Ingested nanomaterials are exposed to many metabolites that are produced, modified, or regulated by members of the enteric microbiota. The adsorption of these metabolites potentially affects the... Show moreIngested nanomaterials are exposed to many metabolites that are produced, modified, or regulated by members of the enteric microbiota. The adsorption of these metabolites potentially affects the identity, fate, and biodistribution of nanomaterials passing the gastrointestinal tract. Here, we explore these interactions using in silico methods, focusing on a concise overview of 170 unique enteric microbial metabolites which we compiled from the literature. First, we construct quantitative structure–activity relationship (QSAR) models to predict their adsorption affinity to 13 metal nanomaterials, 5 carbon nanotubes, and 1 fullerene. The models could be applied to predict log k values for 60 metabolites and were particularly applicable to ‘phenolic, benzoyl and phenyl derivatives’, ‘tryptophan precursors and metabolites’, ‘short-chain fatty acids’, and ‘choline metabolites’. The correlations of these predictions to biological surface adsorption index descriptors indicated that hydrophobicity-driven interactions contribute most to the overall adsorption affinity, while hydrogen-bond interactions and polarity/polarizability-driven interactions differentiate the affinity to metal and carbon nanomaterials. Next, we use molecular dynamics (MD) simulations to obtain direct molecular information for a selection of vitamins that could not be assessed quantitatively using QSAR models. This showed how large and flexible metabolites can gain stability on the nanomaterial surface via conformational changes. Additionally, unconstrained MD simulations provided excellent support for the main interaction types identified by QSAR analysis. Combined, these results enable assessing the adsorption affinity for many enteric microbial metabolites quantitatively and support the qualitative assessment of an even larger set of complex and biologically relevant microbial metabolites to carbon and metal nanomaterials. Show less
Similarity assessment is one of the means of optimally using scarcely available experimental data on the fate and hazards of nanoforms (NFs) for regulatory purposes. For a set of NFs that are shown... Show moreSimilarity assessment is one of the means of optimally using scarcely available experimental data on the fate and hazards of nanoforms (NFs) for regulatory purposes. For a set of NFs that are shown to be similar it is allowed in a regulatory context to apply the information available on any of the NFs within the group to the whole set of NFs. Obviously, a proper justification for such a similarity assessment is to be provided. Within the context of exemplifying such a justification, a case study was performed aimed at assessing the similarity of a set of spherical metallic NFs that different with regard to chemical composition (three metals) and particle size (three different sizes). The endpoints of assessment were root elongation and biomass increase of lettuce (Lactuca sativa L.) seedlings and exposure assessment was performed in order to express the actual exposure concentration in terms of time-weighted average particle concentrations. The results of the study show that for the specific endpoints assessed, chemical composition is driving NF toxicity and this is mostly due to impacts on the fate of the NFs. On the other hand, particle size of Cu NFs had a negligible impact on the dose-response relationships for the specific endpoints assessed. It is thus concluded that hazard data available on spherical Cu NF tested in our case can be used to inform on the hazards of any spherical Cu NF within the size range of 25–100 nm, but only applies for the certain endpoints. Also, toxicity data for the Cu2+-ion are suited for such a similarity assessment. Show less
