Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms

Nanotechnology has rapidly entered into human society, revolutionized many areas, including technology, medicine and cosmetics. This progress is due to the many valuable and unique properties that nanomaterials possess. In turn, these properties might become an issue of concern when considering potentially uncontrolled release to the environment. The rapid development of new nanomaterials thus raises questions about their impact on the environment and human health. This review focuses on the potential of nanomaterials to cause genotoxicity and summarizes recent genotoxicity studies on metal oxide/silica nanomaterials. Though the number of genotoxicity studies on metal oxide/silica nanomaterials is still limited, this endpoint has recently received more attention for nanomaterials, and the number of related publications has increased. An analysis of these peer reviewed publications over nearly two decades shows that the test most employed to evaluate the genotoxicity of these nanomaterials is the comet assay, followed by micronucleus, Ames and chromosome aberration tests. Based on the data studied, we concluded that in the majority of the publications analysed in this review, the metal oxide (or silica) nanoparticles of the same core chemical composition did not show different genotoxicity study calls (i.e. positive or negative) in the same test, although some results are inconsistent and need to be confirmed by additional experiments. Where the results are conflicting, it may be due to the following reasons: (1) variation in size of the nanoparticles; (2) variations in size distribution; (3) various purities of nanomaterials; (4) variation in surface areas for nanomaterials with the same average size; (5) differences in coatings; (6) differences in crystal structures of the same types of nanomaterials; (7) differences in size of aggregates in solution/media; (8) differences in assays; (9) different concentrations of nanomaterials in assay tests. Indeed, due to the observed inconsistencies in the recent literature and the lack of adherence to appropriate, standardized test methods, reliable genotoxicity assessment of nanomaterials is still challenging

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals

High throughput modeling of the effects of mixtures of ToxCast chemicals on steroid hormone cycles in women

Exposure to mixtures of chemicals is an increasing toxicological concern. The availability of exposure data for thousands of chemicals through ExpoCast project, together with the ToxCast results for the hundreds of high throughput in vitro assays, offers the opportunity to explore the toxicity of the chemical mixtures in realistic scenarios. We used computer modeling to predict the size of potential effects of random mixtures of aromatase inhibitors on women's menstrual cycle. We had previously investigated the impact of mixtures on steroidogenesis by a systems biology model for aromatase inhibition in adult female rats. In current work, to consider a larger number of events involved to hormonal balance disruption, we adapted a mathematical model of the hypothalamus-pituitary-ovarian control of estradiol and progesterone concentrations in blood. We used the model (coupled to a pharmacokinetic model of intake and disposition) to predict the effects of a million of chemical mixtures sampled by Monte Carlo simulations. To simulate a realistic exposure scenario, the exposures were also sampled from statistical distributions provided by the ExpoCast database (see illustrated work-flow). We find that a sizable fraction of the mixtures generated led to more than 20% inhibition of estradiol production. In contrast, exposures to chemicals considered individually almost never reach such effect sizes. Those results are discussed in light of the approximations and assumptions made, but demonstrate the possibility to address large scale mixture questions in a predictive toxicology framework, suitable for high throughput risk assessment of endocrine perturbation

Classification nano-SAR modeling of metal oxides nanoparticles genotoxicity based on comet assay data

In nearly a decade of vigorous attempt in the toxicology and exposure research carried out to provide evidence for the assessment of health and environmental risks of nanomaterials (NMs), some progress has been made in generating the health effects and exposure data needed to perform risk assessment and develop risk management guidance. Quantitative Structure Activity Relationship ((Q)SAR) models are a powerful tool for rapid screening of large numbers and types of materials with advantage of saving time, funds and animal suffering. In this work we present first (Q)SAR models developed to predict genotoxicity of metal oxide NMs by using large initial sets of nano descriptors. We used a dataset containing in vitro comet assay genotoxicity for 16 nano metal oxides with different chemical core composition. This multi-source data was retrieved from genotoxicity profiles collected in our previous work. To properly analyse the data, we used a weight of evidence approach for evaluation of quality of the comet in vitro data for (Q)SAR modelling. Subsequently, based on the quality of checked dataset, we assigned genotoxic or non-genotoxic property to each metal core composition. By employing orthogonal partial least squares–discriminant analysis (OPLS-DA) method, nano-(Q)SAR models were derived with significant predictive power: accuracy 0.83 and 1. Conventional molecular descriptors and quantum chemical descriptors together with descriptors based on metal-ligand binding properties have been analysed to discuss the key factors affecting genotoxicity of metal oxide NMs. All derived models involve descriptors that describe possible structural factors influencing genotoxic behaviour of metal oxide NMs

Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties

<p>The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials’ data is resulting from <i>in vitro</i> comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure–activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.</p

High-throughput analysis of ovarian cycle disruption by mixtures of aromatase inhibitors

BACKGROUND: Combining computational toxicology with ExpoCast exposure estimates and ToxCast (TM) assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures. OBJECTIVES: We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles. METHODS: We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast (TM) aromatase assay provided concentration inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus pituitary-ovarian control of ovulation in women. RESULTS: Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, similar to 10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%. CONCLUSIONS: These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action

International audienceRead-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE lipid accumulation. KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed