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Maximizing mRNA vaccine production with Bayesian optimization

Rosa, Sara Sousa; Nunes, Davide; Antunes, Luis; Prazeres, Duarte MF; Marques, Marco PC; Azevedo, Ana M; (2022) Maximizing mRNA vaccine production with Bayesian optimization. Biotechnology and Bioengineering 10.1002/bit.28216. (In press). Green open access

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Abstract

Messenger RNA (mRNA) vaccines are a new alternative to conventional vaccines with a prominent role in infectious disease control. These vaccines are produced in in vitro transcription (IVT) reactions, catalyzed by RNA polymerase in cascade reactions. To ensure an efficient and cost-effective manufacturing process, essential for a large-scale production and effective vaccine supply chain, the IVT reaction needs to be optimized. IVT is a complex reaction that contains a large number of variables that can affect its outcome. Traditional optimization methods rely on classic Design of Experiments methods, which are time-consuming and can present human bias or based on simplified assumptions. In this contribution, we propose the use of Machine Learning approaches to perform a data-driven optimization of an mRNA IVT reaction. A Bayesian optimization method and model interpretability techniques were used to automate experiment design, providing a feedback loop. IVT reaction conditions were found under 60 optimization runs that produced 12 g · L−1 in solely 2 h. The results obtained outperform published industry standards and data reported in literature in terms of both achievable reaction yield and reduction of production time. Furthermore, this shows the potential of Bayesian optimization as a cost-effective optimization tool within (bio)chemical applications.

Type: Article
Title: Maximizing mRNA vaccine production with Bayesian optimization
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/bit.28216
Publisher version: https://doi.org/10.1002/bit.28216
Language: English
Additional information: © 2022 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian optimization, in vitro transcription, machine learning, mRNA, vaccines
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10155409
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