Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm.
10.1016/j.apsb.2021.11.021
- Author:
Wei WANG
1
;
Shuo FENG
2
;
Zhuyifan YE
1
;
Hanlu GAO
1
;
Jinzhong LIN
2
;
Defang OUYANG
1
Author Information
1. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
2. State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China.
- Publication Type:Journal Article
- Keywords:
Formulation prediction;
Ionizable lipid;
LightGBM;
Lipid nanoparticle;
Machine learning;
Molecular modeling;
Vaccine;
mRNA
- From:
Acta Pharmaceutica Sinica B
2022;12(6):2950-2962
- CountryChina
- Language:English
-
Abstract:
Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and 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 prediction. 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, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines 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.
