High-Throughput Algorithmic Optimization of In Vitro Transcription for SARS-CoV-2 mRNA Vaccine Production

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 (by percent intact mRNA) 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. Once the optimized conditions were achieved, an automated, high-throughput screen was conducted to evaluate commercially available T7 RNA polymerases for rate and quality of mRNA production. 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. This novel platform offers a powerful new approach for optimizing IVT reactions for mRNA production.