Meike van der Zande

Distribution, elimination, and toxicity of silver nanoparticles and silver ions in rats after 28-day oral exposure.

We report the results of a 28-day oral exposure study in rats, exposed to <20 nm noncoated, or <15 nm PVP-coated silver nanoparticles ([Ag] = 90 mg/kg body weight (bw)), or AgNO(3) ([Ag] = 9 mg/kg bw), or carrier solution only. Dissection was performed at day 29, and after a wash-out period of 1 or 8 weeks. Silver was present in all examined organs with the highest levels in the liver and spleen for all silver treatments. Silver concentrations in the organs were highly correlated to the amount of Ag(+) in the silver nanoparticle suspension, indicating that mainly Ag(+), and to a much lesser extent silver nanoparticles, passed the intestines in the silver nanoparticle exposed rats. In all groups silver was cleared from most organs after 8 weeks postdosing, but remarkably not from the brain and testis. Using single particle inductively coupled plasma mass spectrometry, silver nanoparticles were detected in silver nanoparticle exposed rats, but, remarkably also in AgNO(3) exposed rats, hereby demonstrating the formation of nanoparticles from Ag(+)in vivo that are probably composed of silver salts. Biochemical markers and antibody levels in blood, lymphocyte proliferation and cytokine release, and NK-cell activity did not reveal hepatotoxicity or immunotoxicity of the silver exposure. In conclusion, oral exposure to silver nanoparticles appears to be very similar to exposure to silver salts. However, the consequences of in vivo formation of silver nanoparticles, and of the long retention of silver in brain and testis should be considered in a risk assessment of silver nanoparticles.

Progress and future of in vitro models to study translocation of nanoparticles

AbstractThe increasing use of nanoparticles in products likely results in increased exposure of both workers and consumers. Because of their small size, there are concerns that nanoparticles unintentionally cross the barriers of the human body. Several in vivo rodent studies show that, dependent on the exposure route, time, and concentration, and their characteristics, nanoparticles can cross the lung, gut, skin, and placental barrier. This review aims to evaluate the performance of in vitro models that mimic the barriers of the human body, with a focus on the lung, gut, skin, and placental barrier. For these barriers, in vitro models of varying complexity are available, ranging from single-cell-type monolayer to multi-cell (3D) models. Only a few studies are available that allow comparison of the in vitro translocation to in vivo data. This situation could change since the availability of analytical detection techniques is no longer a limiting factor for this comparison. We conclude that to further develop in vitro models to be used in risk assessment, the current strategy to improve the models to more closely mimic the human situation by using co-cultures of different cell types and microfluidic approaches to better control the tissue microenvironments are essential. At the current state of the art, the in vitro models do not yet allow prediction of absolute transfer rates but they do support the definition of relative transfer rates and can thus help to reduce animal testing by setting priorities for subsequent in vivo testing.

Translocation of differently sized and charged polystyrene nanoparticles in in vitro intestinal cell models of increasing complexity.

Abstract Intestinal translocation is a key factor for determining bioavailability of nanoparticles (NPs) after oral uptake. Therefore, we evaluated three in vitro intestinal cell models of increasing complexity which might affect the translocation of NPs: a mono-culture (Caco-2 cells), a co-culture with mucus secreting HT29-MTX cells and a tri-culture with M-cells. Cell models were exposed to well characterized differently sized (50 and 100 nm) and charged (neutral, positively and negatively) polystyrene NPs. In addition, two types of negatively charged NPs with different surface chemistries were used. Size strongly affected the translocation of NPs, ranging up to 7.8% for the 50 nm NPs and 0.8% for the 100 nm NPs. Surface charge of NPs affected the translocation, however, surface chemistry seems more important, as the two types of negatively charged 50 nm NPs had an over 30-fold difference in translocation. Compared with the Caco-2 mono-culture, presence of mucus significantly reduced the translocation of neutral 50 nm NPs, but significantly increased the translocation of one type of negatively charged NPs. Incorporation of M-cells shifted the translocation rates for both NPs closer to those in the mono-culture model. The relative pattern of NP translocation in all three models was similar, but the absolute amounts of translocated NPs differed per model. We conclude that for comparing the relative translocation of different NPs, using one intestinal model is sufficient. To choose the most representative model for risk assessment, in vivo experiments are now needed to determine the in vivo translocation rates of the used NPs.

Different responses of Caco-2 and MCF-7 cells to silver nanoparticles are based on highly similar mechanisms of action

Abstract The mode of action of silver nanoparticles (AgNPs) is suggested to be exerted through both Ag+ and AgNP dependent mechanisms. Ingestion is one of the major NP exposure routes, and potential effects are often studied using Caco-2 cells, a well-established model for the gut epithelium. MCF-7 cells are epithelial breast cancer cells with extensive well-characterized toxicogenomics profiles. In the present study, we aimed to gain a deeper understanding of the cellular molecular responses in Caco-2 and MCF-7 cells after AgNP exposure in order to evaluate whether epithelial cells derived from different tissues demonstrated similar responses. These insights could possibly reduce the size of cell panels for NP hazard identification screening purposes. AgNPs of 20, 30, 60, and 110 nm, and AgNO3 were exposed for 6 h and 24 h. AgNPs were shown to be taken up and dissolve intracellularly. Compared with MCF-7 cells, Caco-2 cells showed a higher sensitivity to AgNPs, slower gene expression kinetics and absence of NP size-dependent responses. However, on a molecular level, no significant differences were observed between the two cell types. Transcriptomic analysis showed that Ag(NP) exposure caused (oxidative) stress responses, possibly leading to cell death in both cell lines. There was no indication for effects specifically induced by AgNPs. Responses to AgNPs appeared to be induced by silver ions released from the AgNPs. In conclusion, differences in mRNA responses to AgNPs between Caco-2 and MCF-7 cells were mainly related to timing and magnitude, but not to a different underlying mechanism.

Effects of food-borne nanomaterials on gastrointestinal tissues and microbiota

Ingestion of engineered nanomaterials is inevitable due to their addition to food and prevalence in food packaging and domestic products such as toothpaste and sun cream. In the absence of robust dosimetry and particokinetic data, it is currently challenging to accurately assess the potential toxicity of food‐borne nanomaterials. Herein, we review current understanding of gastrointestinal uptake mechanisms, consider some data on the potential for toxicity of the most commonly encountered classes of food‐borne nanomaterials (including TiO2, SiO2 , ZnO, and Ag nanoparticles), and discuss the potential impact of the luminal environment on nanoparticle properties and toxicity. Much of our current understanding of gastrointestinal nanotoxicology is derived from increasingly sophisticated epithelial models that augment in vivo studies. In addition to considering the direct effects of food‐borne nanomaterials on gastrointestinal tissues, including the potential role of chronic nanoparticle exposure in development of inflammatory diseases, we also discuss the potential for food‐borne nanomaterials to disturb the normal balance of microbiota within the gastrointestinal tract. The latter possibility warrants close attention given the increasing awareness of the critical role of microbiota in human health and the known impact of some food‐borne nanomaterials on bacterial viability. WIREs Nanomed Nanobiotechnol 2018, 10:e1481. doi: 10.1002/wnan.1481 This article is categorized under: 1 Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Abstract In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme

Abstract To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before.