A weight of evidence approach for hazard screening of engineered nanomaterials

Abstract

Hazard identification is an important step in assessing nanomaterial risk and is required under multiple regulatory frameworks in the US, Europe and worldwide. Given the emerging nature of the field and complexity of nanomaterials, multiple studies on even basic material properties often result in varying data pointing in different directions when data interpretation is attempted. Weight of evidence (WOE) evaluation has been recommended for nanomaterial risk assessment, but the majority of WOE frameworks are qualitative in nature and do not satisfy the growing needs for objectivity and transparency that are necessary for regulatory decision making. This paper implements a quantitative WOE framework that utilizes multi-criteria decision analysis methodology for integrating individual studies on nanomaterial hazard resulting from physico-chemical and toxicological properties of nanomaterials. For the first time, a WOE approach explicitly integrates expert evaluation of data quality of available information. Application of the framework is illustrated for titanium dioxide nanoparticles (nano-TiO2), but the approach is designed to compare the relative hazard of several nanomaterials as well as emerging stressors in general.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

“…Several approaches have been proposed to classify NMs and to estimate their risks that include stochastic multicriteria acceptability analysis (SMAA-TRI) (Tervonen et al, 2007), weight of evidence (WOE) (Hristozov et al, 2014;Linkov et al, 2011), grouping (Arts et al, 2014(Arts et al, , 2016, quantitative nanostructure-activity relationship (QSAR) (Singh & Gupta, 2014;Winkler et al, 2014) and Bayesian networks (Linkov et al, 2015;Low-Kam et al, 2015;Money et al, 2014). Combinations of these methods were proposed to integrate and collate heterogeneous information to estimate the risk of NMs under scarcity of data (Hristozov et al, 2012;Linkov et al, 2015).…”

A framework for identifying performance targets for sustainable nanomaterials

“…It is important to note that modeling the variables to reflect more realistic distribution of probabilities can lead to more accurate forecasts and predictions [14,16]. However, for purposes of clarity in communicating this framework, we are proceeding with the above assumptions and recommend the use of probability distributions when practical.…”

A quantitative framework to group nanoscale and microscale particles by hazard potency to derive occupational exposure limits: Proof of concept evaluation

“…Exposure scenarios along the ENM “lifecyle” (production to disposal) are included in some of the conceptual frameworks (Arts et al, 2015; Environmental Defense and Dupont, 2007; Shatkin, 2013). Weight of evidence and decision analysis methods have been proposed in other analysis frameworks (Hristozov et al, 2014; Zuin et al, 2011). Several quantitative structure-activity relationship (QSAR) models have been developed, which describe the important factors influencing the toxicity (Munro et al, 1996; Burello and Worth, 2011; Gernand and Casman, 2014, 2016) and allow for hazard grouping and ranking (Liu et al, 2011, 2013; Oh et al, 2016; Zhang et al, 2012); however, these models have not thus far been used in human health risk assessment.…”