Abstract
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.
Highlights
The rise of nanotechnology and rapid production of nanoscale materials have increased human and ecosystem exposure to NanoParticles (NPs)
Each paper was reviewed focusing on information related to (i) NP type (FeO, SiO2, CuO etc.), (ii) nano-specific descriptors and (iii) study design experimental parameters
After a detailed review of the studies, we identified several properties not mentioned in their article, such as agglomeration/aggregation, shape, surface charge, hydrodynamic size, details on zeta potential, dose, duration etc., While the review study provided the ground for the data extraction, we scanned the papers to extract all available information in greater details
Summary
The rise of nanotechnology and rapid production of nanoscale materials have increased human and ecosystem exposure to NanoParticles (NPs). Many factors that affect NPs toxicity and the underlying mechanisms have been investigated [2]. For instance, can cause Reactive Oxygen Species (ROS), which can induce oxidative stress, resulting in disturbed physiological redox-regulated functions inside a cell. This in turn may lead to DNA damage, unregulated cell signaling, cytotoxicity, apoptosis, and cancer initiation [3]. Size, morphology, surface charge, and other physicochemical (p-chem) properties have all been shown to affect NPs toxicity [1,4,5,6]
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