Version 1
: Received: 9 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (11:12:00 CEST)
How to cite:
Metwally, A. A.; Nayel, A. A.; Hathout, R. M. In Silico Prediction of siRNA Ionizable-Lipid Nanoparticles in vivo Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors. Preprints2021, 2021080254. https://doi.org/10.20944/preprints202108.0254.v1
Metwally, A. A.; Nayel, A. A.; Hathout, R. M. In Silico Prediction of siRNA Ionizable-Lipid Nanoparticles in vivo Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors. Preprints 2021, 2021080254. https://doi.org/10.20944/preprints202108.0254.v1
Metwally, A. A.; Nayel, A. A.; Hathout, R. M. In Silico Prediction of siRNA Ionizable-Lipid Nanoparticles in vivo Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors. Preprints2021, 2021080254. https://doi.org/10.20944/preprints202108.0254.v1
APA Style
Metwally, A. A., Nayel, A. A., & Hathout, R. M. (2021). <em>In Silico</em> Prediction of siRNA Ionizable-Lipid Nanoparticles <em>in vivo</em> Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors. Preprints. https://doi.org/10.20944/preprints202108.0254.v1
Chicago/Turabian Style
Metwally, A. A., Amira A Nayel and Rania M Hathout. 2021 "<em>In Silico</em> Prediction of siRNA Ionizable-Lipid Nanoparticles <em>in vivo</em> Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors" Preprints. https://doi.org/10.20944/preprints202108.0254.v1
Abstract
In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable yet never achieved before. This study aims to computationally predict siRNA nanoparticles in vivo efficacy, which saves time and resources. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log(dose) with R2val = 0.86 – 0.89 and 0.75 – 80 for validation sets one and two respectively. Artificial neural networks resulted in the best R2val for both validation sets. For predictions that have high bias, improvement of R2val from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.
Keywords
siRNA; ionizable lipids; nanoparticles; in vivo; QSAR; machine learning
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.