Development of a nano-QSAR model for predicting the toxicity of nano-metal oxide mixtures to Aliivibrio fischeri

Abstract

Metal oxide nanoparticles (MONPs) have various applications, including cosmetics, detergents, and antibacterial agents, owing to their unique physicochemical properties. MONPs are often mixed and used in products and exist together through various exposure routes in the environment. Toxicity can occur between chemicals at no observed effect concentrations (NOECs) by the cocktail effect (e.g., addition, synergism, potentiation), but a definitive toxic assessment for the nanoparticles is still lacking. There have been several studies on nano quantitative structure–activity relationships (nano-QSAR), but the calculations of the descriptors (<1000 atoms) for the engineered size of the nanoparticles (NPs) based on density functional theory (DFT) are unclear. In this study, we conducted both mixture toxicity assays and molecular dynamics (MD)-based molecular descriptor calculations to develop a nano-mixture QSAR model. A toxicity assay was performed for a mixture of SiO₂, TiO₂, and ZnO NPs of various sizes (8–140 nm), targeting a marine bioluminescent bacterium called Aliivibrio fischeri, a decomposer in aquatic ecosystems. Theoretical molecular descriptors were calculated based on molecular dynamics (MDs) to reflect the characteristics of NPs of different sizes. Two different types of descriptors (total descriptors and the calculated descriptors) were used to develop the models. In this study, four machine-learning algorithms (random forest (RF), support vector machine (SVM), Bayesian regularized neural network (BRNN), and multilinear regression (MLR)) were applied to develop a nano-mixture QSAR model. The proposed model based on MD shows potential for use in the selection of a safer MONPs combination design.