Evaluation of Toxicity Ranking for Metal Oxide Nanoparticles via an in Vitro Dosimetry Model.

It has been argued that in vitro toxicity testing of engineered nanoparticles (NPs) should consider delivered dose (i.e., NP mass settled per suspension volume) rather than relying exclusively on administered dose (initial NP mass concentration). Delivered dose calculations require quantification of NP sedimentation in tissue cell culture media, taking into consideration fundamental suspension properties. In this article, we calculate delivered dose using a first-principles "particles in a box" sedimentation model, which accounts for the particle size distribution, fractal dimension, and permeability of agglomerated NPs. The sedimentation model was evaluated against external and our own experimental sedimentation data for metal oxide NPs. We then utilized the model to construct delivered dose-response analysis for a library of metal oxide NPs (previously used for hazard ranking and prediction making) in different cell culture media. Hierarchical hazard ranking of the seven (out of 24) toxic metal oxide NPs in our library, using EC50 calculated on the basis of delivered dose, did not measurably differ from our ranking based on administered dose. In contrast, simplified sedimentation calculations based on the assumption of impermeable NP agglomerates of a single average size significantly underestimated the settled NPs' mass, resulting in misinterpretation of toxicity ranking. It is acknowledged that in vitro dose-response outcomes are likely to be shaped by complex toxicodynamics, which include NP/cellular association, triggering of dynamic cell response pathways involved in NP uptake, and multiple physicochemical parameters that influence NP sedimentation and internalization.

Simulation tool for assessing the release and environmental distribution of nanomaterials.

An integrated simulation tool was developed for assessing the potential release and environmental distribution of nanomaterials (RedNano) based on a life cycle assessment approach and multimedia compartmental modeling coupled with mechanistic intermedia transport processes. The RedNano simulation tool and its web-based software implementation enables rapid "what-if?" scenario analysis, in order to assess the response of an environmental system to various release scenarios of engineered nanomaterials (ENMs). It also allows for the investigation of the impact of geographical and meteorological parameters on ENM distribution in the environment, comparison of the impact of ENM production and potential releases on different regions, and estimation of source release rates based on monitored ENM concentrations. Moreover, the RedNano simulation tool is suitable for research, academic, and regulatory purposes. Specifically, it has been used in environmental multimedia impact assessment courses at both the undergraduate and graduate levels. The RedNano simulation tool can also serve as a decision support tool to rapidly and critically assess the potential environmental implications of ENMs and thus ensure that nanotechnology is developed in a productive and environmentally responsible manner.

Effect of hydration repulsion on nanoparticle agglomeration evaluated via a constant number Monte-Carlo simulation.

The effect of hydration repulsion on the agglomeration of nanoparticles in aqueous suspensions was investigated via the description of agglomeration by the Smoluchowski coagulation equation using constant number Monte-Carlo simulation making use of the classical DLVO theory extended to include the hydration repulsion energy. Evaluation of experimental DLS measurements for TiO2, CeO2, SiO2, and α-Fe2O3 (hematite) at high IS (up to 900 mM) or low |ζ-potential| (≥1.35 mV) demonstrated that hydration repulsion energy can be above electrostatic repulsion energy such that the increased overall repulsion energy can significantly lower the agglomerate diameter relative to the classical DLVO prediction. While the classical DLVO theory, which is reasonably applicable for agglomeration of NPs of high |ζ-potential| (∼>35 mV) in suspensions of low IS (∼<1 mM), it can overpredict agglomerate sizes by up to a factor of 5 at high IS or low |ζ-potential|. Given the potential important role of hydration repulsion over a range of relevant conditions, there is merit in quantifying this repulsion energy over a wide range of conditions as part of overall characterization of NP suspensions. Such information would be of relevance to improved understanding of NP agglomeration in aqueous suspensions and its correlation with NP physicochemical and solution properties.

Multimedia environmental distribution of engineered nanomaterials.

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.

In silico analysis of nanomaterials hazard and risk.

Because a variety of human-related activities, engineer-ed nanoparticles (ENMs) may be released to various environmental media and may cross environmental boundaries, and thus will be found in most media. Therefore, the potential environmental impacts of ENMs must be assessed from a multimedia perspective and with an integrated risk management approach that considers rapid developments and increasing use of new nanomaterials. Accordingly, this Account presents a rational process for the integration of in silico ENM toxicity and fate and transport analyses for environmental impact assessment. This approach requires knowledge of ENM toxicity and environmental exposure concentrations. Considering the large number of current different types of ENMs and that those numbers are likely to increase, there is an urgent need to accelerate the evaluation of their toxicity and the assessment of their potential distribution in the environment. Developments in high throughput screening (HTS) are now enabling the rapid generation of large data sets for ENM toxicity assessment. However, these analyses require the establishment of reliable toxicity metrics, especially when HTS includes data from multiple assays, cell lines, or organisms. Establishing toxicity metrics with HTS data requires advanced data processing techniques in order to clearly identify significant biological effects associated with exposure to ENMs. HTS data can form the basis for developing and validating in silico toxicity models (e.g., quantitative structure-activity relationships) and for generating data-driven hypotheses to aid in establishing and/or validating possible toxicity mechanisms. To correlate the toxicity of ENMs with their physicochemical properties, researchers will need to develop quantitative structure-activity relationships for nanomaterials (i.e., nano-SARs). However, as nano-SARs are applied in regulatory applications, researchers must consider their applicability and the acceptance level of false positive relative to false negative predictions and the reliability of toxicity data. To establish the environmental impact of ENMs identified as toxic, researchers will need to estimate the potential level of environmental exposure concentration of ENMs in the various media such as air, water, soil, and vegetation. When environmental monitoring data are not available, models of ENMs fate and transport (at various levels of complexity) serve as alternative approaches for estimating exposure concentrations. Risk management decisions regarding the manufacturing, use, and environmental regulations of ENMs would clearly benefit from both the assessment of potential ENMs exposure concentrations and suitable toxicity metrics. The decision process should consider the totality of available information: quantitative and qualitative data and the analysis of nanomaterials toxicity, and fate and transport behavior in the environment. Effective decision-making to address the potential impacts of nanomaterials will require considerations of the relevant environmental, ecological, technological, economic, and sociopolitical factors affecting the complete lifecycle of nanomaterials, while accounting for data and modeling uncertainties. Accordingly, researchers will need to establish standardized data management and analysis tools through nanoinformatics as a basis for the development of rational decision tools.

Analysis of nanoparticle agglomeration in aqueous suspensions via constant-number Monte Carlo simulation.

A constant-number direct simulation Monte Carlo (DSMC) model was developed for the analysis of nanoparticle (NP) agglomeration in aqueous suspensions. The modeling approach, based on the "particles in a box" simulation method, considered both particle agglomeration and gravitational settling. Particle-particle agglomeration probability was determined based on the classical Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and considerations of the collision frequency as impacted by Brownian motion. Model predictions were in reasonable agreement with respect to the particle size distribution and average agglomerate size when compared with dynamic light scattering (DLS) measurements for aqueous TiO(2), CeO(2), and C(60) nanoparticle suspensions over a wide range of pH (3-10) and ionic strength (0.01-156 mM). Simulations also demonstrated, in quantitative agreement with DLS measurements, that nanoparticle agglomerate size increased both with ionic strength and as the solution pH approached the isoelectric point (IEP). The present work suggests that the DSMC modeling approach, along with future use of an extended DLVO theory, has the potential for becoming a practical environmental analysis tool for predicting the agglomeration behavior of aqueous nanoparticle suspensions.