A functional assay-based strategy for nanomaterial risk forecasting

Abstract

The study of nanomaterial impacts on environment, health and safety (nanoEHS) has been largely predicated on the assumption that exposure and hazard can be predicted from physical-chemical properties of nanomaterials. This approach is rooted in the view that nanoöbjects essentially resemble chemicals with additional particle-based attributes that must be included among their intrinsic physical-chemical descriptors. With the exception of the trivial case of nanomaterials made from toxic or highly reactive materials, this approach has yielded few actionable guidelines for predicting nanomaterial risk. This article addresses inherent problems in structuring a nanoEHS research strategy based on the goal of predicting outcomes directly from nanomaterial properties, and proposes a framework for organizing data and designing integrated experiments based on functional assays (FAs). FAs are intermediary, semi-empirical measures of processes or functions within a specified system that bridge the gap between nanomaterial properties and potential outcomes in complex systems. The three components of a functional assay are standardized protocols for parameter determination and reporting, a theoretical context for parameter application and reference systems. We propose the identification and adoption of reference systems where FAs may be applied to provide parameter estimates for environmental fate and effects models, as well as benchmarks for comparing the results of FAs and experiments conducted in more complex and varied systems. Surface affinity and dissolution rate are identified as two critical FAs for characterizing nanomaterial behavior in a variety of important systems. The use of these FAs to predict bioaccumulation and toxicity for initial and aged nanomaterials is illustrated for the case of silver nanoparticles and Caenorhabditis elegans.

Keywords: Dissolution rate; Functional assay; NanoEHS; Nanomaterial risk assessment; Reference systems; Risk framework; surface affinity.