Proposal for a risk banding framework for inhaled low aspect ratio nanoparticles based on physicochemical properties

Abstract

We present a conceptual framework that can be used to assign risk bands to inhaled low aspect ratio nanoparticles starting from exposure bands assigned to a specific exposure situation. The framework mimics a basic physiological scheme that captures the essential mechanisms of fate and toxicity of inhaled nanoparticles and is composed of several models and rules that estimate the result of the following processes: the deposition of particles in the respiratory tract, their (de-)agglomeration, lung burden and clearance, their diffusion through the lung mucus layer, translocation and cellular uptake and local and systemic toxicity. Each model is based on a set of particle's physicochemical properties, including the size and size distribution(s), the zeta potential (or net charge at a specific pH), the surface hydrophobicity or hydrophilicity, the conduction band energy (for metals, metal oxides, quantum dots, etc.) and the solubility at a specific pH. The framework takes the exposure bands as input and predicts, using the above-mentioned models, an internal dose band (module 1). Module 2 assigns a relative hazard ranking depending on the region of particle deposition in the respiratory tract, the likelihood of uptake and whether the toxicological effects are assumed to be local and/or systemic. By combining the results of Module 1 and 2, the framework provides a relative risk ranking.

Grouping of nanomaterials to read-across hazard endpoints: a review

“…The relevance of these approaches lies in the selection of PC properties for hazard classification, and the identification of hazard classes that in some cases are characterised by specific NM properties. Regarding the PC properties, morphology and water solubility are considered in most of the approaches (ANSES 2010;Cornelissen et al 2011;Jensen et al 2014;Oosterwijk et al 2016;Van Duuren-Stuurman et al 2012;Zalk et al 2009). Reactivity is considered as surface reactivity (ANSES 2010;Zalk, Paik, and Swuste 2009) or as ROS formation potential (H€ ock et al 2013), whereas surface coating (or surface chemistry) is considered only in one approach (Jensen et al 2014) in hazard evaluation and in one approach in Table 1 ( RCC 2013aRCC , 2013b and also in Table 3 (Lamon et al 2018).…”

“…Although the classifier is not developed as a CB tool, it is reported in Table 2 because it identifies three hazard classes that are defined according to PC properties and toxicological tests (Fortino and Grevo 2017; NANOSOLUTIONS 2017). Oosterwijk et al (2016) propose a conceptual framework for inhalation exposure, that can be updated and validated once more information on the property-toxicity relationships and on the NMs mechanism of action become available. The hazard module computes different scores according to the accumulation fractions of NMs in different respiratory regions (nasal, tracheobronchial, and pulmonary region) and according to local or systemic toxicity that is considered in the alveolar region.…”

Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices

“…The workflow reflects the key role that exposure data possesses for guiding and informing hazard modeling and testing implicated in the structured NAM‐based safety assessment strategy (c.f. Figure ), and builds on a hierarchical process of estimating exposure to support decisions (Oosterwijk et al).…”

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

“…Oosterwijk et al (2016) describe a conceptual framework using lung dosimetry models to extrapolate rodent pulmonary responses to humans. Hristozov et al (2016) used some of the same data (ENPRA) to estimate BMDLs in rodents as IT mass lung doses, then estimated inhalation BMDLs by assuming that the instilled bolus spreads over one volume of breath.…”

“…Other frameworks have proposed grouping ENMs based on hazard and exposure potential (Arts et al, 2015, 2016; Bos et al, 2015; Braakhuis et al, 2016; Cohen et al, 2013; Gebel et al, 2014; Godwin et al, 2015; Oomen et al, 2015; Oosterwijk et al, 2016; Stone et al, 2014; Walser and Studer, 2015), although many of these have not been tested with quantitative data. Case study data have been used with some of these frameworks (Arts et al, 2016; Gkika et al, 2017; Grieger et al, 2017).…”