Maximizing mRNA vaccine production with Bayesian optimization. Issue 11 (5th September 2022) - Rosa, Sara Sousa; Nunes, Davide; Antunes, Luis; Prazeres, Duarte M. F.; Marques, Marco P. C.; Azevedo, Ana M.
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. Abstract : mRNA, a forefront technology vaccination field, is produced in an in vitro transcription (IVT) reaction catalyzed by RNA polymerase. In this work, a machine learning approach, in particular, Bayesian optimization, was used as a form of incremental adaptive design‐of‐experiments (DoE) to maximise mRNA production. Optimal reaction conditions (with a production of 12 gmRNA.L‐1 in two‐hours) were found in 60 runs, outperforming published industry standards. These results show the potential of Bayesian optimization as a cost‐effective optimization tool for (bio)chemical applications.