We demonstrate for 24 metal oxide (MOx) nanoparticles that it is possible to use conduction band energy levels to delineate their toxicological potential at cellular and whole animal levels. Among the materials, the overlap of conduction band energy (Ec) levels with the cellular redox potential (−4.12 to −4.84 eV) was strongly correlated to the ability of Co3O4, Cr2O3, Ni2O3, Mn2O3 and CoO nanoparticles to induce oxygen radicals, oxidative stress and inflammation. This outcome is premised on permissible electron transfers from the biological redox couples that maintain the cellular redox equilibrium to the conduction band of the semiconductor particles. Both single parameter cytotoxic as well as multi-parameter oxidative stress assays in cells showed excellent correlation to the generation of acute neutrophilic inflammation and cytokine responses in the lungs of CB57 Bl/6 mice. Co3O4, Ni2O3, Mn2O3 and CoO nanoparticles could also oxidize cytochrome c as a representative redox couple involved in redox homeostasis. While CuO and ZnO generated oxidative stress and acute pulmonary inflammation that is not predicted by Ec levels, the adverse biological effects of these materials could be explained by their solubility, as demonstrated by ICP-MS analysis. Taken together, these results demonstrate, for the first time, that it is possible to predict the toxicity of a large series of MOx nanoparticles in the lung premised on semiconductor properties and an integrated in vitro/in vivo hazard ranking model premised on oxidative stress. This establishes a robust platform for modeling of MOx structure-activity relationships based on band gap energy levels and particle dissolution. This predictive toxicological paradigm is also of considerable importance for regulatory decision-making about this important class of engineered nanomaterials.

Using quantitative models to predict the biological interactions of nanoparticles will accelerate the translation of nanotechnology. Here, we characterized the serum protein corona 'fingerprint' formed around a library of 105 surface-modified gold nanoparticles. Applying a bioinformatics-inspired approach, we developed a multivariate model that uses the protein corona fingerprint to predict cell association 50% more accurately than a model that uses parameters describing nanoparticle size, aggregation state, and surface charge. Our model implicates a set of hyaluronan-binding proteins as mediators of nanoparticle-cell interactions. This study establishes a framework for developing a comprehensive database of protein corona fingerprints and biological responses for multiple nanoparticle types. Such a database can be used to develop quantitative relationships that predict the biological responses to nanoparticles and will aid in uncovering the fundamental mechanisms of nano-bio interactions.

Word cloud summary of diverse topics associated with QSAR modeling that are discussed in this review.

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Silver nanoparticles (Ag NPs) are commonly added to various consumer products and materials to impair bacterial growth. Recent studies suggested that the primary mechanism of antibacterial action of silver nanoparticles is release of silver ion (Ag(+)) and that particle-specific activity of silver nanoparticles is negligible. Here, we used a genome-wide library of Escherichia coli consisting of ∼4000 single gene deletion mutants to elucidate which physiological pathways are involved in how E. coli responds to different Ag NPs. The nanoparticles studied herein varied in both size and surface charge. AgNO3 was used as a control for soluble silver ions. Within a series of differently sized citrate-coated Ag NPs, smaller size resulted in higher Ag ion dissolution and toxicity. Nanoparticles functionalized with cationic, branched polyethylene imine (BPEI) exhibited equal toxicity with AgNO3. When we used a genome-wide approach to investigate the pathways involved in the response of E. coli to different toxicants, we found that only one of the particles (Ag-cit10) exhibited a pattern of response that was statistically similar to that of silver ion. By contrast, the pathways involved in E. coli response to Ag-BPEI particles were more similar to those observed for another cationic nanoparticle that did not contain Ag. Overall, we found that the pathways involved in bacterial responses to Ag nanoparticles are highly dependent on physicochemical properties of the nanoparticles, particularly the surface characteristics. These results have important implications for the regulation and testing of silver nanoparticles.

Understanding the relationships between the physicochemical properties of engineered nanomaterials and their toxicity is critical for environmental and health risk analysis. However, this task is confounded by material diversity, heterogeneity of published data and limited sampling within individual studies. Here, we present an approach for analysing and extracting pertinent knowledge from published studies focusing on the cellular toxicity of cadmium-containing semiconductor quantum dots. From 307 publications, we obtain 1,741 cell viability-related data samples, each with 24 qualitative and quantitative attributes describing the material properties and experimental conditions. Using random forest regression models to analyse the data, we show that toxicity is closely correlated with quantum dot surface properties (including shell, ligand and surface modifications), diameter, assay type and exposure time. Our approach of integrating quantitative and categorical data provides a roadmap for interrogating the wide-ranging toxicity data in the literature and suggests that meta-analysis can help develop methods for predicting the toxicity of engineered nanomaterials.

Because of concerns about the safety of a growing number of engineered nanomaterials (ENM), it is necessary to develop high throughput screening and in silico data transformation tools that can speed up in vitro hazard ranking. Here, we report the use of a multi-parametric, automated screening assay that incorporates sub-lethal and lethal cellular injury responses to perform high throughput analysis of a batch of commercial metal/metal oxide nanoparticles (NP) with the inclusion of a quantum dot (QD1). The responses chosen for tracking cellular injury through automated epifluorescence microscopy included ROS production, intracellular calcium flux, mitochondrial depolarization, and plasma membrane permeability. The z-score transformed high volume data set was used to construct heat maps for in vitro hazard ranking as well as showing the similarity patterns of NPs and response parameters through the use of self-organizing maps (SOM). Among the materials analyzed, QD1 and nano-ZnO showed the most prominent lethality, while Pt, Ag, SiO2, Al2O3, and Au triggered sub-lethal effects but without cytotoxicity. In order to compare the in vitro with the in vivo response outcomes in zebrafish embryos, NPs were used to assess their impact on mortality rate, hatching rate, cardiac rate, and morphological defects. While QDs, ZnO, and Ag induced morphological abnormalities or interfered in embryo hatching, Pt and Ag exerted inhibitory effects on cardiac rate. Ag toxicity in zebrafish differed from the in vitro results, which is congruent with this material's designation as extremely dangerous in the environment. Interestingly, while toxicity in the initially selected QD formulation was due to a solvent (toluene), supplementary testing of additional QDs selections yielded in vitro hazard profiling that reflect the release of chalcogenides. In conclusion, the use of a high throughput screening, in silico data handling and zebrafish testing may constitute a paradigm for rapid and integrated ENM toxicological screening.

A compartmental multimedia model was developed to enable evaluation of the dynamic environmental multimedia mass distribution and concentrations of engineered nanomaterials (ENMs). The approach considers the environment as a collection of compartments, linked via fundamental environmental intermedia transport processes. Model simulations for various environmental scenarios indicated that ENM accumulation in the sediment increased significantly with increased ENMs attachment to suspended solids in water. Atmospheric dry and wet depositions can be important pathways for ENMs input to the terrestrial environment in the absence of direct and distributed ENM release to soil. Increased ENM concentration in water due to atmospheric deposition (wet and dry) is expected as direct ENM release to water diminishes. However, for soluble ENMs dissolution can be the dominant pathway for suspended ENM removal from water even compared to advection. Mass accumulation in the multimedia environment for the evaluated ENMs (metal, metal oxides, carbon nanotubes (CNT), nanoclays) was mostly in the soil and sediment. The present modeling approach, as illustrated via different test cases, is suited for "what if" first tier analyses to assess the multimedia mass distribution of ENMs and associated potential exposure concentrations.