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Background The lung epithelial tissue barrier represents the main portal for entry of inhaled nanoparticles (NPs) into the systemic circulation. Thus great efforts are currently being made to determine adverse health effects associated with inhalation of NPs. However, to date very little is known about factors that determine the pulmonary translocation of NPs and their subsequent distribution to secondary organs. Methods A novel two-step approach to assess the biokinetics of inhaled NPs is presented. In a first step, alveolar epithelial cellular monolayers (CMLs) at the air-liquid interface (ALI) were exposed to aerosolized NPs to determine their translocation kinetics across the epithelial tissue barrier. Then, in a second step, the distribution to secondary organs was predicted with a physiologically based pharmacokinetic (PBPK) model. Monodisperse, spherical, well-characterized, negatively charged gold nanoparticles (AuNP) were used as model NPs. Furthermore, to obtain a comprehensive picture of the translocation kinetics in different species, human (A549) and mouse (MLE-12) alveolar epithelial CMLs were exposed to ionic gold and to various doses ( i.e., 25, 50, 100, 150, 200 ng/cm 2 ) and sizes ( i.e., 2, 7, 18, 46, 80 nm) of AuNP, and incubated post-exposure for different time periods ( i.e., 0, 2, 8, 24, 48, 72 h). Results The translocation kinetics of the AuNP across A549 and MLE-12 CMLs was similar. The translocated fraction was (1) inversely proportional to the particle size, and (2) independent of the applied dose (up to 100 ng/cm 2 ). Furthermore, supplementing the A549 CML with two immune cells, i.e., macrophages and dendritic cells, did not significantly change the amount of translocated AuNP. Comparison of the measured translocation kinetics and modeled biodistribution with in vivo data from literature showed that the combination of in vitro and in silico methods can accurately predict the in vivo biokinetics of inhaled/instilled AuNP. Conclusion Our approach to combine in vitro and in silico methods for assessing the pulmonary translocation and biodistribution of NPs has the potential to replace short-term animal studies which aim to assess the pulmonary absorption and biodistribution of NPs, and to serve as a screening tool to identify NPs of special concern.

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.

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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 “well activity” (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 −0.1 and 0.1. The exposure categories agreed for about 25% of the observations and assigned inverse categories 16%–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.

Since WA values are not estimates of ambient pollutant concentrations, this would not have been an appropriate analysis, and we were careful to point out this distinction. ” “Previous study has found that oil and gas preproduction produces ambient air pollutants, including fine particulate matter, nitrous oxides, volatile organic compounds, ozone, carbon monoxide, and hydrogen sulfide.” “The etiology of preterm birth is suspected to include dysregulated inflammation, which may be a response to infection or oxidative stress associated with air pollution, including particulates and nitrous oxides [6].” Because these and other authors imply that these pollutants are part of the biological pathway connecting URD and reported health effects, it was relevant to test these assumptions in our analysis.