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Development of in silico methodology for siRNA lipid nanoparticle formulations
Gao, Haoshi1,2; Kan, Stanislav3; Ye, Zhuyifan1; Feng, Yuchen4; Jin, Lei4; Zhang, Xudong5; Deng, Jiayin1; Chan, Ging1; Hu, Yuanjia1; Wang, Yongjun6; Cao, Dongsheng7; Ji, Yuanhui8; Liang, Mingtao5; Li, Haifeng2; Ouyang, Defang1
2022-08-15
Source PublicationChemical Engineering Journal
ISSN1385-8947
Volume442
Abstract

Small interfering RNA (siRNA) gene silencing therapy has great potential for treating multiple diseases. The lipid nanoparticle (LNP) technology for siRNA delivery succussed in clinical treatment. However, the formulation design of siRNA-LNP still faces enormous challenges. Current research aims to develop an integrated computer methodology for the rational design of siRNA-LNP formulations. The machine learning (ML) algorithm lightGBM was built to predict the knockdown efficiency of siRNA-LNP in vitro and in vivo delivery and reached good accuracy with 80% and 78.89% in the validation set. Further siRNA experiments well validated the ML model. Moreover, molecular dynamic (MD) simulation was utilized to investigate the molecular structure of siRNA-LNP. In conclusion, a novel integrated computer methodology based on ML, experimental, and MD simulation was successfully developed for siRNA-LNP formulation design.

KeywordCationic Lipids Knockdown Efficiency Lipid Nanoparticle Machine Learning Molecular Dynamic Simulation Sirna
DOI10.1016/j.cej.2022.136310
URLView the original
Language英語English
Scopus ID2-s2.0-85128202005
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionInstitute of Chinese Medical Sciences
INSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING
Corresponding AuthorLiang, Mingtao; Li, Haifeng; Ouyang, Defang
Affiliation1.State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, 999078, China
2.Institute of Applied Physics and Materials Engineering, University of Macau, Macau, 999078, China
3.School of Environmental and Life Sciences, The University of Newcastle, Newcastle, 2308, Australia
4.School of Medicine and Public Health, The University of Newcastle, Newcastle, 2308, Australia
5.School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, 2308, Australia
6.Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, 110016, China
7.Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China
8.Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, Nanjing, 211189, China
First Author AffilicationInstitute of Chinese Medical Sciences;  INSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING
Corresponding Author AffilicationINSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING;  Institute of Chinese Medical Sciences
Recommended Citation
GB/T 7714
Gao, Haoshi,Kan, Stanislav,Ye, Zhuyifan,et al. Development of in silico methodology for siRNA lipid nanoparticle formulations[J]. Chemical Engineering Journal,2022,442.
APA Gao, Haoshi,Kan, Stanislav,Ye, Zhuyifan,Feng, Yuchen,Jin, Lei,Zhang, Xudong,Deng, Jiayin,Chan, Ging,Hu, Yuanjia,Wang, Yongjun,Cao, Dongsheng,Ji, Yuanhui,Liang, Mingtao,Li, Haifeng,&Ouyang, Defang.(2022).Development of in silico methodology for siRNA lipid nanoparticle formulations.Chemical Engineering Journal,442.
MLA Gao, Haoshi,et al."Development of in silico methodology for siRNA lipid nanoparticle formulations".Chemical Engineering Journal 442(2022).
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