Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

Abstract

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

Polystyrene Nanoplastic Exposure Induces Developmental Toxicity by Activating the Oxidative Stress Response and Base Excision Repair Pathway in Zebrafish (Danio rerio)

“…Previous reports indicated that the typical nanoplastic concentrations were 150–2400 particles/m 3 , whereas in a harbor adjacent to a plastic production facility, the concentration was 102,000/m 3 . 44 We have tested the toxicological effects of nanoplastics using a median lethal concentration (LC 50 ) assay, 45 followed by for a series of concentrations lower than LC 50 for subsequent experiments. However, for investigating the toxic effects and mechanisms of nanoplastics on aquatic organisms, the complicated environmental factors might disturb the effects of nanoplastics in organisms.…”

Decision tree models to classify nanomaterials according to theDF4nanoGroupingscheme

“…key descriptors) bridging to nanomaterial properties and the observed effects. Oksel et al (2017) proposed three criteria on datasets for modeling: data should be obtained from a preferably single and standardized protocol; its accuracy and suitability for (Q)SAR analysis should be examined; and datasets should be large enough to allow rational division of the data into training and test sets (Oksel et al 2017). Here we have used a unique dataset that fulfills these criteria, in that it comprises a very well characterized panel of nanomaterials that has been evaluated in different assays, both, in vitro and Table 10.…”

In silico prediction of toxicity and its applications for chemicals at work

“…As the number of workers or end users who use them for commercial purposes and are exposed to these chemicals every day increases, the need to assess their potential side effects in a cost-effective manner is growing. These data-driven models can be used to accelerate the decision-making process by screening hazards, and making early identification and management of potential toxicity, and physicochemical properties; and maximizing the use of information that these models provide can also support the industrial, regulatory, and public needs for safer chemicals [64]. In addition, further research is needed on how to use the in silico programs to protect workers' health in industries that manufacture, use, and distribute various chemicals.…”