Literature DB >> 27273889

Controlling the time evolution of mAb N-linked glycosylation - Part II: Model-based predictions.

Thomas K Villiger1, Ernesto Scibona1, Matthieu Stettler2, Hervé Broly2, Massimo Morbidelli1, Miroslav Soos3.   

Abstract

N-linked glycosylation is known to be a crucial factor for the therapeutic efficacy and safety of monoclonal antibodies (mAbs) and many other glycoproteins. The nontemplate process of glycosylation is influenced by external factors which have to be tightly controlled during the manufacturing process. In order to describe and predict mAb N-linked glycosylation patterns in a CHO-S cell fed-batch process, an existing dynamic mathematical model has been refined and coupled to an unstructured metabolic model. High-throughput cell culture experiments carried out in miniaturized bioreactors in combination with intracellular measurements of nucleotide sugars were used to tune the parameter configuration of the coupled models as a function of extracellular pH, manganese and galactose addition. The proposed modeling framework is able to predict the time evolution of N-linked glycosylation patterns during a fed-batch process as a function of time as well as the manipulated variables. A constant and varying mAb N-linked glycosylation pattern throughout the culture were chosen to demonstrate the predictive capability of the modeling framework, which is able to quantify the interconnected influence of media components and cell culture conditions. Such a model-based evaluation of feeding regimes using high-throughput tools and mathematical models gives rise to a more rational way to control and design cell culture processes with defined glycosylation patterns.
© 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1135-1148, 2016. © 2016 American Institute of Chemical Engineers.

Entities:  

Keywords:  N-linked glycosylation; media design; microbioreactors; model-based optimization; process parameters

Mesh:

Substances:

Year:  2016        PMID: 27273889     DOI: 10.1002/btpr.2315

Source DB:  PubMed          Journal:  Biotechnol Prog        ISSN: 1520-6033


  6 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

Review 2.  Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication.

Authors:  Benjamin P Kellman; Nathan E Lewis
Journal:  Trends Biochem Sci       Date:  2020-12-18       Impact factor: 13.807

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.  Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses.

Authors:  Madhuresh Sumit; Sepideh Dolatshahi; An-Hsiang Adam Chu; Kaffa Cote; John J Scarcelli; Jeffrey K Marshall; Richard J Cornell; Ron Weiss; Douglas A Lauffenburger; Bhanu Chandra Mulukutla; Bruno Figueroa
Journal:  iScience       Date:  2019-01-07

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
  6 in total

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