- Title
- Development of in silico methodology for siRNA lipid nanoparticle formulations
- Creator
- Gao, Haoshi; Kan, Stanislav; Cao, Dongsheng; Ji, Yuanhui; Liang, Mingtao; Li, Haifeng; Ouyang, Defang; Ye, Zhuyifan; Feng, Yuchen; Jin, Lei; Zhang, Xudong; Deng, Jiayin; Chan, Ging; Hu, Yuanjia; Wang, Yongjun
- Relation
- Chemical Engineering Journal Vol. 442, Issue 1, no. 136310
- Publisher Link
- http://dx.doi.org/10.1016/j.cej.2022.136310
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2022
- Description
- 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.
- Subject
- siRNA; lipid nanoparticle; cationic lipids; machine learning; molecular dynamic simulation; knockdown efficiency
- Identifier
- http://hdl.handle.net/1959.13/1464400
- Identifier
- uon:46983
- Identifier
- ISSN:1385-8947
- Language
- eng
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