Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP opti-mization primarily relies on screening ionizable lipids by traditional experiments which consumes inten-sive cost , time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R-2 > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model pre-diction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs , mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vac-cines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future. (C) 2022 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.

Automated summary

The study utilized machine learning algorithms and molecular dynamic modeling to accelerate the development of mRNA vaccine LNPs, demonstrating the feasibility of the predictive model. Experimental validation confirmed the accuracy of the predictive model, providing a new approach for virtual screening of LNP formulations in the future.