Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling

Abstract

Magnetic nanoparticles (NPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications. In this paper, we experimentally synthesized and characterized new Fe₃O₄-based NPs, functionalizing its surface with a 5-TAMRA cadaverine modified copolymer consisting of PMAO and PEG. Despite these advancements, many combinations of NP cores and coatings remain unexplored. To address this, we created a new data set of NP systems from public sources. Herein, 11 different AI/ML algorithms were used to develop the predictive AI/ML models. The linear discriminant analysis (LDA) and random forest (RF) models showed high values of sensitivity and specificity (>0.9) in training/validation series and 3-fold cross validation, respectively. The AI/ML models are able to predict 14 output properties (CC₅₀ (μM), EC₅₀ (μM), inhibition (%), etc.) for all combinations of 54 different NP cores classes vs. 25 different coats and vs. 41 different cell lines, allowing the short listing of the best results for experimental assays. The results of this work may help to reduce the cost of traditional trial and error procedures.

Keywords: colon cancer; decorated nanoparticles; drug delivery; machine learning; perturbation theory.