A refined dose metric for nanotoxicology based on surface site reactivity for oxidative potential of engineered nanomaterials

Abstract

The increasing production of engineered nanomaterials (ENMs) raises significant concerns about human and environmental exposure, making it essential to understand the mechanisms of their interaction with biological systems to manage the associated risks. To address this, we propose categorizing ENM reactivity using in chemico methodologies. Surface analysis through methanol chemisorption and temperature-programmed surface reaction allows for the determination of reactive surface sites, providing accurate estimates of effective ENM doses in toxicity studies. Additionally, antioxidant consumption assays (dithiothreitol, cysteine, and glutathione) and reactive oxygen species (ROS) generation assays (RNO and DCFH₂-DA) are employed to rank the oxidative potential of ENM surface sites in a cell-free environment. Our study confirms the classification of ZnO NM-110, ZnO NM-111, CuO, and carbon black as highly oxidant ENMs, while TiO₂ NM-101 and NM-105 exhibit low oxidative potential due to their acidic surface sites. In contrast, CeO₂ NM-211 and NM-212 demonstrate redox surface sites. SiO₂ nanomaterials (NM-200 and NM-201) are shown to be inert, with low oxidation rates and minimal reactive surface density, despite their high surface area. Quantifying reactive surface sites offers a refined dose metric for assessing ENM toxicity, advancing safe-by-design nanomaterial development.

Fig. 1. Temperature-programmed surface reaction products of pre-adsorbed methanol analysed by mass spectroscopy of TiO₂ NM-101 (A), CuO (B), CeO₂ NM-211 (C), TiO₂ NM-105 (D), ZnO NM-110 (E), CeO₂ NM-212 (F), Mn₂O₃ (G), and Fe₂O₃ (H). Formaldehyde (red diamond) is formed at redox surface sites, dimethyl ether (green triangle) at acid surface sites, and carbon dioxide (black square) at basic or highly reactive redox surface sites. No detectable formation of species with mass 60 (methyl formate) or mass 75 (dimethoxymethane) was observed, indicating the absence of bifunctional reactive surface site activity.

Fig. 2. Oxidative potential of the tested ENMs evaluated based on Cys (left) and GSH (center) 24 h consumption and the DTT 1 h oxidation rate (right) normalized by mass (top), surface area (middle) and reactive site (bottom). Averaged values (n = 3) with error bars indicating the standard deviation. Statistical clustering by the k-means algorithm according to reactivity (high-moderate-low) is indicated by horizontal dashed lines.

Fig. 3. ROS production estimation based on DCFH₂ assay (a)–(l) and *OH trapping with RNO (m)–(o). Depleted DCFH₂ at different ENM concentrations measured by using a standard-FDA calibration curve for TiO₂ NM-101 (a), TiO₂ NM-105 (b), CeO₂ NM-211 (c), CeO₂ NM-212 (d), SiO₂ NM-200 (e), SiO₂ NM-201 (f), ZnO NM-110 (g), ZnO NM-111 (h), Fe₂O₃ (i), CuO (j), MWCNT NM-400 (k) and carbon black (l). Carbon based NMs and CuO were only tested between 0 and 12.5 μg mL⁻¹ according to the results of the interference test. RNO depletion is normalized per mass (m), per surface area (n) and per reactive site (o). Averaged values (n = 3) with error bars indicating the standard deviation.

Fig. 4. Heat map for the 14 ENMs evaluated based on their intrinsic oxidative capacity to react with the thiol group in DTT, Cys, and GSH, or their production of ROS that are trapped by RNO and DCFH₂, as well as based on their reactive profile obtained via methanol temperature programmed surface reaction. Clustering was performed by the k-means algorithm.