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=""></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 ( and NanoBRIDGES ( 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> [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>