Bayesian cell therapy process optimization
- Author
- Evan Claes (UGent) , Tommy Heck, Kathleen Coddens, Maarten Sonnaert, Jan Schrooten and Jan Verwaeren (UGent)
- Organization
- Project
- 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. Parallel noisy Bayesian optimization, an efficient iterative experimental design methodology, is evaluated on two in silico bioprocesses and one in vitro bioprocess. The method facilitated a reduction in experimental load of about 50%, compared to the industry state-of-the-art. image
- Keywords
- Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, process optimization, process development, counterflow cell centrifugation, cell therapy, Bayesian optimization, MODEL, DESIGN, QUALITY, GROWTH
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HW4WH3S1X1HEJ09Q2XH1H842
- MLA
- Claes, Evan, et al. “Bayesian Cell Therapy Process Optimization.” BIOTECHNOLOGY AND BIOENGINEERING, vol. 121, no. 5, 2024, pp. 1569–82, doi:10.1002/bit.28669.
- APA
- Claes, E., Heck, T., Coddens, K., Sonnaert, M., Schrooten, J., & Verwaeren, J. (2024). Bayesian cell therapy process optimization. BIOTECHNOLOGY AND BIOENGINEERING, 121(5), 1569–1582. https://doi.org/10.1002/bit.28669
- Chicago author-date
- Claes, Evan, Tommy Heck, Kathleen Coddens, Maarten Sonnaert, Jan Schrooten, and Jan Verwaeren. 2024. “Bayesian Cell Therapy Process Optimization.” BIOTECHNOLOGY AND BIOENGINEERING 121 (5): 1569–82. https://doi.org/10.1002/bit.28669.
- Chicago author-date (all authors)
- Claes, Evan, Tommy Heck, Kathleen Coddens, Maarten Sonnaert, Jan Schrooten, and Jan Verwaeren. 2024. “Bayesian Cell Therapy Process Optimization.” BIOTECHNOLOGY AND BIOENGINEERING 121 (5): 1569–1582. doi:10.1002/bit.28669.
- Vancouver
- 1.Claes E, Heck T, Coddens K, Sonnaert M, Schrooten J, Verwaeren J. Bayesian cell therapy process optimization. BIOTECHNOLOGY AND BIOENGINEERING. 2024;121(5):1569–82.
- IEEE
- [1]E. Claes, T. Heck, K. Coddens, M. Sonnaert, J. Schrooten, and J. Verwaeren, “Bayesian cell therapy process optimization,” BIOTECHNOLOGY AND BIOENGINEERING, vol. 121, no. 5, pp. 1569–1582, 2024.
@article{01HW4WH3S1X1HEJ09Q2XH1H842,
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.
Parallel noisy Bayesian optimization, an efficient iterative experimental design methodology, is evaluated on two in silico bioprocesses and one in vitro bioprocess. The method facilitated a reduction in experimental load of about 50%, compared to the industry state-of-the-art. image}},
author = {{Claes, Evan and Heck, Tommy and Coddens, Kathleen and Sonnaert, Maarten and Schrooten, Jan and Verwaeren, Jan}},
issn = {{0006-3592}},
journal = {{BIOTECHNOLOGY AND BIOENGINEERING}},
keywords = {{Applied Microbiology and Biotechnology,Bioengineering,Biotechnology,process optimization,process development,counterflow cell centrifugation,cell therapy,Bayesian optimization,MODEL,DESIGN,QUALITY,GROWTH}},
language = {{eng}},
number = {{5}},
pages = {{1569--1582}},
title = {{Bayesian cell therapy process optimization}},
url = {{http://doi.org/10.1002/bit.28669}},
volume = {{121}},
year = {{2024}},
}
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