Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles

• Published in: Environment international 2023-07, Vol.177, p.108025-108025, Article 108025
• Main Authors: Zhang, Fan, Wang, Zhuang, Peijnenburg, Willie J.G.M., Vijver, Martina G.
• Format: Article
• Language: English
• Subjects: Advanced nanomaterials; Escherichia coli; Machine Learning; Metal Nanoparticles - toxicity; Mixture toxicity; Nanotoxicity; Neural network; Neural Networks, Computer; Oxides; Quantitative Structure-Activity Relationship; Support vector machine
• ISSN: 0160-4120; 1873-6750
• DOI: 10.1016/j.envint.2023.108025

[Display omitted] •Machine learning-powered QSAR models were capable of predicting mixture toxicity of ENPs to Escherichia coli.•The developed QSAR models outperformed conventional component-based mixture models.•The metal electronegativity and metal oxide energy descriptors were key nano-descriptors.•All the studied binary mixtures of MOX NPs were in the applicability domain.•A secondary literature check confirmed the mechanisms of how these descriptors explain ENP fate and cytotoxicity to E. coli. Research on theoretical prediction methods for the mixture toxicity of engineered nanoparticles (ENPs) faces significant challenges. The application of in silico methods based on machine learning is emerging as an effective strategy to address the toxicity prediction of chemical mixtures. Herein, we combined toxicity data generated in our lab with experimental data reported in the literature to predict the combined toxicity of seven metallic ENPs for Escherichia coli at different mixing ratios (22 binary combinations). We thereafter applied two machine learning (ML) techniques, support vector machine (SVM) and neural network (NN), and compared the differences in the ability to predict the combined toxicity by means of the ML-based methods and two component-based mixture models: independent action and concentration addition. Among 72 developed quantitative structure–activity relationship (QSAR) models by the ML methods, two SVM-QSAR models and two NN-QSAR models showed good performance. Moreover, an NN-based QSAR model combined with two molecular descriptors, namely enthalpy of formation of a gaseous cation and metal oxide standard molar enthalpy of formation, showed the best predictive power for the internal dataset (R2test = 0.911, adjusted R2test = 0.733, RMSEtest = 0.091, and MAEtest = 0.067) and for the combination of internal and external datasets (R2test = 0.908, adjusted R2test = 0.871, RMSEtest = 0.255, and MAEtest = 0.181). In addition, the developed QSAR models performed better than the component-based models. The estimation of the applicability domain of the selected QSAR models showed that all the binary mixtures in training and test sets were in the applicability domain. This study approach could provide a methodological and theoretical basis for the ecological risk assessment of mixtures of ENPs.