Understanding the Manufacturing Process of Lipid Nanoparticles for mRNA Delivery Using Machine Learning

Abstract

Lipid nanoparticles (LNPs), used for mRNA vaccines against severe acute respiratory syndrome coronavirus 2, protect mRNA and deliver it into cells, making them an essential delivery technology for RNA medicine. The LNPs manufacturing process consists of two steps, the upstream process of preparing LNPs and the downstream process of removing ethyl alcohol (EtOH) and exchanging buffers. Generally, a microfluidic device is used in the upstream process, and a dialysis membrane is used in the downstream process. However, there are many parameters in the upstream and downstream processes, and it is difficult to determine the effects of variations in the manufacturing parameters on the quality of the LNPs and establish a manufacturing process to obtain high-quality LNPs. This study focused on manufacturing mRNA-LNPs using a microfluidic device. Extreme gradient boosting (XGBoost), which is a machine learning technique, identified EtOH concentration (flow rate ratio), buffer pH, and total flow rate as the process parameters that significantly affected the particle size and encapsulation efficiency. Based on these results, we derived the manufacturing conditions for different particle sizes (approximately 80 and 200 nm) of LNPs using Bayesian optimization. In addition, the particle size of the LNPs significantly affected the protein expression level of mRNA in cells. The findings of this study are expected to provide useful information that will enable the rapid and efficient development of mRNA-LNPs manufacturing processes using microfluidic devices.

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