A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.
Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.
Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R for internal validation data sets: 0.60-0.80; R for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m/g; and dose, 0-200 ppm.
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