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Development of mathematical modelling for the glycosylation of IgG in CHO cell cultures
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing.
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Chinese hamster ovary (CHO) cells are the most popular expression system for the production of biopharmaceuticals. More than 80% of the approved monoclonal antibodies (mAbs) or immunoglobulin G (IgG) are produced with these cells. Glycosylation is a usual post- translational modification important for therapeutic mAbs. It affects their stability, half-life and immunological activities. Substantial studies have shown that glycosylation can be affected by the culture conditions in manufacturing, e.g. pH, temperature and media components. To achieve a good control of the glycosylation, a number of mathematical models have been developed. However, most of them have been developed for the cell line engineering, while very few can be used to design the media components for matching a given glycoprofile.

This thesis presents developments of mathematical modelling for glycosylation prediction and experimental design of feeding different combinations of carbon sources in CHO cell cultures. The first study investigates the impacts of mannose, galactose, fructose and fucose to the IgG glycoprofile. Specifically, we look at intracellular nucleotide sugars in fed-batch cultures, where glucose is absent and lactate is used as complementary carbon source. The second study is based on the concept of elementary flux mode (EFM) and the mass balance of the glycan residues. A mathematical model named Glycan Residue Balance Analysis (GReBA) is developed for the prediction of the glycosylation profiles of IgG in pseudo perfusion cultures by feeding combinations of glucose, mannose, galactose and lactate. The model is further optimized for a feeding strategy design of perfusion cell cultures to obtain a desired glycoprofile. In the last study, a probabilistic graphic model based on Bayesian network (BN) is developed for glycosylation prediction in cultures under different multiple variable factors affecting the glycosylation.

The results show that the manipulation of different sugars in the media can be used to control the glycosylation. Both the GReBA and PGM models exhibit abilities for glycosylation prediction and experimental design.

Abstract [sv]

Kinesiska hamsteräggstocks celler (CHO) är det mest populära uttryckssystemet för framställning av bioläkemedel, och mer än 80 % av de godkända monoklonala antikropparna (mAbs) produceras med denna cellinje. En vanlig post-translationell modifiering är glykosylering som är viktig för terapeutiska mAbs. Glykosylering kan påverka proteinets stabilitet, halveringstid och immunologiska aktivitet. Omfattande studier har visat att glykosylering kan påverkas av odlingsparametrarna vid tillverkning, t.ex. pH, temperatur och odlingsmediets komponenter. För att få en bra kontroll över glykosyleringen har matematiska modeller utvecklats. Emellertid har de flesta modeller varit utformade för cellinjeutveckling, medans få kan användas för att designa medium komponenter så att en viss mAb glykoprofil uppnås.

Denna avhandling presenterar experimentella studier med tillsatser av olika kombinationer av kolkällor i CHO-cellkulturer och utvecklingen av matematiska modeller för glykosylerings förutsägelse. Den första studien undersöker effekterna av mannos-, galaktos-, fruktos- och fukos-tillsatser på mAb-glykoprofilerna. Vi undersöker mer specifikt de motsvarande intracellulära nukleotid-sockerarterna i cellkulturer, med avsaknad av glukos och laktat användes som komplementär kolkälla. Den andra studien är baserad på Elementary flux modes (EFM) konceptet och massbalansen för glykangrupper, där en matematisk modell med namnet Glycan Residue Balance Analysis (GReBA) utvecklats för förutsägelse av mAb glykosyleringsprofiler. Här används GReBA på pseudo-perfusionscellkulturer med kombinationer av följande tillsatser: glukos, mannos, galaktos och laktat. Modellen optimeras sedan ytterligare för att designa tillsats strategier till perfusionscellkulturer så att en önskad glykoprofil kunde matchas. I den sista studien utvecklas en grafisk sannolikhetsmodell (PGM) för förutsägelse av glykosylering i cellkulturerna med flera variabla faktorer.

Resultaten visar att tillsatserna av olika sockerarter i mediet kan användas för att kontrollera glykosyleringen, och både GReBA och PGM modellerna visar bra förmåga för glykosylerings förutsägelse och experimentell design.

Abstract [zh]

中华仓鼠卵巢细胞 (CHO cells)是生物医药生产中最常用表达系统。目前这种细胞系 被用于大约 80%的抗体药物的生产。糖基化是一种在真核细胞内常见的蛋白翻译后修饰, 同时也是抗体药物的一种重要的质量指标。不同的糖基化糖型会对抗体药物的稳定性, 半 衰期以及免疫学活性产生影响。大量的研究表明抗体药物产生过程中的某些工艺参数会对 抗体糖基化产生影响, 比如细胞培养的 pH, 温度或培养基的成分。目前已有不少针对糖基 化的数学模型被建立出来, 然而, 这些模型大多用于通过细胞系的改造而非培养基的优化 来控制糖基化的糖型。

本篇论文主要针对使用不同碳源进行细胞培养所产生的糖基化差异进行数学建模, 并 通过数学模型对不同培养条件下生产的抗体的糖谱进行预测, 以及对为了得到特定糖谱的 细胞培养条件进行实验设计。首先, 该论文探究了在无葡萄糖且以乳酸作为补充碳源的流 加培养条件下甘露糖、半乳糖、果糖和岩藻糖对抗体糖基化的影响。在第二部分的研究中, 基于基元模式分析和物料守恒, 糖基化守恒分析(GReBA)的数学模型被建立并用于预测拟 灌流培养中不同浓度和配比的葡糖糖、甘露糖和半乳糖对抗体糖基化的影响。之后, 这种 模型被尝试应用于灌流细胞培养的实验设计以得到特定组分的糖基化谱。最后, 论文探究 了通过概率图模型(PGM)对不同细胞培养条件下的糖基化谱进行预测。

实验结果表明调配培养基中的碳源可以起到对抗体药物糖基化糖型的控制。同时两种 数学模型 GReBA 和 PGM 展示出了良好的糖基化预测以及针对特定糖谱的实验设计的能 力。

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2020.
Series
TRITA-CBH-FOU ; 2020:28
Keywords [en]
Chinese hamster ovary cells, glycosylation, IgG, mathematical modelling, experimental design, perfusion, carbon sources, GReBA, Bayesian network, probabilistic graphic model
National Category
Bioprocess Technology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-273343ISBN: 978-91-7873-551-8 (print)OAI: oai:DiVA.org:kth-273343DiVA, id: diva2:1430257
Public defence
2020-06-04, https://kth-se.zoom.us/j/67302879470, Stockholm, 15:00 (English)
Opponent
Supervisors
Projects
SmartFD sponsored by the Sweden's Innovation Agency VINNOVA (diaries nr. 2016-02398)the Competence Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO, funded by the Sweden's Innovation Agency VINNOVA (diaries nr. 2016-05181)
Funder
Vinnova, 2016-02398Vinnova, 2016-05181
Note

QC 2020-05-14

Available from: 2020-05-14 Created: 2020-05-14 Last updated: 2020-05-19Bibliographically approved
List of papers
1. Combined effects of glycosylation precursors and lactate on the glycoprofile of IgG produced by CHO cells
Open this publication in new window or tab >>Combined effects of glycosylation precursors and lactate on the glycoprofile of IgG produced by CHO cells
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2019 (English)In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 289, p. 71-79Article in journal (Refereed) Published
Abstract [en]

The glycosylation profile of therapeutic monoclonal antibodies (mAbs) is a crucial quality parameter for industrial Immunoglobulin G (IgG) production. Several alternative carbon sources, which function as glycosylation precursors, have been reported to impact the glycosylation pattern. Since the cells give priority to glucose uptake, the presence of this substrate can lower the effects of alternative sugars on the glycosylation. In order to get a better understanding of the influence of alternative sugars on the glycosylation and to investigate how they impact each other, combinations of mannose, fructose, galactose and fucose were fed to Chinese hamster ovary (CHO) cells in batch culture when the glucose became depleted and the lactate, accumulated in the culture, was used as carbon source. Feeding with a feed containing mannose or glucose decreased by 3-7% the percentage of high mannose glycans compared to a feed without mannose or glucose. Feeding with a feed containing galactose led to 8-20% increase of monogalactoglycans (G1) glycans and 2-6% rise of digalactoglycans (G2) glycans compared to feeding without galactose or glucose. The cells fed with fucose exhibited a significantly higher concentration of intracellular GDP-Fucose. This work indicates that a feeding strategy based on non-glucose sugars and potentially lactate, could be adopted to obtain a targeted glycosylation profile.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Chinese hamster ovary cells (CHO cells), Monoclonal antibody, mAbs, IgG, Glycosylation, Sugars
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-240693 (URN)10.1016/j.jbiotec.2018.11.004 (DOI)000453380200010 ()30423367 (PubMedID)2-s2.0-85057157868 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2020-05-14Bibliographically approved
2. Glycan Residues Balance Analysis: A novel model for the N-linked glycosylation of IgG produced by CHO cells.
Open this publication in new window or tab >>Glycan Residues Balance Analysis: A novel model for the N-linked glycosylation of IgG produced by CHO cells.
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2020 (English)In: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184, Vol. 57, p. 118-128Article in journal (Refereed) Published
Abstract [en]

The structure of N-linked glycosylation is a very important quality attribute for therapeutic monoclonal antibodies. Different carbon sources in cell culture media, such as mannose and galactose, have been reported to have different influences on the glycosylation patterns. Accurate prediction and control of the glycosylation profile are important for the process development of mammalian cell cultures. In this study, a mathematical model, that we named Glycan Residues Balance Analysis (GReBA), was developed based on the concept of Elementary Flux Mode (EFM), and used to predict the glycosylation profile for steady state cell cultures. Experiments were carried out in pseudo-perfusion cultivation of antibody producing Chinese Hamster Ovary (CHO) cells with various concentrations and combinations of glucose, mannose and galactose. Cultivation of CHO cells with mannose or the combinations of mannose and galactose resulted in decreased lactate and ammonium production, and more matured glycosylation patterns compared to the cultures with glucose. Furthermore, the growth rate and IgG productivity were similar in all the conditions. When the cells were cultured with galactose alone, lactate was fed as well to be used as complementary carbon source, leading to cell growth rate and IgG productivity comparable to feeding the other sugars. The data of the glycoprofiles were used for training the model, and then to simulate the glycosylation changes with varying the concentrations of mannose and galactose. In this study we showed that the GReBA model had a good predictive capacity of the N-linked glycosylation. The GReBA can be used as a guidance for development of glycoprotein cultivation processes.

Keywords
CHO cells, IgG, Mathematical modelling, N-linked glycosylation, Pseudo-perfusion
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-261092 (URN)10.1016/j.ymben.2019.08.016 (DOI)000506206200012 ()31539564 (PubMedID)2-s2.0-85074776776 (Scopus ID)
Note

QC 20191112

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2020-05-14Bibliographically approved
3. Prediction of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE
Open this publication in new window or tab >>Prediction of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

The N-linked glycosylation pattern is an important quality attribute of therapeutic glycoproteins. It has been reported by our group and by others that different carbon sources, such as glucose, mannose and galactose, can differently impact the glycosylation profile of glycoproteins in mammalian cell culture. Acting on the sugar feeding is thus an attractive strategy to tune the glycan pattern. However, in case of feeding of more than one carbon source simultaneously, the cells give priority to the one with the highest uptake rate, which limits the usage of this tuning, e.g. the cells favor consuming glucose in comparison to galactose.

We present here a new feeding strategy (named 'TAFE' for targeted feeding) for perfusion culture to adjust the concentrations of fed sugars influencing the glycosylation. The strategy consists in setting the sugar feeding such that the cells are forced to consume these substrates at a target cell specific consumption rate decided by the operator and taking into account the cell specific perfusion rate (CSPR). This strategy is applied in perfusion cultures of Chinese hamster ovary (CHO) cells, illustrated by ten different regimes of sugar feeding, including glucose, galactose and mannose. Applying the TAFE strategy, different glycan profiles were obtained using the different feeding regimes. Furthermore, we successfully forced the cells to consume higher proportions of non-glucose sugars, which have lower transport rates than glucose in presence of this latter, in a controlled way.

In previous work, a mathematical model named Glycan Residues Balance Analysis (GReBA) was developed to model the glycosylation profile based on the fed carbon sources. The present data were applied to the GReBA to design a feeding regime targeting a given glycosylation profile. The ability of the model to achieve this objective was confirmed by a multi-round of leave-one- out cross-validation (LOOCV), leading to the conclusion, that the GReBA model can be used to design the feeding regime of a perfusion cell culture to obtain a desired glycosylation profile.

Keywords
Glycosylation, perfusion culture, CHO cells, mathematical modelling, feed design, GReBA, galactose, mannose, glucose, antibody
National Category
Bioprocess Technology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-273340 (URN)
Projects
SmartFD sponsored by the Sweden's Innovation Agency VINNOVA (diaries nr. 2016-02398)Competence Centre for Advanced BioProduction by Continuous Processing,AdBIOPRO, funded by the Sweden's Innovation Agency VINNOVA (diaries nr. 2016-05181)
Funder
Vinnova, 2016-02398Vinnova, 2016-05181
Note

QC 20200518

Available from: 2020-05-14 Created: 2020-05-14 Last updated: 2020-05-18Bibliographically approved
4. Probabilistic Graphic Model by Bayesian Network for the Prediction of Antibody Glycosylation in CHO Cell Cultures
Open this publication in new window or tab >>Probabilistic Graphic Model by Bayesian Network for the Prediction of Antibody Glycosylation in CHO Cell Cultures
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Glycosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs). The glycan pattern can have a large impact on the immunological functions, serum half-life and stability. The medium components and cultivation parameters are known to potentially influence the glycosylation profile. Mathematical modelling provides a strategy for rational design and control of the upstream bioprocess. However, the kinetic models usually contain a very large number of unknown parameters, which limit their practical applications. In this article, we consider the metabolic network of N-linked glycosylation as a Bayesian network and calculate the fluxes of the glycosylation process as joint probability using the culture parameters as inputs. The modelling approach is validated with data of different CHO cell cultures in pseudo perfusion, perfusion and fed batch cultures, all showing very good predictive capacities. In cases where a large number of cultivation parameters is available, it is shown here that principal components analysis (PCA) can efficiently be employed for a dimension reduction of the inputs compared to Pearson correlation analysis and feature importance by decision tree. The present study demonstrates that Bayesian network model can be a powerful tool in upstream process and medium development for glycoprotein productions.

Keywords
Chinese hamster ovary (CHO) cells, Monoclonal antibodies (mAbs), Glycosylation, mathematical modelling, Bayesian network
National Category
Bioprocess Technology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-273342 (URN)
Projects
SmartFD sponsored by the Sweden's Innovation Agency VINNOVA (diaries nr. 2016-02398)Competence Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO
Funder
Vinnova, 2016-02398Vinnova, 2016-05181
Note

QC 20200518

Available from: 2020-05-14 Created: 2020-05-14 Last updated: 2020-05-18Bibliographically approved

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  • apa
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  • Other style
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  • en-US
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  • Other locale
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