1
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Arigoni-Affolter I, Losfeld ME, Hennig R, Rapp E, Aebi M. A hierarchical structure in the N-glycosylation process governs the N-glycosylation output: prolonged cultivation induces glycoenzymes expression variations that are reflected in the cellular N-glycome but not in the protein and site-specific glycoprofile of CHO cells. Glycobiology 2024; 34:cwae045. [PMID: 38938083 PMCID: PMC11231950 DOI: 10.1093/glycob/cwae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/18/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024] Open
Abstract
N-glycosylation is a central component in the modification of secretory proteins. One characteristic of this process is a heterogeneous output. The heterogeneity is the result of both structural constraints of the glycoprotein as well as the composition of the cellular glycosylation machinery. Empirical data addressing correlations between glycosylation output and glycosylation machinery composition are seldom due to the low abundance of glycoenzymes. We assessed how differences in the glycoenzyme expression affected the N-glycosylation output at a cellular as well as at a protein-specific level. Our results showed that cellular N-glycome changes could be correlated with the variation of glycoenzyme expression, whereas at the protein level differential responses to glycoenzymes alterations were observed. We therefore identified a hierarchical structure in the N-glycosylation process: the enzyme levels in this complex pathway determine its capacity (reflected in the N-glycome), while protein-specific parameters determine the glycosite-specificity. What emerges is a highly variable and adaptable protein modification system that represents a hallmark of eukaryotic cells.
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Affiliation(s)
- Ilaria Arigoni-Affolter
- Institute of Microbiology, Department of Biology, Swiss Federal Institute of Technology, ETH Zürich, Vladimir-Prelog-Weg 4, 8049 Zürich, Switzerland
| | - Marie-Estelle Losfeld
- Institute of Microbiology, Department of Biology, Swiss Federal Institute of Technology, ETH Zürich, Vladimir-Prelog-Weg 4, 8049 Zürich, Switzerland
| | - René Hennig
- glyXera GmbH, Brenneckestraße 20, 39120 Magdeburg, Germany
| | - Erdmann Rapp
- glyXera GmbH, Brenneckestraße 20, 39120 Magdeburg, Germany
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse, 39106 Magdeburg, Germany
| | - Markus Aebi
- Institute of Microbiology, Department of Biology, Swiss Federal Institute of Technology, ETH Zürich, Vladimir-Prelog-Weg 4, 8049 Zürich, Switzerland
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2
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Gaugler L, Hofmann S, Schlüter M, Takors R. Mimicking CHO large-scale effects in the single multicompartment bioreactor: A new approach to access scale-up behavior. Biotechnol Bioeng 2024; 121:1244-1256. [PMID: 38192095 DOI: 10.1002/bit.28647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/04/2023] [Accepted: 12/21/2023] [Indexed: 01/10/2024]
Abstract
During the scale-up of biopharmaceutical production processes, insufficiently predictable performance losses may occur alongside gradients and heterogeneities. To overcome such performance losses, tools are required to explain, predict, and ultimately prohibit inconsistencies between laboratory and commercial scale. In this work, we performed CHO fed-batch cultivations in the single multicompartment bioreactor (SMCB), a new scale-down reactor system that offers new access to study large-scale heterogeneities in mammalian cell cultures. At volumetric power inputs of 20.4-1.5 W m-3, large-scale characteristics like long mixing times and dissolved oxygen (DO) heterogeneities were mimicked in the SMCB. Compared to a reference bioreactor (REFB) set-up, the conditions in the SMCB provoked an increase in lactate accumulation of up to 87%, an increased glucose uptake, and reduced viable cell concentrations in the stationary phase. All are characteristic for large-scale performance. The unique possibility to distinguish between the effects of changing power inputs and observed heterogeneities provided new insights into the potential reasons for altered product quality attributes. Apparently, the degree of galactosylation in the evaluated glycan patterns changed primarily due to the different power inputs rather than the provoked heterogeneities. The SMCB system could serve as a potent tool to provide new insights into scale-up behavior and to predict cell line-specific drawbacks at an early stage of process development.
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Affiliation(s)
- Lena Gaugler
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
| | - Sebastian Hofmann
- Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany
| | - Michael Schlüter
- Institute of Multiphase Flows, Hamburg University of Technology, Hamburg, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
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3
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Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
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Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
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4
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Kotidis P, Donini R, Arnsdorf J, Hansen AH, Voldborg BGR, Chiang AWT, Haslam SM, Betenbaugh M, Jimenez Del Val I, Lewis NE, Krambeck F, Kontoravdi C. CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells. Metab Eng 2023; 76:87-96. [PMID: 36610518 PMCID: PMC11132536 DOI: 10.1016/j.ymben.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023]
Abstract
Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.
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Affiliation(s)
- Pavlos Kotidis
- Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
| | - Roberto Donini
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Johnny Arnsdorf
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Anders Holmgaard Hansen
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Bjørn Gunnar Rude Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA
| | - Stuart M Haslam
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Michael Betenbaugh
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA; Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | | | - Cleo Kontoravdi
- Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
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5
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Mao L, Schneider JW, Robinson AS. Progress toward rapid, at-line N-glycosylation detection and control for recombinant protein expression. Curr Opin Biotechnol 2022; 78:102788. [PMID: 36126382 DOI: 10.1016/j.copbio.2022.102788] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 08/06/2022] [Accepted: 08/07/2022] [Indexed: 12/14/2022]
Abstract
Proteins continue to represent a large fraction of the therapeutics market, reaching over a hundred billion dollars in market size globally. One key feature of protein modification that can affect both structure and function is the addition of glycosylation following protein folding, leading to regulatory requirements for the accurate assessment of protein attributes, including glycan structures. The non-template-driven, innately heterogeneous N-glycosylation process thus requires accurate detection to robustly generate protein therapies. A challenge exists in the timely detection of protein glycosylation without labor-intensive manipulation. In this article, we discuss progress toward N-glycoprotein control, focusing on novel control strategies and the advancement of rapid, high-throughput analysis methods.
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Affiliation(s)
- Leran Mao
- Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
| | - James W Schneider
- Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
| | - Anne S Robinson
- Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.
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6
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Puranik A, Saldanha M, Chirmule N, Dandekar P, Jain R. Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward Industry 4.0. Biotechnol Prog 2022; 38:e3283. [PMID: 35752935 DOI: 10.1002/btpr.3283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/31/2022] [Accepted: 06/17/2022] [Indexed: 11/09/2022]
Abstract
Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multi-variate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics-enabled complete cell characterization have made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio-manufacturing, with a glimpse into the economic bearing. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Amita Puranik
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
| | - Marianne Saldanha
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
| | | | - Prajakta Dandekar
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, India
| | - Ratnesh Jain
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
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7
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Narayanan H, Sponchioni M, Morbidelli M. Integration and digitalization in the manufacturing of therapeutic proteins. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117159] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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8
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Caso S, Aeby M, Jordan M, Guillot R, Bielser J. Effects of pyruvate on primary metabolism and product quality for a high‐density perfusion process. Biotechnol Bioeng 2022; 119:1053-1061. [DOI: 10.1002/bit.28033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/20/2021] [Accepted: 12/24/2021] [Indexed: 11/09/2022]
Affiliation(s)
- Stefania Caso
- Biotech Process Sciences, Merck KGaA Corsier‐sur‐Vevey Switzerland
| | - Mathieu Aeby
- Biotech Process Sciences, Merck KGaA Corsier‐sur‐Vevey Switzerland
| | - Martin Jordan
- Biotech Process Sciences, Merck KGaA Corsier‐sur‐Vevey Switzerland
| | - Raphael Guillot
- Biotech Process Sciences, Merck KGaA Corsier‐sur‐Vevey Switzerland
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9
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Walsh I, Myint M, Nguyen-Khuong T, Ho YS, Ng SK, Lakshmanan M. Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing. MAbs 2022; 14:2013593. [PMID: 35000555 PMCID: PMC8744891 DOI: 10.1080/19420862.2021.2013593] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.
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Affiliation(s)
- Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Matthew Myint
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Say Kong Ng
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.,Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
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10
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Luo Y, Kurian V, Ogunnaike BA. Bioprocess systems analysis, modeling, estimation, and control. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100705] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Savizi ISP, Motamedian E, E Lewis N, Jimenez Del Val I, Shojaosadati SA. An integrated modular framework for modeling the effect of ammonium on the sialylation process of monoclonal antibodies produced by CHO cells. Biotechnol J 2021; 16:e2100019. [PMID: 34021707 DOI: 10.1002/biot.202100019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Monoclonal antibodies (mABs) have emerged as one of the most important therapeutic recombinant proteins in the pharmaceutical industry. Their immunogenicity and therapeutic efficacy are influenced by post-translational modifications, specifically the glycosylation process. Bioprocess conditions can influence the intracellular process of glycosylation. Among all the process conditions that have been recognized to affect the mAB glycoforms, the detailed mechanism underlying how ammonium could perturb glycosylation remains to be fully understood. It was shown that ammonium induces heterogeneity in protein glycosylation by altering the sialic acid content of glycoproteins. Hence, understanding this mechanism would aid pharmaceutical manufacturers to ensure consistent protein glycosylation. METHODS Three different mechanisms have been proposed to explain how ammonium influences the sialylation process. In the first, the inhibition of CMP-sialic acid transporter, which transports CMP-sialic acid (sialylation substrate) into the Golgi, by an increase in UDP-GlcNAc content that is brought about by the augmented incorporation of ammonium into glucosamine formation. In the second, ammonia diffuses into the Golgi and raises its pH, thereby decreasing the sialyltransferase enzyme activity. In the third, the reduction of sialyltransferase enzyme expression level in the presence of ammonium. We employed these mechanisms in a novel integrated modular platform to link dynamic alteration in mAB sialylation process with extracellular ammonium concentration to elucidate how ammonium alters the sialic acid content of glycoproteins. RESULTS Our results show that the sialylation reaction rate is insensitive to the first mechanism. At low ammonium concentration, the second mechanism is the controlling mechanism in mAB sialylation and by increasing the ammonium level (< 8 mM) the third mechanism becomes the controlling mechanism. At higher ammonium concentrations (> 8 mM) the second mechanism becomes predominant again. CONCLUSION The presented model in this study provides a connection between extracellular ammonium and the monoclonal antibody sialylation process. This computational tool could help scientists to develop and formulate cell culture media. The model illustrated here can assist the researchers to select culture media that ensure consistent mAB sialylation.
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Affiliation(s)
- Iman Shahidi Pour Savizi
- Faculty of Chemical Engineering, Biotechnology Department, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Motamedian
- Faculty of Chemical Engineering, Biotechnology Department, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, La Jolla, California, USA.,School of Medicine, Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, California, USA.,Department of Pediatrics, School of Medicine, University of California, La Jolla, California, USA
| | | | - Seyed Abbas Shojaosadati
- Faculty of Chemical Engineering, Biotechnology Department, Tarbiat Modares University, Tehran, Iran
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12
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Fung Shek C, Kotidis P, Betenbaugh M. Mechanistic and data-driven modeling of protein glycosylation. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100690] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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13
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Štor J, Ruckerbauer DE, Széliová D, Zanghellini J, Borth N. Towards rational glyco-engineering in CHO: from data to predictive models. Curr Opin Biotechnol 2021; 71:9-17. [PMID: 34048995 DOI: 10.1016/j.copbio.2021.05.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/26/2021] [Accepted: 05/07/2021] [Indexed: 12/22/2022]
Abstract
Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.
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Affiliation(s)
- Jerneja Štor
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria
| | - David E Ruckerbauer
- acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria
| | - Diana Széliová
- acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria
| | - Jürgen Zanghellini
- acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria.
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria.
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14
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Zhang L, Wang M, Castan A, Hjalmarsson H, Chotteau V. Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed-batch cell cultures. Biotechnol Bioeng 2021; 118:3447-3459. [PMID: 33788254 DOI: 10.1002/bit.27769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 01/01/2023]
Abstract
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 (BN) 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 Chinese hamster ovary 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 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 BN model can be a powerful tool in upstream process and medium development for glycoprotein productions.
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Affiliation(s)
- Liang Zhang
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden
| | - MingLiang Wang
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Håkan Hjalmarsson
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Stockholm, Sweden.,Digital Futures - KTH Royal Institute of Technology, Stockholm, Sweden
| | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Digital Futures - KTH Royal Institute of Technology, Stockholm, Sweden
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15
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Bryan L, Clynes M, Meleady P. The emerging role of cellular post-translational modifications in modulating growth and productivity of recombinant Chinese hamster ovary cells. Biotechnol Adv 2021; 49:107757. [PMID: 33895332 DOI: 10.1016/j.biotechadv.2021.107757] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 02/06/2023]
Abstract
Chinese hamster ovary (CHO) cells are one of the most commonly used host cell lines used for the production human therapeutic proteins. Much research over the past two decades has focussed on improving the growth, titre and cell specific productivity of CHO cells and in turn lowering the costs associated with production of recombinant proteins. CHO cell engineering has become of particular interest in recent years following the publication of the CHO cell genome and the availability of data relating to the proteome, transcriptome and metabolome of CHO cells. However, data relating to the cellular post-translational modification (PTMs) which can affect the functionality of CHO cellular proteins has only begun to be presented in recent years. PTMs are important to many cellular processes and can further alter proteins by increasing the complexity of proteins and their interactions. In this review, we describe the research presented from CHO cells to date related on three of the most important PTMs; glycosylation, phosphorylation and ubiquitination.
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Affiliation(s)
- Laura Bryan
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Martin Clynes
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Paula Meleady
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
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16
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Kellman BP, Lewis NE. Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication. Trends Biochem Sci 2021; 46:284-300. [PMID: 33349503 PMCID: PMC7954846 DOI: 10.1016/j.tibs.2020.10.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 10/05/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California San Diego School of Medicine, La Jolla, CA, USA.
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17
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Zhang L, Schwarz H, Wang M, Castan A, Hjalmarsson H, Chotteau V. Control of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE. Metab Eng 2020; 65:135-145. [PMID: 33161144 DOI: 10.1016/j.ymben.2020.11.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/15/2020] [Accepted: 11/02/2020] [Indexed: 10/23/2022]
Abstract
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.
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Affiliation(s)
- Liang Zhang
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden
| | - Hubert Schwarz
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden
| | - Mingliang Wang
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden; Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden
| | | | - Håkan Hjalmarsson
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden; Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden
| | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden.
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18
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19
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Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review. Processes (Basel) 2020. [DOI: 10.3390/pr8091088] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.
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Zürcher P, Sokolov M, Brühlmann D, Ducommun R, Stettler M, Souquet J, Jordan M, Broly H, Morbidelli M, Butté A. Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction. Biotechnol Prog 2020; 36:e3012. [DOI: 10.1002/btpr.3012] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 03/24/2020] [Accepted: 04/10/2020] [Indexed: 01/08/2023]
Affiliation(s)
- Philipp Zürcher
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
| | - Michael Sokolov
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
- DataHow AG Zurich Switzerland
| | - David Brühlmann
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Raphael Ducommun
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Matthieu Stettler
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Jonathan Souquet
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Martin Jordan
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Hervé Broly
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Massimo Morbidelli
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
- DataHow AG Zurich Switzerland
| | - Alessandro Butté
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
- DataHow AG Zurich Switzerland
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21
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Zhang L, Wang M, Castan A, Stevenson J, Chatzissavidou N, Hjalmarsson H, Vilaplana F, Chotteau V. Glycan Residues Balance Analysis - GReBA: A novel model for the N-linked glycosylation of IgG produced by CHO cells. Metab Eng 2019; 57:118-128. [PMID: 31539564 DOI: 10.1016/j.ymben.2019.08.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 07/12/2019] [Accepted: 08/22/2019] [Indexed: 02/02/2023]
Abstract
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.
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Affiliation(s)
- Liang Zhang
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden
| | - MingLiang Wang
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden; Department of Automatic Control, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden
| | - Andreas Castan
- GE Healthcare Bio-Sciences AB, Björkgatan 30, 75184, Uppsala, Sweden
| | | | | | - Håkan Hjalmarsson
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden; Department of Automatic Control, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden
| | - Francisco Vilaplana
- Division of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden
| | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden.
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22
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Graham RJ, Bhatia H, Yoon S. Consequences of trace metal variability and supplementation on Chinese hamster ovary (CHO) cell culture performance: A review of key mechanisms and considerations. Biotechnol Bioeng 2019; 116:3446-3456. [PMID: 31403183 DOI: 10.1002/bit.27140] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 07/19/2019] [Accepted: 08/05/2019] [Indexed: 12/18/2022]
Abstract
Trace metals are supplied to chemically-defined media (CDM) for optimal Chinese hamster ovary (CHO) cell culture performance during the production of monoclonal antibodies and other therapeutic proteins. However, lot-to-lot and vendor-to-vendor variability in raw materials consequently leads to an imbalance of trace metals that are supplied to CDM. This imbalance can yield detrimental effects rooted in several primary mechanisms and pathways including oxidative stress, apoptosis, lactate accumulation, and unfavorable glycan synthesis. Recent research endeavors involve supplying zinc, copper, and manganese to CDM in excess to further maximize culture productivity and product quality. These treatments significantly impact critical quality attributes and furthermore highlight the degree to which trace metal availability can affect CHO cell culture performance. This review highlights the role of trace metal variability, supplementation, and interplay on key cellular mechanisms responsible for overall culture performance and the production and quality of therapeutic proteins.
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Affiliation(s)
- Ryan J Graham
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts
| | - Hemlata Bhatia
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts
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23
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Kotidis P, Jedrzejewski P, Sou SN, Sellick C, Polizzi K, Del Val IJ, Kontoravdi C. Model-based optimization of antibody galactosylation in CHO cell culture. Biotechnol Bioeng 2019; 116:1612-1626. [PMID: 30802295 DOI: 10.1002/bit.26960] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/22/2019] [Accepted: 02/21/2019] [Indexed: 01/13/2023]
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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Affiliation(s)
- Pavlos Kotidis
- Department of Chemical Engineering, Imperial College London, United Kingdom
| | - Philip Jedrzejewski
- Department of Chemical Engineering, Imperial College London, United Kingdom
- Department of Life Sciences, Imperial College London, United Kingdom
- Centre for Synthetic Biology and Innovation, Imperial College London, United Kingdom
| | - Si Nga Sou
- Department of Chemical Engineering, Imperial College London, United Kingdom
- Department of Life Sciences, Imperial College London, United Kingdom
- Centre for Synthetic Biology and Innovation, Imperial College London, United Kingdom
| | - Christopher Sellick
- Cell Culture and Fermentation Sciences BioPharmaceutical Development, MedImmune, Granta Park, Cambridge, United Kingdom
| | - Karen Polizzi
- Department of Life Sciences, Imperial College London, United Kingdom
- Centre for Synthetic Biology and Innovation, Imperial College London, United Kingdom
| | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, United Kingdom
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24
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Dynamic metabolic network modeling of mammalian Chinese hamster ovary (CHO) cell cultures with continuous phase kinetics transitions. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2018.11.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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Sumit M, Dolatshahi S, Chu AHA, Cote K, Scarcelli JJ, Marshall JK, Cornell RJ, Weiss R, Lauffenburger DA, Mulukutla BC, Figueroa B. Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses. iScience 2019; 12:102-120. [PMID: 30682623 PMCID: PMC6352710 DOI: 10.1016/j.isci.2019.01.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/19/2018] [Accepted: 01/03/2019] [Indexed: 12/24/2022] Open
Abstract
N-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. Major glycosylated species exhibit temporal dynamics during fed-batch processes Key metabolic pathways linked to N-glycosylation exhibit significant temporal dynamics Dynamics in nucleotide sugar donors (NSDs) directly influences glycoform heterogeneity Glycoform heterogeneity can be mitigated by supplementing NSD biosynthetic precursors
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Affiliation(s)
- Madhuresh Sumit
- Culture Process Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
| | - Sepideh Dolatshahi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - An-Hsiang Adam Chu
- Analytical Research and Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
| | - Kaffa Cote
- Analytical Research and Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
| | - John J Scarcelli
- Cell Line Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
| | - Jeffrey K Marshall
- Analytical Research and Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
| | - Richard J Cornell
- Analytical Research and Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Bhanu Chandra Mulukutla
- Culture Process Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA.
| | - Bruno Figueroa
- Culture Process Development, Bio Therapeutics Pharmaceutical Sciences, Pfizer, 1 Burtt Road, Andover, MA 01810, USA
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Kontoravdi C, Jimenez del Val I. Computational tools for predicting and controlling the glycosylation of biopharmaceuticals. Curr Opin Chem Eng 2018. [DOI: 10.1016/j.coche.2018.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture. Processes (Basel) 2018. [DOI: 10.3390/pr6100176] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The terminal sugar molecules of the N-linked glycan attached to the fragment crystalizable (Fc) region is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) such as immunoglobulin G (IgG). There exists naturally-occurring heterogeneity in the N-linked glycan structure of mAbs, and such heterogeneity has a significant influence on the clinical safety and efficacy of mAb drugs. We previously proposed a constraint-based modeling method called glycosylation flux analysis (GFA) to characterize the rates (fluxes) of intracellular glycosylation reactions. One contribution of this work is a significant improvement in the computational efficiency of the GFA, which is beneficial for analyzing large datasets. Another contribution of our study is the analysis of IgG glycosylation in continuous perfusion Chinese Hamster Ovary (CHO) cell cultures. The GFA of the perfusion cell culture data indicated that the dynamical changes of IgG glycan heterogeneity are mostly attributed to alterations in the galactosylation flux activity. By using a random forest regression analysis of the IgG galactosylation flux activity, we were further able to link the dynamics of galactosylation with two process parameters: cell-specific productivity of IgG and extracellular ammonia concentration. The characteristics of IgG galactosylation dynamics agree well with what we previously reported for fed-batch cultivations of the same CHO cell strain.
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