A novel route for catalytic activation of peroxymonosulfate by oxygen vacancies improved bismuth-doped titania for the removal of recalcitrant organic contaminant

In this work, bismuth-doped titania (BixTiO2) with improved oxygen vacancies was synthesized by sol-gel protocol as a novel peroxymonosulfate (PMS, HSO5−) activator. HSO5− and adsorbed oxygen molecules could efficiently be transformed into their respective radicals through defect ionization to attain charge balance after their trapping on oxygen vacancies of the catalyst. XRD study of BixTiO2 with 5 wt% Bi (5BiT) revealed anatase, crystalline nature, and successful doping of Bi into TiO2 crystal lattice. The particle size obtained from BET data and SEM observations was in good agreement. PL spectra showed the formation rates of •OH by 3BiT, 7BiT, 5BiTC, and 5BiT as 0.720, 1.200, 1.489, and 2.153 μmol/h, respectively. 5BiT catalyst with high surface area (216.87 m2 g−1) and high porosity (29.81%) was observed the excellent HSO5− activator. The catalytic performance of 0BiT, 3BiT, 5BiT, and 7BiT when coupled with 2 mM HSO5− for recalcitrant flumequine (FLU) removal under dark was 10, 27, 55, and 37%, respectively. Only 5.4% decrease in catalytic efficiency was observed at the end of seventh cyclic run. Radical scavenging studies indicate that SO4•− is the dominant species that caused 62.0% degradation. Moreover, strong interaction between Bi and TiO2 through Bi-O-Ti bonds prevents Bi leaching (0.081 mg L−1) as shown by AAS. The kinetics, degradation pathways, ecotoxicity, and catalytic mechanism for recalcitrant FLU were also elucidated. Cost-efficient, environment-friendly, and high mineralization recommends this design strategy; BixTiO2/HSO5− system is a promising advanced oxidation process for the aquatic environment remediation

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

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.Toxicolog

Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques

An increasing number of manufactured nanomaterials (NMs) are being used in industrial products and need to be registered under the REACH legislation. The hazard characterisation of all these forms is not only technically challenging but resource and time demanding. The use of non-testing strategies like read-across is deemed essential to assure the assessment of all NMs in due time and at lower cost. The fact that read-across is based on the structural similarity of substances represents an additional difficulty for NMs as in general their structure is not unequivocally defined. In such a scenario, the identification of physicochemical properties affecting the hazard potential of NMs is crucial to define a grouping hypothesis and predict the toxicological hazards of similar NMs. In order to promote the read-across of NMs, ECHA has recently published “Recommendations for nanomaterials applicable to the guidance on QSARs and Grouping”, but no practical examples were provided in the document. Due to the lack of publicly available data and the inherent difficulties of reading-across NMs, only a few examples of read-across of NMs can be found in the literature. This manuscript presents the first case study of the practical process of grouping and read-across of NMs following the workflow proposed by ECHA. The workflow proposed by ECHA was used and slightly modified to present the read-across case study. The Read-Across Assessment Framework (RAAF) was used to evaluate the uncertainties of a read-across within NMs. Chemoinformatic techniques were used to support the grouping hypothesis and identify key physicochemical properties. A dataset of 6 nanoforms of TiO2 with more than 100 physicochemical properties each was collected. In vitro comet assay result was selected as the endpoint to read-across due to data availability. A correlation between the presence of coating or large amounts of impurities and negative comet assay results was observed. The workflow proposed by ECHA to read-across NMs was applied successfully. Chemoinformatic techniques were shown to provide key evidence for the assessment of the grouping hypothesis and the definition of similar NMs. The RAAF was found to be applicable to NMs

Assessment of <i>in silico</i> models for acute aquatic toxicity towards fish under REACH regulation

<div><p>We evaluated the performance of eight QSAR <i>in silico</i> modelling packages (ACD/ToxSuite™, ADMET Predictor™, DEMETRA, ECOSAR, TerraQSAR™, Toxicity Estimation Software Tool, TOPKAT™ and VEGA) for acute aquatic toxicity towards two species of fish: Fathead Minnow and Rainbow Trout. For the Fathead Minnow, we compared model predictions for 567 substances with the corresponding experimental values for 96-h median lethal concentrations (LC50). Some models gave good results, with <i>r</i>² up to 0.85. We also classified the predictions of all the models into four toxicity classes defined by CLP. This permitted us to assess other parameters, such as the percentage of correct predictions for each class. Then we used a set of 351 substances with toxicity data towards Rainbow Trout (96-h LC50). In this case the predictability was unacceptable for all the <i>in silico</i> models. The calculated <i>r</i>² gave poor correlations (≤0.53). Another analysis was performed according to chemical classes and for mode of action. In the first case, all the classes show a high percentage of correct predictions, in the second case only narcotics and polar narcotics were predicted with good confidence. The results indicate the possibility of using <i>in silico</i> methods to estimate aquatic toxicity within REACH regulation, after careful evaluation.</p></div

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

Developing Data Collections for (Q)SAR Modelling of Nanomaterials

<p>This is a poster delivered at the 16th International Workshop on Quantitative Structure Activity Relationships in Environmental and Health Sciences (QSAR2014), 16-20th June 2014, Milan, Italy: <a href="http://qsar2014.insilico.eu/">http://qsar2014.insilico.eu/</a></p> <p>Disclaimers:</p> <p>(1) this presentation has not undergone peer review</p> <p>(2) this presentation may report preliminary results which may have been revised in subsequent publications</p> <p>(3) no endorsement by third parties should be inferred</p> <p>Presentation abstract:</p><p> There are an increasing number of (Q)SAR models to predict the toxicity and properties of nanomaterials [1]. Indeed, in light of perceived uncertainties regarding their potential health and environmental effects, as well as the drive towards reduced use of animals for toxicity testing, the European Commission has funded a number of projects looking at computational prediction of nanomaterial toxicity. The NanoPUZZLES (www.nanopuzzles.eu) and NanoBRIDGES (www.nanobridges.eu) projects are two such activities charged with developing grouping, read-across and (Q)SAR approaches for modelling of nanomaterial toxicity. These approaches require adequate quantities of high quality toxicological and physicochemical data on well-characterised nanomaterials, which are being collected in both projects. These data need to be available within an electronic database in a consistent and interoperable manner to best support modelling. The NanoPUZZLES project is organising collected data in a suitable electronic format that will be made available to modellers via a publicly accessible database in accordance with the previously noted requirements. Specifically, data are being curated from public domain sources and organised using data collection templates based upon a proposal for a global data exchange standard: ISA-TAB-Nano [2,3]. In order to facilitate their use for modelling and, in particular, their integration with other datasets for future modelling efforts, it is essential that the data are recorded in a standardised fashion. To achieve this objective, the data collection templates being employed record (meta)data using terms linked to ontologies wherever possible. These ontology terms are being retrieved via the BioPortal online resource [4]. Moreover, it is important that modellers are able to assess the quality of (subsets of) the available data. To facilitate this, proposals for assigning data quality scores are being developed. Finally, an overview of the potential usefulness for modelling of some public domain sources, for selected endpoints, will be presented based upon a recent survey of the scientific literature.</p><p> <br></p><p> The research leading to these results has received funding from the European Union Seventh Framework Programme [FP7/2007-2013] under grant agreement n° 309837 (NanoPUZZLES project) and from the NanoBRIDGES project (FP7-PEOPLE-2011-IRSES, Grant Agreement no. 295128).</p><ol><li><p>Winkler, D.A..; Mombelli, E.; Pietroiusti, A.; Tran, L.; Worth, A.; Fadeel, B.; McCall, M.J. <i>Toxicology</i>, <i>313</i>, <b>2013</b>, 15-23.</p></li><li><p>Thomas, D.G.; Gaheen, S.; Harper, S.L.; Fritts, M.; Klaessig, F.; Hahn-Dantona, E.; Paik, D.; Pan, S.; Staffiord, G.A.; Freund, E.T.; Klemm, J.D.; Baker, N.A. <i>BMC Biotechnol.</i>, <i>13</i>, <b>2013</b>, 2.</p></li><li><p>https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano [last accessed 9th of April 2014]</p></li><li><p>Whetzel, P.L.; Noy, N.F.; Shah, N.H.; Alexander, P.R.; Nyulas, C.; Tudorache, T.; Musen, M.A. <i>Nucleic Acids Res.</i>, <i>39</i>, <b>2011</b>, W541-W545.</p></li></ol><p> <br></p