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

Abstract

The large and rapidly growing number of engineered nanomaterials (ENMs) presents a challenge to assessing the potential occupational health risks. An initial database of 25 rodent studies including 1929 animals across various experimental designs and material types was constructed to identify materials that are similar with respect to their potency in eliciting neutrophilic pulmonary inflammation, a response relevant to workers. Doses were normalized across rodent species, strain, and sex as the estimated deposited particle mass dose per gram of lung. Doses associated with specific measures of pulmonary inflammation were estimated by modeling the continuous dose-response relationships using benchmark dose modeling. Hierarchical clustering was used to identify similar materials. The 18 nanoscale and microscale particles were classified into four potency groups, which varied by factors of approximately two to 100. Benchmark particles microscale TiO2 and crystalline silica were in the lowest and highest potency groups, respectively. Random forest methods were used to identify the important physicochemical predictors of pulmonary toxicity, and group assignments were correctly predicted for five of six new ENMs. Proof-of-concept was demonstrated for this framework. More comprehensive data are needed for further development and validation for use in deriving categorical occupational exposure limits.

Current intelligence bulletin 69: NIOSH practices in occupational risk assessment.

“…In current analyses of acute pulmonary inflammation, a set of 16 microscale and nanoscale particles in a training dataset have been grouped into four potency clusters, including three groups for nanoscale particles, which are 4-175 times more potent than a fourth group containing a microscale reference particle. These analyses illustrate proof of concept for grouping particles by pulmonary hazard potency [Drew et al 2017].…”

“…Given the large and growing number of engineered nanomaterials (ENMs) with limited data, as for other emerging and existing substances produced or used in the workplace, alternative test strategies (i.e., toxicological approaches other than primary animal testing) such as high-throughput screening and in vitro exposures may help to fill the gaps by providing data that could be used in validated hazard and risk assessment models [Drew et al 2017;Kuempel et al 2012].…”

“…Given the limited data for developing OELs for ENMs, methods have been developed to prioritize or group ENMs based on the available subchronic or chronic dose-response data for benchmark materials and the utilization of shorter-term in vivo data for many ENMs [Arts et al 2015;Drew et al 2017;Hristozov et al 2016]. Several QSAR models have been developed, which describe the important factors influencing the toxicity and allow for hazard grouping and ranking [Burello and Worth 2011;Gernand and Casman 2014;Hristozov et al 2016;Oh et al 2016]; however, these models have not been used in human health risk assessment.…”

“…Potency is the inverse of dose (i.e., higher potency substances are those with a lower dose associated with an adverse effect). In these ongoing analyses, NIOSH is investigating the dose-response relationships and substance-specific physicochemical data, and it is using statistical learning methods such as Random Forests to identify groups of similarly behaving materials with respect to hazard potency [Drew et al 2017]. The adverse outcomes of interest include pulmonary inflammation, fibrosis, cancer, and systemic effects associated with inhaled nanoscale particles.…”

“…Next steps are to evaluate an in vitro dataset of some of the same materials as in the in vivo dataset to investigate the possible utility of in vitro studies of cellular responses to particle exposure that are involved in the in vivo mechanism of activation of pulmonary inflammation associated with particle exposure, including cytokine, gene transcription, and cell toxicity endpoints. Ultimately, it is envisioned that extended and validated analyses will be used as a framework to develop initial OEL categories or OEBs as hazard inputs into nanomaterial control banding tools [Drew et al 2017;Kuempel et al 2012].…”