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

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough exposure risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models”, with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles

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

A solid-phase extraction procedure coupled to 1H NMR, with chemometric analysis, to seek reliable markers of the botanical origin of honey

The aim of this work was to establish an analytical method for identifying the botanical origin of honey, as an alternative to conventional melissopalynological, organoleptic and instrumental methods (gas-chromatography coupled to mass spectrometry (GC-MS), high-performance liquid chromatography HPLC). The procedure is based on the 1H nuclear magnetic resonance (NMR) profile coupled, when necessary, with electrospray ionisation-mass spectrometry (ESI-MS) and two-dimensional NMR analyses of solid-phase extraction (SPE)-purified honey samples, followed by chemometric analyses. Extracts of 44 commercial Italian honeys from 20 different botanical sources were analyzed. Honeydew, chestnut and linden honeys showed constant, specific, well-resolved resonances, suitable for use as markers of origin. Honeydew honey contained the typical resonances of an aliphatic component, very likely deriving from the plant phloem sap or excreted into it by sap-sucking aphids. Chestnut honey contained the typical signals of kynurenic acid and some structurally related metabolite. In linden honey the 1H NMR profile gave strong signals attributable to the mono-terpene derivative cyclohexa-1,3-diene-1-carboxylic acid (CDCA) and to its 1-O-\u3b2-gentiobiosyl ester (CDCA-GBE). These markers were not detectable in the other honeys, except for the less common nectar honey from rosa mosqueta. We compared and analyzed the data by multivariate techniques. Principal component analysis found different clusters of honeys based on the presence of these specific markers. The results, although obviously only preliminary, suggest that the 1H NMR profile (with HPLC-MS analysis when necessary) can be used as a reference framework for identifying the botanical origin of honey

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

Collection of toxicity, physicochemical and characterisation data to enable modelling of nanomaterial effects

This is a poster presentation delivered at the Nanosafety 2013 conference, November 2013, Saarbruecken, Germany: http://nanosafety.inm-gmbh.de/<br><br>Disclaimers:<br><br>(1) this presentation has not undergone peer review<br><br>(2) this presentation may report preliminary results which may have been revised in subsequent publications<br><br>(3) no endorsement by third parties should be inferred<br><br>Presentation abstract:<br> <p>A number of EU projects have been established to address concerns about the potential health risks posed by nanomaterials. The NanoPUZZLES project is developing new computational methods for predicting the toxicity of nanomaterials based on Quantitative Structure-Activity Relationships (QSARs), chemical category formation and read-across approaches. Successful application of these approaches requires sufficient quantities of high quality toxicological and physicochemical data on well-characterised nanomaterials to be organised self-consistently within an electronic database. NanoPUZZLES is contributing to the development of such a database based on data curated from public domain sources.</p> <p>Initial data collection efforts within NanoPUZZLES yielded a significant number of data points from various peer-reviewed publications. By extending the Klimisch criteria for toxicological data quality assessment, criteria for assessing the quality of data reported for nanomaterials, as well as the suitability of datasets for building QSARs, were developed. However, organising nanomaterial data remains a challenge. The current focus of data collection efforts within NanoPUZZLES is the exploration and evaluation of standards for organising experimental data for nanomaterials: the recently published ISA-Tab-Nano file format is of particular interest. The need for a unique identifier for nanomaterials and minimum information standards for a nanomaterials database is also being addressed. </p> <p>Funding through the European Commission 7th Framework Program NanoPUZZLES (FP7-NMP-2012-SMALL-6, Grant Agreement no. 309837) and NanoBRIDGES (FP7-PEOPLE-2011-IRSES, Grant Agreement no. 295128) projects is gratefully acknowledged.</p><p>N.B. The spreadsheet images provided in this poster, of provisional NanoPUZZLES files, are used with permission from Microsoft. </p> <br