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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] [MESH Headings] [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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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] [MESH Headings] [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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Claes E, Heck T, Coddens K, Sonnaert M, Schrooten J, Verwaeren J. Bayesian cell therapy process optimization. Biotechnol Bioeng 2024; 121:1569-1582. [PMID: 38372656 DOI: 10.1002/bit.28669] [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: 05/31/2023] [Revised: 11/17/2023] [Accepted: 01/22/2024] [Indexed: 02/20/2024]
Abstract
Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.
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Affiliation(s)
- Evan Claes
- Antleron, Leuven, Belgium
- Biovism, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium
| | | | | | | | | | - Jan Verwaeren
- Biovism, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium
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Jiang Z, Dalby PA. Challenges in scaling up AAV-based gene therapy manufacturing. Trends Biotechnol 2023; 41:1268-1281. [PMID: 37127491 DOI: 10.1016/j.tibtech.2023.04.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 05/03/2023]
Abstract
Accelerating the scale up of adeno-associated virus (AAV) manufacture is highly desirable to meet the increased demand for gene therapies. However, the development of bioprocesses for AAV gene therapies remains time-consuming and challenging. The quality by design (QbD) approach ensures bioprocess designs that meet the desired product quality and safety profile. Rapid stress tests, developability screens, and scale-down technologies have the potential to streamline AAV product and manufacturing bioprocess development within the QbD framework. Here we review how their successful use for antibody manufacture development is translating to AAV, but also how this will depend critically on improved analytical methods and adaptation of the tools as more understanding is gained on the critical attributes of AAV required for successful therapy.
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Affiliation(s)
- Ziyu Jiang
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK.
| | - Paul A Dalby
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK.
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Nair A, Horiguchi I, Fukumori K, Kino-oka M. Development of instability analysis for the filling process of human-induced pluripotent stem cell products. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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