Posted in Biochemistry on October 20, 2024

Abstract

The in vitro transcription (IVT) of messenger ribonucleic acid (mRNA) from the linearized deoxyribonucleic acid (DNA) template of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) was optimized for total mRNA yield and purity utilizing machine learning in conjunction with automated, high-throughput liquid handling technology. An iterative Bayesian optimization approach successfully optimized 11 critical process parameters in 42 reactions across 5 experimental rounds. Final conditions showed a 12% yield improvement and a 50% reduction in reaction time, while simultaneously significantly decreasing (up to 44% reduction) the use of expensive reagents.

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McMinn, S.E.; Miller, D.V.; Yur, D.; Stone, K.; Xu, Y.; Vikram, A.; Murali, S.; Raffaele, J.; Holland, D.; Wang, S.C.; Smith, J.P. "High-Throughput Algorithmic Optimization of In Vitro Transcription for SARS-CoV-2 mRNA Vaccine Production." Biochemistry, 2024, 63(21), 2793–2802. https://doi.org/10.1021/acs.biochem.4c00188

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