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Wang B, Chen RQ, Li J, Roy K. Interfacing data science with cell therapy manufacturing: where we are and where we need to be. Cytotherapy 2024; 26:967-979. [PMID: 38842968 DOI: 10.1016/j.jcyt.2024.03.011] [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: 10/05/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 08/25/2024]
Abstract
Although several cell-based therapies have received FDA approval, and others are showing promising results, scalable, and quality-driven reproducible manufacturing of therapeutic cells at a lower cost remains challenging. Challenges include starting material and patient variability, limited understanding of manufacturing process parameter effects on quality, complex supply chain logistics, and lack of predictive, well-understood product quality attributes. These issues can manifest as increased production costs, longer production times, greater batch-to-batch variability, and lower overall yield of viable, high-quality cells. The lack of data-driven insights and decision-making in cell manufacturing and delivery is an underlying commonality behind all these problems. Data collection and analytics from discovery, preclinical and clinical research, process development, and product manufacturing have not been sufficiently utilized to develop a "systems" understanding and identify actionable controls. Experience from other industries shows that data science and analytics can drive technological innovations and manufacturing optimization, leading to improved consistency, reduced risk, and lower cost. The cell therapy manufacturing industry will benefit from implementing data science tools, such as data-driven modeling, data management and mining, AI, and machine learning. The integration of data-driven predictive capabilities into cell therapy manufacturing, such as predicting product quality and clinical outcomes based on manufacturing data, or ensuring robustness and reliability using data-driven supply-chain modeling could enable more precise and efficient production processes and lead to better patient access and outcomes. In this review, we introduce some of the relevant computational and data science tools and how they are being or can be implemented in the cell therapy manufacturing workflow. We also identify areas where innovative approaches are required to address challenges and opportunities specific to the cell therapy industry. We conclude that interfacing data science throughout a cell therapy product lifecycle, developing data-driven manufacturing workflow, designing better data collection tools and algorithms, using data analytics and AI-based methods to better understand critical quality attributes and critical-process parameters, and training the appropriate workforce will be critical for overcoming current industry and regulatory barriers and accelerating clinical translation.
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Affiliation(s)
- Bryan Wang
- Marcus Center for Therapeutic Cell Characterization and Manufacturing, Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA; NSF Engineering Research Center (ERC) for cell Manufacturing Technologies (CMaT), USA
| | - Rui Qi Chen
- H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, GA, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, GA, USA
| | - Krishnendu Roy
- NSF Engineering Research Center (ERC) for cell Manufacturing Technologies (CMaT), USA; Department of Biomedical Engineering, School of Engineering, Vanderbilt University, Nashville, TN, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA; Department of Chemical and Biomolecular Engineering, School of Engineering, Vanderbilt University, Nashville, TN, USA.
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Okamura K, Badr S, Ichida Y, Yamada A, Sugiyama H. Modeling of cell cultivation for monoclonal antibody production processes considering lactate metabolic shifts. Biotechnol Prog 2024:e3486. [PMID: 38924316 DOI: 10.1002/btpr.3486] [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: 10/09/2023] [Revised: 05/10/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024]
Abstract
Demand for monoclonal antibodies (mAbs) is rapidly increasing. To achieve higher productivity, there have been improvements to cell lines, operating modes, media, and cultivation conditions. Representative mathematical models are needed to narrow down the growing number of process alternatives. Previous studies have proposed mechanistic models to depict cell metabolic shifts (e.g., lactate production to consumption). However, the impacts of variations of some operating conditions have not yet been fully incorporated in such models. This paper offers a new mechanistic model considering variations in dissolved oxygen and glutamine depletion on cell metabolism applied to a novel Chinese hamster ovary (CHO) cell line. Expressions for the specific rates of lactate production, glutamine consumption, and mAb production were formulated for stirred and shaken-tank reactors. A deeper understanding of lactate metabolic shifts was obtained under different combinations of experimental conditions. Lactate consumption was more pronounced in conditions with higher DO and low glutamine concentrations. The model offers mechanistic insights that are useful for designing advanced operation strategies. It can be used in design space generation and process optimization for better productivity and product quality.
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Affiliation(s)
- Kozue Okamura
- Department of Chemical System Engineering, The University of Tokyo, Tokyo, Japan
| | - Sara Badr
- Department of Chemical System Engineering, The University of Tokyo, Tokyo, Japan
| | - Yusuke Ichida
- Department of Chemical System Engineering, The University of Tokyo, Tokyo, Japan
| | - Akira Yamada
- Department of Chemical System Engineering, The University of Tokyo, Tokyo, Japan
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, Tokyo, Japan
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Okamura K, Badr S, Murakami S, Sugiyama H. Hybrid Modeling of CHO Cell Cultivation in Monoclonal Antibody Production with an Impurity Generation Module. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Kozue Okamura
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Sara Badr
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Sei Murakami
- Manufacturing Technology Association of Biologics, 2-6-16, Shinkawa, Chuo-ku, 104-0033 Tokyo, Japan
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
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Park J, Cho JH, Braatz RD. Mathematical modeling and analysis of microwave-assisted freeze-drying in biopharmaceutical applications. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107412] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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5
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Quality prediction for multi-grade batch process using sparse flexible clustered multi-task learning. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107320] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Luo L, Xie L, Su H, Mao F. A probabilistic model with spike-and-slab regularization for inferential fault detection and isolation of industrial processes. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.05.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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7
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Duran‐Villalobos CA, Ogonah O, Melinek B, Bracewell DG, Hallam T, Lennox B. Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control. AIChE J 2021. [DOI: 10.1002/aic.17257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Olotu Ogonah
- Department of Biochemical Engineering University College London London UK
| | - Beatrice Melinek
- Department of Biochemical Engineering University College London London UK
| | | | - Trevor Hallam
- Sutro Biopharma, Inc. South San Francisco California USA
| | - Barry Lennox
- Department of Electrical and Electronic Engineering The University of Manchester Manchester UK
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9
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Rolinger L, Rüdt M, Hubbuch J. A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing. Anal Bioanal Chem 2020; 412:2047-2064. [PMID: 32146498 PMCID: PMC7072065 DOI: 10.1007/s00216-020-02407-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 12/01/2022]
Abstract
As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes. Graphical abstract.
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Affiliation(s)
- Laura Rolinger
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany
| | - Matthias Rüdt
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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A PSE perspective for the efficient production of monoclonal antibodies: integration of process, cell, and product design aspects. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.01.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Hong MS, Severson KA, Jiang M, Lu AE, Love JC, Braatz RD. Challenges and opportunities in biopharmaceutical manufacturing control. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2017.12.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Jiang M, Severson KA, Love JC, Madden H, Swann P, Zang L, Braatz RD. Opportunities and challenges of real-time release testing in biopharmaceutical manufacturing. Biotechnol Bioeng 2017; 114:2445-2456. [DOI: 10.1002/bit.26383] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Revised: 06/18/2017] [Accepted: 07/10/2017] [Indexed: 12/21/2022]
Affiliation(s)
- Mo Jiang
- Massachusetts Institute of Technology; Department of Chemical Engineering; Cambridge Massachusetts
| | - Kristen A. Severson
- Massachusetts Institute of Technology; Department of Chemical Engineering; Cambridge Massachusetts
| | - John Christopher Love
- Massachusetts Institute of Technology; Department of Chemical Engineering; Cambridge Massachusetts
| | | | | | - Li Zang
- Biogen; Cambridge Massachusetts
| | - Richard D. Braatz
- Massachusetts Institute of Technology; Department of Chemical Engineering; Cambridge Massachusetts
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Abstract
Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.
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Affiliation(s)
- Leo Chiang
- The Dow Chemical Company, Freeport, Texas 77541;
| | - Bo Lu
- The Dow Chemical Company, Freeport, Texas 77541;
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Eberle L, Sugiyama H, Papadokonstantakis S, Graser A, Schmidt R, Hungerbühler K. Data-driven tiered procedure for enhancing yield in drug product manufacturing. Comput Chem Eng 2016. [DOI: 10.1016/j.compchemeng.2015.12.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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