bjørn G Voldborg; Nathan E Lewis
Journal:  Biotechnol J       Date:  2016-12-28       Impact factor: 4.677

2.  Model-based investigation of intracellular processes determining antibody Fc-glycosylation under mild hypothermia.

Authors:  Si Nga Sou; Philip M Jedrzejewski; Ken Lee; Christopher Sellick; Karen M Polizzi; Cleo Kontoravdi
Journal:  Biotechnol Bioeng       Date:  2017-03-10       Impact factor: 4.530

Review 3.  What can mathematical modelling say about CHO metabolism and protein glycosylation?

Authors:  Sarah N Galleguillos; David Ruckerbauer; Matthias P Gerstl; Nicole Borth; Michael Hanscho; Jürgen Zanghellini
Journal:  Comput Struct Biotechnol J       Date:  2017-01-28       Impact factor: 7.271

4.  Mechanistic reconstruction of glycoprotein secretion through monitoring of intracellular N-glycan processing.

Authors:  Ilaria Arigoni-Affolter; Ernesto Scibona; Chia-Wei Lin; David Brühlmann; Jonathan Souquet; Hervé Broly; Markus Aebi
Journal:  Sci Adv       Date:  2019-11-27       Impact factor: 14.136

5.  Characterization of Monoclonal Antibody Glycan Heterogeneity Using Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry.

Authors:  Sumit K Singh; Kelvin H Lee
Journal:  Front Bioeng Biotechnol       Date:  2022-01-11

6.  Harnessing the potential of artificial neural networks for predicting protein glycosylation.

Authors:  Pavlos Kotidis; Cleo Kontoravdi
Journal:  Metab Eng Commun       Date:  2020-05-15

7.  Osmolality Effects on CHO Cell Growth, Cell Volume, Antibody Productivity and Glycosylation.

Authors:  Sakhr Alhuthali; Pavlos Kotidis; Cleo Kontoravdi
Journal:  Int J Mol Sci       Date:  2021-03-24       Impact factor: 5.923

8.  Population balance modelling captures host cell protein dynamics in CHO cell cultures.

Authors:  Sakhr Alhuthali; Cleo Kontoravdi
Journal:  PLoS One       Date:  2022-03-23       Impact factor: 3.240

  8 in total

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