Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm
https://doi.org/10.1016/j.apsb.2021.11.021
In this new article publication from Acta Pharmaceutica Sinica B, authors Wei Wang, Shuo Feng, Zhuyifan Ye, Hanlu Gao, Jinzhong Lin and Defang Ouyang from University of Macau, Macau, China and Fudan University, Shanghai, China discuss the prediction of lipid nanoparticles for mRNA vaccines by machine learning algorithms.
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 (R2 > 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.
Article reference: Wei Wang, Shuo Feng, Zhuyifan Ye, Hanlu Gao, Jinzhong Lin, Defang Ouyang, Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm, Acta Pharmaceutica Sinica B, Volume 12, Issue 6, 2022, Pages 2950-2962, ISSN 2211-3835, https://doi.org/10.1016/j.apsb.2021.11.021
Keywords: Lipid nanoparticle, Ionizable lipid, mRNA, Vaccine, Formulation prediction, Machine learning, Light, GBM, Molecular modeling
Graphical Abstract: available at https://ars.els-cdn.com/content/image/1-s2.0-S2211383521004597-ga1_lrg.jpg
The AI algorithm was used to find the relationship between ionizable lipids and in vivo efficiencies of mRNA vaccines, which was validated by a mice study. The molecular dynamic simulation further provided mechanical details of lipid nanoparticle (LNP) formulation.
# # # # # #
The Journal of the Institute of Materia Medica, the Chinese Academy of Medical Sciences and the Chinese Pharmaceutical Association.
For more information please visit https://www.journals.elsevier.com/acta-pharmaceutica-sinica-b/
Editorial Board: https://www.journals.elsevier.com/acta-pharmaceutica-sinica-b/editorial-board
APSB is available on ScienceDirect (https://www.sciencedirect.com/journal/acta-pharmaceutica-sinica-b).
Submissions to APSB may be made using Editorial Manager® (https://www.editorialmanager.com/apsb/default.aspx).
CiteScore: 12.5
Impact Factor: 11.614
JIF without self-citation: 10.746
ISSN 2211-3835
# # # # #