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Panjwani S, Almazan A, Hille R, Spetsieris K. Predictive modeling for cell culture in commercial manufacturing of biotherapeutics. Biotechnol Bioeng 2024. [PMID: 39023310 DOI: 10.1002/bit.28813] [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: 03/02/2024] [Revised: 06/18/2024] [Accepted: 07/10/2024] [Indexed: 07/20/2024]
Abstract
The biopharmaceutical industry continually seeks advancements in the commercial manufacturing of therapeutic proteins, where mammalian cell culture plays a pivotal role. The current work presents a novel data-driven predictive modeling application designed to enhance the efficiency and predictability of cell culture processes in biotherapeutic production. The capability of the cloud-based digital data science application, developed using open-source tools, is demonstrated with respect to predicting bioreactor potency from at-line process parameters over a 5-day horizon. The uncertainty in model's prediction is quantified, providing valuable insights for process control and decision-making. Model validation on unseen data confirms the model's robust generalizability. An interactive dashboard, tailored to process scientist's requirements is also developed to streamline biopharmaceutical manufacturing processes, ultimately leading to enhanced productivity and product quality.
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Affiliation(s)
| | | | - Rubin Hille
- Bayer Pharmaceuticals, Berkeley, California, USA
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2
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Singh VK, Jiménez del Val I, Glassey J, Kavousi F. Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation. Bioengineering (Basel) 2024; 11:546. [PMID: 38927782 PMCID: PMC11200465 DOI: 10.3390/bioengineering11060546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.
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Affiliation(s)
- Vishal Kumar Singh
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
| | - Ioscani Jiménez del Val
- School of Chemical & Bioprocess Engineering, University College Dublin, D04 V1W8 Dublin, Ireland;
| | - Jarka Glassey
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Fatemeh Kavousi
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
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3
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Shi Y, Wan Y, Sun Y, Yang J, Lu Y, Xie X, Pan J, Wang H, Qu H. Exploring metabolic responses and pathway changes in CHO-K1 cells under varied aeration conditions and copper supplementations using 1 H NMR-based metabolomics. Biotechnol J 2024; 19:e2300495. [PMID: 38403407 DOI: 10.1002/biot.202300495] [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: 09/19/2023] [Revised: 01/13/2024] [Accepted: 01/17/2024] [Indexed: 02/27/2024]
Abstract
The optimization of bioprocess for CHO cell culture involves careful consideration of factors such as nutrient consumption, metabolic byproduct accumulation, cell growth, and monoclonal antibody (mAb) production. Valuable insights can be obtained by understanding cellular physiology to ensure robust and efficient bioprocess. This study aims to improve our understanding of the CHO-K1 cell metabolism using 1 H NMR-based metabolomics. Initially, the variations in culture performance and metabolic profiles under varied aeration conditions and copper supplementations were thoroughly examined. Furthermore, a comprehensive metabolic pathway analysis was performed to assess the impact of these conditions on the implicated pathways. The results revealed substantial alterations in the pyruvate metabolism, histidine metabolism, as well as phenylalanine, tyrosine and tryptophan biosynthesis, which were especially evident in cultures subjected to copper deficiency conditions. Conclusively, significant metabolites governing cell growth and mAb titer were identified through orthogonal partial least square-discriminant analysis (OPLS-DA). Metabolites, including glycerol, alanine, formate, glutamate, phenylalanine, and valine, exhibited strong associations with distinct cell growth phases. Additionally, glycerol, acetate, lactate, formate, glycine, histidine, and aspartate emerged as metabolites influencing cell productivity. This study demonstrates the potential of employing 1 H NMR-based metabolomics technology in bioprocess research. It provides valuable guidance for feed medium development, feeding strategy design, bioprocess parameter adjustments, and ultimately the enhancement of cell proliferation and mAb yield.
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Affiliation(s)
- Yingting Shi
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yuxiang Wan
- Hisun BioPharmaceutical Co., Ltd., Hangzhou, China
| | - Yan Sun
- Hisun BioPharmaceutical Co., Ltd., Hangzhou, China
| | - Jiayu Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yuting Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xinyuan Xie
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jianyang Pan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Haibin Wang
- Hisun BioPharmaceutical Co., Ltd., Hangzhou, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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4
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Sakaki A, Namatame T, Nakaya M, Omasa T. Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing. Biotechnol Bioeng 2024; 121:605-617. [PMID: 37960996 DOI: 10.1002/bit.28593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/15/2023]
Abstract
To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.
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Affiliation(s)
- Ayumu Sakaki
- Innovation Center, Marketing Headquarters, Yokogawa Electric Corporation, Tokyo, Japan
| | - Tetsushi Namatame
- Innovation Center, Marketing Headquarters, Yokogawa Electric Corporation, Tokyo, Japan
| | - Makoto Nakaya
- Innovation Center, Marketing Headquarters, Yokogawa Electric Corporation, Tokyo, Japan
| | - Takeshi Omasa
- Graduate School of Engineering, Osaka University, Osaka, Japan
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5
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Dodia H, Sunder AV, Borkar Y, Wangikar PP. Precision fermentation with mass spectrometry-based spent media analysis. Biotechnol Bioeng 2023; 120:2809-2826. [PMID: 37272489 DOI: 10.1002/bit.28450] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/13/2023] [Accepted: 05/15/2023] [Indexed: 06/06/2023]
Abstract
Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.
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Affiliation(s)
- Hardik Dodia
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | | | - Yogen Borkar
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
- Clarity Bio Systems India Pvt. Ltd., Pune, India
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Nikita S, Mishra S, Gupta K, Runkana V, Gomes J, Rathore AS. Advances in bioreactor control for production of biotherapeutic products. Biotechnol Bioeng 2023; 120:1189-1214. [PMID: 36760086 DOI: 10.1002/bit.28346] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/11/2023]
Abstract
Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.
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Affiliation(s)
- Saxena Nikita
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Keshari Gupta
- TCS Research, Tata Consultancy Services Limited, Pune, India
| | | | - James Gomes
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
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Saldanha M, Shelar A, Patil V, Warke VG, Dandekar P, Jain R. A case study: Correlation of the nutrient composition in Chinese Hamster Ovary cultures with cell growth, antibody titre and quality attributes using multivariate analyses for guiding medium and feed optimization in early upstream process development. Cytotechnology 2023; 75:77-91. [PMID: 36713064 PMCID: PMC9880107 DOI: 10.1007/s10616-022-00561-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/12/2022] [Indexed: 11/25/2022] Open
Abstract
In this case-study, we demonstrate an approach for identifying correlations between nutrients/metabolites in the spent medium of CHO cell cultures and cell growth, mAb titre and critical quality attributes, using multivariate analyses, which can aid in selection of targets for medium and feed optimization. An extensive LC-MS-based method was used to analyse the spent medium composition. Partial least squares (PLS) model was used to identify correlations between nutrient composition and cell growth and mAb titre and orthogonal projections to latent structures (OPLS) model was used to determine the effect of the changing nutrient composition during the culture on critical quality attributes. The PLS model revealed that the initial concentrations of several amino acids as well as pyruvic acid and pyridoxine, governed the early cell growth, while the concentrations of TCA cycle intermediates and several vitamins highly influenced the stationary phase, in which mAb production was maximum. For the first time, with the help of the OPLS model, we were able to draw correlations between nutrients/metabolites during the culture and critical quality attributes, for example, optimizing the supply of certain amino acids and vitamins could reduce impurities while simultaneously increasing desirable glycoforms. The unique correlations obtained from such an exploratory analysis, utilizing conditions that are commonly adopted in early process development, present opportunities for optimizing the compositions of the growth media and the feed media for enhancing cell growth, mAb production and quality, thereby proving to be a useful preliminary step in bioprocess optimization. Supplementary Information The online version contains supplementary material available at 10.1007/s10616-022-00561-z.
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Affiliation(s)
- Marianne Saldanha
- Department of Biological Sciences and Biotechnology, Institute of Chemical Technology, Matunga, Mumbai, 400019 India
| | - Ashutosh Shelar
- Shimadzu Analytical (India) Private Limited, Rushabh Chambers, Marol, Andheri East, Mumbai, 400059 India
| | - Vaibhav Patil
- Sartorius Stedim India Private Limited, No. 69/2 & 69/3, Jakkasandra, Nelamangala, Bangalore, 562123 India
| | - Vishal G. Warke
- Himedia Laboratories Private Limited, Plot No. C40, MIDC, Wagle Industrial Area, Thane, 400604 India
| | - Prajakta Dandekar
- Department of Pharmaceutical Science and Technology, Institute of Chemical Technology, Matunga, Mumbai, 400019 India
| | - Ratnesh Jain
- Department of Biological Sciences and Biotechnology, Institute of Chemical Technology, Matunga, Mumbai, 400019 India
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Li H, Chiang AWT, Lewis NE. Artificial intelligence in the analysis of glycosylation data. Biotechnol Adv 2022; 60:108008. [PMID: 35738510 PMCID: PMC11157671 DOI: 10.1016/j.biotechadv.2022.108008] [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: 01/08/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022]
Abstract
Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.
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Affiliation(s)
- Haining Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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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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Wang Z, Wang C, Chen G. Kinetic modeling: A tool for temperature shift and feeding optimization in cell culture process development. Protein Expr Purif 2022; 198:106130. [PMID: 35691496 DOI: 10.1016/j.pep.2022.106130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022]
Abstract
Mammalian cells have dominated the biopharmaceutical industry for biotherapeutic protein production and tremendous efforts have been devoted to enhancing productivity during the cell culture process development. However, determining the optimal process conditions is still a huge challenge. Constrained by the limited resources and timeline, usually it is impossible to fully explore the optimal range of all process parameters (temperature, pH, dissolved oxygen, basal and feeding medium, additives, etc.). Kinetic modeling, which finds out the global optimum by systematically screening all potential conditions for cell culture process, provides a solution to this dilemma. However, studies on optimizing temperature shift and feeding strategies simultaneously using this approach have not been reported. In this study, we built up a kinetic model of fed-batch culture process for simultaneous optimization of temperature shift and feeding strategies. The fitting results showed high accuracy and demonstrated that the kinetic model can be used to describe the mammalian cell culture performance. In addition, five more fed-batch experiments were conducted to test this model's predicting power on different temperature shift and feeding strategies. It turned out that the predicted data matched well with experimental ones on viable cell density (VCD), metabolites, and titer for the entire culture duration and allowed selecting the same best condition with the experimental results. Therefore, adopting this approach can potentially reduce the number of experiments required for culture process optimization.
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Affiliation(s)
- Zheyu Wang
- Technology and Process Development (TPD), WuXi Biologics, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai, 200131, China
| | - Caixia Wang
- Technology and Process Development (TPD), WuXi Biologics, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai, 200131, China
| | - Gong Chen
- Technology and Process Development (TPD), WuXi Biologics, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai, 200131, China.
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Flevaris K, Kontoravdi C. Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective. Int J Mol Sci 2022; 23:ijms23095180. [PMID: 35563570 PMCID: PMC9100869 DOI: 10.3390/ijms23095180] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 02/04/2023] Open
Abstract
The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.
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12
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Integrating metabolome dynamics and process data to guide cell line selection in biopharmaceutical process development. Metab Eng 2022; 72:353-364. [DOI: 10.1016/j.ymben.2022.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 02/21/2022] [Accepted: 03/26/2022] [Indexed: 11/20/2022]
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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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Antonakoudis A, Strain B, Barbosa R, Jimenez del Val I, Kontoravdi C. Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107471] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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15
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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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16
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Smiatek J, Clemens C, Herrera LM, Arnold S, Knapp B, Presser B, Jung A, Wucherpfennig T, Bluhmki E. Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes. BIOTECHNOLOGY REPORTS (AMSTERDAM, NETHERLANDS) 2021; 31:e00640. [PMID: 34159058 PMCID: PMC8193373 DOI: 10.1016/j.btre.2021.e00640] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/24/2021] [Accepted: 05/27/2021] [Indexed: 01/02/2023]
Abstract
The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computation and study of growth and metabolite-related upstream process parameters as well as their temporal evolution. Our approach can be used for the control and study of single product-specific large-scale manufacturing runs as well as generic small-scale evaluations for combined processes and products at development stage. The computational results for the product titer as well as various major upstream outcomes in addition to relevant process parameters show a high degree of accuracy when compared to experimental data and, accordingly, a reasonable predictive capability of the RNN models. The calculated values for the root-mean squared errors of prediction are significantly smaller than the experimental standard deviation for the considered process run ensembles, which highlights the broad applicability of our approach. As a specific benefit for platform processes, the generic RNN model is also used to simulate process outcomes for different temperatures in good agreement with experimental results. The high level of accuracy and the straightforward usage of the approach without sophisticated parameterization and recalibration procedures highlight the benefits of the RNN models, which can be regarded as promising alternatives to existing parametric and semi-parametric methods.
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Affiliation(s)
- Jens Smiatek
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
- Boehringer Ingelheim Pharma GmbH & Co. KG, Digitalization Development Biologicals CMC, D-88397 Biberach (Riss), Germany
| | - Christoph Clemens
- Boehringer Ingelheim Pharma GmbH & Co. KG, Focused Factory Drug Substance, D-88397 Biberach (Riss), Germany
| | - Liliana Montano Herrera
- Boehringer Ingelheim Pharma GmbH & Co. KG, Bioprocess Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Sabine Arnold
- Boehringer Ingelheim Pharma GmbH & Co. KG, Bioprocess Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Bettina Knapp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Beate Presser
- Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Alexander Jung
- Boehringer Ingelheim Pharma GmbH & Co. KG, Digitalization Development Biologicals CMC, D-88397 Biberach (Riss), Germany
| | - Thomas Wucherpfennig
- Boehringer Ingelheim Pharma GmbH & Co. KG, Bioprocess Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Erich Bluhmki
- Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany
- University of Applied Sciences Biberach, D-88397 Biberach (Riss), Germany
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Tsopanoglou A, Jiménez del Val I. Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100691] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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18
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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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mAb Production Modeling and Design Space Evaluation Including Glycosylation Process. Processes (Basel) 2021. [DOI: 10.3390/pr9020324] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Due to high demand, monoclonal antibodies (mAbs) production needs to be efficient, as well as maintaining a high product quality. Quality by design (QbD) via predictive process modeling greatly facilitates process understanding and can be used to adjust process parameters to further improve the unit operations. In this work, mechanistic and dynamic kriging models are developed to capture the protein productivity and glycan fractions under different temperatures and pH levels. The design of experiments is used to generate input and output data for model training. The dynamic kriging model shows good performance in capturing the dynamic profiles of cell cultures and glycosylation using only limited input data. The developed model is further used for feasibility analysis, and successfully identifies the operating design space, maintaining high productivity and guaranteed product quality.
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Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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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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