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Chan YJ, Dileep D, Rothstein SM, Cochran EW, Reuel NF. Single-Use, Metabolite Absorbing, Resonant Transducer (SMART) Culture Vessels for Label-Free, Continuous Cell Culture Progression Monitoring. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2401260. [PMID: 38900081 DOI: 10.1002/advs.202401260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 05/16/2024] [Indexed: 06/21/2024]
Abstract
Secreted metabolites are an important class of bio-process analytical technology (PAT) targets that can correlate to cell conditions. However, current strategies for measuring metabolites are limited to discrete measurements, resulting in limited understanding and ability for feedback control strategies. Herein, a continuous metabolite monitoring strategy is demonstrated using a single-use metabolite absorbing resonant transducer (SMART) to correlate with cell growth. Polyacrylate is shown to absorb secreted metabolites from living cells containing hydroxyl and alkenyl groups such as terpenoids, that act as a plasticizer. Upon softening, the polyacrylate irreversibly conformed into engineered voids above a resonant sensor, changing the local permittivity which is interrogated, contact-free, with a vector network analyzer. Compared to sensing using the intrinsic permittivity of cells, the SMART approach yields a 20-fold improvement in sensitivity. Tracking growth of many cell types such as Chinese hamster ovary, HEK293, K562, HeLa, and E. coli cells as well as perturbations in cell proliferation during drug screening assays are demonstrated. The sensor is benchmarked to show continuous measurement over six days, ability to track different growth conditions, selectivity to transducing active cell growth metabolites against other components found in the media, and feasibility to scale out for high throughput campaigns.
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Affiliation(s)
- Yee Jher Chan
- Chemical and Biological Engineering, Iowa State University, Ames, IA, 50011, USA
| | - Dhananjay Dileep
- Chemical and Biological Engineering, Iowa State University, Ames, IA, 50011, USA
| | | | - Eric W Cochran
- Chemical and Biological Engineering, Iowa State University, Ames, IA, 50011, USA
| | - Nigel F Reuel
- Chemical and Biological Engineering, Iowa State University, Ames, IA, 50011, USA
- Skroot Laboratory Inc, Ames, IA, 50010, USA
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2
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Aragão Tejo Dias V, Oliveira Guardalini LG, Leme J, Consoni Bernardino T, da Silveira SR, Tonso A, Attie Calil Jorge S, Fernández Núñez EG. Different modeling approaches for inline biochemical monitoring over the VLP-making upstream stages using Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124638. [PMID: 38880076 DOI: 10.1016/j.saa.2024.124638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/22/2024] [Accepted: 06/09/2024] [Indexed: 06/18/2024]
Abstract
This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 106 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 106 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host.
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Affiliation(s)
- Vinícius Aragão Tejo Dias
- Laboratório de Engenharia de Bioprocessos, Escola de Artes, Ciências e Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, CEP 03828-000, São Paulo, SP, Brazil
| | | | - Jaci Leme
- Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, CEP 05503-900 São Paulo, SP, Brazil
| | - Thaissa Consoni Bernardino
- Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, CEP 05503-900 São Paulo, SP, Brazil
| | | | - Aldo Tonso
- Laboratório de Células Animais, Departamento de Engenharia Química, Escola Politécnica, Universidade de São Paulo, Av. Prof. Luciano Gualberto, travessa do Politécnico, 380, 05508-010 São Paulo, SP, Brazil
| | - Soraia Attie Calil Jorge
- Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, CEP 05503-900 São Paulo, SP, Brazil
| | - Eutimio Gustavo Fernández Núñez
- Laboratório de Engenharia de Bioprocessos, Escola de Artes, Ciências e Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, CEP 03828-000, São Paulo, SP, Brazil.
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3
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Sripada SA, Hosseini M, Ramesh S, Wang J, Ritola K, Menegatti S, Daniele MA. Advances and opportunities in process analytical technologies for viral vector manufacturing. Biotechnol Adv 2024; 74:108391. [PMID: 38848795 DOI: 10.1016/j.biotechadv.2024.108391] [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: 11/14/2023] [Revised: 03/14/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024]
Abstract
Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.
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Affiliation(s)
- Sobhana A Sripada
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Mahshid Hosseini
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA
| | - Srivatsan Ramesh
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Junhyeong Wang
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA
| | - Kimberly Ritola
- North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Neuroscience Center, Brain Initiative Neurotools Vector Core, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Biomanufacturing Training and Education Center, North Carolina State University, 890 Main Campus Dr, Raleigh, NC 27695, USA.
| | - Michael A Daniele
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA.
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4
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Espinel-Ríos S, Morabito B, Pohlodek J, Bettenbrock K, Klamt S, Findeisen R. Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics. Biotechnol Bioeng 2024; 121:366-379. [PMID: 37942516 DOI: 10.1002/bit.28575] [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: 02/04/2023] [Revised: 09/22/2023] [Accepted: 10/14/2023] [Indexed: 11/10/2023]
Abstract
Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.
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Affiliation(s)
- Sebastián Espinel-Ríos
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bruno Morabito
- Yokogawa Insilico Biotechnology GmbH, Stuttgart, Germany
| | - Johannes Pohlodek
- Control and Cyber-Physical Systems Laboratory, Technical University of Darmstadt, Darmstadt, Germany
| | - Katja Bettenbrock
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Rolf Findeisen
- Control and Cyber-Physical Systems Laboratory, Technical University of Darmstadt, Darmstadt, Germany
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5
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Pawar D, Lo Presti D, Silvestri S, Schena E, Massaroni C. Current and future technologies for monitoring cultured meat: A review. Food Res Int 2023; 173:113464. [PMID: 37803787 DOI: 10.1016/j.foodres.2023.113464] [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: 06/07/2023] [Revised: 08/30/2023] [Accepted: 09/10/2023] [Indexed: 10/08/2023]
Abstract
The high population growth rate, massive animal food consumption, fast economic progress, and limited food resources could lead to a food crisis in the future. There is a huge requirement for dietary proteins including cultured meat is being progressed to fulfill the need for meat-derived proteins in the diet. However, production of cultured meat requires monitoring numerous bioprocess parameters. This review presents a comprehensive overview of various widely adopted techniques (optical, spectroscopic, electrochemical, capacitive, FETs, resistive, microscopy, and ultrasound) for monitoring physical, chemical, and biological parameters that can improve the bioprocess control in cultured meat. The methods, operating principle, merits/demerits, and the main open challenges are reviewed with the aim to support the readers in advancing knowledge on novel sensing systems for cultured meat applications.
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Affiliation(s)
- Dnyandeo Pawar
- Microwave Materials Group, Centre for Materials for Electronics Technology (C-MET), Athani P.O, Thrissur, Kerala 680581, India.
| | - Daniela Lo Presti
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Sergio Silvestri
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
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6
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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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7
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Iglesias CF, Ristovski M, Bolic M, Cuperlovic-Culf M. rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing. Bioengineering (Basel) 2023; 10:bioengineering10020229. [PMID: 36829723 PMCID: PMC9951952 DOI: 10.3390/bioengineering10020229] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.
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Affiliation(s)
| | - Milica Ristovski
- Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Miodrag Bolic
- Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council, Ottawa, ON K1A 0R6, Canada
- Department of Biochemistry, Microbiology, and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Correspondence:
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8
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Wainaina S, Taherzadeh MJ. Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review. BIORESOURCE TECHNOLOGY 2023; 369:128421. [PMID: 36462761 DOI: 10.1016/j.biortech.2022.128421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/25/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.
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Affiliation(s)
- Steven Wainaina
- Swedish Centre for Resource Recovery, University of Borås, 50190 Borås, Sweden
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9
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Cheng Y, Bi X, Xu Y, Liu Y, Li J, Du G, Lv X, Liu L. Artificial intelligence technologies in bioprocess: Opportunities and challenges. BIORESOURCE TECHNOLOGY 2023; 369:128451. [PMID: 36503088 DOI: 10.1016/j.biortech.2022.128451] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/01/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.
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Affiliation(s)
- Yang Cheng
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yameng Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China.
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10
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Matanguihan C, Wu P. Upstream continuous processing: recent advances in production of biopharmaceuticals and challenges in manufacturing. Curr Opin Biotechnol 2022; 78:102828. [PMID: 36332340 DOI: 10.1016/j.copbio.2022.102828] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/12/2022] [Accepted: 09/27/2022] [Indexed: 12/14/2022]
Abstract
Upstream continuous processing, or most commonly perfusion processing, for biopharmaceutical production, is emerging as a feasible and viable manufacturing approach. Development in production of recombinant therapeutic proteins as well as viral vectors, vaccines, and cell therapy products, has numerous research publications that came out in previous years. Recent research areas are in perfusion-operation strategies maximizing and controlling bioreactor cell density, adding feed solution designed to supplement basal medium feed stream, combining cell line engineering with bioreactor conditions such as hypoxia, and implementing online process monitoring of cell density by capacitance sensor and metabolites by Raman spectroscopy. Perfusion applications are not limited to production process alone but include other upstream areas where high cell density process is essential such as in cell bank preparation, N-1 seed bioreactor, and combination with intensified fed-batch production process. This review covers recent advances in continuous processing over the last two years for biopharmaceutical production.
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Affiliation(s)
- Cary Matanguihan
- Bayer U.S. LLC, Pharmaceuticals, Biologics Development, 800 Dwight Way, Berkeley, CA 94701, USA.
| | - Paul Wu
- Bayer U.S. LLC, Pharmaceuticals, Biologics Development, 800 Dwight Way, Berkeley, CA 94701, USA
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11
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Swaminathan N, Priyanka P, Rathore AS, Sivaparakasam S, Subbiah S. Cole-Cole modeling of real-time capacitance data for estimation of cell physiological properties in recombinant Escherichia coli cultivation. Biotechnol Bioeng 2021; 119:922-935. [PMID: 34964125 DOI: 10.1002/bit.28028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 11/17/2021] [Accepted: 12/20/2021] [Indexed: 11/12/2022]
Abstract
Real-time estimation of physiological properties of the cell during recombinant protein production would ensure enhanced process monitoring. In this study, we explored the application of dielectric spectroscopy to track the fed-batch phase of recombinant Escherichia coli cultivation for estimating the physiological properties, viz. cell diameter and viable cell concentration (VCC). The scanning capacitance data from the dielectric spectroscopy were pre-processed using moving average (MA). Later, it was modelled through a nonlinear theoretical Cole-Cole model and further solved using a global evolutionary genetic algorithm (GA). The parameters obtained from the GA were further applied for the estimation of the aforementioned physiological properties. The offline cell diameter and cell viability data were obtained from particle size analyzer and flow cytometry measurements to validate the Cole-Cole model. The offline VCC was calculated from the cell viability % from flow cytometry data and dry cell weight concentration (DCW). The Cole-Cole model predicted the cell diameter and VCC with an error of 1.03% and 7.72%, respectively. The proposed approach can enable the operator to take real-time process decisions in order to achieve desired productivity and product quality. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nivedhitha Swaminathan
- Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
| | - Priyanka Priyanka
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Senthilkumar Sivaparakasam
- Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.,Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
| | - Senthilmurugan Subbiah
- Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.,Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
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