Modeling Uptake of Polyethylenimine/Short Interfering RNA Nanoparticles in Breast Cancer Cells Using Machine Learning

Abstract

Polyethylenimine (PEI) is one of the most promising nonviral vectors for delivery of short interfering RNA (siRNA) agents into cancer cells. A promising approach that increases the delivery efficiency of PEI is its modification with hydrophobic substitutions. However, the performance of modified PEIs depends on the nature and extent of substitutions. Herein, machine learning algorithms are used on the basis of quantitative structure activity relationship (QSAR) method to predict the cellular uptake of hydrophobically modified PEI/siRNA nanoparticles (NPs) into various cancer cell lines. To this end, 3 different regression models, namely, random forest (RF), multilayer perceptron (MLP), and linear regression (LR), are used. The results show that RF and MLP regression methods have a better performance than the LR method, suggesting that nonlinear models are better estimators when predicting the cellular uptake of PEI/siRNA NPs. Additionally, critical descriptors that have major contributions to cellular uptake are found to be PEI‐to‐siRNA weight ratio, type of hydrophobic substitution, as well as total numbers of Cs, unsaturated C, and thioester groups on substitutions in each PEI. This study is the first report that predicts cellular uptake with PEI‐based carriers, which provides valuable insight into the design of performance‐enhancing hydrophobic substituents on PEIs.