Concept of assessing nanoparticle hazards considering nanoparticle dosemetric and chemical/biological response...

Engineered nanoparticles (NP) are being developed and incorporated in a number of commercial products, raising the potential of human exposure during manufacture, use, and disposal. Although data concerning the potential toxicity of some NP have been reported, validated simple assays are lacking for predicting their in vivo toxicity. The aim of this study was to evaluate new response metrics based on chemical and biological activity of NP for screening assays that can be used to predict NP toxicity in vivo. Two cell-free and two cell-based assays were evaluated for their power in predicting in vivo toxicity of eight distinct particle types with widely differing physicochemical characteristics. The cell-free systems comprised fluorescence- and electron spin resonance-based assays of oxidant activity. The cell-based systems also used electron spin resonance (ESR) as well as luciferase reporter activity to rank the different particle types in comparison to benchmark particles of low and high activity. In vivo experiments evaluated acute pulmonary inflammatory responses in rats. Endpoints in all assays were related to oxidative stress and responses were expressed per unit NP surface area to compare the results of different assays. Results indicated that NP are capable of producing reactive species, which in biological systems lead to oxidative stress. Copper NP had the greatest activity in all assays, while TiO(2) and gold NP generally were the least reactive. Differences in the ranking of NP activity among the assays were found when comparisons were based on measured responses. However, expressing the chemical (cell-free) and biological (cells; in vivo) activity per unit particle surface area showed that all in vitro assays correlated significantly with in vivo results, with the cellular assays correlating the best. Data from this study indicate that it is possible to predict acute in vivo inflammatory potential of NP with cell-free and cellular assays by using NP surface area-based dose and response metrics, but that a cellular component is required to achieve a higher degree of predictive power.