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Recent studies of unconventional resource development (URD) and adverse health effects have been limited by distance-based exposure surrogates. Our study compared exposure classifications between air pollutant concentrations and &ldquo, well activity&rdquo, (WA) metrics, which are distance-based exposure proxies used in Marcellus-area studies to reflect variation in time and space of residential URD activity. We compiled Pennsylvania air monitoring data for benzene, carbon monoxide, nitrogen dioxide, ozone, fine particulates and sulfur dioxide, and combined this with data on nearly 9000 Pennsylvania wells. We replicated WA calculations using geo-coordinates of monitors to represent residences and compared exposure categories from air measurements and WA at the site of each monitor. There was little agreement between the two methods for the pollutants included in the analysis, with most weighted kappa coefficients between &minus, 0.1 and 0.1. The exposure categories agreed for about 25% of the observations and assigned inverse categories 16%&ndash, 29% of the time, depending on the pollutant. Our results indicate that WA measures did not adequately distinguish categories of air pollutant exposures and employing them in epidemiology studies can result in misclassification of exposure. This underscores the need for more robust exposure assessment in future analyses and cautious interpretation of these existing studies.

Silver nanoparticles are advertised as antimicrobial agents in a wide range of products. The majority of available studies suggest that silver nanoparticle toxicity is mainly caused by silver ions released from the particles. However, it remains challenging to distinguish between the effect of silver nanoparticles and silver ions. Here we used a combination of a short-term in vivo study in rats and an in silico-based toxicokinetic model to determine tissue distribution of administered ionic and nanoparticulate silver, and to estimate mixture ratios of the different silver species, namely primary nanoparticles, ions and secondary particles. Our data indicate that silver nanoparticles and silver ions are not or only marginally bioavailable after oral ingestion of a single, non-toxic dose. Experimental data on organ distribution after intravenous injection were accurately reflected by the predictions of the in silico model. Toxicokinetic modeling suggests systemic distribution of a major proportion of the injected ionic silver as de novo formed secondary nanoparticles, and the presence of such particles was proven by electron microscopy. The observation that silver ions form secondary particles, underlines the difficulties in distinguishing between particle- and ion-dependent effects of silver nanoparticles.

Nano-sized titanium dioxide particles (nano-TiO2) can be found in a large number of foods and consumer products, such as cosmetics and toothpaste, thus, consumer exposure occurs via multiple sources, possibly involving different exposure routes. In order to determine the disposition of nano-TiO2 particles that are taken up, a physiologically based pharmacokinetic (PBPK) model was developed. High priority was placed on limiting the number of parameters to match the number of underlying data points (hence to avoid overparameterization), but still reflecting available mechanistic information on the toxicokinetics of nano-TiO2. To this end, the biodistribution of nano-TiO2 was modeled based on their ability to cross the capillary wall of the organs and to be phagocytosed in the mononuclear phagocyte system (MPS). The model’s predictive power was evaluated by comparing simulated organ levels to experimentally assessed organ levels of independent in vivo studies. The results of our PBPK model indicate that: (1) within the application domain of the PBPK model from 15 to 150 nm, the size and crystalline structure of the particles had a minor influence on the biodistribution; and (2) at high internal exposure the particles agglomerate in vivo and are subsequently taken up by macrophages in the MPS. Furthermore, we also give an example on how the PBPK model may be used for risk assessment. For this purpose, the daily dietary intake of nano-TiO2 was calculated for the German population. The PBPK model was then used to convert this chronic external exposure into internal titanium levels for each organ.

Silver is a strong antibiotic that is increasingly incorporated into consumer products as a bulk, salt, or nanosilver, thus potentially causing side-effects related to human exposure. However, the fate and behavior of (nano)silver in the human body is presently not well understood. In order to aggregate the existing experimental information, a physiologically based pharmacokinetic model (PBPK) was developed in this study for ionic silver and nanosilver. The structure of the model was established on the basis of toxicokinetic data from intravenous studies. The number of calibrated parameters was minimized in order to enhance the predictive capability of the model. We validated the model structure for both silver forms by reproducing exposure conditions (dermal, oral, and inhalation) of in vivo experiments and comparing simulated and experimentally assessed organ concentrations. Therefore, the percutaneous, intestinal, or pulmonary absorption fraction was estimated based on the blood silver concentration of the respective experimental data set. In all of the cases examined, the model could successfully predict the biodistribution of ionic silver and 15–150 nm silver nanoparticles, which were not coated with substances designed to prolong the circulatory time (eg, polyethylene glycol). Furthermore, the results of our model indicate that: (1) within the application domain of our model, the particle size and coating had a minor influence on the biodistribution; (2) in vivo, it is more likely that silver nanoparticles are directly stored as insoluble salt particles than dissolve into Ag+; and (3) compartments of the mononuclear phagocytic system play a minor role in exposure levels that are relevant for human consumers. We also give an example of how the model can be used in exposure and risk assessments based on five different exposure scenarios, namely dietary intake, use of three separate consumer products, and occupational exposure., International Journal of Nanomedicine, 8 (1), ISSN:1176-9114, ISSN:1178-2013