Novel Dynamic Model to Predict the Glycosylation Pattern of Monoclonal Antibodies from Extracellular Cell Culture Conditions

Ohadi, Kaveh; Aghamohseni, Hengameh; Johannes, Gädke; Moo-Young, Murray; Legge, Raymond L.; Scharer, Jeno M.; Hector, Budman M.

doi:10.3182/20131216-3-in-2044.00009

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Previous models have addressed diverse aspects of glycosylation, such as the dynamics governing the initial co-translational oligosaccharide attachment (Shelikoff et al, 1996), sequence motifs correlating with glycosylation site occupancy (Senger and Karim, 2005), and prediction of the total glycan diversity produced by a cell based on enzyme kinetics and expression (Hossler et al, 2007; Kawano et al, 2005; Krambeck and Betenbaugh, 2005). Other models have focused on predicting glycoprofiles on pharmaceutically relevant proteins, such as monoclonal antibodies (del Val et al, 2011; Jedrzejewski et al, 2014; Kaveh et al, 2013). In this work, we follow a similar objective as we aim to address problems arising in the glycoengineering of individual glycoproteins produced in cell culture.…”

Section: Discussionmentioning

confidence: 99%

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

Spahn

Hansen

et al. 2016

Metabolic Engineering

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Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.

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Section: Discussionmentioning

confidence: 99%

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

Spahn

Hansen

et al. 2016

Metabolic Engineering

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“…This was the first step towards coupling cellular metabolism (and therefore measurable variables like glucose uptake) to glycosylation. Kaveh et al [94] pursued this goal and performed a dynamic analysis of extracellular metabolite concentrations via MFA and linked those of glutamine and glucose to nucleotide sugar biosynthesis and glycolysis using the previous models (del Val 2011 [93] and Hossler 2007 [92] ). The model successfully predicted dynamic trends of the glycopatterns of mAb produced in CHO batch culture.…”

Section: Glycosylationmentioning

confidence: 99%

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

Galleguillos

Ruckerbauer

Gerstl

et al. 2017

Computational and Structural Biotechnology Journal

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Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.

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“…These factors are, in turn, influenced by changes in cell culture process parameters including, but not limited to, culture nutrient levels (Fan et al., 2015), pH (Lin et al., 2015, Villiger et al., 2016a), levels of by-products such as lactate and ammonia (Fan et al., 2015, Gawlitzek et al., 2000), and temperature (Ivarsson et al., 2014, Sou et al., 2015). Several groups have developed mathematical models to probe the heterogeneity of glycoforms in a cell culture process (Hossler et al., 2007, Jedrzejewski et al., 2014, Jimenez del Val et al., 2011, Jiménez del Val et al., 2013, Kaveh et al., 2013, Krambeck et al., 2009, Villiger et al., 2016b). Only recently, with the availability CHO genome data (Lewis et al., 2013), several groups have implemented time course omics analysis of CHO fed-batch cultures to probe the system as a whole (Hsu et al., 2017, Mulukutla et al., 2012, Opdam et al., 2017, Sha et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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SummaryN-linked glycosylation affects the potency, safety, immunogenicity, and pharmacokinetic clearance of several therapeutic proteins including monoclonal antibodies. A robust control strategy is needed to dial in appropriate glycosylation profile during the course of cell culture processes accurately. However, N-glycosylation dynamics remains insufficiently understood owing to the lack of integrative analyses of factors that influence the dynamics, including sugar nucleotide donors, glycosyltransferases, and glycosidases. Here, an integrative approach involving multi-dimensional omics analyses was employed to dissect the temporal dynamics of glycoforms produced during fed-batch cultures of CHO cells. Several pathways including glycolysis, tricarboxylic citric acid cycle, and nucleotide biosynthesis exhibited temporal dynamics over the cell culture period. The steps involving galactose and sialic acid addition were determined as temporal bottlenecks. Our results show that galactose, and not manganese, is able to mitigate the temporal bottleneck, despite both being known effectors of galactosylation. Furthermore, sialylation is limited by the galactosylated precursors and autoregulation of cytidine monophosphate-sialic acid biosynthesis.

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Novel Dynamic Model to Predict the Glycosylation Pattern of Monoclonal Antibodies from Extracellular Cell Culture Conditions

Cited by 6 publications

References 9 publications

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

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

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

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